The drag coefficient components of prolate spheroids based on the force element theory at finite Reynolds numbers

Based on the force element theory, the drag on spheroids is decomposed into volume and surface elements, establishing a quantitative linkage between flow structures and forces. This approach identifies key drag source regions. The surface element distribution can determine the separation angle at high angles of attack, and the effects of key parameters on both elements are analyzed. A deep neural-network-based predictive model for both elements is developed. This study is expected to provide theoretical guidance for drag-reduction strategies of spheroids.

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Publication Details

Journal
Physics of Fluids
Published
2026-04-01
DOI
https://doi.org/10.1063/5.0325193
Primary Topic
Fluid Dynamics and Turbulent Flows
Type
article
Field-Weighted Citation Impact
0.00

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article

The drag coefficient components of prolate spheroids based on the force element theory at finite Reynolds numbers

Zhuoyu Xie, Jianfeng Lin, Shizhao Wang
Physics of Fluids
Fluid Dynamics and Turbulent Flows
article

The drag coefficient components of prolate spheroids based on the force element theory at finite Reynolds numbers

Zhuoyu Xie, Jianfeng Lin, Shizhao Wang
article en

Abstract

Based on the force element theory, the drag on spheroids is decomposed into volume and surface elements, establishing a quantitative linkage between flow structures and forces. This approach identifies key drag source regions. The surface element distribution can determine the separation angle at high angles of attack, and the effects of key parameters on both elements are analyzed. A deep neural-network-based predictive model for both elements is developed. This study is expected to provide theoretical guidance for drag-reduction strategies of spheroids.

Physics of FluidsVol. 38(4)
Chinese Academy of Sciences (CN), Institute of Mechanics (CN), University of Chinese Academy of Sciences (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 24%
Fluid Dynamics and Turbulent Flows
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