Multiobjective optimization package of modefrontier the state of the art survey by Amir Mosavi

ÓBUDA UNIVERSITY

Bánki Donát Faculty of Mechanical and Safety Engineering Institute of Mechatronics and Vehicle Engineering ..

MULTIOBJECTIVE OPTIMIZATION PACKAGE OF MODEFRONTIER: THE STATE OF THE ART SURVEY

OE-BGK 2017.

Student’s Name: Registration Number:

Rituraj Rituraj

ÓBUDA UNIVERSITY Donát Bánki Faculty of Mechanical and Safety Engineering Institute of Mechatronics and Vehicle Engineering

THESIS Student’s surname, forename (s): Rituraj Rituraj Registration number: Thesis number:

Neptun code: DQXBWR

Branch of study, specialization: Mechanical and Safety Engineering, MSc Mechatronics

Engineering The proposed title of the thesis: Task description:

1. . 2. 3.

Institutional consultant's name External consultant’s name and workplace: Dr. Amir Mosavi; Institute of Automation Kando Kalman Faculty of Electrical Engineering; Óbudai Egyetem

The limitation period of the theme issued: Subjects of final examination:

Issued: Budapest,

....................................................... Head of Institute

The Thesis is suitable for submission: .......................................................

Institutional consultant

ÓBUDA UNIVERSITY Bánki Donát Faculty of Mechanical and Safety Engineering

DECLARATION OF STUDENT

The undersigned student hereby declares that the thesis as his own results, the literature, and tools used can be identified. Results in the achieved thesis may be used for the purposes and tasks of the university awarding institution free of charge, subject to any restrictions on encryption. .

Budapest, 20................................

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ACKNOWLEDGEMENT First and foremost, word of thanks is to the Almighty God for His faithfulness to see me through and patiently guide me through the whole experiment of difficulties and human disappointments. I am very grateful to have been doing my Master Degree during the course of my two years at Obuda University. Studying here makes me feel at home and gives me opportunities to focus on my studies considerably. Obuda University has all the necessary facilities that I needed during my studies, the wellequipped library, laboratories, and classrooms. I had a chance to attend various practical in and outside the school organized by the school, therefore I gained more field experience from all different field work. My indebted thanks are forwarded to my supervisors Dr. Amir Mosavi for his kind support and thorough guidance. However, my sincere gratitude goes to my classmates for helping me out with their personal time and courage to move on whenever the need arose. In the same vein, I would like to thank my Parents, brothers and sisters for their financial support and timely check-up on studies and research progress, and my roommate. I would like also to thank all colleagues who contributed to my progress in this document, in one way or the other as well as Stipendium Hungaricum Scholarship and Obuda University for the opportunity they gave me. Finally, honor and grateful thanks go to my family for training me how to live with people. Finally, to the fellow countrymates thank you for always being there for me, your support, prayers and best wishes always give me strength to do more and courage to study hard. I believe and promise to myself that I will continue to ride the ladder of education and do more researches proudly with your constant support you give me.

ABSTRACT Multiobjective optimization method of modeFrontier software is becoming popular now a day. The reason is that it utilizes the available resources in an efficient manner and provide results in less duration of time. This thesis survey the algorithm and method of modeFrontier to solve optimization problem in different sectors like Energy, Manufacturing, Material, Transportation, Bioscience, Aerospace, Metal forming, Electrical engineering, Health & Foundry. This research is focused on multiobjective optimization methods of modeFrontier. It involves various optimization methods like Design optimization, Numerical analysis, Computational Fluid Dynamics, Evolutional algorithm etc. These methods are compiled together in modeFrontier that provide an easy workflow for different modules and act as a building block for solving multiple decision issues of product modeling. This work demonstrates the increasing demand of this software in the upcoming years which is justified by graphical representation. This thesis is an art of survey for reviewing the recent and past achievement of multiobjective optimization, involved tools and methods. The data collected from previous studies demonstrate that this software has huge advantage for companies and academics to carry out optimization in product modeling. Keywords: Multiobjective optimization, modeFrontier, Numerical analysis, Design optimization, Computational Fluid Dynamics, Evolutional algorithm.

TABLE OF CONTENT

ACKNOWLEDGEMENT ....................................................................................................... i ABSTRACT........................................................................................................................ ii LIST OF FIGURES............................................................................................................... v LIST OF TABLES .............................................................................................................. vii LIST OF ABBREVIATIONS................................................................................................ viii CHAPTER 1. INTRODUCTION ............................................................................................ 1 1.1 Overview ......................................................................................................................... 1 1.2 Multiple criteria decision-making (MCDM) ................................................................... 2 1.3 Approaches to optimal engineering design ..................................................................... 3 1.4 Contributions ................................................................................................................... 3 1.5 modeFrontier ................................................................................................................... 4 1.6 Aim of the thesis.............................................................................................................. 5 1.7 Thesis outline................................................................................................................... 5 CHAPTER 2. MULTI- OBJECTIVE OPTIMIZATION METHODS................................................ 7 2.1 Overview ......................................................................................................................... 7 2.2 Basic concepts ................................................................................................................. 7 2.3. Weighting Method .......................................................................................................... 8 2.4. Posteriori Method ........................................................................................................... 8 2.5. Priori Method ................................................................................................................. 8 2.6. Genetic Algorithm (GA)................................................................................................. 9 CHAPTER 3. modeFrontier: AN EXCLUSIVE ONE CLICK OPTIMIZER ................................... 10 3.1 Overview ....................................................................................................................... 10 3.2. Comparison .................................................................................................................. 10 3.3. Concept behind modeFrontier ...................................................................................... 11 3.4. Workflow management with modeFrontier ................................................................. 11 3.5. Features and Benefits ................................................................................................... 12 CHAPTER 4. LITERATURE REVIEW.................................................................................... 13 4.1. Energy .......................................................................................................................... 13 4.2. Health ........................................................................................................................... 23 4.3. Materials ....................................................................................................................... 27 iii

4.4 Manufacturing ............................................................................................................... 34 4.5 Transportation ............................................................................................................... 39 4.6. Aerospace ..................................................................................................................... 46 4.7 BioScience ..................................................................................................................... 53 4.8 Foundry Industry ........................................................................................................... 56 4.9 Metal Forming ............................................................................................................... 61 4.10 Electrical Engineering ................................................................................................. 65 CHAPTER 5. SURVEY RESULT ........................................................................................... 71 5.1 State of optimization in industry ................................................................................... 71 5.2 Geographical expansion of modeFrontier ..................................................................... 72 5.3 modeFrontier applications in different sectors .............................................................. 73 CHAPTER 6. DISCUSSION ................................................................................................ 75 6.1 General use of modeFrontier ........................................................................................ 75 6.2. Advantageous to company ........................................................................................... 75 6.3. Optimization and modelling techniques ....................................................................... 75 CONCLUSION ................................................................................................................. 77 REFERENCES .................................................................................................................. 78

LIST OF FIGURES Figure 1: Methods used for multiobjective optimization Figure 2: A coarse structured mesh used for the ideal gas and real gas calculations Figure 3: Use of modeFrontier in energy sector Figure 4: Geographical expansion of modeFrontier in energy sector Figure 5: Geographical expansion of modeFrontier in energy sector Figure 6: FEM in preliminary design Figure 7: Packing Optimization Figure 8: modeFrontier application in health Figure 9: Geographical expansion of modeFrontier in health sector Figure 10: Pareto design of a plastic pot Figure 11: Optimize atomistic models for specific engineering task Figure 12: Integrated Optimization Figure 13: modeFrontier applications in material sector Figure 14: Geographical expansion of modeFrontier in material sector Figure 15: Parameter Optimization FE: Connecting Rod Figure 16: Structural simulation with ANSYS Figure 17: modeFrontier applications in manufacturing sector Figure 18: Geographical expansion of modeFrontier in manufacturing sector Figure 19: System representation in Autonomie Figure 20: The total pressure in the symmetry plane Figure 21: Upper planum internally design Figure 22: CFD models Figure 23: modeFrontier application in transportation sector Figure 24: Geographical expansion of modeFrontier in transportation sector Figure 25: CFD streamlines on torpedo rubbers Figure 26: Original NSGA- II Figure 27: Controlled Elitism NSGA- II Figure 28: MDO- MOO (min EW and FW) without flutter analysis Figure 29: MDO- MOO (min EW and FW) with flutter analysis Figure 30: modeFrontier application in aerospace Figure 31: Geographical expansion of modeFrontier in aerospace Figure 32: modeFrontier application in bioscience Figure 33: Geographical expansion of modeFrontier in bioscience Figure 34: Convention effect of optimization Figure 35: modeFrontier application in foundry Figure 36: Geographical expansion of modeFrontier in foundry Figure 37: Simulation result validation of a rifled pipe v

Figure 38: modeFrontier application in metal foaming Figure 39: Geographical expansion of modeFrontier in metal foaming Figure 40: Optimized magnetic gear topology Figure 41: Fuel economy and weight optimization by GA using 7 designs variables Figure 42: modeFrontier application in electrical engineering Figure 43: Geographical expansion of modeFrontier in electrical engineering Figure 44: Percentage of industry sectors with different years of experience in using m modeFrontier Figure 45: Percentage of companies using optimization at different configuration levels. Figure 46: Percentage of companies using optimization in different domains Figure 47: Percentage of companies using different techniques for modeFrontier Figure 48: Geographical expansion of modeFrontier in 2008 Figure 49: Geographical expansion of modeFrontier in 2016 Figure 50: modeFrontier application in different sectors

LIST OF TABLES Table 1: Review of modeFrontier applications in energy Table 2: Review of modeFrontier applications in health Table 3: Review of modeFrontier applications in material Table 4: Review of modeFrontier applications in manufacturing Table 5: Review of modeFrontier applications in transportation Table 6: Review of modeFrontier applications in aerospace Table 7: Review of modeFrontier applications in bioscience Table 8: Review of modeFrontier applications in foundry Table 9: Review of modeFrontier applications in metal foaming Table 10: Review of modeFrontier applications in electrical engineering

vii

LIST OF ABBREVIATIONS

(1) MOO – Multi Objective Optimization (2) MCDM- Multi Criteria Decision Making (3) DMs– Decision Makers (4) mF – modeFrontier (5) CAE – Computer Aided Engineering (6) GA– Genetic Algorithm (7) DOE- Design of experiment (7) CAD- Computer Aided Design (8) RSM – Response Surface Methodologies (9) MOGA– Multi Objective Genetic Algorithm (10) RLW– Remote Laser Winding (l1) BSFC– Break Specific Fuel Consumption (12) LPG– Liquified Petroleum Gas (13) NSGA – Non -Dominated Sorting Genetic Algorithm (14) RIA – Reliability Index Approach (15) SLWR – Steel Lazy Wave Riser (16) CABS – Climate Adaptive Building Shell (17) UWB – Ultra Wide Range (18) NZEB – Net Zero Energy Building (19) CNG – Compressed Natural Gas (20) OSP – Optimal Shape Problem (21) ANSYS – Analysis System (22) IPMSM – Interior Permanent Magnet Synchronous Motor (23) GHGs – Green House Gases (24) ACP – Automatic Cell Planning (25) MEMS – Micro Electro Magnetic System (26) EH – Energy Harvesting (27) BEM – Boundary Element Method (28) HAWT – Horizontal Axis Wind Turbine (29) CCS – Complex Chemical System (30) FSAE – Formula Society of Automotive Engineers (31) EIP – Enterprise Information Portal Computing (32) MADM – Multiple Attribute Decision Making (33) FPSO– Fuzzy Particle Swarm Optimization (34) FEM – Finite Element Method (35) ITC – Isothermal Titration Calorimetry (36) AVF – Arterio Venous Fistula (37) CAD – Computer Aided Design (38) TTM – Time to Market (39) PI-LPV –Proportional Integrator Linear Parameter Varying (40) FEA – Finite Element Analysis viii

(41) SMEs – Superconducting Magnetic Energy Storage (42) CFD – Computational Fluid Dynamics (43) LVSB – Low Voltage Switchboard (44) R& D – Research & Development (46) EA– Evolutionary Algorithm (47) MOSA – Multi Objective Simulated Annealing (48) H- FAST – Hybrid- FAST (49) MOEA– Multi Objective Evolutionary Algorithm

CHAPTER 1. INTRODUCTION 1.1 Overview In today's era of global competition, when the consumer has become much more smarter and educated. There is also awareness about the competitive and alternatives available in the market. The producer must be smarter to have the profit. For this purpose, there need to optimize the product. The products which produced should satisfy the consumer demands. In order to do that the producer must optimize the resources. To fully utilize the resources so that the prices should be as less as possible. Consequently, the consumer not only look for the prices but also for the quality, performance and features of the products. So, the producers should know the proper way to optimize the things. Today we cannot depend on one objective optimization methods. The optimization should be multi-objective in order to get maximum benefits. For this, there need to focus on many aspects of the available resources. Spanning over several engineering domains. there are clear trends in industries towards more complex products and they must optimize them in such a way that it should be as simple as possible. Besides that, there is a pressure on the producer to develop their products at faster rate, competitive prices with high quality. In order to meet all the demands, the manufacturing company has to focus on their manufacturing process. According to Drucker (1985), in manufacturing process, there are many issues which comes into picture. One of it is to ensure the efficiency of the manufacturing process which can be done by analyzing and managing the design process. Another issue is to develop techniques and tools that must support the complex design products which produce the wealth of the computerized engineering tools as suggested by Colton et al (1991). Today most of the things are getting digitalized due to the advanced evolution of computers. So, the computational capabilities of the computer increase the scope for simulation and numerical optimization. However, in the analytical techniques, the simulation models and the numerical optimization could be of important value and can permit vast improvement in the design of the products statement given by Johan (2001). According to Giuseppe (2008), multiobjective optimization is a solution of finding the vector of decision variables. It satisfies constraints and optimizes a vector function whose elements represent the objective functions of that variables. according to Mosavi (2013) MOO approach is also needed in relating human beings to the computer. This is done by brain- computer optimization approach for solving complicated geometrical decision- making problems. The optimization is needed according to the demand. In many cases, the optimization only related with improving the quality and efficiency but itâ&#x20AC;&#x2122;s not like that, the 1

optimization must also consider the environment. According to Eickhoff et al (1996), MOO deals with contradictory objectives and the â&#x20AC;&#x2DC;bestâ&#x20AC;&#x2122; solution which make a compromise between demand and environment. There are many approaches which is used for MOO, one of it is Pareto set of solutions suggested by Thibault et al. (2003). The most common methods used for this purpose are objective weighting, distance functions, and a method of min-max functions. So, in short, the introduction of a multiobjective optimization framework allows to manage more information (Shaul et al, 2007). Considering the traditional meaning of optimization methods or design, the designer must design the desired structure for a given tasks by selecting the desired variables. According to Mosavi (2009) for hydrodynamic design approach MOO based design method is applied. This is done in two different setups and techniques. 1.2 Multiple criteria decision-making (MCDM) According to Mosavi et al (2010) MCDM is a modelling tool which is used to deal with the complex large scale engineering optimization problems. Optimal engineering design is decision-making process. In this sense, the designing process would overlap with the other theories and methods in further disciplines, e.g., decision sciences, economics. This fact would force the process of design into a complex systems context. The demand that design decisions account for a productâ&#x20AC;&#x2122;s integrated development process. In such process the practical industrial problems need to be considered from different perspectives. This is needed for optimization of several conflicting objectives. It gives idea for decision-making on the several conflicting criteria. In this context, the benefits of utilizing MCDM includes the conflicting design objectives. These are taken into the account simultaneously. It leads to the problems which would deliver a significant and competitive advantage to the engineering design community. According to Mosavi et al (2013), in an optimal engineering design environment the MCDM problem is taken as a combined task of MOO and decision-making. Therefore, MOO approaches for creating Pareto-optimal solutions are considered vital to MCDM community. According to Mosavi et al (2012) utilizes a software architecture for interactive optimization and MCDM for the criteria of mechanical behaviour of the woven industry during the draping. Implementing MCDM task for solving optimal engineering problems is very important. In the same time, complicated approach for engineers is pursued. The problems of this type are mostly non-convex, nonlinear and computationally expensive. This includes numerous variables and several conflicting objectives. According to Mosavi et al (2012), solving the optimal engineering design problems like this, which are mostly referred to black-box optimization problem is not a simple task. Meanwhile engineers prefer to utilize the efficient and easy to use approaches to solve these problems effectively and accurately. Even though the optimization research community has developed numerous approaches to global. The multi-objective optimization is an including meta modeling methodologies, interactive, and evolutionary algorithms. These are mainly surveyed due to the difficulties often 2

associated with the usage. Mosavi (2013) proposed a MCDM software tool which integrates the search heuristic techniques for efficiently solve complex optimization problems. This is based on reactive search optimization methodology. Several requirements mostly associated with increasing the design space. These are not applicable in real-life engineering optimization problems within the industry. Mosavi et al (2010) proposed that the data mining can be used to identify the effective variables of the MCDM systems. Mosavi (2013) proposed a methodology which delivers the capability of handling the very big data. This is associated with production decision making and selection of material. This is also based on MCDM problems. 1.3 Approaches to optimal engineering design For an optimal design, DMs have been urged to extract as much information as possible from a limited number of test runs. This is done due to highly expensive numerical analyses in the engineering and process simulations. The vast number of existing statistical and optimization algorithms are to extract the most relevant qualitative information. This is done from a database of experiments to support the decisions. The MOO algorithms offer a significant competitive advantage in the different fields of engineering optimal design. These are the conflicting objectives considered as leading to the problems. In this context, the task of algorithms selection followed by understanding the true nature of a problem which is vital for an effective approach to the optimal engineering design. According to Joshua et al (2008), algorithms for MCDM are interactive, evolutionary and meta-modeling because of their efficiency. These help in solving the optimal engineering design. Although considering the shape of optimization problems, the aesthetics criterion is a common objective evaluation function. According to Sunith et al (2016), in the optimal design tasks the interactive approaches are found to be more effective. They are capable to support the DM actively in finding the Pareto-optimal solutions. Mosavi et al (2012) based on reactive search optimization proposed a method which involve the decision maker in the optimization problem which help to find a single solution using Pareto optimal set. This is done by continuously involving in the solution process to better guide the search. Utilizing a decision-support system with the ability for reducing the design space. This would help to decrease the complexities. It also provides the ability for understanding the true nature of the problem. Mosavi (2013) explains that decision making algorithm together with evolutionary and interactive optimization used to generate Pareto optimal solutions for any kind of optimization problem. This is done by reactive search methodology. 1.4 Contributions In todayâ&#x20AC;&#x2122;s highly competitive market environments, engineering designs must be optimized in order to succeed in accomplishing design objectives while satisfying the 3

