物理信息潜变量神经算子用于三维压气机叶栅流场预测
Physics-Informed Latent Neural Operator for Three-Dimensional Compressor Cascade Flow Prediction
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中文总结 AI 辅助
提出物理信息潜变量神经算子框架，结合Transolver编码器与PINNs解码器，实现三维压气机叶栅流场的高精度重建与预测，并保持对未知工况的泛化能力。
中文摘要 AI 辅助
提出了一种物理信息潜变量神经算子框架，用于三维压气机叶栅流场预测。该方法结合了基于Transolver的潜变量编码器，该编码器从点云CFD数据中提取流动状态的紧凑全局潜变量表示，以及基于坐标的PINNs解码器，用于重建连续流场，同时在训练过程中融入物理信息残差约束。结果表明，所提出的框架能够准确重建复杂三维叶栅流动的压力和速度分布，并在未见过的运行条件下保持良好的预测能力。该方法展示了在叶轮机械应用中作为高效CFD代理模型和气动预测的广阔潜力。
英文摘要
A physics-informed latent neural operator framework is proposed for three-dimensional compressor cascade flow prediction. The proposed method combines a Transolver-based latent encoder, which extracts compact global latent representations of flow conditions from point-cloud CFD data, with a coordinate-based PINNs decoder to reconstruct continuous flow fields while incorporating physics-informed residual constraints during training. The results show that the proposed framework can accurately reconstruct pressure and velocity distributions of complex three-dimensional cascade flows and maintains good prediction capability under previously unseen operating conditions. The present method demonstrates promising potential for efficient CFD surrogate modelling and aerodynamic prediction in turbomachinery applications.
发表机构
- School of Engineering, University of Liverpool（利物浦大学工程学院）
机构由 AI 辅助整理，请以论文原文为准。
