Parametric Optimization of Façades

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