design constraints. Considering inevitable multiple criteria, e.g., the product development lead-time, cost and performance must be also optimized. This is to ensure affordable and speedy reaction to the changing market needs. Thus, a deep understanding of the computational tools to be used for MOO. According to Narayanan et al (1996), the simulation-based optimal design is critical for supporting the engineering decision-making processes. Therefore, an improved decision procedure is formed. This is according to the limited human memory and data processing capabilities. The research, development, and successful case studies on MCDM and MOO methods suggested to engineering optimal design community are large in number, taking these for instance. The expansion and progress of applicability and popularity of these methods is within engineering design communities have been very slow. This indicates an obvious gap between academic research and the industrial real-life applications. In this way, more challenging real-life applications become the designâ&#x20AC;&#x2122;s new issues the strategy would demand for the further improvements. Such as optimal design of composite textile where the detailed-complex geometry parametrization, big data and increasing the number of ways in decision-making. For this reason, in the improved design strategy the former BURNS-based shape parametrization method is enhanced with a new methodology called generative algorithms. 1.5 modeFrontier With a multi objective approach the belief about the optimization has changed. This is because the longer focus on a single target is no longer exists. Now, this try to improve a set of goals. In short, the ultimate goal of multiobjective optimization is to set an efficient compromise rather than a single solution. To optimize the numerous factors there is one software called â&#x20AC;&#x153;modeFrontierâ&#x20AC;? can be used. It is an integration platform. This is used for multi objective optimization, automation of design process and analytic decision making. This provides seamless coupling with engineering tools within various disciplines. (Alberto, 2015) provides an optimization environment with profile based and modular access. It is developed by ESTECO S.P.A and stable release on 27 May 2016. It works on operating system named Cross-platform. The integration platform of ESTECO for multi-disciplinary and multiobjective optimization. This offers a seamless coupling with third party engineering tools. According to (Hopsan, 2014), it enables the automation of the design simulation process. It also facilitates analytic decision making. It is the first commercial tool which allows a multi-objective optimization. This is applied to any engineering design area. Basically in 1999, Esteco established in Europe and in 2001 in expanded to Asian market and in 2004 it is expanded in North America. Finally, in 2016 the stable configuration of it V5.0 is expanded all over the world. Traditional approach of 4

optimization is to first initialize the configuration, then it need to simulate. After that it need to evaluate the result if it is ok then it will accept the command of optimization. If it is not then it needs modification then again it will start from simulation. But if we look the optimization method from modeFrontier it is very easy and take less time. In this parametric model, design objectives and constraints enters into the modeFrontier and the optimal trade off solution is obtained. These are the design process with powerful workflows. This innovates algorithm and sophisticate post processing tools. It allows the user to perform statistical analysis, data visualization and decision making. It consolidates specialized expertise and streamline teamwork. This is done by allocating software resources. This uses different modules like modeSpace and modeProcess according to the profile of the user. mF2016 discovers the philosophy behind modeFrontier. It reduces complexity, improves the efficiency. It reduces the development time. modeSpace enables efficient license. It plays a role in management within teams. This also includes the sophisticated set of modeFrontier tools. This is for data analysis. This also investigate the problem characteristics both in pre- optimization phase and post processing. modeProcess intends for describing processes in the form of graphical workflows. It is an independent desktop application and specifies the parameters and simulations. These are required to solve an engineering design problems. This is used in different fields like automotive, electronics, aerospace, energy, materials, applications, defense and space, universities and many more fields. modeFrontier is a powerful tool to couple with CAE software. This helps to explore design possibilities. In this tool, any numerical model can be integrated. Many multi objective optimization algorithms and pre/post-processing tools are available in modeFrontier. In short, it helps to design different optimization strategies. 1.6 Aim of the thesis The aim of this thesis is to make a survey data which can give an important data about modeFrontier software. For multiobjective optimization there are various methods, algorithm and software which are used to solve such problems. modeFrontier is a software which is used for solving these problems. The well-organized structure is to be created in order to understand the implementation of this software in ten different sectors. After that an outcome of the survey will be shown with the help of graph. It also shows the geographical expansion of this software in different sectors. 1.7 Thesis outline This thesis is organized in the five sections. Section one is the introduction part which is already discussed. This gives a short view on the multiobjective optimization. It shows the aim of this thesis. It shows the contribution of multiobjective optimization in different sectors 5

Section two, it is aimed to present brief overview on the existing approaches for multiobjective optimization tools. This presents the various methods which is used for such optimization. It gives a short view on basic methods, Posteriori method, Priori methods, Genetic algorithm approach. Section three deals with the explanation of modeFrontier in details. It deals with concept behind it, comparison with other old methodology, workflow management, features and benefit of it and also meta modeling with it. Section four deals with the literature review of almost all the work done for the multiobjective optimization approach using modeFrontier. This review is done in ten different fields. This review is done from year 2008 to 2016. Section five deals with the survey of the review. This shows the importance of the modeFrontier in companies and also the role of it. This section shows the different aspect why it is becoming important and where it is used. Section six deals the discussion and the conclusion about the multiobjective optimization and the modeFrontier.

CHAPTER 2. MULTI- OBJECTIVE OPTIMIZATION METHODS

2.1 Overview Multiple objectives involve many decision and planning problems. There are no unique solutions which exists in such kind of problems. These problems are based on the theoretical background. For solving these problems, a set of mathematically good solutions exists. These solutions are called Pareto optimal solution. Chankong et al (1979) in multiobjective decision-making analysis formulates a Kuhn-Tucker multiplier as tradeoffs. A human DM considers the multiple objectives. Simultaneously it fits a Pareto optimal solution. The solution process needs involvement of the DM which is done in the form of specifying preferences information and the final solution is determined. The preferences of DM an explicit preference model is built in order to find solutions which fit the solution. There can be a list of several properties of multiobjective optimization methods. Some of the desirable properties are: • • • • •

generate Pareto optimal solutions, Pareto optimal solutions help DM, reduce the time, should not too much complicated or demanding, as final one find the most preferred solution.

2.2 Basic concepts For a continuous multiobjective optimization problems an infinite number of Pareto optimal solutions can be find which for a continuous multiobjective optimization problems. However, multiobjective optimization problems have a finite solution. But there is a possibility large number of solutions exists for that single problem and it may be disconnected to each other. Practically there is no relevance of a local Pareto optimal objective vector. It might be possible when it is not global. This is because of the interior feasible objective region. These solutions are always located on its boundaries. Therefore, there is a high possibility to improve it. The non-empty feasible region is compact. Since all these functions are semi-continuous in nature. Therefore, only Pareto optimal solution exists. This depends on the objective functions, domination structure and feasible region of DM. The MOO methods are used to fulfill two requirements: First, in finding any optimal solution and second, in generating only the Pareto optimal solution. There is a methodology to solve these problems which is known by Scalarization. (Eichfelder, 2008) explained that the problem which involves multiple objectives is converted into a single objective optimization problem function is known as Scalarization. There are various methods used for solving the multiobjective optimization. These are discussed below: 7

2.3. Weighting Method Different weights are used in the generation of different Pareto optimal solutions therefore this method can also be known as posteriori method. In this case, the DM can select the most appropriate weights needed for the optimization. If the DM specify the weights then it is used as a priori method. It may also happen that the method does not behave as expected. This is happening in case of nonconvex problem because when the weights of the method alter, it might jump from one vertex to another. This leaves intermediate solution which can be undetected. But always it is not easier to check the convexity in real application. Therefore, it is a very serious and important aspect to find solution through this method. Therefore, for critical MOO problems it is not used frequently. Das and Denis (1997) explained that a set of weights which is distributive in nature but does not produce Pareto optimal set and can be used for MOO problems. But even this is also for the convex problems. 2.4. Posteriori Method This method is used for generating Pareto optimal solutions. It deals with two submethods: •

Method of weighted matrix – This is used to minimized a global criterion between feasible objective region and some reference point. The main aim of this method is to produce solutions by using the matrix methodology for weighting. This approach is called compromise programming. This method uses lexicographic approach to reach any Pareto solution. This results in the increase of computational cost. This will lead to two optimization problems. This can be used as a solution for each Pareto optimal problems (Miettinen et al, 2006).

•

Approximation Method – In the MCDM many method is developed for approximation of Pareto optimal solution. Ruzika and Wiecek (2005), explained the survey of approximation methods. In this method, a number of approximations are made and the value which is near to the optimum value is consider as the final value.

2.5. Priori Method In this method, before the solution process the DM must specify the preference information. The DM does not have to invest too much time in finding the solution. In this method, the scalarizing function approach is used to generate solution. According to Keeney and Raiffa, (1976), the scalarizing function method is valid only if the DM know explicit mathematical formulation of the value function. According to Fishburn (1974), according to the importance of lexicographic ordering the DM arrange the objective function. 8

2.6. Genetic Algorithm (GA) For MOO, GA is introduced by John Holland in 1970. It is an inspiration for the natural selection process. Naturally each member of an ecosystem competes for water, territory and food. According to Konaka et al (2005), the use of genetic algorithm in solving the multiobjective optimization belongs to evolutionary algorithm. This generates solutions to the optimized problems. The techniques used in this algorithm is inspired by natural evolution like mutation, crossover and selection.

Figure 1: Methods used for multiobjective optimization (Xiao-Peng Gan et al, 2015)

CHAPTER 3. modeFrontier: AN EXCLUSIVE ONE CLICK OPTIMIZER

3.1 Overview ESTECO is a company which provides first class software solution. This is focused at perfecting the design process through simulation. Its smart engineering suite brings enterprise- wide solutions for design optimization, automation, and process integration and simulation data management. This technology addresses the entire design workflow by orchestrating best in class simulation tools. This is also done by it streamlines design iterations and lead engineers to new, enhanced solutions. The aim of it to help companies excel across the innovative journey providing them with leading edge technology. This is done to master engineering complexity and accomplish the shift to agile product development. Over 300 international organizations have chosen It consolidates specialized expertise, streamline teamwork and boost product development of industrial sectors. The leading solution of it inspires decision making and accelerates product innovation. This is done by enabling multidisciplinary engineering practices and allocating competence and software resources where needed. There are two simulation techniques through which it operates. â&#x20AC;˘

Volta: According to Carlo (2017), it is a web based collaboration environment that orchestrates enterprise simulation data and multidisciplinary business processes. It enables conscious decision making and innovative product development. It helps to manage all cross functional concurrent design steps by integrating multiple modeling formats. Its service oriented architecture facilitates the execution, sharing and reuse of enterprise engineering knowledge.

â&#x20AC;˘

modeFrontier: It is comprehensive multidisciplinary and multiobjective optimization solution. According to Seiji (2014), its innovative algorithms and effective integration and simulation tools ease the optimization process. In has become essential for understanding of reducing product development time and cost/performance factors.

mF is a collection of technologies. In short it can be defined as a multi-objective optimization and design environment which is written to allow easy coupling to almost any CAE tool. This is used for both commercial or household purposes. This is defined by EnginSoft SpA, 2014 3.2. Comparison Optimization is simply a selection of a best element (regarding some criterion) from some set of available alternatives, statement by Harvey (2003). modeFrontier is a software for design optimization and process integration. It is developed by ESTECO SpA. It was the first commercial tool that allowed a multiobjective optimization applied 10

to any engineering design area. Initially also there are optimization methods but the use of this software makes the optimization quite easier. In traditional optimization design, initial configurations are send for simulation after simulation the result is evaluated then simulator are asked whether the result is “OK” or not. If it is “OK” then accept the simulator command, if it is not then modifying the input configuration again. While in modeFrontier the parametric models and design objectives and constraints are feed in the modeFrontier software and after that the optimal trade- off solution is obtained. So, by using modeFrontier the optimization process become very easy and time saving and cheaper. 3.3. Concept behind modeFrontier There are three steps in which concept of modeFrontier can understand: •

Input Variable: Variables which changes its values according to the parameters. These are the quantities that designer can vary. These are the free parameters. These are of two types:

Continuous variables – these are pints coordinates and process variables. Discrete variables – these are components from a catalogue and number of components. •

Black Box: These are a set of solvers that models and solves in a numerical manner that design problem like CAD and CAE tools. These are a set of experiments that produces some data (Evans, 2005).

•

Output Variables: These are a measure of the system response and/ or performance i.e. acceleration, speed, consumption, confort, deformation, stress, mass, volume, lift, drag, defects, number of failures and cost (Karine et al., 2012).

Optimization means to find a system configurations that meets the objectives and satisfy constraints. The optimization can be multiobjectives. Many softwares can be used to describe the behavior of the system. Mosavi et al (2010) proposed a combination of mF and splining to introduce develop the profile design procedures. This uses CAD and CAE tools as an interface between designer and splines. 3.4. Workflow management with modeFrontier On 26th October 2016, the ESTECO webinar series focus complex workflow management. This presents the new features included in modeFrontier. The designs get more complex and touch more domains. Now it is crucial to understand system interactions and behavior to take informed decisions, based on the overall performance. modeFrontier helps to automate the execution of complex chains of preprocessing and simulation tools (Yang et al., 2015). This has advantage of the very flexible workflow and the wide range of direct integration nodes for the most popular simulation tools. 11

The Optimize design is chosen among innovative algorithms. This determines the set of practical solutions which combines the complex set of opposing objectives. mF 2016 provide advanced features for handling the complex project. This is a dedicated panel for workflow setting, design space node, improved sub process node and many more. The agenda of this workflow depends on the following topics: â&#x20AC;˘ â&#x20AC;˘ â&#x20AC;˘

Scheduling project node: nested optimization strategies Use of modeProcess to enhance modularity of workflow and reduce complexity DOE Designer and Design Space node to complete the full automation of process management.

3.5. Features and Benefits mF helps to deliver shorter development time and improved product management performance. The java technology available in mF which guarantees platform independency and scalability. This is based on complex hardware architectures. It makes it ideal for any company and department. Mosavi (2014) proposed metamodelling approach to evaluate the effectiveness of MOO in 3D wing optimal design. The key benefits of this software are: 1. 2. 3. 4. 5.

reduces the time and cost provides design and performance optimization re-use the historical database provides better and deeper understating for datamining process assists engineers to overcome the common error of giving priority directly to product rather than to the entire process 6. assists in preparing and executing a number of experiments in order to maximize knowledge acquisition 7. has ability to revolutionize product development.

CHAPTER 4. LITERATURE REVIEW

4.1. Energy The role of modeFrontier for optimization in this field is very much popular and important. In this section, its role is discussed: Smajic et al. (2008), uses modeFrontier for simulation of multi-physics phenomena. This is specially focus in power devices and based on 3D simulation. The proposed realization involves an application which drives the field solver. This makes over the memory bus and modeFrontier. It transmits the optimization tool to the field solver and simulates necessary objective function evaluation. After this, it is sent back to the optimizer. This uses multiobjective genetic algorithm (MOGA) and non- dominated sorting genetic algorithm II (NSGA- II). Fujishima (2008), uses modeFrontier in optimization of IPMSM rotor. This is applied for railway traction system. Designers found trade- off relation between Maximizing Torque vs Minimizing Open Circuit Voltage and Maximizing Torque vs. Minimizing Rotor Mass. modeFrontier helps in analyzing the Pareto- Optimal deeply with the multivariate analysis. This involves CAD and MOGA algorithm to carried out the optimization. Matzopoulos et al (2008) uses modeFrontier in optimization of fuel cells design space using GPROM principle. It provides GPROMS users to access different modeFrontier global optimization method. ModeFrontier with help of genetic algorithm gives robust and fast general purpose dynamic modelling. It gives reactor design, catalyst design, energy integration and carbon capture. Pehnec (2008) uses modeFrontier to show workflow based shape optimization of airfoils and blades. This uses chained Bezier curves. The proposed approach is applied to airfoils. This is useful for wind turbine blades. This gives generating object geometries. Optimization gives a definite shape of variables. This is applicable to 2D and 3D cases. modeFrontier provides the exchange of data during the process of changing the geometry in shape optimization. This uses CFD and NACA simulation techniques. Courteille et al. (2009), uses modeFrontier in fuel consumption minimization procedure. This includes the SAIL assisted motor vessel. It is proposed to provide a sea transport route that minimizes the fuel consumption in optimum time. It presents the meshing of the explored area. modeFrontier helps in identifying the best route to minimize the EFC. It also helps in finding optimal route to avoid rough sea conditions. Pehnec et al (2009), uses modeFrontier to couple evolutionary shape optimization and reverse engineering. This is done in reverse design and prototyping. Redesign means 13

facing shape optimization problems, in this case parameters define the geometry and the objectives. modeFrontier saved time and resources by automatically updating optimized results. This is done through ANSYS workflow. Dickson et al. (2010), uses modeFrontier for showing the effect of acceleration on Adidas bounce shoes. This requires the use of optimization techniques. This phenomenon can be described by the term directional energy transfer. The shoe uses tubes with a Î&#x2DC;-shaped cross section. This is assimilated by the shoe sole. modeFrontier showed the optimum design angle. It also helps to calculate the energy transformation using CAE methods. Energy transferred and returned actually improve the runner acceleration. Odom et al (2010) uses modeFrontier for hybrid FSAE vehicle realization. The goal of this to design, optimize and build the race car. modeFrontier used to perform a multiobjective optimization using the genetic algorithm MOGA II. modeFrontier enables the optimal configuration of the braking system maximizing structural rigidity and minimizing weight. With the use of genetic algorithms in modeFrontier, the pedal and brake design were optimized. This is based on a female or male driver Brambilla (2010) uses modeFrontier for preliminary evaluation of real gas. This effect is done in a gasdynamic nozzle. This methodology applied to the design of turbine blades. This couple the fluid-dynamic analysis with a genetic algorithm for the optimization. modeFrontier has implemented a discrete variety of model. modeFrontier makes the analysis simpler and more inaccurate methods. The figure below shows that how modeFrontier helps to calculate the real gas and ideal gas effect on the gasdynamic nozzle.

Figure 2. A coarse structured mesh used for the ideal gas and real gas calculations (Davide, 2010).

