吉林大学学报(工学版) ›› 2022, Vol. 52 ›› Issue (4): 745-753.doi: 10.13229/j.cnki.jdxbgxb20200931

• 车辆工程·机械工程 • 上一篇    

长头重型卡车气动减阻优化

张英朝(),李昀航,郭子瑜,王国华,张喆(),苏畅   

  1. 吉林大学 汽车仿真与控制国家重点实验室，长春 130022
  • 收稿日期:2020-12-02 出版日期:2022-04-01 发布日期:2022-04-20
  • 通讯作者: 张喆 E-mail:yingchao@jlu.edu.cn;zhangzhejlu@jlu.edu.cn
  • 作者简介:张英朝（1978-），男，教授，博士. 研究方向：汽车空气动力学.E-mail： yingchao@jlu.edu.cn
  • 基金资助:
    国家自然科学基金项目(11702109)

Optimization of the aerodynamic drag reduction of a cab behind engine vehicle

Ying-chao ZHANG(),Yun-hang LI,Zi-yu GUO,Guo-hua WANG,Zhe ZHANG(),Chang SU   

  1. State Key Laboratory of Automotive Simulation and Control，Jilin University，Changchun 130022，China
  • Received:2020-12-02 Online:2022-04-01 Published:2022-04-20
  • Contact: Zhe ZHANG E-mail:yingchao@jlu.edu.cn;zhangzhejlu@jlu.edu.cn

RICH HTML

 6   

PDF (PC)

92

摘要：

以长头重型卡车作为研究对象，进行空气动力学性能的减阻优化。优化分为造型初期的外形优化和后期整车阶段的气动优化，优化过程基于Hyperstudy软件实现形状参数化，通过CFD软件仿真进行气动性能评价，并用两种软件构建出了一个自动化流程，实现长头重型卡车气动外形优化。研究确定了8个需要考虑的设计变量（即遮阳板旋转、发舱盖变形、货箱变形、车头顶部导流罩优化、前轮阻风板优化、侧面导流板优化、货箱处添加侧裙、添加货箱尾部导流装置）进行气动优化设计，最终将该车的气动阻力系数从0.432降低至0.387，相比于Base模型降低了45 counts，降幅达10.4%。

关键词: 车辆工程, 长头重型卡车, 空气动力学, 气动减阻, 优化设计

Abstract:

In this paper, the cab behind engine vehicles are taken as the research object, and the entire aerodynamic performance optimization is carried out. The optimization is composed of two steps, the initial shape optimization of the molding surface in the early modeling stage and the aerodynamic optimization in the later vehicle stage. The optimization process is based on Hyperstudy software to realize the shape parameterization, and CFD software simulation is used to evaluate the aerodynamic performance. An automatic process was built to realize the aerodynamic shape optimization of cab behind engine vehicles with these two kinds of software. Eight design variables that need to be considered (i.e. sunshield rotation, hood deformation, container deformation, fairing optimization, front wheel baffle optimization, side deflector optimization, side skirt at the container, and rear guide device of the container) were determined for aerodynamic optimization design. Finally, the drag coefficient of the vehicle was reduced from 0.432 to 0.387, compared with that of the vehicle base model reduced 45 counts by 10.4%.

Key words: vehicle engineering, cab behind engine vehicles, aerodynamics, aerodynamic drag reduction, optimization design

中图分类号: 

  • U270.1

图1

数值仿真模型"

图2

整车面网格"

图3

网格加密区示意图"

表1

网格敏感性检查结果"

A/mmB/mmC/mm网格数量/万Cd值
326412830010.4327
32649630520.4325
32489631810.4322
24489634200.4321
24486435120.4320
24326440170.4320
16326447620.4319

图4

优化流程示意图"

图5

遮阳板变形示意图"

表2

遮阳板优化结果"

旋转角度α/（°）CdΔCd/%
00.4320
-20.428-0.86
-40.428-0.97
-60.427-1.25
-80.427-1.16
-100.426-1.30
-120.426-1.44
-140.425-1.64
-160.426-1.34
-180.428-1.00

图6

优化前后Y=0截面压力云图"

图7

变形示意图"

图8

RBF拟合结果"

图9

优化前、后车头压力云图侧视图"

表3

优化结果统计表"

