Università Iuav di Venezia Dipartimento di Architettura e Culture del Progetto Tesi di Laurea Eric Danzo
PARAMETRIC OPTIMIZATION OF FAÇADES
UniversitĂ Iuav di Venezia FacoltĂ di Architettura Dipartimento di Architettura e Culture del Progetto
Tesi di Laurea Studente: Eric Danzo Mat. 280068 Relatore: Luigi Schibuola Correlatore: Massimiliano Scarpa Correlatore esterno: Alfonso Senatore (University of East London)
PARAMETRIC OPTIMIZATION OF FAÇADES
INTRODUCTION
INTRODUCTION: CONVENTIONAL DRAWING TO GENERATIVE DESIGN Architects have always drawn, an act that dife-
rentiates architecture from the mere construction. Drawings have been the architects medium to organize ideas, resources, space, etc... and
represent the architect’s faculty to predict design outcomes. As methods of representation have
evolved, new styles have emerged. Tools such
as perspective in the Reinassance and projective geometry in Modernism have marked leap forward in design. However, these tools have
been dependant on a stable set of instruments
for centuries: paper, drawing utensils, ruler and the compass. In this model each creative act is
translated into a geometric alphabet by gestures
PalĂĄcio do Planalto Sketch, late 1950s by Oscar Niemeyer
which establish a direct link between the idea and the sign.
Traditional drawing is an additive process, in
which complexity is achieved by the addittion
and the overlap of independent sign traced on
paper. No associative relations can be managed. The internal consistency of a drawing is not guaranteed by the medium, but is entrustend to the designer. As follows, the drawing is not a smart
medium, but rather, a code based on standards and conventions.
The additive logic of the traditional drawing
implies two limits: first, the act of drawing dif-
fers from cognitive mechanism, underlying the creative proces, which by establishing interre-
lations rather than adding information. Second,
the drawing process exclude physically relevant aspects that in the real world drive the generation of forms. For example, the traditional
drawing cannot manage forces (such as gravity)
8
Brick House Plan, 1923/24 by Mies an Der Rohe
and constraints which affects and restrict deformations and displacements. These limits have restricted the exploitation of the drawing and
designers have been forced to reiterate definitive tectonic systems rather than innovating. Initially
these limits were not overcome by the computer; CAD software simply improved the ability to per-
form repetitive tasks without affecting the method of design. Similar to traditional drawing, CAD
entrusted the designer to determine the overall
consistency by adding digital signs or geometric primitives on a digital sheet/space and control-
ling CAD layers; this method can be seen as the translation of the addittive logic within the digital realm.
From ‘60s the architectural avant-garde tried to force drawing’s limits using several methods to represent forces and processes that drive the
generative process. For example, Eisenman’s diagram for House IV (below) impressed the
entire sequence of geometric operations that led to the final object
Peter Eisenman, House IV, Connecticut, USA, 1971
9
INTRODUCTION: CONVENTIONAL DRAWING TO GENERATIVE DESIGN Despite limitations, drawings have been the
stable medum of architecture over the centuries and this was possible as the architects have
relied on typology, i.e. the use of well proven,
precoinceived solutions and tectonic systems. Typology made the drawing not only a com-
munication medium but a system that enabled designers to explore and refine variations
(form-making approach) within a specific set of formal and structural constraints.
The conventional drawing was first attacked by a new approach, - the form finding - emerged in architecture in the late 19th century - whi-
ch aimed to investigate novel and optimized
structures found through complex and asso-
ciative relations between materials, shape and structures.
Pioneers like Antoni GaudĂŹ, Frei Otto and Segio Musmeci have rejected typology and looked
to self-formation processes in nature as a way
to organize buildings. Since the form could not descend from proven solutions, the traditional drawing could not be used as a tool to predict design outcomes.
For this reason form-finding pioneers relied
on physical models such as: soap films which
found minimal surfaces, and suspended fabric
which found compression-only vaults and branched structures. In other words, the drawing as
a medium to investigate form was replaced with physical form finding relying on analogue devices which demonstrated how dynamic forces could mould new self-optimized architectural forms.
10
Antoni Gaudí near his hanged model for Güell Chapel, Scale 1:10, Santa Coloma de Cervelló, Barcelona, 1898-1906
Güell Chapel, Barcelona, Early 1900s
Frei Otto’s hanging models of branching systems, Stuttgart, 1970s
Terminal 3, Stuttgart Airport, 1974
11
INTRODUCTION: CONVENTIONAL DRAWING TO GENERATIVE DESIGN Over the last decades the increasing complexity of buildings has made form-finding an important strategy in determinating the shape and form of indeterminate structures. Strucutral optimiza-
tion through physical modeling was mono-pa-
rametric (gravity based) and marked trajectory
towards multi-para,etric form-finding which aims to interact with heterogeneous data: geometry, dynamic forces, environment, social data.
12
Heinz Isler studies on correlation between forces and form
Heinz Isler. Service Station in Deitingen, Solothurn, Switzerland, 1968
Heinz Isler. Pasento Bridge, Potenza, Italy, 1971-1975
13
INTRODUCTION: FROM ADDITIVE TO ASSOCIATIVE LOGIC Italian architect Luigi Moretti invented the defini-
tion “Architettura Parametrica” in 1939. His research on “the relations between the dimensions
dependent upon various parameters were linked to viewing angles and economic feasibility in
these projects: the final shape was generated by calculating pseudo isocurves, that attempted to
optimize views from every position in the stadium. Moretti’s research was a collaboration with the mathematician Bruno De Finetti, wherewith he
founded the Institute for Mathematical Research
Moretti’s Parametric Stadium Model, 1960
in Architecture (I.R.M.O.U.)