Boulougouris et al. (2010) uses modeFrontier for energy efficient parametric design tool. This optimizes the framework of holistic ship design. This reduces the emissions from maritime greenhouse gases (GHGs). modeFrontier provides Pareto-optimum 14

solutions. It provides optimum design with highly efficient models. This complete mapping of the design space in a comprehensive way. Hubar, (2011), design the renewable micro generation technologies to supply and off grid community energy demand. An off-grid community has an optimized energy system with 100% renewable micro-generation technologies. The renewable energy technologies satisfy the electrical and thermal energy demand. modeFrontier results in renewable energy technology system configurations. It also modifies the capacities and power based on off-design indoor air temperatures. Benkhelifa et al. (2011) design and optimize the Energy Harvesting (EH) MicroElectromechanical-Systems (MEMS). The features of the framework are to enables the enhancement of MEMS-DO. modeFrontier helps in automation of the optimization process. It reduces the time and cost of the procedure. It uses the CFD methods to get the harvesting patterns. Poles (2011) uses modeFrontier for designing a wind turbine is a multidisciplinary process. This is characterized by subsequent transformations and phases. This study is aimed to optimize the wind turbine with regard to maximizing efficiency and minimizing cost. HAWT optimization is commonly based on BEM techniques. modeFrontier conducts full 3D CFD simulations. Results are presented in terms of chord and pitch angle distributions. Results shown by modeFrontier for optimizing HAWT will be used in future studies on multiobjective optimization of small and medium wind turbines. Lee et al (2011), analyses and optimizes the power generation. It is done with the help of CCS. It stands for carbon capture and storage. This is useful to control atmospheric levels of CO2. It helps to present a modular framework. This is used for the analysis and optimization of power generation systems. This is done with help of modeFrontier. It provides the algorithm which gives the idea to analyze and optimize the power generation. Furthermore, CFD and MOGA methods is used to find the final result. Costa et al (2012) explains the numerical procedure which couples the 3D model of the cylinder. This procedure is done with modeFrontier. This gives the detail idea about moderate-load and moderate speed condition. This model is applied where the air-tofuel ratio needs to maintain. In this case modeFrontier minimize the fuel consumption. It also increases the useful work with respect to the case injection. It resorts the split injections. This is to improve the quality of the charge stratification. This is done under lean operation. Jenovencio et al (2012) explains the composite structural optimization with the help of modeFrontier and ANSYS composite prepost. This focus on composites structures. It can be optimized easily. This can be done using ACP and modeFrontier. An optimization is performed using a parametric geometry and CFD simulations. This is 15

couple with Structural Analysis. modeFrontier runs ACP which provides several possible designs. It reduces the weight and minimizes the risk of failure. Andrade (2012) explains the application of a riser connected to a vessel. This is used for production and exploration in deep water. The high offsets and vertical motions imposed by the vessel at the top of the riser. The steel lazy-wave riser (SLWR) has an adequate solution. This is due to its structural dynamic behavior. modeFrontier defines a model that could achieve all design verification phases more easily. This optimization procedure facilitates the design of a SLWR connected to a FPSO unit offshore of Brazil. Loonen et al. (2012) show the use of modeFrontier in exploring the potential of climate. This is done by making adaptive building shell. Building shells with adaptive properties offer opportunities for comfort enhancements and energy savings. It also explores and quantifies the latent potential of climate adaptive building shell (CABS) by using building performance simulation. TRNSYS simulations generates the most optimal building shell design. This is done by embedding using modeFrontier. It minimizes the sum of heating and cooling energy demand [kJ]. It also minimizes the number of hours per year that temperature exceeds 25Â°C. According to Alessandro et al. (2013), modeFrontier can be used to design a decision support system. This is done because of the increasing demand for sustainable energy supply. It is done by combining multiobjective and multi attribute analysis together. This is done in three steps: [1] an estimation of the total EIP emissions; [2] utilizes a MADM methodology; [3] focusing on the sustainability benefits of combined heating and power plants. modeFrontier provides the best Pareto solution. It also improves the efficiency of the energy plant. Farias et al (2013), uses modeFrontier to simulate and optimize zero energy industrial halls. In practice Net-zero energy building (NZEB) requires high investment cost. Therefore, it is very important to investigate the amount of the additional capital investment. This is recouped from the energy saving (or generation). Building energy performance simulate and optimize in order to integrated design approach from the demand side to the generation side. modeFrontier provides maximized energy production capability and minimized additional capital investment. It also gives integrated design approach. Huang et al. (2013), uses modeFrontier to simulate the laminated lithium ion battery. These batteries are having multiple thermal parameters of large format. These batteries have longer life cycles and higher energy density. Multiple thermal parameters are estimated. This is done by minimizing the residuals using modeFrontier. It optimizes simulation process and measure the temperature rise. It achieves the simultaneous measurement for multiple thermal parameters. 16

Percebon et al. (2013) optimizes the wind farm layout. It is done by using genetic algorithm and modeFrontier. The optimal position of each turbine can be determined by the windâ&#x20AC;&#x2122;s condition. This is the combination of the characteristics and number of wind turbines. For the calculation of energy production in wind farm a code is developed in MATLAB. This code is based on a wake model. modeFrontier improves the efficiency of the farm. It also reduces shadow of multiple cumulative impact. It also reduces the thrust coefficient in the turbine blade. Donateo et al (2013), proposes a method which is based on empirical models and CFD simulations. This evaluates the design alternatives. It also helps in the conversion of a diesel engine to either CNG dedicated or dual fuel engines. Two engine configurations are discussed in this paper. Engine A, which converts diesel combustion to CNG. This is used for cogeneration purpose. Engine B, is a single cylinder optical engine. It converts direct injection diesel combustion to dual fuel mode. modeFrontier perform the final choice of the combustion chamber. It also reduces conversion cost of the engine. Martin (2014) uses modeFrontier for optimization and automation design of fluiddynamic system. This basically focused on optimal shape of wind turbine blades. To solve an optimal shape problem (OSP) many approaches are proposed like: NelderMead method, Quasi-Newton-method, SQP-method. But for final optimization of it NTB CFD Automatic Design Toolbox is proposed. This is basically done with the help of modeFrontier software. This helps to solve to CFD optimal shape problems. It also allows CFD engineers to enjoy the benefits of high-end solution-techniques. This can be done without deeply diving into the intricacies of modern optimization theory. Giovanni et al (2014) researched on uranium enrichment cascades modeling with optimized stage. This is done by mixing parameters for non-proliferation analysis. This uses the application of modeFrontier. In the last years, the numerical approach in the U isotopes separation process. A numerical tool for the UF6 enrichment cascades simulation has been developed by â&#x20AC;&#x153;The Institute of Trans-Uranium Elements of the Joint Research Centreâ&#x20AC;?. In this modeFrontier provides a coherent information data for analysis and simulation. It also demonstrates the possibility to use the simulation as a valid additional tool when some information on the real plant performance is missing or is inaccurate. Bhat, et al (2014) demonstrated the eco evaluation for remote laser welding. The purpose of eco-evaluation is to provide tools for planners. This is to understand the consequences of parameter settings and help them. This project is the part of the FP7 RLW Navigator project. modeFrontier software gives a quantification method to provide estimates for savings. It also concentrates on energy calculations for the

process. This also contributes for, the robot motion, the laser power for the stitches and the cooling. Perrone (2014) researched grid geometry effects on pressure drops and heat transfer in an EMbaffle heat exchanger. This uses the modeFrontier software to carried out the experimental result. This is a patented shell and tube heat exchanger technology. This is designed to improve performance and reduce operating costs. This is done by reducing fouling deposition and pressure drops. This designed tube is fully supported by expanded metal grids (EM-Grids). This allows a pure longitudinal flow pattern so that dead zones are avoided and tube vibration does not occur. To simulate the behaviour of the fluids inside an its exchanger, a parametric 3D numerical CFD model is designed. ANSYS Workbench is used to build up the model and ANSYS CFX as solver. modeFrontier help to investigate the influence of different EM-Grids on the development/propagation of turbulence in the shell-side fluid. It also helps to developed the link between turbulence propagation and performance of the exchanger. Paolo et al (2015) made a structural modeling of pipelay vessels dedicated to the laying of long and deep water submarine pipelines. Pipelay vessels have to store on board thousands of pipe tons to perform welding and non-destructive control operations in few minutes. This leads to lay the pipe while controlling its state of stress and strain through dedicated long structures. The proposed methodology adopted to convert the structural drawings in finite element model. This apply static loads such as weights and tank pressures to manage hydrostatic and hydrodynamic loads. modeFrontier gives an idea to apply inertia loads associated to the vessel motion. It also provides the coupled use of global and more detailed local models. Pasteur et al (2015) uses a new method for current pulsation calculation. This method is done in induction machine driving reciprocating compressor. In this case, the steadystate characteristics are used in the analytical solution. The model of induction motor has enhanced by adding the effects of the air-gap torque. The method is validated by simulation of the complete system through the use of LMS AMESim. It also used as multi-physics simulation software. modeFrontier provides more accurate design than the traditional one. It also analyzes and simulate the torsional natural behavior of the system more completely. Rottoli et al (2016) uses the CFD analysis of annular distributors for shell & tube heat exchangers. This heat exchangers are widely used in many industries. These are widely used because of its robustness and affordability properties. It reduces the vibration potential, lowering the velocity of the fluid. This provides an impingement protection to the first tubes. It also prevents them from erosion issues. The analysis for simulation is done with the help of modeFrontier software. ANSYS workbench uses the geometry

of a gas-gas heat exchanger. It creates a model and a mesh. It also characterizes the flow in the distributor and compare different configurations. Raciti (2016) proposed an analytical and FEM modelling of a large turbogenerator. This is done for the determination of the induced currents in rotor components. Such as the damper windings in order to assure a safe operation. The rotor body of a turbo generators is normally made as a single heat-treated forging. This means only the electromagnetic phenomena can provide to rotor by means of the excitation winding located into the rotor slots. This creates the rotating magnetic field source. In this paper, the numerical and analytical calculation methodologies developed in ANSYS. This is done to optimize the turbo-generator damper windings design. In this modeFrontier calculates the temperature reached in the rotor hot spots. This is done by means of the standard CFD analysis technique. Leonid et al. (2016) proposed an idea for automatic selection of closure relations for TUFFP. This is two phases flow unified model. This unified model is developed by Tulsa University Fluid Flow Project research group. This model allows to determine flow pattern. It calculates liquid hold-up and pressure losses with appropriate accuracy. There need to define a number of settings which best suit specific flow parameters of the pipeline to apply this method. The purposed methodology allows to automate closure relations selection. This is based on the base preliminary processing of available databases of experimental results. modeFrontier provides a database for experimental results. It also embeded correspondent knowledge of best closure relations for different regions of parameters. Venturin et al (2016) proposed an idea for multiobjective optimization of a hydrogen production process powered by Solar Energy. Solar-powered thermo-chemical and hybrid cycles are capable of transforming concentrated sunlight into chemical energy. This is done by a series of chemical and electrochemical reactions. This study analyzes the whole production chain and process flowsheet. This connects them with multiobjective design and optimization algorithms. modeFrontier helps to exploits metamodeling techniques. It also minimizes hydrogen production costs and maximizes the share of renewables in the energy used in the process. Genetic algorithm helps to provide the optimization algorithm and gives the easiest and efficient methods to harvest the solar energy. NO 1

Project New Opportunities in an old industry power products â&#x20AC;&#x201C; Challenges in field analysis and optimization Application of multidisciplinary

Authors Bogdan CranganuCretu and Jasmin Smajic

Algorithm MOGA and NSGA- II

Company ABB Schweiz AG

Location Switzerland

Year 2008

Yasushi Fujishima

MOGA and CAD

CD-adapco

Italy

2008

optimization to rotor design of interior permanent magnet synchronous motor Investigting the design space using gPROMS first‐ principles models in modeFrontier WorkflowBased Shape Optimization of Airfoils and Blades using Chained Bezier Curves Fuel Consumption Minimization Procedure of Sailassisted Motor Vessel based on a Systematic Meshing of the Explored Area Coupled Evolutionary Shape Optimization and Reverse Engineering in Product Design and Virtual Prototyping Effect of acceleration on optimization of Adidas bounce shoes Hybrid FSAE vehicle realization Preliminary evaluation of real gas effect in a gasdynamic nozzle Energy efficiency parametric design tool in the framework of holistic ship design optimization Design concept for optimizing the renewable micro generation technologies to supply and offgrid community energy demand ModeFrontier: A Facilitator for MEMS Design Optimisation Integration Multidisciplinary and MultiObjective Optimization of a Wind Turbine,

Mark Matzopoulos, Tom Williams

MOGA, CFD, ANSYS

Igor Pehnec, Damir Vučina, Željan Lozina

CAD

S. Marie & Courteille

Trieste

2008

Croatia

2008

modeFrontie r

Université Européenne de Bretagne

France

2009

Igor Pehnec, Damir Vučina, Željan Lozina

ANSYS

University Split

Croatia

2009

Mathew James Dickson, Franz Konstantin Fuss Edwin Odom, Steven Beyerlein, and Joe Law Davide Brambilla

CAE

RMIT University

Australia

2010

MOGA- II

University Idaho

2010

CFD

Australia

2010

E K Boulougouris, A D Papanikolaou, A Pavlou

Pareto optimum optimization

Facolt´a Ingegneria Industriale National Technical University Athens

Greece

2010

Jeroen van Hellenberg Hubar

modeFrontie r, CFD

Eindhoven University of Technology

Netherlands

2011

Dr Elhadj Benkhelifa

CFD

Cranfield University

Italy

2011

Silvia Poles

HAWT, CFD

EnginSoft

Italy

2011

Process Systems Enterprise Limited University Split

A Modular framework for the analysis and optimization of power generation system with CCS Increasing energy efficiency of a gasoline direct injection engine through optimal synchronization of single or double injection strategies. Composites structural optimization modeFrontier + ANSYS composite pre-post Optimization procedure for alternative configurations of risers

David C. Miller, John C. Eslick, Andrew Lee, Juan E. Morinell

CCS

U.S. Department of Energy

USA

2011

Michela Costa, Luigi Allocca, Ugo Sorge, stituto Motori

CFD

University Naples

Federico

2012

Guilherme Jenovencio, Rodrigo Ferraz

ANSYS, CFD

ESSS,

Italy

2012

Edmundo Queiroz de Andrade

modeFrontie r

Petrobras

Italy

2012

Exploring the potential of climate adaptive building shells

TRNSYS

Eindhoven University of Technology

Italy

2012

Towards zero energy industrial halls simulation and optimization with integrated design approach Simultaneous estimation of multiple thermal parameters of large format laminated Lithium ion batteries Optimization of wind farm layout using Genetic Algorithm

Roel C.G.M. Loonen, Marija Trcka, and Jan L.M. Hensen Bruno Lee, Jan L.M. Hensen

modeFrontie r

Eindhoven University of Technology

Verona

2013

Zhang, Bin Wu, Zhe Li, Jun Huang

CFD

Tsinghua University Jianbo

Beijing

2013

Tales G. do Couto, Bruno Farias, Alberto Carlos G.C. Diniz, Marcus Vinicius G. de Morais Teresa Donateo, Federica Tornese, Domenico Laforgia Martin Bünner

Genetic Algorithm

University Brasília

Brasilia

2013

GA, MatLab

Università del Salento

Italy

2013

NelderMead method; QuasiNewton method; SQP method; NTB- CFD

NTB, Institute for Computational Engineering

Germany

2014

Computer Aided conversion of an engine from diesel to methane.

Optimization & Automatic Design of Fluid-Dynamical Systems: Towards Optimal Shapes for Wind Turbine Blades,

Uranium Enrichment Cascades Modeling with Optimized StageMixing Parameters for nonproliferation analysis ECO evaluation for remote laser welding Grid geometry effects on pressure drops & heat transfer in an EMbaffle heat exchanger New Method for Current Pulsation Calculation in Induction Machines Driving Reciprocating Compressor Assessment of costoptimality and technical solutions in high performance multi-residential buildings in the Mediterranean area Assessment of costoptimality and technical solutions in high performance multi-residential buildings in the Mediterranean area An analytical and FEM modelling of a large turbo generator for the determination of the induced currents in rotor components An analytical and FEM modelling of a large turbo generator for the determination of the induced currents in rotor components Automatic selection of closure relations for TUFFP two phase flow Unified model, Multiobjective optimization of a hydrogen production process powered by solar energy

Giovanni Mercurio, Patrice Richir,

CFD

Joint Research Centre European Commission

Italy

2014

Kiran Bhat, Ian Stroud, Jumyung Um, Francesco Perrone

CFD, MOGA

Cambridge University

2014

ANSYS, CFX, CFD

EMbaffle

Italy

2014

Andrea Fusi, Alessandro Ussi, Francesco Grasso, Florent Pasteur

LMS, AMESIM

Siemens

Italy

2015

Ilaria Zacàa, Delia D’Agostino b, Paolo Maria Congedoa, Cristina Baglivo

CFD

University Salento

Italy

2015

Ilaria Zacàa, Delia D’Agostino b, Paolo Maria Congedoa, Cristina Baglivo

CFD

University Salento

Italy

2015

Michele Raciti, Ansaldo Energia,

FEM. ANSYS, CFD

Joint Research Centre European Commission

Italy

2016

Michele Raciti, Ansaldo Energia,

FEM. ANSYS, CFD

Joint Research Centre European Commission

Italy

2016

Leonid Korelshteyn, Alexey Babenko

modeFrontie r, CFD, GA

Italy

2016

Manolo Venturin, Raffaele Liberatore

CFD

Piping System Research & Engineering Co (NTP Truboprovod) EnginSoft, Mariarosaria Ferrara University

Italy

2016

Table 1: Reviewing of modeFrontier applications in energy

The figure below gives about the expansion the modeFrontier over past years. From the graph, it is realized that the use of this software is increasing year by year due to its simplicity and quick optimization methods. This shows that modeFrontier application has increased at very high rate in recent four years.

Figure 3. Use of modeFrontier in energy sector

The use of this software is global. Almost every developed and developing countries need optimization for the generation of energy. Therefore, for such kind of problem the companies of these countries use this softwares. The geographical expansion of this software can be realized through the figure given below.

Figure 4. Geographical expansion of modeFrontier in energy sector

4.2. Health Sugase et al. (2008) use modeFrontier to demonstrate the short-term memory trace. It is a rapidly adapting synapse of inferior temporal cortex. To study short-term memory â&#x20AC;&#x153;two brain areasâ&#x20AC;? of monkeys are recorded. modeFrontier helps in allocating the working area in mind. It also helps to find out the delay period. It also refers intervening and non-matching stimuli wipe out the delay-period activity in ITC neurons. Gustavo (2011) develop a system control for compressor valves. This evaluates the potential of suction's valve movement. This is done in order to reduce the noise level 23

of a hermetic compressor. This is used in domestic refrigeration systems. This also modify the opening movement of suction valve. modeFrontier helps to change pressure pulsation in the suction chamber. It also helps hermetic compressor in simulating the program recip. It also simulates the compressor of the suction valve. It helps to maintain the order of 10 dB in the suction chamber. Bogdan et al. (2016) describe the transitional flow in patient-specific arteriovenous fistulae. This is widely used for hemodialysis. Arteriovenous fistula (AVF) is used for hemodialysis patients. These have high failure rates. This is due to development of neointimal hyperplasia in the juxta-anastomotic vein. This research investigates the blood flow in patient-specific AVF. This is done in order to characterize local wall shear stress acting on the AVF walls. The 3D model of AVF from contrast-free magnetic resonance images. After that it is performed high-resolution CFD simulations. In this the modeFrontier characterized the flow field and categorized disturbed flow areas. It impairs the physiological response of endothelial cells and trigger NH formation. Andrea (2016) uses modeFrontier in optimization of fluid-structure interaction analysis of polymeric membrane. The study is mainly focused to reproduce real stress and deformation fields into a polymeric membrane that works inside complex industrial filling valve. A CFD model was developed. This is done in order to estimate loads distribution. This acts over membrane during the closure procedure of valve. In this case modeFrontier creates real characterization data. It also generates a model allows to reproduce 1 way fluid structure interaction analysis. The figure below shows the modeFrontier analysis methods which is carried out through CAD system.

Figure 5. FEM in preliminary design (Stefano, 2016)

Christian et al. (2016) uses modeFrontier to demonstrate real neurons-nanoelectronics architecture with memristive plasticity. The artificial neural networks run on conventional von Neumann computers. This is run with the help of modeFrontier software. It appears in terms of speed, robustness energy efficiency and adaptability. This research is done by self-organizing connectivity, merging data storage, 24

reconfiguring and processing into single electronic devices. Nano and micro transducers allows direct electrical interfacing network of neurons. modeFrontier is used to provide new and unique adaptive system. It also helps in self-organizing and evolving properties deriving from the fusion of natural and artificial neuronal elements. This leads into a new plastic entity. The figure shows the various stages where modeFrontier is effective. The first shows the original design and the next two shows the optimized structure.

