Convolutional neural networks-based surrogate model for fast computational fluid dynamics simulations of indoor airflow distribution

Computational fluid dynamics (CFD) is a powerful but time-consuming simulation tool for building indoor environment analysis. Artificial intelligence (AI)-based surrogate models, especially artificial neural networks (ANN)-based models which are the dominated ones, have demonstrated a great potential in accelerating CFD simulations. However, the published AI-based models are not good at capturing local spatial features in indoor airflow distribution, leading to poor local simulation accuracy. To overcome this challenge, this study proposes a convolutional neural networks (CNN)-based surrogate model for fast CFD simulations of indoor airflow distribution. Compared with other published AI-based models, this model can capture local spatial features in indoor airflow distribution datasets simulated by CFD, leading to higher accuracy. To enable this model to process room geometry information, a geometry representation strategy is proposed to convert room geometry information into inputs suitable for CNN models. Simulation data of 2000 indoor airflow velocity fields with various boundary conditions are generated using COMSOL Multiphysics. Five cases with various model training strategies are designed based on these simulation data to verify the performance of the CNN-based model by comparing this model with ANN-based and GNN-based models. The results show that the CNN-based model outperforms other models in all cases. The CNN-based and GNN-based models have significantly smaller local simulation errors than the ANN-based model. The simulation accuracy of the CNN-based model is improved by an average of 45.55 % and 32.90 % compared with the ANN-based and GNN-based models, respectively. Moreover, the computational time of the CNN-based model is reduced to about 0.05 % of the computational time of CFD simulations.