参 数计算结果ΔCd/%
Base Cd0.432-
预测Cd0.420-2.78
变量M1.4-
变量N0-
实际计算Cd0.422-2.31
预测误差/%0.46-

图10

货箱变形示意图"

表4

货箱处优化结果"

方 案下压距离M/mCdΔCd/countsΔCd/%
10.150.4254-6.60-1.53
20.200.4224-9.60-2.22
30.250.4205-11.5-2.66
40.300.4198-12.2-2.82

图11

优化前、后Y=0截面速度矢量对比图"

图12

导流罩优化示意图"

表5

导流罩优化结果"

方 案

X方向

平移/m

Y方向

平移/m

CdΔCd/counts
10.100.0800.4313-0.7
20.090.0720.43250.5
30.080.0640.4313-0.7
40.070.0560.4298-2.2
50.060.0480.4302-1.8
60.050.0400.4306-1.4
70.040.0320.4305-1.5
80.030.0240.4308-1.2

图13

优化前、后压力云图对比"

图14

优化前、后车头压力对比图"

图15

RBF拟合结果"

表6

优化结果统计表"

参 数差 值ΔCd/%
Base Cd0.42800.926
预测Cd0.4236-1.94
变量H/mm126.7-
变量β/（°）17.4°-
实际计算Cd0.4241-1.83
预测误差0.12-

图16

优化前、后压力云图对比图"

图17

导流板示意图"

图18

RBF拟合结果"

表7

优化结果统计表"

参 数差 值ΔCd/%
Base Cd0.4320
优化导流板预测Cd0.4226-2.17
变量L/mm0.93-
变量β/（°）6.82-
实际计算Cd0.4249-1.64
预测误差/%0.54-

图19

优化前后Y=0截面湍流动能对比示意图"

图20

货箱尾部变形示意图"

表8

货箱尾部优化结果"

X向移动距离/mCd值ΔCd/countsΔCd/%
0.20.4267-5.3-1.23
0.40.4256-6.4-1.48
0.60.4254-6.6-1.53
0.80.4253-6.7-1.55
1.00.4245-7.5-1.74
1.20.4239-8.1-1.88
1.40.4231-8.9-2.08
1.60.4265-5.5-1.27
1.80.459527.56.37
2.00.446614.63.38

图21

优化前、后Y=0截面速度矢量图"

图22

最终方案累积力发展曲线对比图"

图23

优化前、后车头压力云图主视图对比图"

图24

优化前、后车头压力云图侧视图对比图"

