This study utilized high-speed camera and large eddy simulation methods to explore the cavitating flow mechanisms and turbulence structures of R134a refrigerant inside a Venturi tube under varying cavitation numbers (CNs). Data-driven modal analysis approaches, proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD), were introduced to identify and extract the energy hierarchy and transient characteristics within the cavitating flow. The analysis of grayscale images indicated that the cavitating flow gradually transitioned from quasi-periodic to unsteady flow as the CN decreased, and the severity of cavitation correlates with lower peak frequencies. The POD analysis facilitated the extraction of coherent structures in the cavity's temporal evolution, and the results indicate that the quasi-ordering shedding and collapse of large-scale cavity clouds predominantly occur under low cavitation intensity conditions. As the CN increases, the influence of small-scale cavity shedding becomes more significant. The first 30 most energetic modes occupied over 75% of the entire energy, and they were used to reconstruct the cavitating flow, achieving good consistency with transient flow snapshots. Additionally, the DMD results of the cavitating flow yield three frequency spans, including several prominent characteristic frequencies. These spans are closely linked to the cavity cloud structures of varying scales, unveiling the structural characteristics of unsteady cavitating flow.

中文翻译：

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文丘里管中 R134a 制冷剂空化流的数据驱动模态分解

本研究利用高速摄像机和大涡模拟方法，探讨了不同空化数（CN）下文丘里管内 R134a 制冷剂的空化流动机制和湍流结构。引入数据驱动模态分析方法、适当正交分解（POD）和动态模态分解（DMD）来识别和提取空化流内的能量层次和瞬态特征。灰度图像分析表明，随着CN的降低，空化流逐渐从准周期流转变为非定常流，并且空化的严重程度与较低的峰值频率相关。 POD分析有助于提取空穴时间演化中的相干结构，结果表明，大尺度空穴云的准有序脱落和塌陷主要发生在低空化强度条件下。随着CN的增加，小范围空腔脱落的影响变得更加显着。前30个能量最高的模态占据了整个能量的75%以上，它们被用来重建空化流，与瞬态流快照取得了良好的一致性。此外，空化流的 DMD 结果产生三个频率跨度，包括几个突出的特征频率。这些跨度与不同尺度的空腔云结构紧密相连，揭示了非定常空化流的结构特征。