In Moretti’s words “The parameters and their
interrelationship become the code of the new arcitectural language, the “structure” in the original
sense of the word. The setting of parameters and
their relation must be supported by the techniques and tools offered by the most current sciences, in particular by logics, mathematics and computers.
Computers give the possibility to express parameters and their relations through a set of self correcting routines”
It is evident from this quote, that Moretti imme-
diately undesrtood the potentials of the computer applied to design. Following Moretti, the first ap-
plication for design utilizing the computer occured in 1963 when computer scientist Ivan Sutherland developed the Sketchpad, which is the first inte-
ractive Computer-Aided-Design (CAD) program. After the explosion of CAD systems in the last
decades many designer soon realized that more sofisticated programs could manage complexity
beyond human imagination by structuring routines and procedures.
This type of modeling relies on programming
languages which express instruction in a form that can be executed by the computer through a stepby-step procedure: the algorithm 14
Moretti’s Parametric Stadium Studies Late ‘1950s
INTRODUCTION: ALGORITHMIC MODELING An Algorhithm is a procedure used to return a
solution to a question - or to perform a particular
task - through a finite list of basic and well-defined instructions. Algorithms follow the human aptitude to split a problem into a set of simple steps that can be easily computed, and although they are
strongly associated with the computer, algorithms
could be defined independently from programming languages.
In this research the algorithm-based software is
Grasshopper, a plug-in for Rhinoceros, which is a node-based editor developed by David Rutten at Robert McNeel & Associates in 2008.
In just a few years the plug-in gained a vast community of users and developers
And according to Patrik Schumacher (Co-founder
at Zaha Hadid Architects) is now the main working tool for architectural avant-garde of the newborn movement “Parametricism”
The “algorithmic-approach” can provide lots of
new possibilities for designers and it can perform searches and insights never experienced before.
Schematic representation of an algorithm
15
INTRODUCTION: ALGORITHMIC ENVIRONMENTAL ANALYSIS Several software applications are available in
the field of environmental design; most however, are back-end analysis and give small constribu-
tions in exploring holistic design solutions. Due to difficulties in making changes during advanced
stages of modeling/drawing designers often rely
on personal judgement deduced from experience, over analysis. Thereby, narrowing the possibilities to explore a large set of environmental-informed designs.
The combined use of environmental and parametric packages can guide the generation of
form from the initial sketches to the final design.
The software find forms through realtime analysi
based on insolation, lighting, etc... These techni-
ques, supported by a deep understanding of data can lead to environmental-conscious designs.
Design Process
Optimized Design Process
16
INTRODUCTION: RESEARCH PREMISES This research push forward the knowledge of ex-
ternal shading’s impact on building’s inner space, and seek to provide an innovative tool for designers.
While designing a façade the two main aspect to consider are:
1- Ensure a good quality of daylighting, which
means to provide a comfort illuminance for the longest time possible.
2- Reduce energy costs related to an excessive heating due to direct solar irradiation.
Basically, these two aspects works in opposite
directions because daylight prefers a less invasi-
ve solution where the façade is lighter in terms of glass-shade ratio; while at the same time to save more energy the façade should be as opaque as possible.
To get the best setup is fundamental to maximi-
ze the daylight and minimize the primary energy consumption.
Here is where the study of optimization comes in
play, integrating design and structural decisions to create optimal solutions within set parameters.
In this case the set parameter will be the orienta-
tion of shading’s elements and the fitness value to minimize will be the primary energy consumption, but only if according to a 50% minimum of sDA.
sDA stands for spatial Daylight Autonomy, and it’s the percentage of the overall inner surface that
exceed, for at least 50% of the year, the minimum illuminance treshold, in this case is set to 300 lux
17
INTRODUCTION: SOFTWARE TOOLS Here there’s the list of the software tools that I
used to develop this thesis, on the right page there’s a scheme that explain their interconnections
Rhinoceros is a commercial 3D computer graphics and computer-aided design (CAD) application software. Rhinoceros geometry is based on the
NURBS mathematical model, which focuses on producing mathematically precise representa-
tion of curves and freeform surfaces in computer graphics.
Grasshopper is a visual programming language and environment developed by David Rutten at
Robert McNeel & Associates, that runs within the
Rhinoceros application. Programs are created by
dragging components onto a canvas. The outputs to these components are then connected to the inputs of subsequent components.
DIVA is a highly optimized daylighting and energy modeling plug-in for Rhinoceros NURBS mo-
deler. The plug-in was initially developed at the
Graduate School of Design at Harvard University. DIVA-for-Rhino allows users to carry out a series
of environmental performance evaluations of individual buildings and urban landscapes including Single Thermal Zone Energy and Load Calculations. Runs both in Grasshopper and Rhino
EnergyPlus is a energy simulation program that engineers, architects, and researchers use to
model both energy consumption—for heating,
cooling, ventilation, lighting and plug and process loads—and water use in buildings.
E.P. is the thermal calculator engine included in the DIVA suite 18
Radiance is a suite of tools for performing lighting
simulation originally written by Greg Ward. It includes a render engine as well as many other tools for measuring the simulated light levels. It uses
ray tracing to perform all lighting calculations, accelerated by the use of an octree data structure. It pioneered the concept of high dynamic range imaging
Daysim is a validated, Radiance-based dayli-
ghting analysis software that models the annual amount of daylight in and around buildings. It
allows users to model dynamic facades systems ranging from standard venetian blinds to state-
of-the-art light redirecting elements, switchable glazings and combinations thereof.
