发表机构
Luminary AI（Luminary AI）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出基于风洞PSP测量的航空航天代理模型数据融合框架，通过训练修正网络学习CFD与实验的偏差，在不修改预训练模型的前提下提升预测精度，且优于测量条件的直接插值。
AI 中文摘要
基于高保真CFD数据训练的气动代理模型可准确复现标量输出与全场的数值预测，但其预测保真度受限于CFD与实验观测之间的系统偏差。本文提出一种实验修正框架，利用风洞PSP测量数据调整经CFD训练的深度学习代理模型。Geotransolver代理模型基于2300组NASA CRM翼身构型的高保真CFD仿真训练，覆盖几何变化、马赫数0.70-0.85、迎角0-4度范围，其复现的CFD积分气动力与俯仰力矩的R²大于0.99，但与实验数据不匹配。为在不重新训练代理模型的前提下融入实验信息，本文在相同迎角范围的两个自由流马赫数（0.70和0.85）下，对空间配准的PSP测量数据训练修正网络，学习代理模型预测与实验测量的表面压力分布之间的偏差。在马赫数0.85时，该修正网络大幅提升了与PSP数据的一致性，尤其在机翼吸力峰值、激波位置及后续压力恢复区域，既减小了预测误差的幅度，也降低了Cp（压力系数）误差超过0.05的浸润表面占比，且该改进基于有限的实验数据集，未修改预训练代理模型的参数。在预留的迎角条件下，经实验修正的代理模型与测量值的偏差在测量Cp范围的2.3%-2.7%以内，且在所有测试状态下均优于测量条件间的直接插值。因此，实验测量可通过学习CFD与实验之间的系统偏差，在保留代理模型泛化能力与计算效率的前提下，实现对大规模仿真训练代理模型的实验修正。
英文摘要
Aerodynamic surrogate models trained on high-fidelity CFD data reproduce numerical predictions of both scalar outputs and entire fields accurately, yet their predictive fidelity is limited by systematic discrepancies between CFD and experimental observations. We present an experimentally grounded correction framework that adapts a CFD-trained deep learning surrogate using wind-tunnel PSP measurements. A Geotransolver surrogate trained on 2,300 high-fidelity CFD simulations of the NASA CRM wing-body configuration, spanning geometric variation, Mach 0.70-0.85, and angles of attack 0 to 4 degrees, reproduces the CFD integrated aerodynamic forces and pitching moment to R2 > 0.99 but does not match the experimental data. To incorporate experimental information without retraining the surrogate, a correction network is trained on spatially registered PSP measurements at two freestream Mach numbers (0.70 and 0.85) across the same angle-of-attack range, learning the discrepancy between the surrogate-predicted and experimentally measured surface-pressure distributions. At Mach 0.85 the correction substantially improves agreement with PSP, particularly at the wing suction peak, shock location, and subsequent pressure recovery, reducing both the magnitude of the prediction error and the fraction of wetted surface on which it exceeds 0.05 in Cp, and it does so from a limited experimental dataset without modifying the pretrained surrogate parameters. On held-out angles of attack the grounded surrogate agrees with measurement to within 2.3-2.7% of the measured Cp range, and outperforms direct interpolation between the measured conditions at every state tested. Experimental measurements can therefore ground a large-scale simulation-trained surrogate by learning systematic CFD-to-experiment discrepancies while preserving its generalization capability and computational efficiency.
