A Framework for Optimizing Biomimetic Opaque Ventilated Façades Using CFD and Machine Learning

This paper addresses the challenge of improving the thermal performance of building envelopes in hot arid climates by identifying optimal configurations for biomimetic opaque ventilated façade (OVF) designs. To overcome the complexity of parameter interactions in such systems, a multi-objective optimization framework is developed using computational fluid dynamics (CFD) simulations integrated with parametric modeling and machine learning surrogate models. A central contribution of this research is the application of machine learning-based surrogate models to predict CFD simulation outcomes with high accuracy. This predictive capability enables the rapid generation and evaluation of thousands of façade design alternatives without the need for full-scale CFD runs, significantly reducing computational effort and time. The proposed workflow establishes a direct connection between parameterized biomimetic geometries and thermal performance indicators, allowing for a comprehensive exploration of the design space through automated optimization. The optimization process relies on response surface modeling to approximate system behavior and evaluate design performance across multiple objectives. The final results reveal that the computationally optimized biomimetic façades achieved superior thermal performance compared to the initial bio-inspired design. To validate and extend the findings, additional simulations were carried out to evaluate the performance of selected designs under varying wind conditions and solar exposures. The larger wide mound configuration consistently performed best, offering a strong balance across the defined objectives. This solution was then applied to three-floor and five-floor commercial buildings in Riyadh, Saudi Arabia, where it showed a clear reduction in the average inner skin surface temperature of the OVF. The design proved suitable for construction with conventional methods and could be integrated into a range of architectural styles without major changes to the façade. These results reinforce the potential of combining biomimetic design strategies with computational optimization to develop high-performance façade systems for hot desert climates. The novelty of this work lies in combining biomimetic design principles with machine learning-driven optimization to systematically explore the design space and identify configurations that balance thermal efficiency with material economy.