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arXiv Machine Learning By Qineng Wang, Zhendong Guo, Liming Song, Tianyuan Liu

A panoramic aerodynamic performance prediction method for turbomachinery cascades using transformer-enhanced neural operator

Read the original on arXiv Machine Learning →

The paper introduces a panoramic aerodynamic performance prediction framework for turbomachinery cascades that first predicts basic Navier-Stokes parameters (temperature, pressure, density) and then derives key turbine stage performance metrics. A transformer‑enhanced neural operator (TNO) is trained on Rotor 37 blades to accurately predict transonic compressor blade performance, outperforming state‑of‑the‑art deep learning operators like FNO and DeepONet. The TNO also supports downstream tasks such as sensitivity analysis and optimization, achieving CFD‑like results while cutting computational cost by four orders of magnitude.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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