1 傅立敏. 汽车空气动力学[M]. 北京:机械工业出版社, 2006.
2 傅立敏. 用空气动力学附加装置降低国产货车气动阻力措施的探讨[J]. 汽车工程, 1994(3):144-148.
Fu Li-min. An investigation to the effects of add-on devices on reducing aerodynamic drag of domestic trucks[J]. Automotive Engineering, 1994(3):144-148.
3 Gong X, Gu Z, Li Z, et al. Aerodynamic shape optimization of a container-truck's wind deflector using approximate model[C]∥ SAE Paper, 2010-01-2035.
4 许亮. 重型卡车非光滑车身气动减阻特性研究[D]. 长沙:湖南大学机械与运载工程学院, 2014.
Xu Liang. Research on non-smooth body aerodynamic drag reduction characteristics of heavy trucks[D]. Changsha: College of Mechanical and Vehicle Engineering, Hunan University, 2014.
5 张凯歌. 重型卡车气动性能优化与实验研究[D]. 长沙:湖南大学机械与运载工程学院,2016.
Zhang Kai-ge. The aerodynamic perfomance optimization and experiment study of heavy duty truck[D]. Changsha: College of Mechanical and Vehicle Engineering, Hunan University,2016.
6 Cooper K R. Truck aerodynamics reborn - lessons from the past[C]∥ SAE Paper,2003-01-3376.
7 Englar R J. Development of pneumatic aerodynamic devices to improve the performance, economics, and safety of heavy vehicles[C]∥SAE Paper, 2000-01-2208.
8 Englar R J. Advanced aerodynamic devices to improve the performance, economics, handling, and safety of heavy vehicles[C]∥SAE Paper, 2001-01-2072.
9 Englar R J. Improved pneumatic aerodynamics for drag reduction, fuel economy, safety and stability increase for heavy vehicles[C]∥SAE Paper, 2005-01-3627.
10 Salati L, Cheli F, Schito P. Heavy truck drag reduction obtained from devices installed on the trailer[C]∥SAE Paper,2015-01-2898.
11 Woodiga S, Salazar D M, Wewengkang P, et al. Skin-friction topology on tail plate for tractor—trailer truck drag reduction[J]. Journal of Visualization, 2018,21(6):1017-1029.
12 金希东. 遗传算法及其应用[D]. 成都:西南交通大学电气工程学院, 1996.
Jin Xi-dong. Genetic algorithm and its application[D]. Chengdu: School of Electrical Engineering,Southwest Jiaotong University, 1996.
13 Deb K, Pratap A, Agarwal S, et al. A fast and elitist multi-objective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2):182-197.
14 Kleijnen J P C. Simulation Optimization[M]. New York: Elsevier, 2015:101-138.
[1] 李杰,陈涛,郭文翠,赵旗. 汽车非平稳随机振动空间域虚拟激励法及应用[J]. 吉林大学学报(工学版), 2022, 52(4): 738-744.
[2] 姚玉权,仰建岗,高杰,宋亮. 基于性能-费用模型的厂拌再生沥青混合料优化设计[J]. 吉林大学学报(工学版), 2022, 52(3): 585-595.
[3] 张立杰,阿喜塔,田笑,李稳. 基于Gamma过程的加速退化试验多目标优化设计[J]. 吉林大学学报(工学版), 2022, 52(2): 361-367.
[4] 赵泓荀,杨兆军,陈传海,田海龙,王立平. 考虑参数权重的数控机床电主轴加速试验优化设计[J]. 吉林大学学报(工学版), 2022, 52(2): 409-416.
[5] 段亮,宋春元,刘超,魏苇,吕成吉. 基于机器学习的高速列车轴承温度状态识别[J]. 吉林大学学报(工学版), 2022, 52(1): 53-62.
[6] 庄蔚敏,陈沈,王楠. 温度对车身钢铝胶铆连接结构热应力变化的影响[J]. 吉林大学学报(工学版), 2022, 52(1): 70-78.
[7] 陈剑斌,周宋泽,费峰永,陈永龙,凌国平. 过盈量及滚花方式对装配式凸轮轴压装失效的影响[J]. 吉林大学学报(工学版), 2021, 51(6): 1959-1966.
[8] 胡兴军,张靖龙,罗雨霏,辛俐,李胜,胡金蕊,兰巍. 冷却管结构及进气方向对空冷中冷器性能的影响[J]. 吉林大学学报(工学版), 2021, 51(6): 1933-1942.
[9] 罗勇,韦永恒,黄欢,肖人杰,任淋,崔环宇. 驾驶员意图识别的P2.5插混构型双离合器起步控制[J]. 吉林大学学报(工学版), 2021, 51(5): 1575-1582.
[10] 曾小华,宋美洁,宋大凤,王越. 基于车联网信息的公交客车行驶工况数据处理方法[J]. 吉林大学学报(工学版), 2021, 51(5): 1692-1699.
[11] 马超,高云凯,刘哲,段月星,田林雳. 骨架式车身多材料及梁截面形状和尺寸优化[J]. 吉林大学学报(工学版), 2021, 51(5): 1583-1592.
[12] 兰凤崇,李继文,陈吉清. 面向动态场景复合深度学习与并行计算的DG-SLAM算法[J]. 吉林大学学报(工学版), 2021, 51(4): 1437-1446.
[13] 杨建,夏琦,周海超,王国林. 修正胎体弦轮廓载重子午线轮胎的降噪机理[J]. 吉林大学学报(工学版), 2021, 51(4): 1198-1203.
[14] 龙江启,向锦涛,俞平,王骏骋. 适用于非线性主动悬架滑模控制的线性干扰观测器[J]. 吉林大学学报(工学版), 2021, 51(4): 1230-1240.
[15] 陈鑫,于贵申,张彪,潘凯旋,杨立飞. 搅拌摩擦点焊接头拉伸-剪切行为的等效建模[J]. 吉林大学学报(工学版), 2021, 51(4): 1190-1197.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 《吉林大学学报(工学版)》编辑部
地址：长春市人民大街5988号　电话：+86-431-85095297　传真：+86-431-85094074　E-mail: xbgxb@jlu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发