Improving Indoor Multiphysics Prediction with Local Measurements Based on Data Assimilation

Abstract

Accurately mastering the distribution of multi-physical field is an important prerequisite for rationally formulating building environment construction scheme. In practical engineering projects, sensor monitoring can obtain more accurate environmental state parameter values. However, due to the constraints of investment cost, spatial limitations and other factors, the number of on-site measured monitoring points is limited. On the contrary, CFD simulation can obtain global distribution information of the physical field, but the uncertainty of parameters such as boundary conditions seriously affects the reliability of simulation results. In view of the above problems, based on Ensemble Kalman Filter (EnKF), which is a sequential data assimilation algorithm, a technical framework for accurate indoor multiphysics simulation is established. We evaluated the performance of this method with reduced-scale model experiments, verifying that the simulation errors can be significantly reduced. The proposed method has a positive impetus for realizing the global monitoring of the physical field of the building space.