Figure 6. Packing Optimization (Vincenzo, 2016)

Stefano (2016) uses modeFrontier to demonstrate the part of numerical simulation in product development. This paper uses the example of bicycle. The numerical simulation is intensively used in the product development cycle. For this FEM, robust and carbon fibre designs are created. modeFrontier helps to introduce the concept of external support for the bigger chain ring to improve lateral stiffness. It also gives an output text result file with a number of calculated quantities. This is directly related to wheelâ&#x20AC;&#x2122;s performance. FEM analysis is also required for a new product development which is done with the help of modeFrontier software. Vincenzo (2016) did a case study to show the role of modeFrontier in optimize packaging protection. Numerical simulations were used to investigate. LS-DYNAÂŽ is used for analyzing the large assembled parts of the final product and also the packaging. This study is carried out to guarantee the integrity of the product from factory to customer. Therefore, it reduces the customers service calls. In this case, the FE model reproduces the testing conditions i.e. defined by internal Electrolux regulations. The result obtained from modeFrontier is used to improve overall performances. Edge drop tests and inclined planes are studied in modeFrontier. modeFrontier improves the speed up of product development. It also reduces the time to market (TTM).

S.No.

Project

Authors

Short- term memory trace in rapidly adapting synapses of inferior temporal cortex

Yasuko SugaseMiyamoto, Zheng Liu, Barry J. Richmond, Matthew C. Wiener, Lance M. Optican

CFD, ANSYS

Development of system control compressor valves

Gustavo Myrria Neto

CFD

Transitional Flow in Patient-Specific Arteriovenous Fistulae for Hemodialysis Fluid-structure interaction analysis of polymeric membrane Real neuronsnanoelectronics Architecture with Memristive Plasticity

Bogdan EneIordache, Andrea Remuzzi

CFD, MOGA, Numerical Analysis

The role of numerical simulation in product development: examples from bicycle industry Impact simulations on home appliances to optimize packaging protection: a case study

Algorithm

Company

Location

Year

Merck Research Laboratories

USA

2008

Universidade Federal de Santa Caterina IRCCS

Brazil

2011

Italy

2016

Andrea Piazza

SIDEL

Italy

2016

Marco Fanciulli, Giacomo Indiveri, Christian Mayr, Themis Prodromakis, Sabina Spiga, Grazia Tallarida, Ralf Zeitler Stefano Garbin

University of Padova

Italy

2016

FEM

Campagnolo

Italy,

2016

Vincenzo Colozzo

LS- DYNA

Electrolux

Italy

2016

Table 2: Reviewing of modeFrontier applications in health

Figure 7. modeFrontier application in health

From the above graph, it is seen that the demand of this software is increasing year by year. Even on the geographical level this software is expanding from one countries to another. This can be seen in the figure shown below.

Figure 8. Geographical expansion of modeFrontier in health sector

4.3. Materials Robin (2008) shows the use of modeFrontier in radome optimization for airborne jammers. This needs to examine the parameters which affect the construction of modern radome. This helps to minimize the time of laborious work. In this, ANSYS used to calculates the strength of radome structure. MATLAB is used to calculate electromagnetic data and cost models of unusual sizes and materials. For optimization modeFrontier picks data from the program and solve the optimization algorithm. It helps to find out the less material requirement. It also helps to find lower manufacturing costs. This results in the cheapest solution possible. AndrĂŠ et al. (2008) uses modeFrontier for design optimization of layered composite structures. An expensive composite material can be utilized with the use of appropriate design and analysis methods. The approach used, involves specific issues that are not addressed in general finite element analysis tools. Tools are used for the description composite optimization problems. This includes the dependencies between zones and sub-laminates This ignores the composite specific failure modes in design. modeFrontier provides multi-objective optimization capabilities, process integration environment, visualization of results. It represents an industry proven solution. Marco et al (2009) uses modeFrontier for material model calibration at automobile Lamborghini. This procedure calibrates the parameters of LS-DYNA. It is advanced material models. After, this model is used for predicting the design optimization and robustness analysis. modeFrontier reduces the cost of expensive experimental tests. It allows the influence of physical and geometrical variables. This depends on the 27

composite dynamic structural response. This also used to obtain improved solutions for industrial case study. Ronan et al (2009) uses an optimization strategy to the design of an injected plastic pot. The research is basically focused to the minimize the part weight while meeting structural and rheological constraints. This pertains to the production line consumption strategy. The deformation is controlled by conditioning and injection molding. modeFrontier helps in reduction of weight and material cost. It also deals with polymer processing issues. It enables to generate a parameterized design. It also validates the behavior of the part submitted to a mechanical constraint and controls the injection process. The figure shows the Pareto design obtained in modeFrontier.

Figure 9. Pareto design of a plastic pot (David et al., 2009)

Vincent et al (2011) design a robust PI- LPV tension control with elasticity observer. This is designed for Roll to Roll system. This needs multiobjective optimization approach which is done by modeFrontier. It helps to maintain web tension in the entire processing line. This paper give an idea to design linear parameter varying PI controllers with quadratic performance using genetic algorithm. In this case modeFrontier helps in minimization of the cost function. Cavaliere et al (2011) uses modeFrontier for having multiobjective optimization of steel fusion welding process. This process is characterized by phase transformations. This paper deals with different properties of steel welds hardness, residual stresses, phase transformations, tensile, and fatigue. The welding approach is achieved by employing a multi-objective optimization software. modeFrontier is used to employ data to build a predictive database for industrial welding procedures. It also evaluates the mechanical properties of the structural materials and include the weld metals. It gives broad experimental campaign performed on different steel joints obtained with different processing parameters.

Gerhard (2012) demonstrated the integration of materials and engineering modelling. This is done by integrating materials computational tools and information with computational and analytical tools which are already in use. The process is carried on composite materials (Glass bead composites, glass fibre polymer composites) with the help of DIGIMAT Technology (Digimat-MF). DIGIMAT-MF integration is done in modeFrontier. It shows variation of glass bead fraction, minimizes thermal anisotropy and also maximizes stress at full load as indicator of strength. The figure 5 shows the engineering optimized workflow. This shows how the multiobjective optimization helps in getting the optimum trade- off solution. The figure shows the optimized engineering task model through different models and constraints.

Figure 10. Optimize atomistic models for specific engineering task (Gerhard (2012)

Neilson et al. (2012) use the modeFrontier for investigating the optimized composite scarf. This repairs with practical constraints. The composite materials are used in the aerospace industry. They are widely used because of their high specific properties. This investigates the effectiveness of scarf repair optimization for minimum adhesive shear stress concentration. This is done with genetic algorithm together with modeFrontier software. The finite element analysis (FEA) packages is used for the scarf repair modelled. modeFrontier helps to find each ply angle as a variable. It also limits the design space to repair stacking sequences. It also helps to find the scarf angle of the repair and the number of plies within the repair. The figure shows the engineering optimized workflow. This shows how the multiobjective optimization helps in getting the optimum trade- off solution. The figure shows the modeFrontier effect on the integrated optimization techniques.

Figure 11. Integrated Optimization (Pullumbi et al, 2012)

Hensen et al (2012) demonstrated that optimized Design Solutions are robust for industrial halls. This is done with Varying Process Loads and Occupancy Patterns. This deals with the most optimal combinations of values of demand side parameters. This minimizes the total energy consumption of ventilation, heating, and lighting for a typical industrial hall. The building energy performance simulation is done with program TRNSYS. This is used to perform the energy analysis for cooling and heating demands. For this MOGA is the optimization algorithm chosen in modeFrontier. This gives an idea to minimize the total energy consumption for designing industrial halls. Silvia et al (2013) use modeFrontier to design a tool named BENIMPACT Suite for zeb performance assessment. This tool is used for designing and realizing the NZEB (nearly zero energy building) and EPBD (energy performance of buildings directive)building structure. This is developed by EnginSoft (Italy). This allows to verify how the building performance level is influenced. modeFrontier provides simulation tasks. This helps in compared with monitored data. It helps in providing an idea related to energy consumption. Andreas (2014) uses CAE method in modeFrontier for successful additive layer manufacturing. This is also known as 3D Printing. It is the forthcoming advanced manufacturing technique. This paper discusses the enormous potential of light weight designs and the barriers of commercialization. It also presented the set of CAD and CAE methodology to unlock the ALM potential. There are several benefits of ALM techniques like high buy to fly ratio, net shape manufacturing, flexibility in stiffness and many more. modeFrontier in this case provide to get the optimum solution for lighter weight designs. It also provides complex biomimetic structures. Dominique et al (2014) analyzed the web wrinkles in industrial Roll-to-Roll plants system using finite element modeling. These systems handle web material such as papers, polymers, textiles or metals. In these systems, troughs and wrinkles of the web are the most common and expensive defects. This paper deals with industrial production 30

lines to simulate the dynamic 3D behavior of the moving web. This is done by using finite element modeling. modeFrontier helps to understand which parameters lead to web wrinkles and which are the most influent. Alexandre et al. (2014) uses modeFrontier for modelling of transverse segregation on centrifugally-cast functionally graded composites. In this paper, a modelling effort is undertaken. This is done to quantify the effects of the acceleration ramp as well as predicting the occurrence of transverse segregation. This also modifies the Stokesâ&#x20AC;&#x2122; equation. modeFrontier helps to contemplate the specific effects of the solidification front on particle distribution. It also helps to implement the GPROMS software environment. Elisabeth et al (2014) uses modeFrontier for experimental and numerical simulation of titanium flow forming. The main objective of this is to improve the production of safe parts without damage and with good final mechanical properties in a very short time. This is done by modelling the incremental processes like flow forming process. modeFrontier provides adapts mesh management. It also gives mesh optimization, computing time and validation through experimental tests. Bartolozzi et al (2015) gives simplified FE model of rolling tires. This is given for the simulation of dynamic forces at hub level. The main aim was to develop an accurate and easy to use methodology to obtain an FE model of a tire. It also determines the hub forces of a rolling tire on a drum test bench. This also includes the tire model in a fullvehicle simulation. modeFrontier enforced displacement to all the nodes included in the contact patch area. It also gives a rigid plane approaches the tire and compresses it until the measured static loaded radius is reached. Ilaria et al (2015) published a paper which shows the application of a modeFrontier to identify cost-optimal levels in new residential buildings located in the Mediterranean area. It gives multi-objective optimization methodology to assess energy and cost effectiveness in new buildings. This is carried out to reach diverse types of highly energy efficient external walls for the Mediterranean climate. modeFrontier helps to simulate for economical high efficient buildings. Its solution provides a cost-optimal design. Dylan et al (2016) uses modeFrontier to find an optimize methodology for the determination of cyclic plasticity model. This paper presents the improvements to achieve the multi-component AF Model with Multiplier (MAFM). This is done through a numerical optimization methodology. In this variety of strain/stress loading cases at different plasticity levels have been examined. modeFrontier is used with the genetic algorithm MOGA II algorithm. It helps to drastically improve the number of different loading cases. It also recommends adjusting 1y, 2y and 4y when using the MAFM model. 31

Martin et al (2016) identify the mechanical properties based on nanoindentation experiments. This is presented to extract material properties from the sample material. For this, the experiment is conducted in a multidisciplinary laboratory for space research, SPACE-S. Mechanical properties of specimen materials are identified with physical experimentation. Then the simulation and optimization is conducted. It provides mechanical properties in the form of the Young modulus, work hardening exponent and material hardness. modeFrontier provides the optimization module and brought quick results in this methodology. S.No. 1.

10.

Project Radom optimization for airborne jammers Design Optimization of Layered Composite Structures LS-DYNA AND modeFrontier for material model calibration at automobile Lamborghini Multi-objective optimization applied to the mass reduction of a plastic part Robust PI–LPV Tension Control with Elasticity Observer for Roll– to–Roll Systems Multi-objective optimization of steel fusion welding Integration of materials and engineering modelling: getting down to atoms Investigation into optimized composite sarf repairs with practical constraints Towards zero energy industrial halls-simulation and optimization with integrated design approach Beninpact suite a tool for ZEB performance assessment

Authors

Algorithm

Company

Location

Year

Robin Brorsson

ANSYS, MATLAB

Saab AB Avitronics

Dubai

2008

André Mönicke & Harri Katajisto & Markku Palanterä

MOGA

Componeering Inc

Italy

2008

Marco Perillo, Vito Primavera, Luca Fuligno, Giulia Fabbri, Casper Steenbergen, Nicolò Pasini Ronan Le Goff, Thierry Burret, David Garcia Pôle Européen de Plasturgie Vincent Gassmann, Dominique Knittel

LS- DYNA

EnginSoft

Italy

2009

MOGA

EnginSoft

Italy

2009

PI controllers, GA

National Institute of Applies Sciences, Strasbourg

Italy

2011

P. Cavaliere, A. Perrone

CFD

University of Salento

Italy

2011

Dr Gerhard Goldbeck

DIGIMAT

Goldbeck Consulting

Pharma

2012

S.V. Neilson, A.C. Orifici, C.H. Wang,

FEA

RMIT University

Melbourne

2012

Bruno Lee, Jan L.M. Hensen

TRNSYS, MOGA

Eindhoven University of Technology

Netherland

2012

Silvia Demattè,Cristina Grillo, Angelo Messina, Antonio Frattari,

CFD

EnginSoft

Italy

2013

11.

12.

13.

14.

15.

16.

17.

18.

CAE Key to Successful Additive Layer Manufacturing Analysis of web wrinkles in industrial Roll-toRoll plants using finite element modeling Materials Research Centre, Modeling of transverse segregation on centrifugally-cast functionally graded composites Experimental and Numerical Simulation of Titanium Flow Forming Simplified FE Modeling of Rolling Tires for the Simulation of Dynamic Forces at Hub Level Assessment of costoptimality and technical solutions in high performance multiresidential buildings in the Mediterranean area An Optimisation Methodology for the Determination of Cyclic Plasticity Models’ Parameters Identification of mechanical properties based on Nanoindentation experiment

Andreas Vlahinos

CAE, CAD, ALM

EnginSoft

Italy

2014

Dominique Knittel, Yannick Martz

CFD

University of Strasbourg

Italy

2014

Alexandre Velhinho, Gonçalo Rodrigues, José Paulo Mota, Rodrigo Martins

gpROMS

Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia; CENIMAT/I3N

Verona

2014

Elisabeth Massoni, Dorian Depriester

CEMEF MINES ParisTech

Verona

2014

Bartolozzi, G., Danti, M., Nierop, G., and Camia

FIAT Chrysler automobiles

Pharma

2015

Ilaria Zacàa, Delia D’Agostino b, Paolo Maria Congedoa, Cristina Baglivo

CFD

University of Salento

Italy

2015

Dylan Agius, Mladenko Kajtaz, Kyriakos I. Kourousis

MAFM, MOGA

RMIT University

Italy

2016

Martin Lamut

GA, CFD

Space-SI

Australia

2016

Table 3: Reviewing of modeFrontier applications in material

Figure 13. modeFrontier applications in material sector

From the graph shown above it can be seen that there is high increasing demand for this software per year. Due to its userâ&#x20AC;&#x2122;s friendly behaviour and quick response it is widely accepted to various countries. The geographical expansion of this software can be seen from the figure shown below.

Figure 14. Geographical expansion of modeFrontier in material sector

4.4 Manufacturing Amos (2008) uses modeFrontier to optimize the fatigue life of a diesel engine. The main objective is to maximize the inter-cylinder walls. This also includes the maximization of minimum bead pressure. This is during the low temperature phases of combustion. The robust design of modeFrontier algorithms help to address the multiobjective design optimization. This is done by allowing one variable and three constants to be defined as stochastic. It automatically created a set of sample designs during the optimization. This able to improve the Fatigue Safety Factor by 15%. The figure below shows the amount of maximum pressure reduction due to the optimization technique.

Figure 15. Parameter Optimization FE: Connecting Rod (Motoren, 2008)

Nikbay et al (2010) design an optimization of an aircraft wing. This is a reliability based structure. In aircraft industry, there need to design new aircraft which are faster and quieter. This paper proposed an implementation of a homemade RBDO code. This is based on Reliability Index Approach (RIA). It is done into a structural optimization framework. The modeFrontierÂŽ optimization tool provides a reliable and efficient structure of aircraft wing. It includes genetic algorithm and CFD. Iwane et al (2011) describes a numeric approach to multiobjective optimization in manufacturing design. This is done by using the modeFrontier software. This aims at boosting the yield rate. This paper deals with a new optimization methods called quantifier elimination. This is based on symbolic algorithm and numerical computation. The total efficiency of the design process can be improved by reducing the number of numerical yield-rate evaluations. modeFrontier improve the total efficiency of design process. It produces accurate relations among design parameters or objective function. Guan et al (2011) develop the most favorable material property parameters. This develop finite element models of rat skull bone samples. This is used to predict the reaction of traumatic brain injuries in humans. modeFrontier used to develop a material based optimization methods. This is done to minimize the differences of three-point bending test responses. modeFrontier automatically updated input parameters and submitted to LS-DYNA. It also reduces the time needed in completely the task. Kudriavtsev et al (2011) uses modeFrontier for optimization of radiative heating. This is done for manufacturing of silicon solar cells. This paper deals with the minimization of thermal variation. This is across single substrate and a group of substrates. This is done during radiant heating stage. This optimization is done through a variety of high temperature thermal processes. ANSYS workbench develops lamp heating surface to surface design. This is a thermal conduction-radiation model. modeFrontier provides unique insights into system behavior. This lead to innovative enabling design solutions. Zeguer (2012) uses modeFrontier to find out whether upfront simulation and CAE driven design. These are reality or long term dream. This paper explains the business drivers for simulation. It deals with the MDO requirements and issues. The 35

requirements for MDO are: Own Excel template, create project workflow, post processing / optimization. CAE that uses trial and error analysis and can be used to drive the design. modeFrontier simulation code LS-DYNA support for business needs. Alessandro et al (2012) present a structural design for stochastic improvement. This includes trial and error procedures drafting, preliminary evaluations, verifications and many more. This paper presents the probabilistic analysis and optimization of structural details of aeronautical. The work is set to be run by ANSYS, LS-DYNA. The input values are embedded in an input file for ANSYS preprocessor. This is processed by LSDyna to simulate the riveting operation. modeFrontier perform 3-level DOE analysis of the problem. It built the response surface of the problem to save time. Aslam (2013) analyzes the manufacturing of supply chains using system dynamics and multiobjective optimization. This paper focused on introducing a methodology to address supply chain management problems. This is done within a truly Pareto-based multi-objective optimization. In this the simulation is based on system dynamics. The optimization utilizes multi-objective meta-heuristic search algorithm. For this simulation software VensimInterface is coupled with modeFrontier. It leads to optimal results in SCM decision-making. Agazzi (2015) presents â&#x20AC;&#x153;Field tests vs CFDâ&#x20AC;? results in gas applications. This is done by EMbaffle technology. This is a patented shell and tube heat exchanger technology. This is designed to improve performance and reduce pressure drops. The CFD analysis outcomes granted a basic comprehension of the mechanism of turbulence generation. This test campaign on gas fluids. This increases Reynolds number confirmed the correctness of the HTRI SW algorithms. modeFrontier gives a straight comparison between experimental tests data and CFD results. Bresciani (2015) uses modeFrontier for knowledge management in LVSB R&D. This is done by ABB SACE software simulated in modeFrontier. This software is based on Excel spreadsheet. This paper deals with the ABB organized global divisions: Power products, power system, discrete automation and motion. Low voltage products and process automation. It basically deals with the different types of MCBs. In this modeFrontier allow to evolve the existing calculation. Desando et al (2016) describes the heat transfer numerical analysis applied to a quenching process. For modern technology applications, there is high temperature requirements of thermal and structural stresses. Because manufacturing process utilizes this to produce more durable materials. This paper deals with the explanation of the application of literature correlations with water and oil as quenching mediums. This is done on different geometries. The main objective is to estimate the heat transfer behavior during the thermal process. A numerical model with thermal transient features has been implemented to simulate the quenching process. A mesh sensitivity is also 36

carried out. modeFrontier is used to evaluate the cell thickness influences and the accuracy of the cooling rate curves. Esposito (2016) conducts the simulation of real defects with the industrial computed tomography. This is done with help of modeFrontier. The goal of the work is: first, to produce a relayable procedure to perform structural simulation of real components. This is done by considering their real defects, instead of the ideal CAD. Second, test this procedure to assess its reliability. This follows the following procedure: selection of a repeatable benchmark. This allows to obtain repeatable and verifiable results, Tomographic scan of the benchmark. The benchmark of the procedure is the additive manufacturing tensile sample with designed internal defects. modeFrontier is used for strain gauge results from the defect. Simulation results from the defect using real material properties and from the defect using average material properties. The figure below gives the modeFrontier optimized structure. This shows the difference between the tensile test result with and without optimization methods.

