Session: Student Poster Competition

Submission Number: 186437

A General Framework for Enhancing Film Cooling Predictions via Data Assimilation and Turbulence Model Correction 

Data assimilation has been widely employed to enhance the predictive capability of turbulence models, demonstrating significant potential especially in complex flow fields where experimental data are scarce or costly to obtain. In engineering applications such as turbine blade film cooling, the Reynolds-averaged Navier–Stokes (RANS) model is commonly adopted due to its relatively low computational cost and practicality for engineering use; however, its predictive accuracy is limited by model simplifications and inherent assumptions. Based on an ensemble Kalman inverse (EKI) framework, this study develops a generalized approach for turbulence model correction. To address the systematic deviations of the RANS model in predicting cooling effectiveness, this research introduces important improvements to traditional ensemble generation methods. An innovative physics-informed ensemble generation strategy is proposed based on the spatial distribution characteristics of the eddy viscosity coefficient. By employing a continuous distributed eddy viscosity perturbation method, the approach prevents the emergence of non-physical modes, thereby significantly enhancing the numerical stability and convergence efficiency of the data assimilation process. Validation via typical film cooling cases demonstrates the excellent correction capability of the framework. Despite using sparse experimental data, the method demonstrates a high capability by reducing the prediction error of cooling effectiveness (η) from approximately 30% to below 2% after assimilation, achieving close agreement with reference data.

Presenting Author: Shengwei Yang beihang university

Presenting Author Biography: Shengwei Yang is a Ph.D. student at the School of Energy and Power Engineering, Beihang University, Beijing, China. His research focuses on turbulence modeling, data assimilation, and computational fluid dynamics for turbomachinery applications, with particular interest in improving film cooling predictions through model correction methods.

Authors:

Shengwei Yang beihang university
Zilong Song Beihang University
Jianqin Zhu beihang University
Lu Qiu beihang university