Software Tools interconnections
19
INTRODUCTION: SETTINGS The analysis are made modeling a box with the following dimensions: Width: 5 meters
Depth: 4 meters
Height: 3 meters This box represent an ideal portion of a building
with one face representing a window, this strategy
is often used by designer due to its caracteristic of neutrality, using a simple box in fact is much way
better to measure the impact of the shading instead of adding complexity using a whole building.
The space is set as an office, which mean an occupancy from 9am to 6pm in weekdays.
The occupancy is set a 1 person/10 square meters The electric light power is set at 7 kW/sq
Internal equipment (such as PCs) is set at 4 kW/sq The location is set in New York City (USA).
The whole box is set as adiabatic except for the
window that is a Double Pane 80 mm Air glass (from standard Radiance library), in this way the model
response will be as near as possible as real building portion.
The following research will analize 4 different façade solutions for 8 different building orientation, plus the
“shading-free” solution that will be set as a benchmark.
20
Rhino-Grasshopper Workspace
Grasshopper Workflow Overview
21
BENCHMARK
BENCHMARK SOUTH
Boundary Conditions
Primary Energy
10151 [kWh]
Heating Energy
1338 [kWh]
Cooling Energy
2331 [kWh]
Electric Energy
8690 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
24
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
25
BENCHMARK SOUTH-EAST
Boundary Conditions
Primary Energy
9287 [kWh]
Heating Energy
1382 [kWh]
Cooling Energy
2041 [kWh]
Electric Energy
7777 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
26
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
27
BENCHMARK EAST
Boundary Conditions
Primary Energy
7804 [kWh]
Heating Energy
1559 [kWh]
Cooling Energy
1517 [kWh]
Electric Energy
6101 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
28
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
29
BENCHMARK NORTH-EAST
Boundary Conditions
Primary Energy
6306 [kWh]
Heating Energy
1853 [kWh]
Cooling Energy
946 [kWh]
Electric Energy
4282 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
30
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
31
BENCHMARK NORTH
Boundary Conditions
Primary Energy
5421 [kWh]
Heating Energy
1970 [kWh]
Cooling Energy
628 [kWh]
Electric Energy
3270 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
32
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
33
BENCHMAARK NORTH-WEST
Boundary Conditions
Primary Energy
6692 [kWh]
Heating Energy
1906 [kWh]
Cooling Energy
1048 [kWh]
Electric Energy
4610 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
34
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
35
BENCHMARK WEST
Boundary Conditions
Primary Energy
8468 [kWh]
Heating Energy
1672 [kWh]
Cooling Energy
1686 [kWh]
Electric Energy
6642 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
36
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
37
BENCHMARK SOUTH-WEST
Boundary Conditions
Primary Energy
9906 [kWh]
Heating Energy
1485 [kWh]
Cooling Energy
2200 [kWh]
Electric Energy
8283 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
38
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
39
CASE STUDY 1
CASE 1
CASE STUDY 1 INTRODUCTION This first solution come from a University of East
London’s research, there is no example on a real building and represent the “fully-experimental” solution of this work.
The geometry consist of an array of 0.5 m large and 0.5m tall square components that are splitted in two parts, revealing two triangle shaped
elements that rotates around the square’s component diagonal.
42
EVALUATED POSITIONS
43
CASE STUDY 1 SOUTH Optimized Position
Boundary Conditions
40°
Primary Energy
4659 [kWh]
Heating Energy
1912 [kWh]
Cooling Energy
394 [kWh]
Electric Energy
2353 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
44
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
45
CASE STUDY 1 SOUTH-EAST
Optimized Position
Boundary Conditions
30°
Primary Energy
4665 [kWh]
Heating Energy
1816 [kWh]
Cooling Energy
374 [kWh]
Electric Energy
2682 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
46
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
47
CASE STUDY 1 EAST
Optimized Position
Boundary Conditions
30°
Primary Energy
4602 [kWh]
Heating Energy
1889 [kWh]
Cooling Energy
314 [kWh]
Electric Energy
2539 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
48
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
49
CASE STUDY 1 NORTH-EAST
Optimized Position
Boundary Conditions
40°
Primary Energy
4528 [kWh]
Heating Energy
1967 [kWh]
Cooling Energy
302 [kWh]
Electric Energy
2379 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
50
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
51
CASE STUDY 1 NORTH
Optimized Position
Boundary Conditions
50°
Primary Energy
4415 [kWh]
Heating Energy
2032 [kWh]
Cooling Energy
264 [kWh]
Electric Energy
2196 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
52
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
53
CASE STUDY 1 NORTH-WEST
Optimized Position
Boundary Conditions
50°
Primary Energy
4621 [kWh]
Heating Energy
2025 [kWh]
Cooling Energy
337 [kWh]
Electric Energy
2409 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
54
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
55
CASE STUDY 1 WEST
Optimized Position
Boundary Conditions
30°
Primary Energy
4711 [kWh]
Heating Energy
1917 [kWh]
Cooling Energy
330 [kWh]
Electric Energy
2677 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
56
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
57
CASE STUDY 1 SOUTH-WEST
Optimized Position
Boundary Conditions
30°
Primary Energy
4684 [kWh]
Heating Energy
1897 [kWh]
Cooling Energy
354 [kWh]
Electric Energy
2612 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
58
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
59
CASE STUDY 1
The solution performance has been quite im-
pressive, totaling near 50% of energy savings,
provide a good spatial Daylight Autonomy, while reducing the glare by 80% in the whole year.
