基于神经网络的减阻沟槽壁面形状优化

李超群; 唐硕; 李易; 耿子海

doi:10.13224/j.cnki.jasp.20210683

基于神经网络的减阻沟槽壁面形状优化

doi: 10.13224/j.cnki.jasp.20210683

李超群¹,
唐硕¹,
李易^1, ,,
耿子海^2,3

1.
西北工业大学航天学院，西安 710072
2.
西北工业大学航空学院，西安 710072
3.
中国空气动力研究与发展中心低速空气动力研究所，四川绵阳 621000

基金项目: 装备预研

详细信息

作者简介:
李超群（1995-），男，博士生，主要从事微结构壁面减阻与优化研究。

通讯作者:
李易（1984-），男，副教授，博士，主要从事飞行器设计研究。E-mail：dr_liyi@nwpu.edu.cn

中图分类号: V211.3
计量
- 文章访问数: 73
- HTML浏览量: 35
- PDF量: 46
- 被引次数: 0
出版历程
- 收稿日期: 2021-11-30
- 刊出日期: 2022-03-28

Sub-optimization of riblet shape based on neural networks

LI Chaoqun¹,
TANG Shuo¹,
LI Yi^{1
, ,},
GENG Zihai^2,3

1.
School of Astronautics，Northwestern Polytechnical University，Xi'an 710072，China
2.
School of Aeronautics，Northwestern Polytechnical University，Xi'an 710072，China
3.
Low Speed Aerodynamics Institute， China Aerodynamics Research and Development Center，Mianyang Sichuan 621000，China

摘要

摘要: 针对沟槽外形减阻问题，采用基于神经网络的方法对沟槽壁面形状进行外形优化。模型采用槽道流动模型，控制方程为黏性不可压缩Navier-Stokes（NS）方程，流动求解采用直接数值模拟（DNS）方法，对于对流项的离散采用紧致4阶中心格式，对黏性项的离散采用4阶中心格式，时间推进采用3阶Runge-Kutta格式。在神经网络优化过程中，约束方程为不可压NS方程，采用基于在线学习的自适应控制器，使用基于抑制展向切应力的控制律，控制量的产生由壁面变形提供。优化结果表明，壁面最大减阻效果可达17.41%。对于优化后的壁面，湍流强度降低了19.68%，同时壁面的涡量与雷诺切应力亦有所降低。由于湍流流动非定常，因此优化得到的壁面形状亦是时变的，但变化的过程中整体上仍呈现流向沟槽的形状。
- 神经网络 /
- 槽道流动 /
- 壁面形状优化 /
- 减阻沟槽 /
- 流动控制
Abstract: For the riblet drag reduction，a neural network-based method was used to optimize the shape of the riblet surface.This work employed the channel flow model and the governing equations were the viscous incompressible Navier-Stokes （NS） equations.The turbulent flow inside the channel was resolved by the direct numerical simulation （DNS） method.According to the numerical method，a compact fourth-order central scheme was used for the discretization of the convective term，a fourth-order central scheme was applied to the discretization of the viscous term，and a third-order Runge-Kutta scheme was employed for the time advancement.In the neural network sub-optimization process，the constraint equation was the incompressible NS equation，and an adaptive controller based on online learning was used.In the optimization procedure，the control law was based on weakening the spanwise shear stress，and the control quantities were provided by the wall deformation.The optimization results demonstrated that the maximum wall drag reduction reached 17.41%.For the wall optimization，the turbulence intensity was reduced by 19.68%，besides，the vorticity and Reynolds shear stress at the wall also declined.Since the turbulent flow was unsteady，the shape of the optimized wall also varied with time，but the overall shape of the riblets still illustrated a streamwise riblet-like configuration.
- neural networks /
- channel flow /
- shape sub-optimization /
- drag-reducing riblet /
- flow control

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