Figure 16. Structural simulation with ANSYS 17.0 (Esposito 2016).

No. 1.

Project Advanced numerical optimization methods in the rapid product development process of diesel engines Reliability based structural optimization of an aircraft wing

Authors Amos Giovannini

Melike Nikbay, Necati Fakkusoglu, Muhammet N. Kuru

Algorithm FE Analysis

RBDO RIA

code,

Company BMW

Location Austria

Year 2008

Istanbul Technical University

Istanbul

2010

10.

11.

12.

A SymbolicNumeric Approach to Multi-Objective Optimization in Manufacturing Design Application of optimization methodology and Specimen specific finite element models for investigating material properties of rat skul Multiobjective Optimization of radiative heating for silicon solar cells manufacturing Upfront simulation and CAE driven design reality or long term dream Stochastic improvement of structural design Analysis of manufacturing supply chains using system dynamics and multiobjective optimization Field tests vs CFD results in gas applications for EMbaffle technology Knowledge management in LVSB R&D (An ABB SACE experience) Heat Transfer Numerical Analysis Applied to a Quenching Process, The simulation of real defects with the Industrial Computed Tomography

Hidenao Iwane, Hitoshi Yanami, Hirokazu Anai Fengjiao Guan, Xu Han, Haojie Mao, Christina Wagner, Yener N. Yeni, King H. Yang Vladimir Kudriavtsev, Terry Bluck,

Quantifier elimination

Fujitsu Laboratories Ltd

Japan

2011

FE model, LSDYNA

Henry Ford Hospital

USA

2011

ANSYS

INTEVAC

USA

2011

Dr Tayeb Zeguer, Jaguar Landrover Soprano Alessandro and Caputo Francesco Tehseen Aslam

CAE/ DYNA

LS-

Jaguar Landrover

2012

LSDYNA, ANSYS

Second University of Naples

Italy

2012

Pareto Optimization

University of Skovde

Sweden

2013

Daniele Agazzi, Brembana & Rolle

CFD

EMbaffle B.V.

Verona

2015

Nicola Bresciani

CFD, MOGA

ABB SACE

Verona

2015

Alessio Desando, Elena Campagnoli,

Numerical analysis, CFD

Polytechnic University of Turin

Parma

2016

Fabio Esposito

CFD, GA

TEC Eurolab

Parma

2016

Table 4: Sample of modeFrontier applications in manufacturing

Figure 17. modeFrontier applications in manufacturing sector

From the graph and discussion above it is clear that the use of this software is increasing every year. Furthermore, the use of this is not only increasing in limited countries for the manufacturing process, it is also expanding in different countries. Most of developing countries and developed countries are using this software to carry out the optimization problems.

Figure 18. Geographical expansion of modeFrontier in manufacturing sector

4.5 Transportation In the field of transportation, the modeFrontier has several applications. According to the analyst the global automotive and transportation industries are recovering so fast. However, it also faces pressure to develop vehicles that are smarter, safer and cheaper. This reduces the fuel emission and consumption. So, with the help of modeFrontier 39

anyone can reduce the development cycle time, lower cost, and increases the degree of product innovation. These can be done by taking the advantage of computer-based optimization, sensitivity analysis, and robust design. There are number of factors in this system which needs to be optimized. Francesco et al. (2014) uses modeFrontier in optimization of the liquid movement in automotive fuel tank. A complex activity is needed for advanced modeling of liquid movement in automotive fuel tank. This is done to enhance design and validation of automotive fuel tanks. This includes fluid-dynamic simulations, track tests and a new bench tests. The target is to prevent fuel spill to vapor canister and vapor pressure build up. CFD simulations have been used to assess the accuracy of bench tests compared to road tests. This allows to simulate vehicle accelerations. This is done by using a combination of tank motion and tilt on all axes. Numerical analyses have been performed to explore fluid dynamic inside the tank. This targeted to evolve the CFD approach (modeFrontier) into a design validation and optimization tool. Sylvain et al. (2014), state a complex system engineering simulation through distribution. Co-simulations is a diverse tool which needs to model different components of a vehicle. In addition, the complexity and size of such models require a better use of computing resources. This is done with the objective of providing system models quickly by assembling these system models in a â&#x20AC;&#x153;plug-and-playâ&#x20AC;? architecture. It also providing control over the models computing load distribution between multiple cores and machines. CFD methods is used in modeFrontier. It helps to give a simulated design within very less time. Sylvain et al. (2014) demonstrate these functionalities through the example of a Simulink vehicle model. This communicates with detailed sub-models in their expert tools. In this case modeFrontier is used to interface different simulations processes together and save time for optimization. In automotive manufacturing, Robotic Remote Laser Welding (RLW) is emerging as a powerful and promising technology. This welding is done with the help of modeFrontier software. RLW can easily create joints in distinct locations of the product through simple robot repositioning. Laser beam redirection from a remote distance. RLW takes advantage of the three main characteristics of laser welding: high power beam capable of creating a joint in fraction of second, non-contact and single side joining technology. The figure below shows the system representation inside the software.

Figure 19. System representation in Autonomie (Philips et al., 2014)

According to Pasquale et al. (2014), design a system for synthesis of robotic remote laser welding. It needs simulation and optimization of assembly processes with nonideal compliant parts joined using RLW. This involves workstation configurator and planner; part variation modeler, RLW Process Parameter Optimizer. In this modeFrontier facilitate design optimization for improved part and assembly performance. It also provides initial optimized locator strategy for input to assembly fixture 3D design. It also reduces process and equipment commissioning time. Oliver et al, (2014) demonstrate the dynamic testing of a car body. This is done on a virtual test rig of a reliable fatigue assessment of mechanical components. This is done by testing on hydro-pulse rigs. For this CAE methods are used in parallel. It is necessary to correctly reproduce the loading and dynamic response of the virtual component. An improved fatigue calculation of dynamically loaded parts by applying correctly identified loads. This includes the nonlinear contact behavior within the deformable structure. The correct loading is calculated by using the method of virtual iteration on a simulation model of a test rig. The simulation process is done by modeFrontier combines several technologies. An iteration loop for determining realistic loading structure is carried out by FEMFAT Lab virtual module. The figure below shows the analysis process done in modeFrontier. According to Lombardi et al. (2014), the use of computational fluid dynamics techniques is characterized by specific peculiarities. In the cooperation between the University of Pisa and Piaggio an analysis of a CFD approach for the study of the aerodynamics of a scooter is analyzed. This approach can be considered a useful engineering tool in the scooter design. From the CFD a complete description of the flows with and without the spoiler are obtained and compared. This approach which is done by modeFrontier has advantage of providing a complete description of the flow 41

field. It also gives the forces acting on different elements of the scooter. Therefore, a better understanding of the physical behavior is obtained. The figure below shows the pressure calculated in the symmetry plane. This is the result which comes out from the modeFrontier.

Figure 20. The total pressure in the symmetry plane (Baldini et al., 2014)

Bhartendu (2015) states that in the field of automotive there needs highly progressive improvement in overall efficiency. This is done by integrating the intercooler with air intake manifold. This will lead to an increase in overall systemâ&#x20AC;&#x2122;s performance. It also opens the room to provide more technological alternatives for psychological comfort of the end user. If the fluid dynamic behavior of the air is optimized, it will lead to improvements in the heat exchange through intercooler. It also reduces the air temperature going into the engine. It also minimizes the overall pressure loss of the system. ANSYS tool used to analyze and optimize the complex phenomena. This will make the flow uniform. By using the modeFrontier the turbulence of intercooler can easily be reduced. This can be done by internally designing the upper planum. The given figure shows the difference between the base approach design and the optimized design.

Figure 21. Upper planum internally design (Bhartendu, 2015)

Emanuela et al, (2015) presented a paper for predicting the most influential structural resonances for a variable oil pump. The adoption of criteria aimed at improving the vibro-acoustic behavior of products. It has assumed a growing importance. The calculation done with modeFrontier provides the list of natural frequencies together with the respective modes of vibration of the structure. After an analysis of the sound emission generated by the pump a consequence of the application of unitary impact forces to a set of chosen points on the structure. The simulation process done with the help of modeFrontier activity accompanied by an experimental activity. This acts as a validation support of the calculation results. It makes the process simple. Andrea et al. (2016) carried out a project in VM motori company for multiobjective optimization of a V6 3.0L diesel engine under different operating condition. The studied carried out on different operating conditions like full load, emission area and transient condition. The influence of several engine design parameters on engine performance in terms of Time to Torque, Torque and Air/Fuel ratio and Brake Specific Fuel Consumption (BSFC) are observed. In an automated way, the integrated work of the two software, GT-SUITE and modeFrontier are manage. The high number of parameters involved and for the strong conflict of optimization objectives. It has been possible in modeFrontier to choose the final engine configuration amongst all optimal solution identified by the optimization software (Pareto frontier). The optimized engine ensures a significant improvement of performance in transient condition at the same time and reduces the fuel consumption and emissions. Ferdinando (2016) focus on the increasing the use of alternative fuels for automotive application. This allows an improvement of the environmental sustainability and the strictest international standards. It requires high performance for the system and its components in different operating condition. In the LPG gaseous injection system, the fuel that comes from the tank is a mixture liquid-vapor. It must be completely vaporized before it reaches the injectors. The evaporator-pressure regulator ensures the completely vaporization of the fuel. The correct pressure of injection in the different engine operating condition is installed on-board system. The evaporator developed by Landi Renzo S.p.A. It is a heat exchanger of a liquid-bi-phase mixture. To optimize the heat exchange and increasing the heat transfer to the LPG a conjugate heat transfer analysis has been performed. This is done to simulate the different operating conditions of the component in terms of water flow, water temperature, LPG flow rate and LPG composition. The CFD simulations done by modeFrontier have allowed realizing an optimized geometry ensuring the excellent performances in the presence of very low environmental temperature. This simulation has proved to be an excellent tool to define

the characteristic curve of the evaporator performance. The figure below shows the CFD model for the injection system in the automobiles.

Figure 22. CFD models (Ferdinando, 2016)

For an optimum design of exhaust system of a ship a study is carried out by Hasan et al. (2016) in EnginSoft (Turkey) and Istanbul Technical University. During this study, the 3-dimensional model of the system has been constructed. The CFD analyzes covered flow mechanism of back pressure. By using optimization platform modeFrontier the acoustics and back pressure at the same time are derived. No.

Project

Authors

Algorithm

Advanced modeling of liquid movements in automotive fuel tanks

Francesco Fortunato

CFD

Fiat Group Automobiles

Verona

2014

Complex System Engineering Simulation through Distributed Cosimulations

Sylvain Pagerit, P. Sharer, A. Rousseau

CFD

Argonne National Laboratory

Verona

2014

Design Synthesis of Robotic Remote Laser Welding Assembly System with Compliant Non-Ideal Parts

Pasquale Franciosa

FEA

University Warwick

Verona

2014

Dynamic testing of a car body on a virtual test rig

Oliver Grieshofer

CAE, FEMFAT LAB

Engineering Center Steyr GmbH & Co KG

Verona

2014

Company

Location

Year

Analysis of a CFDapproach for the study of the aerodynamics of a scooter

G. Lombardi

CFD

University Pisa

Verona

2014

New Methodology: Intercooler Integration Space & Efficiency Optimization

Bhartendu Tavri

ANSYS, MATLAB

Magneti Marelli

Verona

2015

Critical frequencies and acoustic emission of an automotive Variable Displacement Oil Pump: some numerical analyses and validations

Emanuela LigarĂ˛, Riccardo Maccherini, Raffaele Squarcini

CFD, MOGA

Pierburg Pump Technology

Verona

2015

An automatic procedure for the optimization of pin bore profile for highly loaded pistons Conjugate heat transfer analysis of a LPG evaporatorpressure regulator for automotive application

Andrea Negrisolo

GT- Suite

Duraldur

Parma

2016

Ferdinando Ciardiello

CFD

LANDI RENZO

Parma

2016

Optimum Design of Exhaust System of a Ship

Hasan Avsar, Nasser Ghassembaglou, BulvarÄą, Sanayi Mahallesi, Pendik, Levent Kavurmacioglu

EnginSoft

Parma

2016

Table 5: Reviewing of modeFrontier applications in transportation

Figure 23. modeFrontier application in transportation sector

Transportation is very high demanding sector for any country. Each country has to focus on this sector to enhance the service to the citizen and also to the tourist. The graph shown above it is clear that its demand is increasing in each year. For the countries like India and China where the population density is very high it requires managing this sector. Therefore, MOO is requiring to efficiently manage it. From the figure shown below it is clear that for such optimization problem mF is widely used in different countries.

Figure 24. Geographical expansion of modeFrontier in transportation sector.

4.6. Aerospace Every country requires a powerful weapon system to protect its boundaries from the enemy country. The weapon system can be waterways, airways or its army. Many organizations and companies use to make this defense and aerospace equipment. Many optimization techniques are needed to make it up to date and up to the requirements. Many optimization techniques in several aspects are discussed below. According to Bernardo (2014), the parameters affects the combustion at target temperature with the reverse calculation of chemical equilibrium models. This is approached using modeFrontier design space exploration and response surface modeling techniques. The use of these techniques in modeFrontier significantly simplified the design problem resulting in an ethanol-fueled engine. This is done as 46

propellant-efficient as a kerosene-fueled engine with the same thrust but designed without such techniques. In 2008, the design of the L75 rocket engine is started. This requires liquid oxygen/kerosene as propellant pair i.e. 75 KN of thrust in vacuum and an estimated specific impulse of 324 s. According to Giulia (2014), for underwater defense system design a project is carried out by Wassi company. It is found that a modern underwater weapon system cannot be separated from the use of FEM simulations. This is because of the costs associated with the development, combined with the high performance and short lead time. It requires the execution of design activities to minimize design change iterations. It also reduces the risks associated with the test, often destructive. The use of FEM simulations and modeFrontier also help the designer to understand some physical phenomena. This helps to observe during test in the operative environment, CFD analysis are important to limit the number of test. Also, the ANSYS transient dynamic analysis is used to verify the installation and testing process. The figure below shows the optimized rubber structure done with the help of CFD.

Figure 25. CFD streamlines on torpedo rubbers (Giulia, 2014)

According to Stefano et al, (2014), the optimization of a EMA (Electro-Mechanical Actuator) system consists of two main components. First, recirculating ball screw and second, electric motor is presented. The main parameters like efficiency, mass, maximum force a consistent set of designs optimum can be considered. To achieve the design optimization of an electro mechanical actuator several optimization algorithms are applied in the Umbra Group Private limited. All the optimization process is done there by the integrating the optimization software modeFrontier with the electrical design Maxwell and an Umbra proprietary code FALICOST. In this case modeFrontier presents the algorithms based on response surfaces methodology as FAST algorithm. It also presents both NSGA- II controlled elitism and MOGT allow a wide exploration of the design space. The two figures shown below shows the comparison of the original and controlled algorithm. It also the its effect over the optimization methods. 47

Figure 26. Original NSGA- II (Stefano, 2014)

= Figure 27. Controlled Elitism NSGA- II (Stefano, 2014)

Raffaelo et al, (2014), demonstrate big evaluation of new production technology and CAE in Aerospace industry. Today this industry represents one of the main test bench for employment of innovative materials and automated production processes. The design, engineering and production phases are complex. All the operations and parameters must be managed effectively. But there is evolution in the manufacturing processes and the continuous upgrades of the virtual prototyping tools. This allows to support all the design phases efficiently. Compositi Avanzati, the company involved in the design and production of composite material structures. The CAE and modeFrontier software provide the element that should be removed or not through an iterative numerical procedure. Stefan (2015) explains, Lufthansa Technik carries out recalculations in a systematic matter to improve maintenance costs and fuel burn of modern and mature engines. The flow analysis of the fan module of a classic turbofan engine can be consider as an example of it. This describes the general flow topology of that design and possible room for improvements. This could not be detected with the tools available in the design phase of that type in the late â&#x20AC;&#x2DC;70s. modeFrontier provides the efficient design of the engine which reduces the cost. It also helps to reduce the time. It also uses genetic algorithm and ANSYS algorithm. Philip and Stefan, (2015) illustrates the use of numerical simulation in the maintenance of civil aero engines. This allows sensible economic decisions about maintenance workscopes. This affect the engine performance. CFD calculation which is done by 48

using the modeFrontier software can help in maintenance sector to find the right decision and to offer the most cost- effective maintenance to the customer. This makes necessary introduction of advanced optimization approaches to obtain overall optimal designs. Stefania (2015) proposed the Over-the-Wing-Nacelle which is a Multidisciplinary Design Optimization for the preliminary design of an unconventional aircraft concept. In this case, a complex MDA framework has been implemented through modeFrontier. This is done to account the interdisciplinary interaction and to provide self-consistent analyses. The problem of multi-objective optimization of an unconventional aircraft has been solved through aeroelastic assessment using modeFrontier, FEMWING, MSC.Nastran, FLOPSâ&#x201E;˘, Cart3D. Thus, the optimization criteria include minimum empty weight and minimum fuel weight, taking into account of structural, aeroelastic and mission constraints. modeFrontier give consistent and realistic tail sizes for conceptual design. It also reduces FEMWING. modeFrontier helps aero- structural analysis of the complete model, compromising airframe and subsystems weights from FLOP. This provides structural static, aerospace static flexibility and flutter. The two figure, shown below shows the effect of flutter analysis done with the help of software in the MDO and MOO.

Figure 28. MDO- MOO (min EW and FW) without flutter analysis (Gamma, 2015).

