Reynolds Averaged Navier Stokes

Reynolds-Averaged Navier-Stokes (RANS) equations are a simplified approach to modeling turbulent fluid flows, aiming to predict time-averaged flow properties. Current research focuses on improving RANS accuracy and efficiency through data-driven methods, including machine learning techniques like neural networks (e.g., Physics-Informed Neural Networks, GANs) and data assimilation approaches (e.g., Ensemble Kalman Filtering) to calibrate existing models or learn closures directly from data. These advancements are significant because they offer the potential to reduce the computational cost of complex fluid simulations, enabling faster design optimization and improved predictions in various engineering applications, such as gas engine design and aerodynamic analysis.