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August 1, 2025Physics of Fluids

Prediction model of the three-dimensional flow field in compressor cascades using shallow neural networks based on sparse pressure data

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Authors

SCShuaitong ChenPYPengcheng YangSCShaowen Chen

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Overview

Data-driven framework predicts total pressure distribution in compressor cascades, suggesting improved flow control technology.

Key Points

  • The prediction model achieves over 97.3% of test cases with a Pearson correlation coefficient above 0.85.
  • Using only 13 sparse measurements, the model predicts total pressure with millisecond-level speeds.
  • Shallow neural networks leverage wall pressure data to estimate complex 3D flow in compressor components.
  • Findings indicate potential for enhanced real-time control in aircraft engines, while highlighting prediction errors due to corner separation flow.

Cite This Study

Chen et al. (2025) studied this question.

synapsesocial.com/papers/68af55dead7bf08b1eadcb93https://doi.org/10.1063/5.0282844
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Research on a Rapid Three-Dimensional Compressor Flow Field Prediction Method Integrating U-Net and Physics-Informed Neural Networks2025
  2. 2A Corner Separation Sensing Method Based on Shallow Neural Networks in Compressor Cascade2025
  3. 3Implicit Neural Representation For Accurate CFD Flow Field Prediction2024
  4. 4Investigation on compressor flow field reconstruction using deep neural networks with a fine-tuned strategy2025
  5. 5Investigation of Compressor Cascade Flow Using Physics-Informed Neural Networks with Adaptive Learning Strategy2024 · 7 citations