用稀疏压力数据精准重建扰流气动特性并量化预测不确定性
Low-Order Flow Reconstruction and Uncertainty Quantification in Disturbed Aerodynamics Using Sparse Pressure Measurements
- 结合机器学习与概率回归，从稀疏噪声数据中重构气动流场和升力系数
- 在极端工况下仍保持高精度，计算效率优于传统方法
- 适合需要实时在线估计的飞行器气动监测系统
本文提出一种新型机器学习框架，从稀疏、噪声干扰的表面压力测量中重构气动翼型遭遇阵风时的低阶流场与升力系数。研究深入分析了传感器对阵风-翼型相互作用的时间响应特性，揭示了最优传感器布置规律。为应对深度学习预测中的不确定性，采用概率回归策略建模认知不确定性（epistemic）与随机不确定性（aleatoric）。认知不确定性通过蒙特卡洛丢弃法近似贝叶斯变分推断，将神经网络视为随机实体；随机不确定性则通过学习的统计参数捕捉输入噪声，并传播至最终预测结果。实验表明，该双重不确定性量化策略在极端条件下仍能准确预测气动行为，同时保持良好计算效率，展现出在真实场景下基于传感器的在线流场估计中的应用潜力。
原文摘要 · Abstract (English)
This paper presents a novel machine-learning framework for reconstructing low-order gust-encounter flow field and lift coefficients from sparse, noisy surface pressure measurements. Our study thoroughly investigates the time-varying response of sensors to gust-airfoil interactions, uncovering valuable insights into optimal sensor placement. To address uncertainties in deep learning predictions, we implement probabilistic regression strategies to model both epistemic and aleatoric uncertainties. Epistemic uncertainty, reflecting the model's confidence in its predictions, is modeled using Monte Carlo dropout, as an approximation to the variational inference in the Bayesian framework, treating the neural network as a stochastic entity. On the other hand, aleatoric uncertainty, arising from noisy input measurements, is captured via learned statistical parameters, which propagates measurement noise through the network into the final predictions. Our results showcase the efficacy of this dual uncertainty quantification strategy in accurately predicting aerodynamic behavior under extreme conditions while maintaining computational efficiency, underscoring its potential to improve online sensor-based flow estimation in real-world applications.
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