220 likes | 366 Views
Daylight as an Evolutionary Architectural Form Finder. Authors:Tarek Rakha and Khaled Nassar. Tarek Rakha The American University in Cairo. Paper id: 121. Paper Contents. Introduction Objectives Literature Review Ceiling Form Optimization Methodology
E N D
Daylight as an Evolutionary Architectural Form Finder. Authors:TarekRakha and KhaledNassar. Tarek Rakha The American University in Cairo Paper id: 121
Paper Contents • Introduction • Objectives • Literature Review • Ceiling Form Optimization Methodology • Daylighting/Ceiling Problem Formulation • Optimization Process • Ceiling Example, Results & Discussion • Optimization Results • Ceiling Form & Daylighting Analysis • Conclusion
Introduction Evolution of form generation is becoming based on performance (performative) strategies • Emphasis shifts from the • “Generation of Form” to the • “Finding of Form”. Kolarevic B (2005) Computing the performative. In: Kolarevic B and Malkawi A, eds. Performative Architecture: Beyond Instrumentality. New York: Spon Press. Introduction Methodology Results / Discussion Conclusion
Introduction • Objective • Develop a Computer Aided Architectural Design (CAAD) procedure/tool for optimizing a generic curvilinear ceiling form in accordance with daylight uniformity. • Literature Review • Geometry of Form • Shading Devices • Window Design Introduction Methodology Results / Discussion Conclusion
Ceiling Form Optimization Methodology What is a Genetic Algorithm (GA)? Stage 1: Stage 2: Stage 3: Yi, Y. and Malkawi A., (2009). Optimizing building form for energy performance based on hierarchical geometry relation. Automation in Construction 18: 825-833. Introduction Methodology Results / Discussion Conclusion
Ceiling Form Optimization Methodology • Daylighting/Ceiling Problem Formulation Introduction Methodology Results / Discussion Conclusion
Ceiling Form Optimization Methodology Chromosome Gene P2(z2,y2) Sweeped B-spline Ceiling Introduction Methodology Results / Discussion Conclusion
Ceiling Form Optimization Methodology • Optimization Process Fitness Functions: Daylight Uniformity Introduction Methodology Results / Discussion Conclusion
Ceiling Form Optimization Methodology • Optimization Process Crossover Mutation Introduction Methodology Results / Discussion Conclusion
Ceiling Form Optimization Methodology • Optimization Process Introduction Methodology Results / Discussion Conclusion
Ceiling Form FindingExample, Results & Discussion • Example Case Introduction Methodology Results / Discussion Conclusion
Ceiling Form FindingExample, Results & Discussion • Example Case • Simulation parameters were as follows: • Location: Cairo, Egypt (Latitude: 29.8, Longitude: 31.3). • Date and time: June 21st (summer), 12 Noon. • Sky condition: clear sky with sunshine. • Ground reflectance: 20% - medium colored stone. • Walls reflectance: 56% - off white color paint. • Ceiling reflectance: 85.7% - plasterboard. • Floor reflectance: 59.2% - grey colored concrete. • Glass visible light transmittance (VLT): 85%. • Analysis grid: 20 measuring point in a grid of 2.5m *2.5m at a height of 0.75m. • Four B-spline ceiling control nodes (P0, P1, P2 and P3) with limitations: (2.0m < z1, z2 < 3.5m), (2.2m < z0, z3 < 3.3m), • (0.1m < y1 < 5.4m) and (5.4m < y2 < 10.79m) Introduction Methodology Results / Discussion Conclusion
Ceiling Form FindingExample, Results & Discussion • Optimization Results Introduction Methodology Results / Discussion Conclusion
Ceiling Form FindingExample, Results & Discussion • Optimization Results Introduction Methodology Results / Discussion Conclusion
Ceiling Form FindingExample, Results & Discussion • Ceiling Form & Daylighting Analysis Selected chromosomes of ceiling form changes. Introduction Methodology Results / Discussion Conclusion
Ceiling Form FindingExample, Results & Discussion • Ceiling Form & Daylighting Analysis Optimized Unfit Introduction Methodology Results / Discussion Conclusion
Ceiling Form FindingExample, Results & Discussion • Discussion Ceiling geometry that gives comparable performance, allowing choice in optimum design (50 p/i). Introduction Methodology Results / Discussion Conclusion
Conclusion Through this procedure, the code demonstrated different novel directions for performativly fit geometry, which leaves the architect with a variety of choices for design. The computer now becomes more than a visualization tool; an unbiased tireless partner in design with extraordinary ways of approaching problems. Introduction Methodology Results / Discussion Conclusion
Conclusion Further Research Introduction Methodology Results / Discussion Conclusion