基于学习的强扰动流场中物体联合流动与运动状态估计

中文摘要 AI 辅助

从稀疏测量中准确估计非定常气动流场仍然是一个基本挑战，尤其是在存在强扰动、未知物体运动学和观测不完整的情况下。本研究提出了一种数据驱动的序贯估计框架，用于从稀疏测量中联合重建非定常流场、气动载荷和翼型运动学。该方法将运动学感知的非线性流场自编码器与流式测量的在线滤波相结合。在所得的降阶表示中，预测算子和观测算子直接从数据中学习，每个预测-同化周期仅需几毫秒。该框架在受到随机涡旋阵风作用并经历任意俯仰运动的二维不可压缩翼型绕流上进行了评估。结果表明，利用表面压力传感器、垂直线上的涡量传感器以及具有代表性阴影区域的合成测速数据，可以实现准确重建。压力传感器的瞬态信息量通过其对主导观测模态的时变贡献进行量化。虽然升力由主导模态重建，但阻力、俯仰角和角速度需要更高阶模态。将有限的离体测量与表面压力相结合，可提高可观测性并降低估计误差和不确定性。

英文摘要

Accurate estimation of unsteady aerodynamic flows from sparse measurements remains a fundamental challenge, particularly in the presence of strong disturbances, unknown body kinematics, and incomplete observations. This study presents a data-driven sequential estimation framework for joint reconstruction of unsteady flow fields, aerodynamic loads, and airfoil kinematics from sparse measurements. The approach combines a kinematics-aware nonlinear flow autoencoder with online filtering of streaming measurements. In the resulting reduced-order representation, the forecast and observation operators are learned directly from data, with each forecast-assimilation cycle requiring only a few milliseconds. The framework is evaluated on two-dimensional incompressible flow over an airfoil subjected to random vortical gusts while undergoing arbitrary pitch-up motions. Results demonstrate accurate reconstruction from surface pressure sensors, vertical lines of vorticity sensors, and synthetic velocimetry data with representative shadow regions. The transient informativeness of pressure sensors is quantified through their time-varying contributions to dominant observation modes. While lift is reconstructed from the leading modes, drag, pitch angle, and angular velocity require higher modes. Incorporating limited off-body measurements alongside surface pressure improves observability and reduces estimation error and uncertainty.

发表机构

• University of California, Los Angeles（加州大学洛杉矶分校）

机构由 AI 辅助整理，请以论文原文为准。