Enhancing the accuracy of physics-informed neural networks for indoor airflow simulation with experimental data and Reynolds-averaged Navier-Stokes turbulence model

Physics-informed neural network (PINN) has aroused broad interest among fluid simulation researchers in recent years, representing a novel paradigm in this area where governing differential equations are encoded to provide a hybrid physics-based and data-driven deep learning framework. However, the lack of enough validations on more complex flow problems has restricted further development and application of PINN. Our research applies the PINN to simulate a two-dimensional indoor turbulent airflow case to address the issue. Although it is still quite challenging for the PINN to reach an ideal accuracy for the problem through a single purely physics-driven training, our research finds that the PINN prediction accuracy can be significantly improved by exploiting its ability to assimilate high-fidelity data during training, by which the prediction accuracy of PINN is enhanced by 53.2% for pressure, 34.6% for horizontal velocity, and 40.4% for vertical velocity, respectively. Meanwhile, the influence of data points number is also studied, which suggests a balance between prediction accuracy and data acquisition cost can be reached. Last but not least, applying Reynolds-averaged Navier-Stokes (RANS) equations and turbulence model has also been proved to improve prediction accuracy remarkably. After embedding the standard k-ϵ model to the PINN, the prediction accuracy was enhanced by 82.9% for pressure, 59.4% for horizontal velocity, and 70.5% for vertical velocity, respectively. These results suggest a promising step toward applications of PINN to more complex flow configurations.