MECHANICAL ENGINEERING & MATERIALS SCIENCE
Daniel G. Cole, PhD, PE
605 Benedum Hall | 3700 O’Hara Street | Pittsburgh, PA 15261
Associate Professor Director, Stephen R. Tritch Program in Nuclear Engineering
P: 412-624-3069 dgcole@pitt.edu
Bayesian Techniques for Control
The Cole Group’s focus is instrumentation and controls and the application of dynamic systems, measurement and control theory to modern industrial and energy systems. Current research efforts are investigating Bayesian techniques for system estimation and control, the application of advanced computing tools for control, supervisory control of nuclear and energy systems, and cyber security for industrial control systems.
Feedback control is very good at handling uncertainty in systems. As systems become more complex, there is a need to develop advanced approaches for handling uncertainty. Probability theory provides flexible and comprehensive techniques for describing uncertainty, and Bayes’ rule provides an important tool for statistical inference and updating probabilities and estimates with new measurements. This research looks at applying probabilistic tools, like Bayes’ rule, Bayesian networks, and Monte Carlo techniques, to control problems. These tools provide techniques for describing control systems that can be used at different time scales and levels of hierarchy.
Advanced Computing for Control Supercomputers have been pushed to incredible speeds and are ever more capable at handling huge computational problems. Oddly, the tools and techniques of highperformance computing (HPC) have found little use in control systems, which often depend upon single processors or highly decentralized controllers. This research is investigating the application of advanced computing tools, like parallel computing, to controls. These advanced tools can be used for controller design, using for example HPC, or for controller implementation, using parallel banks of controllers.
Supervisory Control of Nuclear Systems A significant challenge for making viable SMRs (small modular reactors) is the need to reduce demands for labor-intensive surveillance, testing, and inspection. In this research, we are using advanced control techniques to enable automated online, in-situ monitoring of SMR instrumentation and control components. This will allow engineers to rapidly measure system dynamics and loop processes, and tune these loops to optimize conditions for
peak performance. A significant advantage is that these tools can be used during reactor operation to monitor components. This effort includes modeling and control of light-water, moltensalt, and advanced high-temperature reactors, the development of supervisory control to manage multi-unit SMR plants, and advanced decision-making techniques to meet economic and safety objectives.
Cyber Security for Industrial Control Systems As the industrial internet grows, more industrial systems will be digital, enabling robust and resilient operation of industrial processes, and improved system monitoring, fault tolerance, and data acquisition. To achieve this, I&C systems will include networks that connect systems, and supervisory control will require the transfer of information throughout the
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control system hierarchy from basic process and safety systems to site command and control to corporate decision making. These interdependencies presents the possibility of cyber incidents compromising plant safety, security, and emergency preparedness. This research is developing cybersecurity approaches for industrial systems that diagnose and inform decision
makers about potential attacks and that provide procedures and protocols to react to such attacks. Specifically, this research will address the challenges unique to the nuclear power enterprise. The research will develop methods and approaches that can rigorously evaluate the vulnerabilities of cyber-physical systems, and will propose tools that can mitigate these vulnerabilities.
DEPARTMENT OF MECHANICAL ENGINEERING AND MATERIALS SCIENCE
