Abstract

The computational fluid dynamics (CFD)-based adjoint method may be appropriate for the inverse design of indoor environments, considering both accuracy and efficiency, but a single design still requires tens of hours with the use of a personal computer. To speed up the inverse design process, this study evaluated four fast fluid dynamics (FFD) models in terms of solving the Navier–Stokes equations, integration with turbulence models, and solving the adjoint equations. This study implemented the FFD solvers in OpenFOAM and validated them for predicting steady-state and transient indoor airflow. This study then validated the FFD solvers for solving the adjoint equations and the FFD-based adjoint method for inverse identification problems and inverse designs in indoor environments. The results showed that FFD was 20 times faster than CFD in predicting transient indoor airflow, and similar computational accuracy could be maintained; the FFD-based adjoint method was 4–16 times faster than the CFD-based adjoint method in the inverse design process.

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