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Pressure Estimation from PIV like Data of Compressible Flows by Boundary Driven Adjoint Data Assimilation

机译:通过边界驱动伴随数据同化的可压缩流的PIV的压力估计

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Particle image velocimetry (PIV) is one of the major tools to measure velocity fields in experiments. However, other flow properties like density or pressure are often of vital interest, but usually cannot be measured non-intrusively. There are many approaches to overcome this problem, but none is fully satisfactory. Here the computational method of an adjoint based data assimilation for this purpose is discussed. A numerical simulation of a flow is adapted to given velocity data. After successful adaption, previously unknown quantities can be taken from the - necessarily complete - simulation data. The main focus of this work is the efficient implementation of this approach by boundary driven optimisation. Synthetic test cases are presented to allow an assessment of the method.
机译:粒子图像速度(PIV)是测量实验中的速度场的主要工具之一。然而,与密度或压力等的其他流动性质通常是至关重要的兴趣,但通常不能侵入性地测量。有许多方法来克服这个问题,但没有完全令人满意。这里讨论了用于此目的的伴随基于数据同化的计算方法。流程的数值模拟适用于给定速度数据。在成功的适应后,可以从 - 必然完全 - 模拟数据采取先前未知的数量。这项工作的主要重点是通过边界驱动优化有效地实现了这种方法。提出了合成试验案例以允许评估该方法。

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