压气机叶栅开口叶尖间隙流动的VAE潜空间自适应CFD修正

中文摘要 AI 辅助

压气机叶栅中开口叶尖间隙流动的CFD预测与实验结果相比存在偏差，而实验观测数据稀疏，且缺乏高分辨率的实验真值。本研究提出一种基于变分自编码器（VAE）和潜空间自适应的非侵入式修正方法。首先，利用166个参数化采样的CFD总压损失场数据集训练一个VAE，以学习这些场的低维统计表示。随后，冻结该VAE，并仅使用12个配对的CFD-实验工况训练一个低秩潜空间适配器。一个观测算子将修正后的高分辨率场映射到实验观测空间，从而仅在可用的测量位置和测量的周向窗口内施加监督。在当前12折交叉验证中，平均绝对误差从0.1335降至0.0473，均方根误差从0.1717降至0.0621，相对$L_2$误差从0.5108降至0.1871。结果表明，该方法在不修改RANS求解器或构造人工高分辨率实验标签的情况下，提高了CFD预测与开口叶尖间隙流动稀疏实验观测之间的一致性。

英文摘要

CFD predictions of open tip clearance flow in compressor cascades are subject to discrepancies relative to experiments, while experimental observations are sparse and high-resolution experimental ground truth is unavailable. This study proposes a non-intrusive correction method based on a variational autoencoder (VAE) and latent-space adaptation. A VAE is first trained using a dataset of 166 parametrically sampled CFD total pressure loss fields to learn a low-dimensional statistical representation of these fields. The VAE is then frozen, and a low-rank latent-space adapter is trained using only 12 paired CFD--experiment operating conditions. An observation operator maps the corrected high-resolution fields to the experimental observation space, allowing supervision to be applied only at the available measurement locations and within the measured pitchwise windows. In the current 12-fold cross-validation, the mean absolute error decreases from 0.1335 to 0.0473, the root mean square error from 0.1717 to 0.0621, and the relative $L_2$ error from 0.5108 to 0.1871. These results indicate that the method improves agreement between CFD predictions and sparse experimental observations of open tip clearance flow without modifying the RANS solver or constructing artificial high-resolution experimental labels.

发表机构

• School of Aeronautics and Astronautics, Shanghai Jiao Tong University（上海交通大学航空航天学院）
• Global Institute of Future Technology, Shanghai Jiao Tong University（上海交通大学未来技术学院）

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