Machine learning (ML)-based intelligent CFD simulation for interactive design exploration of built environments

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Understanding the effect of airflow in enclosed/indoor environments is of great interest due to its close relationship to occupant's safety, thermal comfort, and energy efficiency. Optimally placed air conditioning could increase the comfort of the inhabitants. In hospitals, airflow can distribute germs and can pose a significant health hazard. Indoor airflow patterns can be very complicated, and computer simulations are an invaluable tool for understanding their characteristics during the initial design phase when designers can explore various design scenarios, which consider multiple constraints (infection risk, energy efficiency) without compromising the critical factor of indoor air quality (IAQ). However, the complex and dynamic nature of the problem makes it challenging to perform the fluid simulations quickly to explore 100 to 1000s of design options interactively to identify the optimum.