带后分流板的凸轮圆柱减阻控制流场的数值方法

IF 1.9 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Sunil Chamoli, Amit Joshi, Sumit Rana, Suvanjan Bhattacharaya, Ashutosh Gupta, Siddharth Ghansela, Chinaruk Thianpong, Smith Eiamsa-ard
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引用次数: 1

摘要

在2019年发表的一篇研究文章中,引入了一种凸轮形状的圆柱体,结果表明它具有防止涡流脱落的能力。这使得凸轮形圆柱体比圆形圆柱体性能更好。这项工作是对过去工作的延伸,目的是通过在凸轮形圆柱体上附加一个向后的分离器板来进一步减少阻力。为了比传统的分流板控制装置更有效地减小阻力和调节尾迹,本文提出了一种低雷诺数流动范围(Re = 50-200)的分流板流动偏离控制装置。当板长增加时,阻力、升力和斯特罗哈尔数等积分参数不是单调变化的。斯特罗哈尔数(St)随D2/Deq的减小而增大,但平均阻力分别随Re的增大和D2/Deq的减小而减小。在减小阻力方面,当前的凸轮圆柱体与后置分流板相连接,显示出其优于其他钝体的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Numerical Methodology to Reduce the Drag and Control Flow around a Cam-Shaped Cylinder Integrated with Backward Splitter Plate
After publishing a research article in the year 2019, a cam-shaped cylinder was introduced, and the results expressed its ability to prevent the vortex from shedding. This makes the cam-shaped cylinder a better performer than the circular cylinder. This work is an extension of past work with the aim of further reducing drag by attaching a backward splitter plate to a cam-shaped cylinder. In an attempt to decrease drag and regulate the wake regime more efficiently than the traditional splitter plate control devices, a splitter plate flow departure control device is presented in this paper for a low Reynolds number flow range (Re = 50–200). It has been noted that when plate length increases, integral parameters like drag, lift, and Strouhal number do not change monotonically. The Strouhal number (St) increases with a drop in D2/Deq, but the average drag reduces with a rise in Re and a decrease in D2/Deq, respectively. In terms of decreased drag, the current cam-shaped cylinders attached to a rearward splitter plate have shown their superiority to other bluff bodies.
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来源期刊
Computation
Computation Mathematics-Applied Mathematics
CiteScore
3.50
自引率
4.50%
发文量
201
审稿时长
8 weeks
期刊介绍: Computation a journal of computational science and engineering. Topics: computational biology, including, but not limited to: bioinformatics mathematical modeling, simulation and prediction of nucleic acid (DNA/RNA) and protein sequences, structure and functions mathematical modeling of pathways and genetic interactions neuroscience computation including neural modeling, brain theory and neural networks computational chemistry, including, but not limited to: new theories and methodology including their applications in molecular dynamics computation of electronic structure density functional theory designing and characterization of materials with computation method computation in engineering, including, but not limited to: new theories, methodology and the application of computational fluid dynamics (CFD) optimisation techniques and/or application of optimisation to multidisciplinary systems system identification and reduced order modelling of engineering systems parallel algorithms and high performance computing in engineering.
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