The best solutions are set around 30,40 & 50° positions.
Cooling energy consumption reduction is really drastic.
60
Optimized shading elements rotation following
Primary Energy consumption following
building orientation
building orientation
Overall Faรงade Performance
61
CASE STUDY 1: OPTIMIZATOR RESULTS
Or 40° - P.E. 4659 [kWh]
Or 30° - P.E. 4665 [kWh]
Or 30° - P.E. 4602 [kWh]
Or 40° - P.E. 4528 [kWh]
Or 30° - P.E. 4704 [kWh]
Or 40° - P.E. 4695 [kWh]
Or 40° - P.E. 4658 [kWh]
Or 50° - P.E. 4611 [kWh]
Or 50° - P.E. 4738 [kWh]
Or 50° - P.E. 4755 [kWh]
Or 50° - P.E. 4764 [kWh]
Or 30° - P.E. 4691 [kWh]
Or 60° - P.E. 5006 [kWh]
Or 20° - P.E. 4829 [kWh]
Or 20° - P.E. 4788 [kWh]
Or 60° - P.E. 4810 [kWh]
Or 20° - P.E. 5167 [kWh]
Or 60° - P.E. 4985 [kWh]
Or 60° - P.E. 5070 [kWh]
Or 20° - P.E. 5001 [kWh]
Or 10° - P.E. 5387 [kWh]
Or 10° - P.E. 5227 [kWh]
Or 10° - P.E. 5231 [kWh]
Or 70° - P.E. 5068 [kWh]
Or 70° - P.E. 5415 [kWh]
Or 70° - P.E. 5357 [kWh]
Or 70° - P.E. 5465 [kWh]
Or 80° - P.E. 5333 [kWh]
Or 80° - P.E. 5962 [kWh]
Or 80° - P.E. 5850 [kWh]
Or 80° - P.E. 5922 [kWh]
Or 10° - P.E. 5420 [kWh]
Or 90° - P.E. 6919 [kWh]
Or 90° - P.E. 6392 [kWh]
Or 90° - P.E. 6373 [kWh]
Or 90° - P.E. 5599 [kWh]
SOUTH
62
SOUTH-EAST
EAST
NORTH-EAST
Or 50° - P.E. 4415 [kWh]
Or 50° - P.E. 4621 [kWh]
Or 30° - P.E. 4771 [kWh]
Or 30° - P.E. 4684 [kWh]
Or 60° - P.E. 4481 [kWh]
Or 40° - P.E. 4631 [kWh]
Or 40° - P.E. 4798 [kWh]
Or 40° - P.E. 4760 [kWh]
Or 40° - P.E. 4498 [kWh]
Or 60° - P.E. 4749 [kWh]
Or 50° - P.E. 4985 [kWh]
Or 50° - P.E. 5004 [kWh]
Or 70° - P.E. 4587 [kWh]
Or 30° - P.E. 4779 [kWh]
Or 20° - P.E. 5042 [kWh]
Or 20° - P.E. 5050 [kWh]
Or 30° - P.E. 4660 [kWh]
Or 70° - P.E. 4899 [kWh]
Or 60° - P.E. 5241 [kWh]
Or 60° - P.E. 5360 [kWh]
Or 80° - P.E. 4679 [kWh]
Or 20° - P.E. 5048 [kWh]
Or 70° - P.E. 5585 [kWh]
Or 10° - P.E. 5562 [kWh]
Or 90° - P.E. 4789 [kWh]
Or 80° - P.E. 5060 [kWh]
Or 80° - P.E. 5955 [kWh]
Or 70° - P.E. 5816 [kWh]
Or 20° - P.E. 5088 [kWh]
Or 90° - P.E. 5186 [kWh]
Or 10° - P.E. 5604 [kWh]
Or 80° - P.E. 6371 [kWh]
Or 10° - P.E. 5621 [kWh]
Or 10° - P.E. 5650 [kWh]
Or 90° - P.E. 6305 [kWh]
Or 90° - P.E. 6873 [kWh]
NORTH
NORTH-WEST
WEST
SOUTH-WEST
63
CASE STUDY 2: ALBAHR TOWERS
ALBAHR TOWERS
CASE STUDY 2: ALBAHR TOWERS INTRODUCTION The geometry refers to the faรงade of the Al Bahr Towers in the Emirates, designed by AEDAS.
The triangle shaped components in this reserch are 60 cm tall.
Geometry refers to the tipical arabic blinds solution called mashrabiyya, famous for its caracteristic of filtering light, is perfect to cut down the internal heat in arid climates.
The Al Bahr towers provide a dynamic shading system while in my study is meant to be static.
