周期神经映射用于非定常转子叶片压力与气动弹性载荷预测
Periodic Neural Mapping for Unsteady Rotor-Blade Pressure and Aeroelastic Load Prediction
- Cenaero（塞纳罗航空航天研究中心）
- Safran Helicopter Engines（赛峰直升机发动机公司）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
针对涡轮机械非定常气动载荷预测难题，提出周期傅里叶神经映射（p-FNM），嵌入时间周期性并独立预测压力场，在最大数据集上较基线显著提升压力场与广义气动力预测精度，展现降阶建模潜力。
中文摘要 AI 辅助
在涡轮机械设计中，非定常气动载荷的准确预测仍是一项重大挑战。高保真计算流体力学（CFD）模拟成本高昂，而气动弹性关注量（QoI）对压力场的时间演化高度敏感。本工作提出了周期傅里叶神经映射（p-FNM），一种神经算子框架，用于预测基于谐波（chorochronic）数值假设模拟的涡轮转子叶片上的非定常压力分布。该架构将时间周期性嵌入模型中，学习从运行条件和时间到压力场的连续映射。与顺序潜在空间方法不同，p-FNM可在任意时刻独立预测压力场，在保持时间连续性的同时避免误差累积。该模型在一个非定常转子叶片模拟数据库上进行了评估，并与基于变分自编码器和循环神经网络的降阶基线（称为时间预测模型（TPM））进行了比较。性能评估针对压力场和广义气动力（GAFs）——主要的气动弹性QoI。在所有训练数据集上，p-FNM均一致优于TPM。在最大数据集上，p-FNM实现了压力场平均绝对百分比误差0.46%和GAF幅值预测误差4.42%，分别对应60.7%和77.6%的提升。最小加权相位误差达到0.060弧度，表明准确保留了气动响应的时间特征。结果表明，GAF预测比压力场预测更具挑战性，且时间相干性对于准确预测频谱气动量至关重要。这些发现展示了周期神经算子在涡轮机械降阶建模和气动弹性分析中的潜力。
英文摘要
Accurate prediction of unsteady aerodynamic loads remains a major challenge in turbomachinery design. High-fidelity Computational Fluid Dynamics (CFD) simulations are expensive, while aeroelastic Quantities of Interest (QoI) depend sensitively on the temporal evolution of the pressure field. This work introduces periodic Fourier Neural Mapping (p-FNM), a neural-operator framework for predicting unsteady pressure distributions on turbine rotor blades simulated using the chorochronic numerical hypothesis. The architecture embeds temporal periodicity into the model and learns a continuous mapping from operating conditions and time to pressure fields. Unlike sequential latent-space approaches, p-FNM predicts pressure fields independently at any time, avoiding error accumulation while preserving temporal continuity. The model is evaluated on a database of unsteady rotor-blade simulations and compared with a reduced-order baseline based on a variational autoencoder and recurrent neural network, refered as the Temporal Prediction Model (TPM). Performance is assessed for pressure fields and Generalized Aerodynamic Forces (GAFs), the primary aeroelastic QoI. Across all training datasets, p-FNM consistently outperforms TPM. On the largest dataset, p-FNM achieves a pressure-field mean absolute percentage error of 0.46% and a GAF-magnitude prediction error of 4.42%, corresponding to improvements of 60.7% and 77.6%, respectively. The minimum weighted phase error reaches 0.060 rad, demonstrating accurate preservation of the temporal characteristics of the aerodynamic response. The results show that GAF prediction is more challenging than pressure-field prediction and that temporal coherence is critical for accurately predicting spectral aerodynamic quantities. These findings demonstrate the potential of periodic neural operators for reduced-order modeling and aeroelastic analysis in turbomachinery.
