| Autor: |
Wang, Zihao, Xing, Xiuqing, Sun, Teizhi, Zhang, Guiyong |
| Předmět: |
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| Zdroj: |
Physics of Fluids; May2023, Vol. 35 Issue 5, p1-16, 16p |
| Abstrakt: |
Clustering applied to unsteady flow fields can simplify flow field data and partition the flow field into regions of interest. Unfortunately, these areas are often unexplored when applied to complex fluid mechanics problems because multivariate data are difficult to express, and the relationships between flow field snapshots in a time series are difficult to preserve. In this paper, we use joint principal component analysis (JPCA) and fusion principal component analysis (FPCA) to process multivariate data to obtain the static and dynamic characteristics of the cavitation flow field. Based on the static characteristics of the flow field, we use the K-means algorithm and cohesive hierarchical clustering to obtain static flow field segmentation at different levels. Based on the dynamic characteristics of the flow field, we use the proposed time series K-means (TK-means) algorithm and cohesive hierarchical clustering to obtain dynamic flow field segmentation at different levels. The results show that JPCA or FPCA is effective in expressing multivariate features. Static flow field segmentation can obtain time-invariant, physically related structures of unsteady flow. Dynamic flow field segmentation can obtain time-varying, physically related structures of unsteady flow. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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