Nonlinear reduced-order modeling of compressible flow fields using deep learning and manifold learning

Physics of Fluids, Journal Year: 2025, Volume and Issue: 37(3)

Published: March 1, 2025

This paper presents a nonlinear reduced-order modeling (ROM) framework that leverages deep learning and manifold to predict compressible flow fields with complex features, including shock waves. The proposed DeepManifold (DM)-ROM methodology is computationally efficient, avoids pixelation or interpolation of field data, adaptable various grids geometries. consists four main steps: First, convolutional neural network-based parameterization network extracts shape modes directly from aerodynamic Next, applied reduce the dimensionality high-fidelity output fields. A multilayer perceptron-based regression then trained map input modes. Finally, back-mapping process reconstructs full predicted low-dimensional DM-ROM rigorously tested on transonic RAE2822 airfoil test case, which includes waves varying strengths locations. Metrics are introduced quantify model's accuracy in predicting wave strength location. results demonstrate achieves prediction error approximately 3.5% significantly outperforms reference ROM techniques, such as proper orthogonal decomposition (POD)-ROM isometric mapping (ISOMAP)-ROM, for training sample sizes.

Language: Английский

Efficient large-scale graph learning for predicting the 3D multi-physics flow fields of axial compressor Rotor37 with variable geometry

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 136083 - 136083

Published: April 1, 2025

Language: Английский