Figure 29. MDO- MOO (min EW and FW) with flutter analysis (Gamma, 2015)

Michele (2015) explains the understanding of the complex physics for spray atomization plays a significant role. This is of fundamental importance in different industries. The aims of this is to deliver technologies for combustion emission 49

reduction. The CFS and ANSYS allow the commercial code in the field of sprays atomization. modeFrontier predicts the spray distribution. It also helps in evaluation of NOx, CO and soot emissions. Fredric (2016) states that the prototype validation to product qualification can be used for the development cycle of a product is performed by physical test. These are quite costly but the demands are growing day by day. These are helpful for qualification tests like endurance vibration testing to be more representative of the real world. These are also growing in demand because they assess the durability of items based on FEA simulation. HBM France explains how to set up a CAE-based test and how to correlate the results with some physical measurements. FEA modelling is used for design and also for virtual testing. modeFrontier make sure that the representation should be in real environment. It also reduces test duration and simplify the test specifications. It also validates boundary conditions, stiffness and density. It set up the magnitude of the stress response. It validates the meshing and loading. This figure shows the role of modeFrontier in finding the fatigue failure within the design). Guiseppe et al, (2016) give the idea for the employment of CAE processes for the development and optimization of high quality products has become worldwide famous. There are a wide parameter ranges which needs to cover. For this a large number of simulations must be performed which depends on the optimization strategy. It is often unfeasible for SMEs to provide the required capacities for complex flow simulations. A solution for this would be the usage of open source technology in a HPC cloud. The aim of CloudSME project funded by EU Commission is to create the HPC-based approaches to the rapid and effective deployment of CAE software in the cloud. In order to enable this in particular SMEs companies the use of both at affordable conditions (pay-on-demand basis) is necessary. In order to achieve this goal, 29 project partners have been working on the implementation of different cases (MSaaS). The software developer DHCAE Tools provides access from within their modelling environment. In the cloudSME HPC environment, CastNet to open-source solver technology (OpenFOAM for CFD, CalculiX of structural analysis) has been used for the simulation and the technical design of model helicopter rotors and their blades. modeFrontier can helps in extending services for existing customers. It also reduces effort for installation guidance. Giovanni et al. (2016) focused on a safety assessment performed on CubeSat satellites. The simulation of blast waves in maintenance has increases its role in fluid dynamics. This may be due to its due to its many important applications primarily in the aerospace, defense and the oil & gas sectors. This is to evaluate the effects of the thermal runaway phenomena originating inside a batter. This verify the interaction between the blast of battery and the structure of CubeSat, and assess the derived space debris production. 50

To simulate wave and pressure development, battery structural failure and solid- fluid parts ejection from the assembly. A finite element model of the ignition is built. The software used to model the phenomenon is LS-Dyna. This approach can be subjected to dangerous environment conditions leading to an explosive thermal runaway. This is done so that a suitable containing structure can be easily integrated. modeFrontier correlate experimental data and implement experimental data like materials, pressure, times etc. It also helps in system correlations. No.

Project

Authors

Algorithm

Company

Location

Year

Applications of modeFrontier in liquid propellant rocket engine design

Bernardo Souza

CFD, GA

DCTA/FUNDEP

Verona

2014

S-DYNA and modeFrontier for material model calibration at automobile Lamborghini,

Marco Perillo, Vito Primavera, Luca Fuligno, Giulia Fabbri, Casper Steenbergen, NicolĂ˛ Pasini

FEM, ANSYS, CFD, LSDYNA

Enginsoft SpA, Automobili Lamborghini SpA

Verona

2014

Design Optimization of an Electro Mechanical Actuator: what are the suitable algorithms to solve this complex constrained problem of optimization

Stefano Toro, Stefano Siontas

FALICOST, NSGA, MOGA

Umbra Group

Verona

2014

The role of new production technologies and CAE in the evolution of Aerospace industry

Raffaele Acierno, Fabio Rossetti

CAE

Compositi Avanzati, EnginSoft

Verona

2014

DFAM: Design for Additive Manufacturing of the case for a rugged pc for aeronautical applications

Stefano Scardino, Mauro Faga, Federico Valente, Ilaria Schiavi, Manuel Lai

GA, ANSYS

ITACAe Srl

Verona

2015

Recalculation of the fan design of a classic aero engine with high thrustclass from a

Stefan Kuntzagk;

CFD

Lufthansa Technik

Verona

2015

maintenance perspective FLOPS™, Cart3D, FEMWING

Sapienza University Rome

Michele Andreoli

CFD, ANSYS

Use Virtual Vibration Tests to Optimize Physical Shaker Tests

Frederic Kihm,

Model helicopter configuration and optimisation using OpenFOAM®based CFD solver technology in a HPC-based Cloud

Cubesat battery safety: Simulation of blasting phenomena in vacuum

Multi-Disciplinary and MultiObjective Optimization of an Unconventional Aircraft Concept using modeFrontier™

Stefania Gemma

Verona

2015

An overview of the FIRST project: CFD can be a valuable tool for a deeper understanding of the atomization process

EnginSoft

Verona

2015

FEA, CAE,

HBM France

Parma

2016

Giuseppe Padula

CAE, CLOUDSM E, CFD, OPENFOA M

University of Repubblica di San Marino

Parma

2016

Giovanni Gambacciani

LS- DYNA

Aviospace,

Parma

2016

Table 6: Reviewing of modeFrontier applications in aerospace

The figure below gives about the expansion the modeFrontier over past years. From the graph, it is realized that the use of this software is increasing year by year due to its simplicity and quick optimization methods. In this initial rate of using this software is very slow but from the previous three years the use of this software has increased with high demand.

Figure 30. modeFrontier application in aerospace

In this sector, the optimization method is increasing in each year. The demand of this software is also increasing at geographical level. From the figure below it can be seen that how modeFrontier is expanding throughout the globe.

Figure 31. Geographical expansion of modeFrontier in aerospace

4.7 BioScience Thor et al. (2012) uses multiobjective optimization with modeFrontier applied to system biology. The main aim is to identify multiple solutions with acceptably small errors, also to identify different sets of solutions. For this a modelling and simulation software based on MathModelica software which run through modeFrontier. A Partitive Clustering Analysis was carried out on the data. For Multivariate Analysis (MVA), understood using the normal tools available in the Design Space. This can be done by using Partitive Clustering Analysis. This is tool available in modeFrontier. Valtorta et al. (2014) studied on numerical simulation of a transcatheter aortic valve. This is the replacement of the aortic valve of the heart through the blood vessels. The interaction of blood, stent and aorta are of importance in determining this valve for implants lifetime. A numerical modeling procedure is developed together with CAD, FEM. This is done for implanting a transcatheter percutaneous aortic valve. This study 53

discussed information about the evolution of forces, pressures and stresses during a heart pulse. It also deals with their effects on working conditions, mechanical integrity and lifetime assessment of the implant. modeFrontier used to find out the most critical locations in the stent and compared the fatigue properties of the nitinol stent. Antiga et al (2014) study about the image-based intracranial aneurysm modeling into clinical settings. This study is carried out by Orobix Srl in collaboration with the University of New York at Buffalo. This further developed a clinically-oriented application for image-based modeling of intracranial aneurysm morphology and hemodynamics. This allows to process a case in 5 to 10 minutes of user interaction time. In this case modeFrontier helps in characterization of cerebral aneurysms for assessing risk of rapture. This also use CFD simulations to perform neurosurgery. Leyva et al (2014) gave a theory about emergence and dynamics in complex networks of neurons. Complex networks of neural system in the large scale improve the understanding of the integration-segregation function balance of different brain areas. This also covers the microscale and dynamic of the living neural tissue. modeFrontier is used to experimentally investigate the morphological evolution of self-organized ensembles of cells into networks in vitro primary cultures neurons. Numerical models of modeFrontier identify the physical processes ruling the structure transformations and the evolution of the dynamics of the entire system. Yazar et al (2014) use the modeFrontier for showing the efforts on medical education simulation center establishment. This is done with the help of Gulhane Military Medical Academy experience. In this campus of emergency and intensive care units, transportation of the patientâ&#x20AC;&#x2122;s simulators with an ambulance made available. Under a sophisticated scenario, a simulation process is done with the help of modeFrontier. Due to this a CBRN training opportunities, training with real anesthesia gases, emergency applications are provided. It also provides an opportunity to practice on real patients or in order to overcome the medical malpractices. No.

Project

Authors

Algorithm

Multiobjective optimization with modeFrontier applied to system biology

Adam Thor, Elin Nyman

Math Modelica

EnginSoft Nordic, LinkĂśping University

Austria

2012

Numerical simulation of a transcatheter aortic valve

D. Valtorta, Grognuz1, Baenninger1, Schwenter, Wind

CAD, FEM

ADMEDES SCHUESSLER GmbH

Verona

2014

Taking image-based intracranial

Luca Antiga, Simone Manini,

CFD

Orobix

Verona

2014

J. U. B. M.

Company

Location

Year

aneurysm modeling into clinical settings

Lorenzo Botti, Pietro Rota

Emergence and dynamics in complex networks of neurons

I. Leyva, I. SendiĂąa-Nadal, D. de SantosSierra, A. Navas

Numerical Analysis

Centre Biomedical Technology

for

Verona

2014

Efforts on Medical Education Simulation Center Establishment

Fatih Yazar, Ahmet Korkmaz, SĂźleyman Ceylan, Orhan Kozak

CBRN

Gulhane Military Medical Academy

Verona

2014

Table 7: Reviewing of modeFrontier applications in Bioscience

Figure 32. modeFrontier application in bioscience

The map below shows the geographical expansion of this software in the field of foundry sector. It is clear from the map that the demand of this software is increasing in every part of the world.

Figure 33. Geographical expansion of modeFrontier in bioscience

4.8 Foundry Industry Piccininni (2014) carried out a numerical simulation of the cooling phase of a sand casting. This is done for the evaluation of residual stresses. The prediction of residual stress enables the negatively influence on the performance of the component and its manufacturing. These predictions come from the numerical simulations. These simulations are based on robust numerical models of modeFrontier. For this, both mechanical and thermo-physical properties of the investigated alloy are implemented. A reliable temperature evolves during different casting zones which is the key factor in order to effectively and efficiently model the thermal stresses. In the casting procedure, an optimized inverse analysis procedure is used based on finite element within the modeFrontier workflow. modeFrontier allows to determine the interface heating transfer Coefficients. The Finite Element model reproduce the thermal field of the casting during the cooling. Spaccasassi et al (2014) uses modeFrontier software for aerospace castings simulation. This paper deals with the exploitation of MAGMA5® simulator for Aerospace Premium Castings. It also presents an Aluminum Engine Gear Box. It focuses on the filling and solidification phases. In this case, modeFrontier helps in time reduction and quality improvements on the firsts prototypes and start-up phase. Boaster et al (2014) did a thermal analysis of a step-like test block in super duplex material. This is done by subjecting a solution of annealing heat treatment and water quenched together. The aim of this work to follow the thermal evolution of a step-like super duplex test block. The numerical simulation is done by using the software Magmasoft® in modeFrontier. The test block is conducted in 4 different parts corresponding to each step and samples for mechanical, corrosion, metallographic tests. modeFrontier used for the analysis of each metallographic sample. It also reveals the correspondence between evaluations obtained by the observation of CCT curves and the intermetallic phases. Treachi et al (2014) designed a predictive method for residual stress of railway wheels. This is done with the use of CAE instrument using modeFrontier. The residual stresses on the railway wheel are mainly due to the parameters of the heat treatment process. This basically consists of an austenitizing followed by rim quenching and tempering. This process is simulated through Magmasoft using modeFrontier. The results obtained from this simulation compared with the residual stresses distribution calculated through ANSYS according to prescriptions of the US standard AAR S-669. Fiorese et al (2015) defines the role of analytical computation of the plunger kinematic parameters in HPDC. It is the most used process for manufacturing Al alloy components. It is also considered a “defect generating process”. This is because it 56

detects the high scrap percentage. This paper proposed an analytical approach for computing the novel parameters like mechanical properties and porosity of the castings. This starts from the plunger displacement curve or its notable points. Optimization process using modeFrontier is achieved by choosing the best plunger kinematic parameters affecting quality of castings. Jakaj (2015) proposed an idea for simulation and process control in steel foundry. The main aspect is to analyze about Oil & Gas sector. Materials such as Ti and Ni alloy require during the manufacturing process due to their consequences of errors on final on-service behavior. This paper deals with the use of virtual simulation done with the help of modeFrontier. This allows the designer to process the production and obtain the maximum cast quality increasing the production yield. Santi (2015) deals in cost reduction using virtual optimization for automotive component. In cold chamber of an aluminum alloy, the target production process causes the high-pressure die casting. The paper proposed a new casting system and a print increase. The simulation approach is done by modeFrontier. It permits an effective description of the work-flow. A rapid development of the design is done. The workâ flow of the design develops a structural automotive component. Trevisan (2015) use modeFrontier for core production. In this Optical 3D metrology is used which is assisted by Virtual simulation. This gives the possibility to measure and virtualize accurately real part geometries. This is why the 3D scanning is used as the starting point to investigate on the production process with the simulation code. The mesh obtained with a Comet L3D. 5M is optimized into MAGMA, Core& Mold simulation in modeFrontier software. This also helps in minimization of energy consumption. Miceli (2016) studied the role of modeFrontier for the optimization of process parameters in pouring of slim steel ingots. The purpose of study is to Optimize the pouring parameters in a slim round multi sides ingot in 11CrMo9-10 steel. This is done to obtain maximum compositional homogeneity. Optimize casting parameters via virtual DoE. This is done with the help of various processes like solidification, pouring, validation Segregation of carbon, Molybdenum, Chromium, Manganese. The main target of optimization to have homogeneity of composition. The goal of modeFrontier is to minimize the difference between maximum and minimum value of the concentration of Mn in the ingot. The figure below shows the effect of optimization on the given design.

Figure 34. Convention effect of optimization (Miceli, 2016)

Bertuzzi (2016) uses modeFrontier software for structural optimization of heavy section ductile iron components. This paper discusses the integration and optimization of casting process that can improve their design. This design reduces the components like mass and cost. Further, it maintains a high safety and quality target. The main aim of work done is to find the most efficient shape and coupled with the casting process. This allows large freedom in terms of geometry complexity. This paper analyzed and compared. This is based on three different optimization workflows applied to a ductile iron casting. First, a structural optimization without considering local mechanical properties. Second, mechanical properties are introduced in the optimization loop. This is used to calculate the component reliability. Third, structural and process optimization. These are fully coupled. The casting process is optimized in the "virtual foundry". This is done to reach and improve the structural target. modeFrontier helps to reduce the total cost and increase the quality level of the component. Polli (2016) describes the problems in solving and optimizing the use of MagmaSoft in hot chamber zinc diecasting. The process of realizing the products through high pressure injection of fluid metal into the mould is called "Hot-chamber zinc die casting". The fundamental help in mould design is to analyze and predict the behavior of the metal during filling and solidification. This paper describes the products affected by typical defects of this process. It deals with shrinkage porosity, cavitation and turbulent flow. CFD simulation by MagmaSoft within modeFrontier has been used to analyze the problem and find suitable solutions. Quaglia (2016) use modeFrontier to describe the integration between numerical simulation and sampling in the die casting tool standard production process. "Die casting technology" use to convert many components into light alloys production aluminum and magnesium. This uses the process simulation MAGMA5 modeFrontier software. It helps to analyze and compare the results of the numerical simulations. It also reduces the production time.

No.

Project

Authors

Algorithm

Company

Location

Year

Numerical simulation of the cooling phase of a sand casting for the evaluation of residual stresses

Piccininni

GA, CFD

Politecnico di Bari

Verona

2014

Aerospace Castings Simulation

Danilo Spaccasassi, Roberto Pilone

MAGMA 5

AvioAero

Verona

2014

Thermal analysis of a step-like test block in superduplex material subjected to a solution annealing heat treatment and water quenched

Matteo Bosatra, Marco Fusar Poli

MagmaSoft

Fondinox

Verona

2014

Residual stress of railway wheels â&#x20AC;&#x201C; A predictive method,

Mirko Treachi, Alberto Ronchi

MagmaSoft, ANSYS

Lucchini RS

Verona

2014

Analytical computation of the plunger kinematic parameters affecting quality in HPDC

Elena Fiorese, Dario Richiedei, Franco Bonollo

CFD, CAE

University Padova

Verona

2015

Simulation and Process Control in Steel Foundry

Edmond Jaka

GA, MOGA

ECA

Verona

2015

Cost Reduction using virtual Optimization for Automotive component

Mauro De Santi

Numerical Analysis

Studio DSM

Verona

2015

Core production assisted by virtual simulation

Lorenzo Trevisan, Andrea Pasqualetto

MAGMA, Comet L3D

EnginSoft SpA

Verona

2015

Optimization of process parameters in pouring of slim steel ingots

Daniele Miceli, Acciaierie Rubiera

DoE, Numerical Analysis

EnginSoft SpA

Parma

2016

Structural optimization of heavy section ductile iron

Giacomo Bertuzzi

Structural Optimization

SACMI IMOLA

Parma

2016

components. How the integration and optimization of casting process can improve their design 11

Problem solving and optimization: the use of MagmaSoft in hot chamber zinc diecasting

Stefano Polli

MagmaSoft, CFD

Bruschi

Parma

2016

The integration between numerical simulation and sampling in the die casting tool standard production process

Gianluca Quaglia

MAGMA 5

SAEN

Parma

2016

Table 8: Reviewing of modeFrontier applications in Foundry

Figure 35. modeFrontier application in foundry

The map below shows the geographical expansion of this software in the field of foundry sector. It is clear from the map that the demand of this software is increasing in every part of the world.

Figure 36. Geographical expansion of modeFrontier in foundry

4.9 Metal Forming Meng et al (2011) uses optimization techniques on an axisymmetric forging with a hammer. This is done with initial billet and forging die design for product quality control. The main objective is to obtain some optimal parameters of the initial billet and forging dies shape. To develop the metal forming process, the optimization system requires several numerical tools like CATIA, V5TM, ABAQUS, modeFrontier. This paper gives an idea of a two-step axisymmetric metal forming project. By using simulation model of modeFrontier 581 correct real simulation results are obtained. Based on all the real values the surrogate meta-models and Pareto points are built for a two-objective optimization process. A solution in all Pareto points give the best values. Sartori et al (2014) describe the external piloting for ring rolling application with FORGE. This process has the ability to maintain the centered ring and drive the grow of height and diameter. It avoids non-round shapes and defects. This paper discusses the interface during ring rolling production process in real-time. During the calculation, the position of some virtual sensors pass informationâ&#x20AC;&#x2122;s (position, loads) to an external routine. This able to calculate corrections of the kinematic of all the tools and write back these corrections in Forge. modeFrontier guarantees the results obtained in the simulation are close to the real one. Valsecchi (2014) design and optimize the hard metal tools for cold metal forming processes. Hard metal is a powder metallurgy material of Tungsten Carbide and a binding element like Cobalt. These are used for cold metal forming. This paper deals with the analysis of application issues and customer needs. This is done in order to provide advices on the best tool design or configuration. It introduced the FEM simulator Trasvalor ColdForm using modeFrontier. It straightens the capabilities of CAE tools design. It analyses the wear and stress on the tools in order to improve their design. The figure below shows the comparison of optimized design to the given design. This shows that the optimized design has the decreased cross section area and the constraints are decreased for the optimized design. 61

Figure 37. Simulation result validation of a rifled pipe (Valsecchi, 2014).