I evaluted 7 different positions as shown on the right page
AlBahr Towers by AEDAS
66
EVALUATED POSITIONS
67
CASE STUDY 2: ALBAHR TOWERS SOUTH Optimized Position
Boundary Conditions
6
Primary Energy
4945 [kWh]
Heating Energy
1712 [kWh]
Cooling Energy
467 [kWh]
Electric Energy
3076 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
68
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
69
CASE STUDY 2: ALBAHR TOWERS SOUTH-EAST
Optimized Position
Boundary Conditions
6
Primary Energy
4731 [kWh]
Heating Energy
1755 [kWh]
Cooling Energy
413 [kWh]
Electric Energy
2814 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
70
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
71
CASE STUDY 2: ALBAHR TOWERS EAST
Optimized Position
Boundary Conditions
6
Primary Energy
4697 [kWh]
Heating Energy
1852 [kWh]
Cooling Energy
365 [kWh]
Electric Energy
2674 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
72
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
73
CASE STUDY 2: ALBAHR TOWERS NORTH-EAST
Optimized Position
Boundary Conditions
6
Primary Energy
4715 [kWh]
Heating Energy
1851 [kWh]
Cooling Energy
364 [kWh]
Electric Energy
2782 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
74
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
75
CASE STUDY 2: ALBAHR TOWERS NORTH
Optimized Position
Boundary Conditions
5
Primary Energy
4611 [kWh]
Heating Energy
1954 [kWh]
Cooling Energy
326 [kWh]
Electric Energy
2007 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
76
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
77
CASE STUDY 2: ALBAHR TOWERS NORTH-WEST
Optimized Position
Boundary Conditions
6
Primary Energy
4869 [kWh]
Heating Energy
1873 [kWh]
Cooling Energy
400 [kWh]
Electric Energy
2737 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
78
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
79
CASE STUDY 2: ALBAHR TOWERS WEST
Optimized Position
Boundary Conditions
6
Primary Energy
5025 [kWh]
Heating Energy
1793 [kWh]
Cooling Energy
452 [kWh]
Electric Energy
2727 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
CASE STUDY 2: ALBAHR TOWERS SOUTH-WEST
Optimized Position
Boundary Conditions
6
Primary Energy
4897 [kWh]
Heating Energy
1791 [kWh]
Cooling Energy
445 [kWh]
Electric Energy
2940 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
82
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
83
CASE STUDY 2: ALBAHR TOWERS
The solution performed the best sDA performance, good energy saving and glare reduction.
The optimized position is equal almost in every orientation (except North).
As seen in Case1 there’s a higher consumption in the west side
84
Optimized shading elements rotation following
Primary Energy consumption following
building orientation
building orientation
Overall Faรงade Performance
85
CASE STUDY 2: ALBAHR TOWERS OPTIMIZATOR RESULTS
86
Pos. 6 - P.E. 4945 [kWh]
Pos. 6 - P.E. 4731 [kWh]
Pos. 6 - P.E. 4697 [kWh]
Pos. 6 - P.E. 4715 [kWh]
Pos. 7 - P.E. 4981 [kWh]
Pos. 5 - P.E. 4846 [kWh]
Pos. 7 - P.E. 4799 [kWh]
Pos. 5 - P.E. 4735 [kWh]
Pos. 5 - P.E. 5001 [kWh]
Pos.7 - P.E. 4854 [kWh]
Pos. 6 - P.E. 4816 [kWh]
Pos. 4 - P.E. 4835 [kWh]
Pos. 4 - P.E. 5411 [kWh]
Pos.4 - P.E. 5181 [kWh]
Pos. 4 - P.E. 5043 [kWh]
Pos. 7 - P.E. 4926 [kWh]
Pos. 3 - P.E. 5898 [kWh]
Pos. 3 - P.E. 5638 [kWh]
Pos. 3 - P.E. 5372 [kWh]
Pos. 3 - P.E. 4998 [kWh]
Pos. 2 - P.E. 6497 [kWh]
Pos.2 - P.E. 6147 [kWh]
Pos. 2 - P.E. 5733 [kWh]
Pos. 2 - P.E. 5162 [kWh]
Pos. 1 - P.E. 6661 [kWh]
Pos.1 - P.E. 6803 [kWh]
Pos. 1 - P.E. 6181 [kWh]
Pos. 1 - P.E. 5416 [kWh]
SOUTH
SOUTH-EAST
EAST
NORTH-EAST
Pos. 5 - P.E. 4611 [kWh]
Pos. 6 - P.E. 4869 [kWh]
Pos. 6 - P.E. 5025 [kWh]
Pos. 6 - P.E. 4933 [kWh]
Pos. 4 - P.E. 4627 [kWh]
Pos. 5 - P.E. 4870 [kWh]
Pos. 7 - P.E. 5131 [kWh]
Pos. 7 - P.E. 5015 [kWh]
Pos. 3 - P.E. 4687 [kWh]
Pos. 4 - P.E. 5029 [kWh]
Pos. 5 - P.E. 5131 [kWh]
Pos. 5 - P.E. 5086 [kWh]
Pos. 6 - P.E. 4746 [kWh]
Pos. 7 - P.E. 5125 [kWh]
Pos. 4 - P.E. 5376 [kWh]
Pos. 4 - P.E. 5485 [kWh]
Pos. 2 - P.E. 4775 [kWh]
Pos. 3 - P.E. 5213 [kWh]
Pos. 3 - P.E. 5756 [kWh]
Pos. 3 - P.E. 5993 [kWh]
Pos. 1 - P.E. 4914 [kWh]
Pos. 2 - P.E. 5417 [kWh]
Pos. 2 - P.E. 6189 [kWh]
Pos. 2 - P.E. 6578 [kWh]
Pos. 7 - P.E. 4952 [kWh]
Pos. 1 - P.E. 5699 [kWh]
Pos. 1 - P.E. 6684 [kWh]
Pos. 1 - P.E. 7286 [kWh]
NORTH
NORTH-WEST
WEST
SOUTH-WEST
87
CASE STUDY 3: VENETIAN BLINDS
VENETIAN BLINDS
CASE STUDY 3: VENETIAN BLINDS INTRODUCTION This is probably the most famous kind of blinds,
these horizontal shading comes in many different forms and shape, can be internal, external or static, dynamic, ecc...
