Dynamic Mode Decomposition for the Comparison of Engine In-Cylinder Flow Fields from Particle Image Velocimetry (PIV) and Reynolds-Averaged Navier–Stokes (RANS) Simulations
Dynamic Mode Decomposition for the Comparison of Engine In-Cylinder Flow Fields from Particle Image Velocimetry (PIV) and Reynolds-Averaged Navier–Stokes (RANS) Simulations
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DOI：
10.1007/s10494-023-00424-3
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发表时间：
2023-05
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通讯作者：
Samuel J. Baker;X. Fang;Li Shen;C. Willman;Jason M. B. Fernandes;F. Leach;M. Davy
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作者：
Samuel J. Baker;X. Fang;Li Shen;C. Willman;Jason M. B. Fernandes;F. Leach;M. Davy
利用粒子图像测速仪（PIV）测量的流场等实验数据对雷诺平均纳维尔-斯托克斯（RANS）模拟结果进行验证，仍然是热推进系统发展的一个挑战。这部分是由于空气运动中的周期间变化（CCV），部分是由于PIV测量技术的不确定性，使RANS模型的公平验证目标的问题复杂化。事实上，不适当的验证目标可能会误导RANS模型的后续调整。在这项工作中，系综平均PIV场首先研究其适用性作为RANS模拟的验证目标。相关性指数和速度直方图距离被用作定量指标，以评估整体平均场与单个PIV循环的完整数据集的相似性。虽然在平均PIV流场和滚流平面上的单个循环之间可以看到很高的相似性，但是在交叉滚流平面上的相似性较低并且变化更大，其中存在显著的CCV。标准（空间，相位相关）适当的正交分解（POD）作为一种替代方法的数据处理，目的是提供一个更公平的比较RANS模拟。标准POD模式的周期依赖性被证明是导致许多验证目标和过宽的验证范围的一个方面，限制了其在这种情况下的效用。动态模式分解（DMD）和稀疏促进动态模式分解（SPDMD），然后提出作为替代解决方案，能够提取在特定频率的流结构。背景0 Hz SPDMD模式能够产生更真实的流场，其速度幅度明显更接近单个循环。
Validation of Reynolds-averaged Navier–Stokes (RANS) simulation results against experimental data such as flow measurements from particle image velocimetry (PIV) remains a challenge for the development of thermal propulsion systems. This is partly due to cycle-to-cycle variations (CCVs) in the air motion and partly due to uncertainties in the PIV measurement technique, complicating the question of what constitutes a fair validation target for the RANS model. Indeed, an inappropriate validation target can misguide subsequent adjustments of a RANS model. In this work, the ensemble-averaged PIV field is first investigated for its suitability as a validation target for RANS simulations. The relevance index and the velocity histogram distance are used as quantitative metrics to assess the similarity of the ensemble-averaged field to the full dataset of individual PIV cycles. While a high similarity is seen between the average PIV flow field and the individual cycles on the tumble plane, the similarity is lower and more variable on the cross-tumble plane, where there are significant CCVs. Standard (space-only, phase-dependent) proper orthogonal decomposition (POD) is employed as an alternative method of data processing with the aim of providing a fairer comparison to RANS simulations. The cycle-dependence of the standard POD modes is shown to be an aspect that results in many validation targets and an excessively broad validation range, limiting its utility in this context. Dynamic mode decomposition (DMD) and sparsity-promoting dynamic mode decomposition (SPDMD) are then proposed as alternative solutions, capable of extracting flow structures at specific frequencies. The background 0 Hz SPDMD modes exhibit an ability to produce more realistic flow fields with velocity magnitudes that are significantly closer to the individual cycles.