Reggiani et al (2014) optimize the extrusion process. In light alloy extrusion process used for structural applications. This guarantees adequate mechanical characteristics. This shows the impact of a limited number of input parameters on a specific output. This paper deals with the profile quality and the die life. The main aim is to extend this contribution to account for several input and output variables. This is achieved by COMSOL Multiphysics® integrated with modeFrontier. The optimized solutions are extracted from the Pareto frontier. Fracasso et al (2014) uses forging optimization for tool joints. This paper deals with Forge 3D method. It is a reliable design instrument that uses to fulfill customer satisfaction. This is used to check the complete die filling at the end of a reverse extrusion operation for a tool joint production. modeFrontier analyzes a global error in production within 5%. Perrotta (2015) uses finite element analysis in fastener industry. This paper aims to show the main benefits achieved by Forge® 3D simulations. This shows successful analysis of process chain’s optimization and detection and correction of microscopic folds through the grain flow simulation. Forge® optimization done with the help of modeFrontier demonstrates proper heating conditions of the initial billet and predict an unexpected material behavior at high temperature. It helps in saving time and cost. Marini and Maggio (2015) described a winning synergy between multibody simulation and finite element analysis to design mechanical presses. Multibody dynamics is a simulation process used to investigate the mechanism parts move and the forces behind the motion. During this the working loads are directly transferred to a Finite Element Analysis software for the structural assessment. ANSYS® Workbench environment in modeFrontier, propose an approach to made a reliable sizing of the mechanical parts while reducing the overall design time. Pegie (2015) uses FORGE® NxT simulation techniques to find out the latest features about graphical user interface. FORGE® NxT v1.0 was delivered in 2014. This is widely used in the field of induction heating, open die forging with real manipulators, flat rolling, mandrel forging. This is delivered in Q1-⁠2015 with a major update 62

regarding sensors and marking grids. This gives a baseline for the future result and optimization techniques. BaĂš (2016) provides a wide range of forging and drop forging products. This paper discusses related to markets like transport railways and marine, oil & gas and energy. These demands shaft, rings, bushes, valve bodies, wheels, pipes, connecting rods, hooks, gears and planet carriers. The implementation of numerical simulation within modeFrontier allows the material usage and quality prediction. Fioletti (2016) uses modeFrontier to Develop a new shaped ring rolling processes via numerical simulation. This paper introduces the experience of a SME during the introduction of Forge FEM forming simulation tool. This tool is used for the development of a new shaped ring rolling processes.

No. 1

Project

Authors

Algorithm

Initial billet and forging dies shape optimization application on an axis symmetrical forging with a hammer External Piloting with FORGE for Ring Rolling Application: A collaboration between Muraro and Transvalor Design and optimization of hard metal tools for cold metal forming processes Optimization of the extrusion process by means of a novel comprehensive approach Tool Joints Forging Optimization

Fanjuan Meng, Carl Labergere, and Pascal Lafon

Catia, VSTM, ABAQUS

Angelo Sartori, Patrice Lasne, Marcello Gabrielli

Finite Element Analysis in fastener industry A Winning Synergy between Multibody Simulation and Finite Element Analysis to Design Mechanical Presses

Location

Year

University of Technology of Troyes

Italy

2011

CAD, MOGA

MURARO Spa, Transvalor S.A., EnginSoft Spa

Verona

2014

Antonello Valsecchi

FEM, CAE

Ceratizit Italia

Verona

2014

B. Reggiani, M. Broccoli, L. Donati, L. Tomesani

COMSOL

University Bologna

Verona

2014

L. Fracasso, C. Contri, Hydromec Fabrizio Perrotta

FORGE 3D

Hydromec

Verona

2014

FORGE 3D

VIMI Fasteners

Verona

2015

Davide Marini, Fabiano Maggio

ANSYS

EnginSoft SpA

Verona

2015

Company

FORGE® NxT: Latest features about graphical user interface Advantages and outlooks of implementation of numerical simulation on steel hot forging Development of New Shaped Ring Rolling Processes via Numerical Simulation

Laëtitia Pegie

FORGE NxT

TRANSVALOR

Verona

2015

Stefano Baù

modeFrontier, CFD

F.O.C. Ciscato SpA

Parma

2016

Fabio Fioletti

FEM

FELB

Parma

2016

Table 9: Reviewing of modeFrontier applications in metal foaming

Figure 38. modeFrontier application in metal foaming

The map below shows the geographical expansion of this software in the field of metal foaming sector. It is clear from the map that the demand of this software is increasing in every part of the world.

Figure 39. Geographical expansion of modeFrontier in metal foaming

4.10 Electrical Engineering Civita et al (2008) uses modeFrontier for studying FERMI@Elettra undulator frame project. This needs installation of linear and elliptical polarization undulators. This paper focuses on mechanical structure. This guarantees minimum displacement of the girders. It supports the magnet arrays. modeFrontier helps to achieve minimum transversal deformation. It simulates LPU and EPU frame. This makes the same topology and bar dimension. Poian et al (2008) design an antenna through multiobjective optimization using modeFrontier. In this case, the optimization is a full batch process. This is monitored from a run-log graphic console. In this case, modeFrontier provides clear, well-defined and non-weighted approach. It helps to understand helps the physics and explore the design space problems. Bocchio (2010) uses modeFrontier to optimize System-on-Chip Platform. This is an integration of all components of an electronic system into a single IC. It contains digital, analog, radio frequency functions and mixed signal. This paper deals with low power processor design space. These design spaces are analyzed with M3Explorer for statistical analysis. All designs were evaluated by modeFrontier on a computer cluster at ST for obtaining the real Pareto front. modeFrontier optimization algorithm helps to reduce DoE. This means reduction in time- to- market. Benkhelifa (2010) uses modeFrontier for MEMS design optimization integration. This includes motion, sound, biochemistry, radio waves, light, computation and chemistry. This all is on a single chip. modeFrontier gives highest power generated from MEMS. The optimization results also predicts maximum power that can be generated from this model. Percebon et al (2011) presents a new topology for high torque permanent magnetic gear. This gives high performance of magnetic gears. It proposed a topology which involves series of simulations with ANSYS. This is an electromagnetic solver. modeFrontier is 65

used to achieve high torque values. It also improves the robustness for rotor eccentricities. The figure below gives the optimized magnetic gear structure obtained through the software.

Figure 40. Optimized magnetic gear topology (Ferraz et al, 2011)

Tessarolo et al (2011) design a surface permanent magnet slotless alternator. This is used for a small power wind generation. This is done through modeFrontier. Wind generation has been gaining increasing importance. This paper deals with the development of a prototype slotless surface permanent magnet generator. This is conceived for a 20-kW power station. In this case, design optimization, carried out with stochastic algorithms. modeFrontier helps to optimize the electric generation system between wind turbines and the grid. It also used CFD and MOGA algorithm to have the efficient gear structure. Valerio et al (2012) configure a low cost dual polarized printed antennas. This is used for ultra-wide-band (UWB) arrays. The development of UWB antennas provides a new solution. This paper discusses the various arrangements of printed dipoles of different lengths. This form an approximately rhombic-shaped element. This reduces the 7mutual interactions among adjacent elements in array environment. modeFrontier achieves the best input matching at the desired frequency band. It uses a MOGA method with multi search elitism. This enhances the robustness. Berbecea (2013) uses multi- level approach for the optimal system design in railways. In this paper two optimization approach are mentioned: first, metamodelâ&#x20AC;?based design optimization and second, decompositionâ&#x20AC;?based complex systems optimal design. The metamodelâ&#x20AC;?based optimization approach of modeFrontier intended to address the optimization of devices. In this case, the FEA in electromagnetics helps the design engineer make an informed decision. Garstecki et al (2013) shows how the engineers of Whirlpool are involved modeFrontier in modeling and simulation of household appliances. These modeling products provide 66

optimized solutions at a system level. Modeling of Attribute performance starts with a specific performance metric with empirical test data. The Attribute models are further integrated into the modeFrontier full system flow. In this case, CAE models used to generate response surface output. modeFrontier combines the attribute models. It evaluates product performance without specific requirements. It also allows to reach a high level of performance given product requirements. Kato (2014) design optimization of permanent magnet synchronous motors for HEV and EV. The main challenge discussed in this paper is the design of fuel economy optimized HEV and EV motors. Furthermore, this paper describes a design search method which enables automatic searches for motor dimensions that will optimize fuel economy. Optimization is conducted in modeFrontier using 7 design variable parameters. Also, genetic algorithm and MOGA- II are made to find minimum loss design. modeFrontier produced design that is based on criteria of weight and fuel economy. It also adds a new value to design environment of motors for HEVs and EVs. The figure below gives the optimized structure of the motor.

Figure 41. Fuel economy and weight optimization by GA using 7 designs variables (Kato, 2014)

Tallini (2014) describes multiobjective electromagnetic optimization of RF components. The main challenge in doing this to identify the best robust RF design solutions. The solution can be obtained by coupling the CST STUDIO SUITE with modeFrontier using the direct integration node. The algorithms are piloted with the CST simulations. This is to reach the design objectives while respecting the set constraints. modeFrontier sensitivity analysis tools identify the variables affecting the RF component design. It allows engineers to save time and focus on their specific tasks. Craciun (2014) uses multiobjective optimization approach for medium voltage reclosers. The main challenge described in this paper is to find the right configuration for the components. Recloser lifetime enables excellent switching properties and improvs energy performance. According to the paper, ABB engineers built a two-step optimization framework. This incorporates the energy efficiency constraints by working initially on the actuator model. The paper deals with the optimizing the FEM with modeFrontier software. The numerical simulation is completed with physical 67

calibration with a Hardware-in-the-Loop (HIL) optimization process. This ensures the entire system desired performance. modeFrontier helps to achieve the optimal timing and switching frequencies for the closing operation. It also enhances the performance, robustness and lifetime of the recloser design. Mezzarobba (2016) uses modeFrontier for optimization of a SPM machine. This is done by using a non- isotropic magnetic wedge. To estimate the cogging torque analytical methods are required. This torque minimization requires genetic optimization approach. FEA simulations predicts the cogging torque. This paper suggests that the analytical optimization done in modeFrontier gives the same result as of FEA but it less time. No 1

Project Fermi @ elettra undulator frame study

Multiobjective optimization for antenna design

Optimization of System-on-Chip Platform using modeFrontier modeFrontier: A Facilitator for MEMS Design Optimisation Integration Modelling of a Magnetic Gear Considering Rotor Eccentricity

Multiobjective design optimization of a surface permanent magnet slotless alternator for small power wind generator New configuration of low cost dual polarized printed antennas for UWB arrays

Authors D. La Civita, R. Bracco, B. Diviacco, G. Tomasin, D. Zangrando, Sincrotrone M. Poian, S. Poles, F. Bernascon, E. Leroux, W. SteffĂŠ, M. Zolesi Sara Bocchio

Algorithm FERMI @ Elettra

Company Trieste SCpA

Location Italy

Year 2008

Pareto optimizatio n

Thales Space

Thales

2008

M3 explorer algorithm

AST â&#x20AC;&#x201C; Ultra Low Power Platform

Italy

2010

Dr Elhadj Benkhelifa

MEMS

Cranfield University

Trieste

2010

Leandro Alberto Percebon, Rodrigo Ferraz, Mauricio Valencia Ferreira da Luz Tessarolo A., Venuti V, Luise F., Raffin P

ANSYS

Universidade Federal de Santa Catarina

Trieste

2011

CFD, MOGA

University of Trieste, Ansald

Trieste

2011

Guido Valerio, Simona Mazzocchi, Alessandro Galli, Matteo

MOGA, FEA

SELEX S.I. S.P. A

Italy

2012

Alenia

Multi- level approaches for optimal system design in railway applications Attribute Modeling and System Level Performance Optimization for Household Appliances Design optimization of permanent magnet synchronous motors for HEV and EV Multiobjective electromagnetic optimization of RF components Multiobjective optimization of medium voltage reclosers Optimization of a SPM machine using a non-isotropic magnetic wedge with an analytical method for cogging torque estimation

Ciattaglia, Marco Zucca Alexandru Berbecea

CFD, GA

University Lille Nordâ&#x20AC;?deâ&#x20AC;?France

Italy

2013

Greg Garstecki

CAE MOGA II, GA

Whirlpool Corporation

Italy

2013

Shingo Kato

MOGA II, GA

Honda R&D Co. Ltd

Italy

2014

Davide Tallini

CST STUDIO SUITE

CST AG Computer Simulation Technology

Italy

2014

Octavian Craciun

FEM, HIL

ABB

Italy

2014

Tessarolo, A., Branz, L., & Mezzarobba, M.

FEA, GA

International Conference in IEEE.

Switzerland

2016

Table 10: Reviewing of modeFrontier applications in electrical engineering

The figure below shows the application of modeFrontier in the electrical engineering sector from the 2008 to 2016. This shows that the use of it is becoming very famous and it is widely used for MOO.

Figure 42: modeFrontier application in electrical engineering

The map below shows the geographical expansion of this software in the field of electrical engineering. It is clear from the map that the demand of this software is increasing in every part of the world.

Figure 43. Geographical expansion of modeFrontier in electrical engineering

CHAPTER 5. SURVEY RESULT

5.1 State of optimization in industry This section discusses the number of year that different sectors are using the modeFrontier software for optimization. From the below figure, it can be seen that this software is widely used for more than five years. In starting two years itâ&#x20AC;&#x2122;s use was restricted only to 17%. In next three years, itâ&#x20AC;&#x2122;s use was increased by 16%.

Figure 44. Percentage of industry sectors with different years of experience in using modeFrontier

modeFrontier is used at different configuration level in the companies. These levels can be component, system and sub- system. From the figure shown below, it can be seen that only 52% of all the companies deals with the service level.

Figure 45. Percentage of companies using optimization at different configuration levels .

During the survey, it is also seen that different companies uses modeFrontier in different domains. Design and manufacturing are found to most common domain for optimization. The figure shown below design and manufacturing domains needs more optimization.

Figure 46. Percentage of companies using optimization in different domains.

modeFrontier is combination of different of different algorithms and methods. This survey also gives an idea about the percentage of different algorithms and methods used by the companies for optimization problems. The figure shown below states that NSGA algorithm is widely used for solving this problem.

Figure 47. Percentage of companies using different techniques for modeFrontier.

5.2 Geographical expansion of modeFrontier This software is developed in Europe. Due to its simplicity in past few years itâ&#x20AC;&#x2122;s demand goes on increasing. Now this software is widely spread and most demanding software for optimization problems. This is use in every continent. It is widely spread in 72

developed and many developing countries. The figure shown below gives different geographical areas where modeFrontier is used for optimization problems. The first figure shows the expansion of the software in 2008 in different countries and continents. The second figure shows the expansion of this software. From both the figure it can be seen that the software is drastically used for optimization problems in different countries.

Figure 48. Geographical expansion of modeFrontier in 2008.

Figure 49. Geographical expansion of modeFrontier in 2016

Note: In all the geographical maps shown above, darker the color represents larger the intensity of modeFrontier application at that place. 5.3 modeFrontier applications in different sectors This software is used in different sectors. The survey is done in ten different sectors. Among those sector this software is widely applicable in energy sector. Furthermore, other than the mentioned sectors it is also widely used for research institution and universities these days. The graph shown below states the application of this software in different sectors from the year 2008 to 2016.

Figure 50. modeFrontier application in different sectors

CHAPTER 6. DISCUSSION

6.1 General use of modeFrontier Optimization is becoming very important task to solve the engineering problems. It is a daily task in the companies. From more than 10 years the companies related to different sectors are using this method. Now a day almost every companies are using multi objective optimization approach. Out of these companies more than 86% 0f the companies are using mF software to carry out the MOO problems. Furthermore, we can see that 15% of the companies are only optimizing at component level. The deployment of mF enables virtual testing, physical experiments and management of a process. This provides many benefits like time reduction, cost reduction, design and performance optimization. Its metamodelling technique provides in re-using the historical database. It also can be used for better and deeper understanding of the available data. It accelerates the design to market. 6.2. Advantageous to company Today, many companies are using this software for many optimization problems. Many companies use virtual prototyping methods for design process. mF provides the combination of virtual prototyping and decision process. This makes it an emerging tool for MOO approach. It has the ability to revolutionize the product development. Many companies are using this software. For process integration, it has the ability to verify 5000 design configurations in the same time as taken to verify 50 configurations with techniques. Avio company saves 15% air mass flow in gas turbine blade cooling by using this software. Indesit company save 38% time in experimental campaigns of a full glass oven by using this software. By using this software ABB company reduces maximum force for a damper by 30%. Many more companies like FIAT group, HPE of Piero Ferrari, Sulzer pumps, Piaggio & C., use this software for different purposes. 6.3. Optimization and modelling techniques Among all the companies surveyed the modelling approach is almost used by every companies. This approach mostly uses CAD/ CAE, FEA and CFD methods. These are mostly used to solve uncertainly related problems. 70% of the metaheuristic problems are solved by EA. The EA involves the algorithm like NSGA- II, GA, MOGA and CMS- ES. This software widely used also in the academic and research fields. Most importantly this software is used for academic problems. The evaluation time for robust problems is longer than standard problems. mF uses NSGA- II algorithm to minimize this time. Only in less than 10% of problem an implicit knowledge is requires among 50% of the companies. Therefore, mF is expanding in different part of world because 75

of its simplicity and less time requirement than the other methods to solve the optimization problems.

CONCLUSION This Thesis work is focused on multiobjective optimization method through modeFrontier software. The environment of modeFrontier provide an easy workflow linking together to the different modules. This helps the user to save their time and use resource efficiently. It shows the way to set up and run any type of application methods. It is concluded that Computational Fluid Dynamics application (CFD) is used in many processes of multiobjective optimization. It helps to maximize the driving force and minimize the heeling moments. This paper also discuss various methods in which optimization problem are used to solve. The modeFrontier provides a quick and efficient optimized solution. Therefore, it is becoming popular now a day. The modeFrontier act as an integration platform for multiobjective optimization. It also offers flexibility by integration with the third-party engineering tools. This enables the automation of simulation process. It also facilitates to take analytical decision. This thesis work survey the research done with the help of different algorithm like MOGA, CFD, ANSYS etc. in modeFrontier. In case of many numerical simulation, modeFrontier play a vital role in different sectors. From the above discussion, it can be concluded that the role of modeFrontier is increasing year by year. This is because of its simplicity, efficiency and quick response. It consists of multiple optimization algorithm and technique. It can also be concluded that modeFrontier is a widely-used software for multiobjective optimization in different fields, out of which the use in energy sector is very high and demanding.

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of optimization methodology and specimen specific finite element models for investigating material properties of rat skul, modeFrontier Users' meeting, Italy. 54. Fatih Yazar, Ahmet Korkmaz, SĂźleyman Ceylan, Orhan Kozak, Gulhane, (2014, October) Military Medical Academy, Efforts on Medical Education Simulation Center Establishment, International CAE Conference, Verona, Italy. 55. Fabrizio Perrotta, (2015, October) VIMI Fasteners, Finite Element Analysis in fastener industry, International CAE Conference, Verona, Italy. 56. Fanjuan Meng, Carl Labergere, and Pascal Lafon, University of Technology of Troyes, Initial billet and forging dies shape optimization application on an axis symmetrical forging with a hammer, modeFrontier usermeeting conference, Italy, 2011. 57. Fabio Fioletti, (2016, October) FELB, Development of New Shaped Ring Rolling Processes via Numerical Simulation, International CAE Conference, Parma, Italy. 58. Francesco Fortunato, (2014, October) Fiat Group Automobiles, Advanced modeling of liquid movements in automotive fuel tanks, International CAE Conference, Italy. 59. Giovanni Mercurio, Patrice Richir, Joint Research Centre - European Commission, Uranium Enrichment Cascades Modeling with Optimized Stage- Mixing Parameters for Non- Proliferation Analysis, International CAE Conference, Verona, 2014 60. Guilherme Jenovencio, Rodrigo Ferraz, ESSS, Composites structural optimization modeFrontier + ANSYS composite pre-post, modeFrontier usermeeting, August 2012. 61. Giacomo Bertuzzi, (2016, October) SACMI IMOLA, Structural optimization of heavy section ductile iron components. How the integration and optimization of casting process can improve their design, International CAE Conference, Parma, Italy. 62. Gustavo Myrria Neto, (2015, December) Universidade Federal de Santa Caterina, Development of system control for compressor valves, usermeeting conference, Austria. 63. Gianluca Quaglia, (2016, October) SAEN, The integration between numerical simulation and sampling in the die casting tool standard production process, International CAE Conference, Parma, Italy. 64. Guido Valerio, Simona Mazzocchi, Alessandro Galli, Matteo Ciattaglia, Marco Zucca, (2012, May) Selex S.I. S.P.A, New configurations of low-cost dual polarized printed antennas for UWB arrays, ModeFrontier User Meeting, Italy. 65. Greg Garstecki, (2013, May) Whirlpool Corporation, Attribute Modeling and System Level Performance Optimization for Household Appliances, modeFrontier user meeting, Italy. 66. Giuseppe Carlo, (2008, October) Calafiore, Politecnico di Torino, Multi-period portfolio optimization with linear control policies, Automatica Volume 44, Beijing.

