Fast and accurate prediction of air temperature and velocity field in non-uniform indoor environment under complex boundaries

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.