2021 年 35 巻 3 号 p. 437-444
Building energy simulation (BES) is commonly conducted in architecture design process to evaluate building energy performance. The coupling between computational fluid dynamics (CFD) and BES not only improves energy simulation accuracy but also makes it possible to simultaneously consider energy consumption and indoor environment. Nevertheless, to conduct high fidelity CFD simulation is generally time-consumed and thus it is almost impractical to carry out a long-term coupled simulation that requires multiple CFD executions. A fast and accurate prediction method is therefore required to serve as a surrogate for CFD in the coupled simulation. Inspired by successful applications of deep learning neural networks (NNs) in various fields due to the high computation speed and prediction accuracy, the authors proposed a deep learning-based prediction method to achieve fast and accurate prediction of indoor air distributions. This paper provides a general introduction to the proposed prediction method with regard to its principle, implementation, performance, and application. Predictions of two-dimensional isothermal flow and three-dimensional non-isothermal flow are demonstrated. The results confirmed the feasibility of NN models for fast and accurate indoor airflow prediction. Meanwhile, as an example of practical application, the NN model is coupled with a BES tool to implement a coupled simulation framework for fast energy simulation considering non-uniform indoor environment. The coupling scheme is introduced and validation of the crucial functionality of the framework is presented.
