基于CFD⁃EnKF数据同化的室内多物理场精确预测技术的初步探讨与应用

Discussion and Application of Indoor Multiphysics Accurate Prediction Technology Based on CFD⁃EnKF Data Assimilation

准确地获取室内温湿度分布信息是精准控制建筑热湿环境的重要前提,然而,在实际工程项目中,一方面,受空间局限性等因素约束,现场实测监测点数量有限;另一方面,系统边界条件参数的不确定性会给CFD模拟结果带来较大误差。针对此问题,本研究引入数据同化算法——集合卡尔曼滤波(Ensemble Kalman Filter,EnKF),基于该算法校正热湿环境CFD模拟的边界条件参数,构建了室内多物理场精确模拟技术框架,并以北京市某大型博物馆古代书画展厅为例进行了应用测试和讨论分析。结果表明,以实测值为基准,经数据同化技术处理后,各测点位置对应温度模拟误差降低83%以上,室内湿度模拟误差减小75%以上,有效提高了模拟精度。该技术将CFD模拟与温湿度场监测有机结合,在工程项目中具有较高的应用潜力。

Accurate acquisition of indoor temperature and humidity distribution information is an important prerequisite for precise control of building thermal and humidity environment.However,in actual engineering projects,on the one hand,due to the constraints of spatial limitations and other factors,the number of on⁃site measured monitoring points is limited.On the other hand,the uncertainty of boundary condition parameters will bring large error to CFD simulation results.In response to this issue,the Ensemble Kalman Filter(EnKF),was introduced in this paper.Based on this algorithm,the boundary condition parameters of CFD simulation of thermal and humidity environment were corrected,and the indoor multiphysics accurate prediction technology was constructed.Taking the ancient painting and calligraphy exhibition hall of the National Museum of China as an example,the application test,discussion and analysis were made.The results show that the simulation errors of temperature and indoor humidity are reduced by more than 83%and 75%respectively based on the measured values after data assimilation,effectively improving the simulation accuracy.This technology organically combines CFD simulation with temperature and humidity field monitoring and has high application potential in engineering projects.