AI 中文总结
本研究针对类车辆钝体尾流的多平面PIV数据，提出伴随定位的高效3DVar方法，可降低内存占用，提升湍流模型表现，揭示数据覆盖要求，为三维分离流的工业应用提供支撑。
AI 中文摘要
我们采用离散伴随方法进行三维变分数据同化（3DVar），以优化时间平均动量方程。实验数据为在类车辆钝体尾流中沿12个横向平面采集的稀疏立体粒子图像测速（PIV）测量值，基于流向体长的雷诺数Re_L为5.64×10^5。本文提出并实现了伴随定位技术，通过将控制变量空间限制在用户定义的子域内，减少3DVar中空间变化控制变量的离散伴随方法内存占用。将控制变量限制为全部控制空间的12%，可使峰值内存最大降低64%，同时产生的同化场在平均速度和优化动量强迫场方面具有相当的保真度。伴随定位案例优于基准Spalart–Allmaras湍流模型，且能恢复复杂三维（3D）回流泡的正确非对称拓扑结构。同化的雷诺剪切应力与实验结果吻合良好，同化的平均压力与面内涡度场相关时表现出物理一致性。还开展了数据效率研究，逐步减少用于同化的平面数量，结果表明数据覆盖范围必须至少延伸至主回流泡末端，才能充分约束近尾流动力学。伴随定位带来的效率对于在精细网格上同化稀疏实验数据以解决感兴趣的工业问题至关重要，这类数据适用于三维分离流的相关研究。
英文摘要
We perform three-dimensional variational data assimilation (3DVar) using a discrete adjoint approach to optimise the time-averaged momentum equations. The experimental data consist of sparse stereoscopic particle image velocimetry (PIV) measurements collected along $12$ cross-stream planes in the wake of a vehicle-like bluff body at a Reynolds number $Re_L = 5.64 \times 10^5$ based on the streamwise body length. Adjoint localisation is proposed and implemented to reduce the memory footprint of the discrete adjoint method for spatially-varying control variables in 3DVar by confining the control variable space to a user-defined subdomain. Restricting the control variable to $12$ % of the full control space yields a maximum reduction in peak memory of $64$ %, while producing assimilated fields of comparable fidelity with respect to mean velocity and the optimised momentum forcing field. The localised adjoint case improves upon the baseline Spalart--Allmaras turbulence model and recovers the correct asymmetric topology of the complex three-dimensional (3D) recirculation bubble. The assimilated Reynolds shear stress agrees well with the experiment, and the assimilated mean pressure is shown to be physically consistent when correlated with the in-plane vorticity fields. A data efficiency study is also performed, in which the number of planes provided for assimilation is progressively reduced, demonstrating that the data coverage must extend at least to the end of the primary recirculation bubble to adequately constrain the near-wake dynamics. The efficiency that adjoint localisation affords is crucial for assimilating sparse, experimental data for 3D separated flows on fine meshes that can tackle industrial problems of interest.
Comments42 pages, 25 figures
