Phenotype Variability Mimicking as a Process for the Test and Optimization of Dynamic Facade Systems

Area of Science:

• Building Science
• Computational Intelligence
• Materials Science

Background:

• Dynamic façade systems are crucial for optimizing building energy performance.
• Adaptive building envelopes require sophisticated design and control strategies.
• ETFE (Ethylene tetrafluoroethylene) cushions offer potential for innovative façade solutions.

Purpose of the Study:

• To design a dynamic multi-layered façade system using computational intelligence.
• To enable real-time adaptation of the façade to environmental conditions and occupant requirements.
• To optimize the thermal efficiency of ETFE cushion-based façade systems.

Main Methods:

• Deployment of a genetic algorithm (GA) for performance optimization.
• Utilization of an artificial neural network (ANN) for real-time adaptation.
• Modeling façade cushions as artificial neurons within a digital framework.
• Simulating phenotypical adaptations based on environmental data and GA-optimized gene configurations.

Main Results:

• The genetic algorithm successfully optimized façade cushion performances.
• The artificial neural network enabled learning from environmental data models.
• The computational model demonstrated phenotypical adaptations for thermal efficiency.
• The proposed façade system showed maximized thermal efficiency across various scenarios.

Conclusions:

• The integration of genetic algorithms and artificial neural networks is effective for designing adaptive façade systems.
• The developed computational model successfully optimizes thermal performance in dynamic conditions.
• This approach offers a promising solution for energy-efficient and responsive building envelopes.