In this case well’be considering a static external
shade similar, in shape, to the fire station façade shown in the photo on the right.
Elements width is 50 cm, I optimized the solution minimizing primary energy consumption consi-
dering 7 different positions as seen on the right page.
90
Detail of the fire station in Berlin by Sauebruch & Hutton, 2002-2004
EVALUATED POSITIONS
91
CASE STUDY 3: VENETIAN BLINDS SOUTH Optimized Position
Boundary Conditions
80°
Primary Energy
4430 [kWh]
Heating Energy
1590 [kWh]
Cooling Energy
440 [kWh]
Electric Energy
2694 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
92
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
93
CASE STUDY 3: VENETIAN BLINDS SOUTH-EAST
Optimized Position
Boundary Conditions
70°
Primary Energy
4448 [kWh]
Heating Energy
1737 [kWh]
Cooling Energy
390 [kWh]
Electric Energy
2491 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
94
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
95
CASE STUDY 3: VENETIAN BLINDS EAST
Optimized Position
Boundary Conditions
50°
Primary Energy
4621 [kWh]
Heating Energy
1848 [kWh]
Cooling Energy
346 [kWh]
Electric Energy
2603 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
96
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
97
CASE STUDY 3: VENETIAN BLINDS NORTH-EAST
Optimized Position
Boundary Conditions
70°
Primary Energy
4663 [kWh]
Heating Energy
1895 [kWh]
Cooling Energy
389 [kWh]
Electric Energy
2592 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
98
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
99
CASE STUDY 3: VENETIAN BLINDS NORTH
Optimized Position
Boundary Conditions
70°
Primary Energy
4647 [kWh]
Heating Energy
1941 [kWh]
Cooling Energy
373 [kWh]
Electric Energy
2527 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
100
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
101
CASE STUDY 3: VENETIAN BLINDS NORTH-WEST
Optimized Position
Boundary Conditions
70°
Primary Energy
4735 [kWh]
Heating Energy
1922 [kWh]
Cooling Energy
407 [kWh]
Electric Energy
2635 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
102
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
103
CASE STUDY 3: VENETIAN BLINDS WEST
Optimized Position
Boundary Conditions
50°
Primary Energy
4751 [kWh]
Heating Energy
1899 [kWh]
Cooling Energy
347 [kWh]
Electric Energy
2677 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
104
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
105
CASE STUDY 3: VENETIAN BLINDS SOUTH-WEST
Optimized Position
Boundary Conditions
70°
Primary Energy
4606 [kWh]
Heating Energy
1773 [kWh]
Cooling Energy
413 [kWh]
Electric Energy
2420 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
106
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
107
CASE STUDY 3: VENETIAN BLINDS
Unexpectedly and differently from the previous cases, the consumption in the north side is
higher than the south, the sDA is good and also the simulated energy saving.
Best positions fluctuates between 40 and 70°
108
Optimized shading elements rotation following
Primary Energy consumption following
building orientation
building orientation
Overall Faรงade Performance
109
CASE STUDY 3: VENETIAN BLINDS OPTIMIZATOR RESULTS
Or. 80° - P.E. 4420 [kWh] Or. 70° - P.E. 4448 [kWh]
Or. 50° - P.E. 4621 [kWh]
Or. 70° - P.E. 4735 [kWh]
Or. 70° - P.E. 4534 [kWh] Or. 80° - P.E. 4468 [kWh]
Or. 60° - P.E. 4649 [kWh]
Or. 60° - P.E. 4791 [kWh]
Or. 90° - P.E. 4567 [kWh] Or. 70° - P.E. 4472 [kWh]
Or. 40° - P.E. 4655 [kWh]
Or. 50° - P.E. 4873 [kWh]
Or. 60° - P.E. 4580 [kWh] Or. 60° - P.E. 4619 [kWh]
Or. 70° - P.E. 4681 [kWh]
Or. 80° - P.E. 4896 [kWh]
Or. 50° - P.E. 4625 [kWh] Or. 90° - P.E. 4678 [kWh]
Or. 80° - P.E. 4762 [kWh]
Or. 90° - P.E. 5014 [kWh]
Or. 40° - P.E. 4857 [kWh] Or. 40° - P.E. 4720 [kWh]
Or. 30° - P.E. 4911 [kWh]
Or. 40° - P.E. 5083 [kWh]
Or. 30° - P.E. 5224 [kWh] Or. 30° - P.E. 5042 [kWh]
Or. 90° - P.E. 4957 [kWh]
Or. 30° - P.E. 5434 [kWh]
EAST
NORTH-EAST
SOUTH
110
SOUTH-EAST
Or. 70° - P.E. 4647 [kWh] Or. 70° - P.E. 4735 [kWh]
Or. 50° - P.E. 4751 [kWh]
Or. 70° - P.E. 4606 [kWh]
Or. 60° - P.E. 4706 [kWh] Or. 60° - P.E. 4791 [kWh]
Or. 70° - P.E. 4927 [kWh]
Or. 60° - P.E. 4632 [kWh]
Or. 50° - P.E. 4721 [kWh] Or. 50° - P.E. 4873 [kWh]
Or. 60° - P.E. 4795 [kWh]
Or. 80° - P.E. 4748 [kWh]
Or. 80° - P.E. 4736 [kWh] Or. 80° - P.E. 4896 [kWh]
Or. 40° - P.E. 5005 [kWh]
Or. 50° - P.E. 4760 [kWh]
Or. 90° - P.E. 4855 [kWh] Or. 90° - P.E. 5014 [kWh]
Or. 80° - P.E. 5038 [kWh]
Or. 40° - P.E. 4966 [kWh]
Or. 40° - P.E. 5021 [kWh] Or. 40° - P.E. 5083 [kWh]
Or. 90° - P.E. 5329 [kWh]
Or. 90° - P.E. 5062 [kWh]
Or. 30° - P.E. 5361 [kWh] Or. 30° - P.E. 5434 [kWh]
Or. 30° - P.E. 5428 [kWh]
Or. 30° - P.E. 5344 [kWh]
WEST
SOUTH-WEST
NORTH
NORTH-WEST
111
CASE STUDY 4: VERTICAL BLINDS
VERTICAL BLINDS
CASE STUDY 4: VERTICAL BLINDS INTRODUCTION If the previous case was the most famous kind of blinds, this is probably the second.
