[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122489-en":3,"doc-seo-122489-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122489,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","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, a multi-objective optimization framework integrates computational fluid dynamics (CFD) simulations with parametric modeling and machine-learning surrogate models. Surrogates predict CFD outcomes accurately, enabling rapid evaluation of thousands of façade alternatives without full-scale CFD runs. Response-surface modeling supports automated exploration of design space across multiple objectives, and optimized biomimetic façades outperform the initial design. Validation uses simulations under varied wind and solar exposures, and the best-performing wide mound configuration reduces average inner-skin surface temperature in Riyadh commercial building cases.","Article  \nA Framework for Optimizing Biomimetic Opaque Ventilated Façades Using CFD and Machine Learning  \nAhmed Alyahya 1, *, Simon Lannon 2 and Wassim Jabi 2  \nAcademic Editors: Xing Jin and Apple L.S. Chan  \nReceived: 14 September 2025  \nRevised: 3 November 2025  \nAccepted: 13 November 2025  \nPublished: 17 November 2025  \nCitation: Alyahya, A.; Lannon, S.; Jabi, W. A Framework for Optimizing Biomimetic Opaque Ventilated Façades Using CFD and Machine Learning. Buildings 2025, 15, 4130 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)buildings15224130  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 College of Architecture and Planning, Imam Abdulrahman Bin Faisal University, Dammam 31451, Saudi Arabia  \n2 Welsh School of Architecture, Cardiff University, Cardiff CF10 3NB, UK; [lannon@cardiff.ac.uk](lannon@cardiff.ac.uk) (S.L.); [jabiw@cardiff.ac.uk](jabiw@cardiff.ac.uk) (W.J.)  \n* Correspondence: [aaalyahya@iau.edu.sa](aaalyahya@iau.edu.sa)  \nAbstract  \nThis 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 offaçade 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 learningdriven optimization to systematically explore the design space and identify configurations that balance thermal efficiency with material economy.  \nKeywords: Opaque Ventilated Façades; Machine Learning; CFD; building envelope; computational design optimization; parametric design; biomimetic façade; Ansys DesignXplorer  \n1. Introduction  \nIn performance-based architectural design, optimizing faç","cbCaisDP0qoblvlh","https://ap.wps.com/l/cbCaisDP0qoblvlh","pdf",14431805,1,32,"English","en",105,"# Abstract\n# Introduction\n## Performance-based façade optimization and OVFs\n## CFD cost challenge and proposed framework\n## Parametric modeling and surrogate-based workflow","[{\"question\":\"What problem does this framework target in hot arid climates?\",\"answer\":\"It targets improved thermal performance of building envelopes by finding optimal biomimetic opaque ventilated façade (OVF) configurations where geometric and thermal parameters interact in complex ways.\"},{\"question\":\"How does the method reduce the cost of exploring many façade designs?\",\"answer\":\"It combines CFD with parametric modeling and machine-learning surrogate models so thousands of design alternatives can be evaluated quickly without running full CFD simulations for each case.\"},{\"question\":\"What design configuration performed best in the reported results?\",\"answer\":\"The wide mound configuration consistently achieved superior performance, providing a strong balance across the defined optimization objectives under varying wind conditions and solar exposures.\"}]","A Framework for Optimizing Biomimetic Opaque Ventilated Façades Using CFD and Machine Learning | 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problem does this framework target in hot arid climates?","Question",{"text":75,"@type":76},"It targets improved thermal performance of building envelopes by finding optimal biomimetic opaque ventilated façade (OVF) configurations where geometric and thermal parameters interact in complex ways.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the method reduce the cost of exploring many façade designs?",{"text":80,"@type":76},"It combines CFD with parametric modeling and machine-learning surrogate models so thousands of design alternatives can be evaluated quickly without running full CFD simulations for each case.",{"name":82,"@type":73,"acceptedAnswer":83},"What design configuration performed best in the reported results?",{"text":84,"@type":76},"The wide mound configuration consistently achieved superior performance, providing a strong balance across the defined optimization objectives under varying wind conditions and solar 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