Abstract：
The indoor environment has a significant impact on our wellbeing. Accurate prediction of the indoor air distribution can help to create a good indoor environment. Reynolds-averaged Navier-Stokes (RANS) models are commonly used for indoor airflow prediction. However, the Boussinesq hypothesis used in the RANS model fails to account for indoor anisotropic flows. To solve this problem, this study developed a data-driven RANS model by using a nonlinear model from the literature. An artificial neural network (ANN) was used to determine the coefficients of high-order terms. Three typical indoor airflows were selected as the training set to develop the model. Four other cases were used as testing sets to verify the generalizability of the model. The results show that the data-driven model can better predict the distributions of air velocity, temperature, and turbulent kinetic energy for the indoor anisotropic flows than the original RANS model. This is because the nonlinear terms are accurately simulated by the ANN. This investigation concluded that the data-driven model can correctly predict indoor anisotropic flows and has reasonably good generalizability.
摘要：
室内环境对我们的健康有重大影响。准确预测室内空气分布有助于创造良好的室内环境。雷诺平均Navier-Stokes（RANS）模型通常用于室内气流预测。然而,RANS模型中使用的Boussinesq假设未能解释室内各向异性流动。为了解决这个问题,本研究使用文献中的非线性模型建立了数据驱动的RANS模型。使用人工神经网络（ANN）来确定高阶项的系数。选择了三种典型的室内气流作为训练集，以开发模型。另外四个案例作为测试集来验证模型的泛化性。结果表明,数据驱动模型能较好地预测风速分布,温度,与原始RANS模型相比，室内各向异性流的湍流动能。这是因为非线性项被ANN精确地模拟。这项研究得出结论，数据驱动模型可以正确预测室内各向异性流，并且具有良好的泛化性。
