Fast and accurate prediction of air temperature and velocity field in non-uniform indoor environment under complex boundaries. (15th February 2023)

• Record Type: Journal Article
• Authors: Luo, Yongqiang; Cui, De'en; Song, Yixiao; Tian, Zhiyong; Fan, Jianhua; Zhang, Ling
• Is Part Of: Building and environment. Volume 230(2023)
• Publication Date: 2023-02-15
• Subjects: Non-uniform environment -- Indoor air -- CFD -- Proper orthogonal decomposition -- Radial basis function -- Fast simulation; Buildings -- Environmental engineering -- Periodicals; Building -- Research -- Periodicals; Constructions -- Technique de l'environnement -- Périodiques; Electronic journals; 696
• DOI: 10.1016/j.buildenv.2023.109987
• Languages: English
• ISSNs: 0360-1323

Abstract

Abstract: The thermal environment in a built indoor space is usually non-uniform in temperature and many other index distributions. Simulation, analysis and understanding the non-uniform indoor environment is critical to a healthy, energy-efficient and comfort indoor space. Conventional CFD model consumes huge computational resources and time for a final result, which may not be suitable for coupling with energy system, and some other applications that needs fast simulation. This study proposes a new model that is built upon Proper Orthogonal Decomposition (POD) method and Radial Basis Function (RBF), with a special Taguchi method served for data preparation. A series of tests are conducted and the results show that the POD-RBF model is fast in computation, accurate in prediction, robust in implementation, and flexible in applications. The construction of the model takes less than 3 s and each case computation takes less than 1 s. The averaged model error in predicting temperature and velocity field is about 0.3% and 8% across different tests. Highlights: The model can realize fast prediction of indoor environment within 1 s. The error for predicted temperature and velocity is around 0.3% and 8%. The curve of accumulated energy of basis mode is nearly flat. The model is robust even using very low sampling density.