阻力
阻力系数
湍流
机械
雷诺数
寄生阻力
流量(数学)
压力降
粒子图像测速
大涡模拟
赫尔肖流
物理
经典力学
作者
Puxuan Li,Matthew Campbell,Ning Zhang,Steven J. Eckels
出处
期刊:Journal of Fluids Engineering-transactions of The Asme
[ASME International]
日期:2022-05-19
卷期号:144 (10)
被引量:2
摘要
Abstract This study proposes a numerical model to collect and analyze relationships between flow structures and drag forces on a microfin enhanced surface. We utilized a large eddy simulation (LES) with a localized, dynamic kinetic energy, subgrid-scale model (LDKM) to predict turbulent flow structures. The accuracy of the numerical model was verified by a telescopic particle image velocimetry (PIV) system. Of special note was the strong match of PIV flow structures with numerical flow structures simulated with LES. To detect two main flow structures, lateral and longitudinal, a new method based on the correlation coefficient of velocity fluctuation was developed. Two main types of drag, form, and skin-friction, were discussed and analyzed as occurring on complex near-surface engineered enhancements. Several problems about the relationships were discussed and solved. First, the study determined which drag force dominated the pressure drop (Δp) with different Reynolds numbers. Second, the study analyzed how turbulent flow structures affected form drag and friction drag, respectively. Third, the study explained why the microfins in the paper designed by Webb et al. were better suited for the high Reynold number cases (Reynolds number ≈ 28,000). The goal of the paper was not to find a new Reynolds number-based correlation but to find flow structures responsible for pressure drop and understand the mechanisms causing it.
科研通智能强力驱动
Strongly Powered by AbleSci AI