67. Giuseppe Padula, (2016, October) University of Repubblica di San Marino, Model helicopter configuration and optimisation using OpenFOAM®-based CFD solver technology in a HPC-based Cloud, International CAE Conference, Parma. 68. Giovanni Gambacciani, (2016, October) Aviospace, Cubesat battery safety: Simulation of blasting phenomena in vacuum, International CAE Conference, Parma. 69. Gramegna, Nicola, Emilia Della Corte, and Silvia Poles, (2011, March) Manufacturing process simulation for product design chain optimization, modeFrontier usermeeting, Italy. 70. G. Lombardi, (2014, October) University of Pisa, Analysis of a CFD approach for the study of the aerodynamics of a scooter, International CAE Conference, Verona. 71. Hasan Avsar, Nasser Ghassembaglou, Bulvarı, Sanayi Mahallesi, Pendik, Levent Kavurmacioglu, (2016, October) EnginSoft, Optimum Design of Exhaust System of a Ship, International CAE Conference, Parma. 72. Hopson, (2014, July) Tutorial- Optimizing models with modeFrontier. 73. Hidenao Iwane, Hitoshi Yanami, Hirokazu Anai, (2011, February) Fujitsu Laboratories Ltd, A Symbolic-Numeric Approach to Multi-Objective Optimization in Manufacturing Design, Springer Basel AG. 74. I. Leyva, I. Sendiña-Nadal, D. de Santos-Sierra, A. Navas, (2015, October) Centre for Biomedical Technology, Emergence and dynamics in complex networks of neurons, International CAE Conference, Verona, Italy. 75. Igor Pehnec, Damir Vučina, Željan Lozina, (2008, January) University of Split, Workflow- Based Shape Optimization of Airfoils and Blades using Chained Bezier Curves, Croatia. 76. Igor Pehnec, Damir Vučina, Željan Lozina, (2009, January) University of Split, Coupled Evolutionary Shape Optimization and Reverse Engineering in Product Design and Virtual Prototyping, Springer, Croatia. 77. Ilaria Zacàa, Delia D’Agostino b, Paolo Maria Congedoa, Cristina Baglivo, (2015, October) University of Salento, Assessment of cost-optimality and technical solutions in high performance multi-residential buildings in the Mediterranean area, Joint Research Centre (JRC) – European Commission, Ispra, VA, Italy. 78. Jeroen van Hellenberg Hubar, (2011, December) Eindhoven University of Technology, Design concept for optimizing the renewable micro generation technologies to supply and off grid community energy demand, Netherlands. 79. Johan Andersson, (2015, March) Linkoping University, A survey of multiobjective optimization in engineering design, Technical Report No: LiTH-IKP. 80. Keeney, R.L. and Raiffa, H. (1976) Decisions with Multiple Objectives: Performances and Value Trade-Offs, Wiley, New York. 81. Kevin Chen, Dannie Durand, and Martin Farach, (2004, June) Colton, A Program for Dating Gene Duplications and Optimizing Gene Family Trees, Journal of Computational Biology. 83

82. Kiran Bhat, Ian Stroud, Jumyung Um, (2014, October) Ecole Poly technique, Fédérale de Lausanne, Cambridge University, ECO evaluation for remote laser welding, International CAE Conference, Italy. 83. Keeney, R.L. and Raiffa, H. (1976) Decisions with Multiple Objectives: Performances and Value Trade-Offs, Wiley, New York. 84. Kaisa Miettinen, Francisco Ruiz, Andrzej P. Wierzbicki, (2008, May) Multiobjective Optimization: Interactive Approaches, Heidelberg, Springer. 85. Frederic Kihm, (2016, October) HBM France, Use Virtual Vibration Tests to Optimize Physical Shaker Tests, International CAE Conference, Parma. 86. Leonid Korelshteyn, Alexey Babenko, (2016, October), Piping System Research & Engineering Co (NTP Truboprovod), Automatic selection of closure relations for TUFFP two phase flow Unified model, International CAE Conference, Parma, Italy. 87. Luciano Moro, P. Boscariol, J. Srnec Novak, F. De Bona, A. Gasparetto, (2014, October) Fire doors for naval applications: numerical analysis and innovative solutions, International CAE Conference, Verona, Italy. 88. Lorenzo Trevisan, Andrea Pasqualetto, (2015, October) EnginSoft SpA, Core production assisted by virtual simulation, International CAE Conference, Verona, Italy. 89. Luca Antiga, Simone Manini, Lorenzo Botti, Pietro Rota, (2014, October) Orobix, Taking image-based intracranial aneurysm modeling into clinical settings, International CAE Conference, Verona, Italy. 90. L. Fracasso, C. Contri, Hydromec, (2015, October) Hydromec, Tool Joints Forging Optimization, International CAE Conference, Verona, Italy. 91. Laëtitia Pegie, (2015, October) TRANSVALOR, FORGE® NxT: latest features about graphical user interface, International CAE Conference, Verona, Italy. 92. Leandro Alberto Percebon, Rodrigo Ferraz, (2011, May)Mauricio Valencia Ferreira da Luz, Universidade Federal de Santa Catarina, Modelling of a Magnetic Gear Considering Rotor Eccentricity, ModeFrontier User Meeting, Trieste. 93. Mark Matzopoulos, Tom Williams, (2008) Process Systems Enterprise Limited, Investigting the design space using gPROMS first‐ principles models in modeFrontier, modeFrontier users' meeting, Trieste, 94. Mathew James Dickson, Franz Konstantin Fuss, (2010) RMIT University, Effect of acceleration on optimization of Adidas bounce shoes, modeFrontier usermeeting, Australia. 95. Michela Costa, Luigi Allocca, Ugo Sorge, (2012, May) Stituto Motori – CNR, University of Naples Federico II, Increasing energy efficiency of a gasoline direct injection engine through optimal synchronization of single or double injection strategies, modeFrontier usermeeting 96. Mattiussi Alessandro, Simeoni Patrizia, Rosano Michele, (2013, October) University of Udine, Curtin University, A decision support system for sustainable 84

energy supply combining multiobjective and multi attribute analysis- an Australian case study, modeFrontier usermeeting, Australia. 97. Martin Bünner, (2014, October) NTB, Institute for Computational Engineering, Optimization & Automatic Design of Fluid-Dynamical Systems: Towards Optimal Shapes for Wind Turbine Blades, International CAE Conference, Italy. 98. Marco Rottoli, Thomas Odry, (2016, October) Brembana & Rolle, CFD analysis of annular distributors for shell&tube heat exchangers, International CAE Conference, Parma. 99. Michele Raciti, Ansaldo Energia, (2016, October) An analytical and FEM modelling of a large turbogenerator for the determination of the induced currents in rotor components, International CAE Conference, Parma, Italy. 100. Michele Andreoli, (2015, October) EnginSoft, An overview of the FIRST project: CFD can be a valuable tool for a deeper understanding of the atomization process, International CAE Conference, Verona. 101. Manolo Venturin, Raffaele Liberatore, (2016, October) EnginSoft, Mariarosaria Ferrara University, Multiobjective optimization of a hydrogen production process powered by solar energy, International CAE Conference, Parma, Italy. 102. Marco Fanciulli, Giacomo Indiveri, Christian Mayr, Themis Prodromakis, Sabina Spiga, Grazia Tallarida, Ralf Zeitler, (2016, October) University of Padova, Real neurons-nanoelectronics Architecture with Memristive Plasticity, International CAE Conference, Parma, Italy. 103. Marco Perillo, Vito Primavera, Luca Fuligno, Giulia Fabbri, Casper Steenbergen, Nicolò Pasini, (2009, October) Enginsoft SpA, Automobili Lamborghini SpA, LSDYNA and modeFrontier for material model calibration at automobile Lamborghini, modeFrontier Users' meeting, Trieste, Italy. 104. Melike Nikbay, Necati Fakkusoglu, Muhammet N. Kuru, (2010, October) Istanbul Technical University, Reliability based structural optimization of an aircraft wing, modeFrontier Users' meeting, Istanbu. 105. Martin Lamut, (2016, October) Space-SI, Identification of mechanical properties based on Nanoindentation experiment, modeFrontier Users' meeting, Melbourne, Australia. 106. Matteo Bosatra, Marco Fusar Poli, (2015, October) Fondinox, Thermal analysis of a step-like test block in superduplex material subjected to a solution annealing heat treatment and water quenched, International CAE Conference, Verona, Italy. 107. Mirko Treachi, Alberto Ronchi, (2014, October) Lucchini RS, Residual stress of railway wheels – A predictive method, International CAE Conference, Verona, Italy.

108. Mauro De Santi, (2015, October) Studio DSM, Cost Reduction using virtual Optimization for Automotive component, International CAE Conference, Verona, Italy. 109. M. Poian, S. Poles, F. Bernascon, E. Leroux, W. SteffĂŠ, M. Zolesi, (2008) ESTECO, CST, Thales Alenia Space, Multiobjective optimization for antenna design, modeFrontier usermeeting, Italy. 110. Mosavi, A. (2010). Multiple criteria decision-making preprocessing using data mining tools. 111. Mosavi, A., & Vaezipour, A. (2012). Reactive search optimization; application to multiobjective optimization problems. 112. Mosavi, A. (2014). Data mining for decision making in engineering optimal design. Journal of AI and Data Mining. 113. Mosavi, A., Milani, A. S., Hoffmann, M., & Komeili, M. (2012, June). Multiple criteria decision making integrated with mechanical modeling of draping for material selection of textile composites. In Proceedings of 15th European Conference on Composite Materials. 114. Mosavi, A. (2010). On engineering optimization the splined profiles. In Proceedings of International modeFRONTIER Usersâ&#x20AC;&#x2122; Meeting. 115. Mosavi, A. (2013). A multicriteria decision making environment for engineering design and production decision-making, International Journal of Computer Applications, 69(1). 116. Mosavi, A. (2009, November). Hydrodynamic design and optimization: application to design a general case for extra equipments on the submarine's hull. In Computer Technology and Development, 2009. ICCTD'09. International Conference on IEEE. 117. Mosavi, A. (2013). A MCDM software tool for automating the optimal design environments, with an application in shape optimization. In Proceedings of International Conference on Optimization and Analysis of Structures. 118. Mosavi, A. (2013). Optimal Engineering Design. Tech. Rep. 2013. University of Debrecen, Hungary. 119. Mosavi, A., & Hoffmann, M. (2010). Design of curves and surfaces by multiobjective optimization; utilizing IOSO and modeFRONTIER packages. In Poster in Proceedings of Enginsoft International Conference on CAE Technologies for Industries, Bergamo, Italy. 120 Mosavi, A. (2013). Brain-computer optimization for solving complicated geometrical decision making problems. In Proceedings of PEME VI. Ph. D. Conference.

121. Nicola Bresciani, (2015, October) ABB Sace, Knowledge management in LVSB R&D, An ABB SACE experience, International CAE Conference, Verona, Italy. 122. Octavian Craciun, (2014, May) ABB, Multiobjective optimization of medium voltage reclosers, modeFrontier User Meeting, Italy. 123. Oliver Grieshofer, (2014, October) Engineering Center Steyr GmbH & Co KG, Dynamic testing of a car body on a virtual test rig, International CAE Conference, Verona, Italy. 124. Paolo Monti, Aldo Marciano, Agostino Morrone, Cristian Rossetti, (2015, October) Saipem SpA, The structural modelling of pipelay vessels dedicated to the laying of long and deep water submarine pipelines, Verona, Italy. 125. P. Cavaliere, A. Perrone, (2011, March) University of Salento, Multi-objective optimization of steel fusion welding, Workshop IGF, Forni di Sopra (UD), Italy. 126. Piccininni, (2014, October) Politecnico di Bari, Numerical simulation of the cooling phase of a sand casting for the evaluation of residual stresses, International CAE Conference, Verona, Italy. 127. Pasquale Franciosa, (2014, October) University of Warwick, Design Synthesis of Robotic Remote Laser Welding Assembly System with Compliant Non-Ideal Parts, International CAE Conference, Verona, Italy. 128. Peter F. Drucker, (1985) Claremont Graduate University, Innovation and Entrepreneurship, American Management Consultant, 1985 129. Roel C.G.M. Loonen, Marija Trcka, and Jan L.M. Hensen,(2012) Eindhoven University of Technology, Exploring the potential of climate adaptive building shells, um12. 130. Robin Brorsson, (2008) Saab AB AVITRONICS, Radom optimization for airborne jammers, usermeeting, Järfäll. 131. Raffaele Acierno, Fabio Rossetti, (2014, October) Compositi Avanzati, EnginSoft; The role of new production technologies and CAE in the evolution of Aerospace industry; International CAE Conference,Verona. 132. Ronan Le Goff, Thierry Burret, David Garcia, (2009, October) Pôle Européen de Plasturgie, EnginSoft France, Multi-objective optimization applied to the mass reduction of a plastic part, EnginSoft International Conference, Italy. 133. S. RuzikaM. M. Wiecek, (2005, September) Approximation Methods in Multiobjective Programming, Journal of Optimization Theory and Applications. 134. S. Marie & E Courteille, (2009) Université Européenne de Bretagne, Fuel Consumption Minimization Procedure of Sail-assisted Motor Vessel based on a Systematic Meshing of the Explored Area, INSA-LGCGM, France. 135. Silvia Poles, (2011) EnginSoft, Multidisciplinary and Multiobjective Optimization of a Wind Turbine, modeFrontier usermeeting. 87

136. Stefano Garbin, (2016, October) Campagnolo, The role of numerical simulation in product development: Example from bicycle industry, International CAE Conference, Parma, Italy, 137. Stefania Gemma, (2015, October) Sapienza University of Rome, MultiDisciplinary and Multi-Objective Optimization of an Unconventional Aircraft Concept using modeFrontier™, International CAE Conference, Verona. 138. S.V. Neilson, A.C. Orifici, C.H. Wang, (2012) RMIT University, Investigation into optimized composite sarf repairs with practical constraints, 28th International congress of the aeronautical sciences, Melbourne, Victoria Australia. 139. Silvia Demattè, Cristina Grillo, Angelo Messina, Antonio Frattari, (2013, October) EnginSoft, Beninpact suite a tool for ZEB performance assessment, modeFrontier Users' meeting, Trieste, Italy. 140. Sylvain Pagerit, P. Sharer, A. Rousseau, (2014, October) Argonne National Laboratory, Complex System Engineering Simulation through Distributed Cosimulations, International CAE Conference, Verona, Italy. 141. Stefan Kuntzagk, (2015, October) Lufthansa Technik AG, Recalculation of the fan design of a classic aero engine with high thrust-class from a maintenance perspective, International CAE Conference, Verona. 142. Soprano Alessandro, Caputo Francesco, (2012) Second University of Naples, Stochastic improvement of structural design, modeFrontier Users' meeting, Italy. 143. Stefano Polli, Bruschi, (2016, October) Problem solving and optimization: the use of MagmaSoft in hot chamber zinc diecasting, International CAE Conference, Parma, Italy. 144. Stefano Baù, (2016, October), F.O.C. Ciscato SpA, Advantages and outlooks of implementation of numerical simulation on steel hot forging, International CAE Conference, Parma, Italy. 145.Stefano Toro, Stefano Siontas, (2014, October) Umbra Group, Design Optimization of an Electro Mechanical Actuator: what are the suitable algorithms to solve this complex constrained problem of optimization, International CAE Conference, Verona. 146. Stefano Scardino, Mauro Faga, Federico Valente, Ilaria Schiavi, Manuel Lai, (2015, October) ITACAe Srl, DFAM: Design for Additive Manufacturing of the case for a rugged pc for aeronautical applications, International CAE Conference, Verona. 147. Sara Bocchio, (2010, June) AST – Ultra Low Power Platform, Optimization of System-on-Chip Platform using modeFrontier, modeFrontier user meeting, Italy. 148. Shingo Kato, (2014, May) Honda R&D Co. Ltd., Design optimization of permanent magnet synchronous motors for HEV and EV, ModeFrontier User Meeting.

149. Tales G. do Couto, Bruno Farias, Alberto Carlos G.C. Diniz, Marcus Vinicius G. de Morais, (2013, May) University of Brasília, Optimization of wind farm layout using genetic algorithm, user meeting. 150. Teresa Donateo, Federica Tornese, Domenico Laforgia, (2013, May) Università del Salento, Computer aided conversion of an engine from diesel to methane, usermeeting. 151. Tehseen Aslam, (2013, May) University of Skovde, Analysis of manufacturing supply chains using system dynamics and multiobjective optimization, modeFrontier Users' meeting, Sweden. 152. Tessarolo A., Venuti V, Luise F., Raffin P., (2011, May) Multiobjective design optimization of a surface permanent magnet slotless alternator for small power wind generator, ModeFrontier User Meeting, Trieste. 153. Vincent Gassmann, Dominique Knittel, (2011, September) National Institute of Applies Sciences, Strasbourg, Robust PI–LPV Tension Control with Elasticity Observer for Roll–to–Roll Systems, 18th IFAC World Congress, Milano Italy. 154. Vladimir Kudriavtsev, Terry Bluck, (2011, November) Intevac, Multiobjective optimization of radiative heating for silicon solar cells manufacturing, modeFrontier Users' meeting. 155. Vincenzo Colozzo, (2016, October) Electrolux, Impact simulations on home appliances to optimize packaging protection: a case study, International CAE Conference, Italy. 156. Yasushi Fujishima,(2008, October)CD-adapco, Application of multidisciplinary optimization to rotor design of interior permanent magnet synchronous motor, users' meeting, Trieste Italy. 157. Yasuko Sugase-Miyamoto, Zheng Liu, Barry J. Richmond, Matthew C. Wiener, Lance M. Optican, (2008, May) National Institutes of Health USA, Merck Research Laboratories, National Institutes of Health, Short term memory trace in rapidly adapting synapses of inferior temporal cortex, usermeeting conference. 158. Xiao-Peng Ganb, Ashutosh Tiwaria, Paula Noriega Hoyosa, Windo Hutabarata, , Chris Turnera, , Nadir Inceb, Neha Prajapatb, (2015, May), Cranfield University, Survey on the use of computational optimisation in UK engineering companies, CIRP Journal of Manufacturing Science and Technology, UK 159. Yacov Y. Haimes, (1979, January) Chankong, Kuhn-Tucker multipliers as tradeoffs in multiobjective decision-making analysis, Automatica, Germany 160. Zhang, Bin Wu, Zhe Li, Jun Huang, (2013, August) Tsinghua University Jianbo, Simultaneous estimation of multiple thermal parameters of large format laminated Lithium ion batteries, modeFrontier usermeeting, Beijing