These horizontal shading as well comes in many different forms and shape, usually external, can be static or dynamic
In this case I considered a static external shade similar, in shape, to the building shown in the photo on the right.
Elements width is 1 m, I optimized the solution
minimizing primary energy consumption consi-
dering 7 different positions as seen on the right page.
114
Net Center, Padova
EVALUATED POSITIONS
115
CASE STUDY 4: VERTICAL BLINDS SOUTH Optimized Position
Boundary Conditions
160°
Primary Energy
5064 [kWh]
Heating Energy
1736 [kWh]
Cooling Energy
470 [kWh]
Electric Energy
3167 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
116
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
117
CASE STUDY 4: VERTICAL BLINDS SOUTH-EAST
Optimized Position
Boundary Conditions
160°
Primary Energy
4977 [kWh]
Heating Energy
1868 [kWh]
Cooling Energy
407 [kWh]
Electric Energy
2702 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
118
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
119
CASE STUDY 4: VERTICAL BLINDS EAST
Optimized Position
Boundary Conditions
160°
Primary Energy
4651 [kWh]
Heating Energy
1958 [kWh]
Cooling Energy
285 [kWh]
Electric Energy
2512 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
120
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
121
CASE STUDY 4: VERTICAL BLINDS NORTH-EAST
Optimized Position
Boundary Conditions
140°
Primary Energy
4622 [kWh]
Heating Energy
1909 [kWh]
Cooling Energy
349 [kWh]
Electric Energy
2363 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
122
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
123
CASE STUDY 4: VERTICAL BLINDS NORTH
Optimized Position
Boundary Conditions
40°
Primary Energy
4558 [kWh]
Heating Energy
1909 [kWh]
Cooling Energy
349 [kWh]
Electric Energy
2300 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
124
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
125
CASE STUDY 4: VERTICAL BLINDS NORTH-WEST
Optimized Position
Boundary Conditions
40°
Primary Energy
4613 [kWh]
Heating Energy
1996 [kWh]
Cooling Energy
321 [kWh]
Electric Energy
2493 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
126
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
127
CASE STUDY 4: VERTICAL BLINDS WEST
Optimized Position
Boundary Conditions
160°
Primary Energy
4940 [kWh]
Heating Energy
1793 [kWh]
Cooling Energy
431 [kWh]
Electric Energy
2982 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
128
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
129
CASE STUDY 4: VERTICAL BLINDS SOUTH-WEST
Optimized Position
Boundary Conditions
160°
Primary Energy
4727 [kWh]
Heating Energy
1741 [kWh]
Cooling Energy
393 [kWh]
Electric Energy
2825 [kWh]
Heating and Cooling Energy Consumption trends (monthly)
Primary Energy Consumption (monthly)
130
Useful Daylight Illuminance Distribution
Istant Glare Analysis: Worst Annual Condition
Istant Glare Analysis: Average Annual Condition Annual Glare Evaluation
131
CASE STUDY 4: VERTICAL BLINDS
The solution performed the best overall performance, totaling near 50% of energy savings
provide a good spatial Daylight Autonomy while
reducing the glare by 87% during the whole year. The best solutions aren’t oscillating between a
value but are at the opposites, showing that for this solution, is crucial to catch undirectly the sunlight.
Cooling energy consumption is a bit lower than the others, glare reduction is the highest.
