Enhancing CFD computational efficiency using hybrid data-driven and physics-based modeling

Computational Fluid Dynamics (CFD) is commonly used to simulate the transport of heat in closed spaces. The resulting airflow and temperature predictions facilitate improved designs of Heating, Ventilation, and Air Conditioning (HVAC) systems. However, CFD is highly expensive to apply to large domains. This paper presents a novel approach that is a hybridization of artificial intelligence (AI) with CFD modeling, which improves computational speed and predictive accuracy. Specifically, CFD data from a forced air-conditioned room is used to train an Adaptive Network-based Fuzzy Inference System (ANFIS) with temperature taken to be the dependent variable. The trained ANFIS predicts the temperature distribution on…

• NN
  National Natural Science Foundation of China

  Award: 52179060