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September 18, 2025Physics of Fluids

Aerodynamic data fusion for low-Reynolds-number compressors based on film-Re physics-guided multi-fidelity network

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Authors

RCRuoyu ChenXRXun RenMWMingyang Wang

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Overview

This study integrates RANS and LES methods for better flow predictions in low-reynolds-number compressors, suggesting significant improvements in accuracy.

Key Points

  • The FP–MFN model predicts flow fields more accurately at low-reynolds numbers, outperforming traditional methods.
  • When the fidelity fusion coefficient is optimized at 0.5, the model achieves the best balance between RANS and LES accuracy.
  • The incorporation of a Reynolds number modulation network improves flow prediction under various conditions.
  • The physics-guided loss term ensures consistent physical behavior in predicted flow fields, enhancing model reliability.

Cite This Study

Chen et al. (2025) studied this question.

synapsesocial.com/papers/68d461cb31b076d99fa611bahttps://doi.org/10.1063/5.0284884
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Also Consider

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  1. 1Investigation on Aerodynamic Data Fusion for Low-Reynolds-Number Compressors Based on Multi-Fidelity Deep Neural Networks2025
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  4. 4Numerical Design of Experiments for Repeating Low-Pressure Turbine Stages Part I: Computational Opportunities and Methodology2025
  5. 5RenaNet: Reynolds-Aware Neural Network for Rapid Flow Field Prediction via Lattice Boltzmann Simulations2026