132
Optimized shading elements rotation following
Primary Energy consumption following
building orientation
building orientation
Overall Faรงade Performance
133
CASE STUDY 1: VERTICAL BLINDS
Or 40° - P.E. 4659 [kWh]
Or 30° - P.E. 4665 [kWh]
Or 30° - P.E. 4602 [kWh]
Or 40° - P.E. 4528 [kWh]
Or 30° - P.E. 4704 [kWh]
Or 40° - P.E. 4695 [kWh]
Or 40° - P.E. 4658 [kWh]
Or 50° - P.E. 4611 [kWh]
Or 50° - P.E. 4738 [kWh]
Or 50° - P.E. 4755 [kWh]
Or 50° - P.E. 4764 [kWh]
Or 30° - P.E. 4691 [kWh]
Or 60° - P.E. 5006 [kWh]
Or 20° - P.E. 4829 [kWh]
Or 20° - P.E. 4788 [kWh]
Or 60° - P.E. 4810 [kWh]
Or 20° - P.E. 5167 [kWh]
Or 60° - P.E. 4985 [kWh]
Or 60° - P.E. 5070 [kWh]
Or 20° - P.E. 5001 [kWh]
Or 10° - P.E. 5387 [kWh]
Or 10° - P.E. 5227 [kWh]
Or 10° - P.E. 5231 [kWh]
Or 70° - P.E. 5068 [kWh]
Or 70° - P.E. 5415 [kWh]
Or 70° - P.E. 5357 [kWh]
Or 70° - P.E. 5465 [kWh]
Or 80° - P.E. 5333 [kWh]
Or 80° - P.E. 5962 [kWh]
Or 80° - P.E. 5850 [kWh]
Or 80° - P.E. 5922 [kWh]
Or 10° - P.E. 5420 [kWh]
Or 90° - P.E. 6919 [kWh]
Or 90° - P.E. 6392 [kWh]
Or 90° - P.E. 6373 [kWh]
Or 90° - P.E. 5599 [kWh]
SOUTH
134
SOUTH-EAST
EAST
NORTH-EAST
Or 50° - P.E. 4415 [kWh]
Or 50° - P.E. 4621 [kWh]
Or 30° - P.E. 4771 [kWh]
Or 30° - P.E. 4684 [kWh]
Or 60° - P.E. 4481 [kWh]
Or 40° - P.E. 4631 [kWh]
Or 40° - P.E. 4798 [kWh]
Or 40° - P.E. 4760 [kWh]
Or 40° - P.E. 4498 [kWh]
Or 60° - P.E. 4749 [kWh]
Or 50° - P.E. 4985 [kWh]
Or 50° - P.E. 5004 [kWh]
Or 70° - P.E. 4587 [kWh]
Or 30° - P.E. 4779 [kWh]
Or 20° - P.E. 5042 [kWh]
Or 20° - P.E. 5050 [kWh]
Or 30° - P.E. 4660 [kWh]
Or 70° - P.E. 4899 [kWh]
Or 60° - P.E. 5241 [kWh]
Or 60° - P.E. 5360 [kWh]
Or 80° - P.E. 4679 [kWh]
Or 20° - P.E. 5048 [kWh]
Or 70° - P.E. 5585 [kWh]
Or 10° - P.E. 5562 [kWh]
Or 90° - P.E. 4789 [kWh]
Or 80° - P.E. 5060 [kWh]
Or 80° - P.E. 5955 [kWh]
Or 70° - P.E. 5816 [kWh]
Or 20° - P.E. 5088 [kWh]
Or 90° - P.E. 5186 [kWh]
Or 10° - P.E. 5604 [kWh]
Or 80° - P.E. 6371 [kWh]
Or 10° - P.E. 5621 [kWh]
Or 10° - P.E. 5650 [kWh]
Or 90° - P.E. 6305 [kWh]
Or 90° - P.E. 6873 [kWh]
NORTH
NORTH-WEST
WEST
SOUTH-WEST
135
CONCLUSIONS
CONCLUSIONS The study returned interesting and singular con-
siderations, both graphically and numerical, over
the impact of the shading devices, and drived the
creation of a tool that can be used by designers to evaluate better the impact of their work.
The Grasshopper workflow I created can also be used to evaluate every tipology of geometry in
every location by only changing a few elements of the diagram.
In conclusion, this work doesn’ t want to be a
celebration of the role of computers in the design process. The recent developments in architecture and design are actually an outcome of a long
research supported by a deep control of digital tools, that paradoxically is freeing the designer from software constraints , making the tool a “neutral” instrument of inquiry and investigation. Parametric design is becoming a paradigm capable of
responding to the growing complexity of design
problems through an alternative approach, whi-
ch puts into perspective the established roles of
process and outcome and see in computers just a natural ally, but not its raison d’etre.
138
PROCESS IS MORE IMPORTANT THAN THE OUTCOME WHEN THE OUTCOMES DRIVES THE PROCESS WE WILL ONLY EVER GO TO WHERE WE’VE ALREADY BEEN IF PROCESS DRIVES THE OUTCOME WE MAY NOT KNOW WHERE WE’RE GOING, BUT WE WILL KNOW WE WANT TO BE THERE
Bruce Mau, Rule 3 of the Incomplete Manifesto for Growth, 1998 139
BIBLIOGRAPHY AD. Architectural Design Parametricism 2.0. Rethinking architecture’s agenda for the 21st Century, Profile N. 240 (March/April 2016)
Carpo M. The DigitalnTurn in Architecture 1992-2012 John Wiley and Sons Publications 2013 Dunn N. Digital Fabrication in Architecture Laurence King Publishing 2012 Griffa C. Smart Creatures. Progettazione parametrica per architetture sostenibili Edilstampa 2012 Iwamoto L. Digital Fabrications. Architectural and Material Tecniques Princeton Architectural Press 2009
Neufert E. Enciclopedia pratica per progettare e costruire. Manuale a uso di progettisti, costruttori, docenti e studenti. IX Edizione Ulrico Hoepli Editore 2013
Tatano V. Rossetti M. Schermature Solari. Evoluzione, progettazioe e soluzioni tecniche Maggioli Editore 2012
Tedeschi A. Architettura Parametrica. Instroduzione a Grasshopper Le Penseur Editore 2010
- AAD_Algorithmic Aided Design. Parametric strategies using Grasshopper Le Penseur Editor
2014
140
141