Tianjin Key Laboratory of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin, China.
School of Mechanical Engineering, Tianjin University, Tianjin, China.
Indoor Air. 2022 Oct;32(10). doi: 10.1111/ina.13123.
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.
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。雷诺平均纳维-斯托克斯（RANS）模型常用于室内气流预测。然而，RANS 模型中使用的 Boussinesq 假设无法考虑室内各向异性流动。为了解决这个问题，本研究通过使用文献中的非线性模型，开发了一种数据驱动的 RANS 模型。人工神经网络（ANN）用于确定高阶项的系数。选择三种典型的室内气流作为训练集来开发模型。另外四个案例用作测试集来验证模型的泛化能力。结果表明，数据驱动模型比原始 RANS 模型能更好地预测室内各向异性流动的空气速度、温度和湍流动能分布。这是因为 ANN 准确地模拟了非线性项。本研究得出结论，数据驱动模型可以正确预测室内各向异性流动，具有较好的泛化能力。
