基于ESGA遗传算法的水射流自驱旋转喷头优化设计

陈源捷; 陈正寿; 杜炳鑫; 谢应孝; 姜华

doi:10.11883/bzycj-2022-0155

基于ESGA遗传算法的水射流自驱旋转喷头优化设计

doi: 10.11883/bzycj-2022-0155

1.
浙江海洋大学船舶与海运学院，浙江舟山 316022
2.
太平洋海洋工程（舟山）有限公司，浙江舟山 316057

基金项目: 国家自然科学基金（41776105）；舟山市科技计划项目（2019C21010）；浙江省属高校基本科研业务费（2021JD002）

详细信息

作者简介:
陈源捷（1995－　），男，硕士研究生，1101693713@qq.com

通讯作者:
陈正寿（1979－　），男，博士，教授，aaaczs@163.com

中图分类号: O358
计量
- 文章访问数: 632
- HTML全文浏览量: 184
- PDF下载量: 99
- 被引次数: 0
出版历程
- 收稿日期: 2022-04-12
- 修回日期: 2022-07-20
- 网络出版日期: 2022-09-13
- 刊出日期: 2023-02-25

Optimum design of self-driven rotary water-jet sprayer based on ESGA genetic algorithm

1.
Naval Architecture and Maritime College, Zhejiang Ocean University, Zhoushan 316022, Zhejiang, China
2.
Paxocean Engineering Co. Ltd., Zhoushan 316057, Zhejiang, China

摘要

摘要: 超高压水射流自驱旋转型喷头是目前广泛应用于船壁除锈的一种装置，其布局方式直接影响船壁除锈的效率和质量，目前喷头布局多依赖工程经验，缺少准确的理论分析和优化技术支持。针对水射流自驱旋转型喷头的布局优化问题，在传统遗传算法（genetic algorithm，GA）的基础上，提出一种基于“锦标赛选择”的精英策略遗传算法（elitist strategy genetic algorithm，ESGA），该算法通过采用种群进化过程中精英个体直接保留到下一代的进化策略，从而有效提高算法的全局收敛能力和算法的鲁棒性。结合旋转喷头扫掠冲击性能和轨迹特征，以喷头移动路径垂直打击面上的能量分布均匀度为衡量标准，建立超高压水射流自驱旋转型喷头的螺旋扫掠冲击离散化时间优化模型，并分别利用两种遗传算法对其进行优化改进。对一字形水射流自驱旋转型喷头的布局优化研究发现，经ESGA算法优化的旋转喷头，其扫掠冲击能量分布均匀度较原喷头布局提升了47.2%，其收敛精度也高于GA算法。经对ESGA算法优化后的喷头实验验证发现，ESGA优化方案较原设计方案除锈效率提高了42.0%。改进的ESGA优化算法可行性强，能够在收敛迭代次数较少的情况下得到水动力性能更好的喷头布局方案，为旋转型喷头布局优化设计提供了理论依据和应用支持。
- 船壁除锈 /
- 旋转喷头 /
- 遗传算法 /
- 扫掠冲击 /
- 布局优化
Abstract: The self-driven rotary sprayer using ultra-high-pressure water jet is widely used in the rust removal of ship hulls, and its layout directly affects the efficiency and quality of ship derusting. Hitherto, the design of sprayer layout primarily depends on practical engineering experiences, due to the lack of support from accurate optimization techniques and theoretical analysis. In order to solve the layout optimization problem associated with self-driven rotary sprayers using ultra-high-pressure water jet, an improved elitist strategy genetic algorithm (ESGA) based on conventional genetic algorithm (GA) is proposed. By designing appropriate evolutionary operations, the ESGA algorithm can skip crossover and mutation operations on the fittest individuals in the population, and then directly copy the fittest individual to the next generation. Thus, the global convergence ability and robustness of the algorithm are improved effectively. By fully combining the sweep impinging performance and trajectory characteristics of rotary sprayer, a sweep impinging discrete-time model for self-driven rotary sprayer using ultra-high-pressure water jet is developed to quantify the evenness of impinging energy distribution on target surface perpendicular to the sprayer movement path. Aiming at enhancing the evenness of impinging energy distribution and improving hydrodynamic performance, the layout of self-driven rotary sprayer is optimized via the GA and ESGA algorithms. It is found that the evenness of the impinging energy distribution related to the self-driven rotary sprayer with a rod-like shape, which is optimized by the ESGA algorithm, is improved by 47.2% compared with that of the original layout scheme. The ESGA algorithm provides faster convergence speed and higher convergence precision, superior to the conventional GA. The experimental test results indicated that the rust-removing efficiency of self-driven rotary sprayer, optimized by the ESGA algorithm, is increased by 42.0% when compared with original layout scheme. It is worth noting that the improved ESGA algorithm optimization approach is feasible, and some sprayer layouts with better hydrodynamic performance can be easily achieved in fewer convergence iterations, providing adequate theoretical basis and application support for the layout optimization.
- rust removal of ship hull /
- rotary sprayer /
- genetic algorithm /
- sweep impinging /
- layout optimization

HTML全文

图 1 船体锈蚀与海洋附着物

Figure 1. Hull corrosion and marine attachments

下载: 全尺寸图片幻灯片

图 2 船壁面高压水射流除锈

Figure 2. Rust removal on ship wall using high-pressure water jet

下载: 全尺寸图片幻灯片

图 3 直圆锥收敛型喷嘴结构示意图

Figure 3. Structure diagram of straight cone convergent nozzle

下载: 全尺寸图片幻灯片

图 4 自驱型水射流旋转喷头工作示意图

Figure 4. Working schematic diagram of self-driven rotary water jet sprayer

下载: 全尺寸图片幻灯片

图 5 自驱旋转射流喷头结构

Figure 5. Structure of self-driven rotary water jet sprayer

下载: 全尺寸图片幻灯片

图 6 常规旋转射流喷头布置

Figure 6. Layout of conventional rotary water jet sprayer

下载: 全尺寸图片幻灯片

图 7 水射流旋转喷头除锈作业运动模型

Figure 7. Motion model of rotary water jet sprayer for rust removal

下载: 全尺寸图片幻灯片

图 8 单喷嘴运动扫掠模型

Figure 8. Sweep motion model of single nozzle

下载: 全尺寸图片幻灯片

图 9 喷头扫掠冲击时间模型及观测点局部放大图

Figure 9. Sweep impinging-time model of water jet sprayer and local enlargement around observation point

下载: 全尺寸图片幻灯片

图 10 目标函数值的搜索过程

Figure 10. Search process of the objective function

下载: 全尺寸图片幻灯片

图 11 原设计方案与ESGA优化设计方案对比

Figure 11. Comparison between original design and ESGA optimal design

下载: 全尺寸图片幻灯片

图 12 原布局与ESGA优化布局方案冲击扫掠轨迹对比

Figure 12. Comparison of sweep impinging trajectories between original layout and optimized layout by ESGA

下载: 全尺寸图片幻灯片

图 13 原喷头布局与ESGA算法优化后的冲击时间与能量分布对比

Figure 13. Comparisons of impinging time and energy distribution between original and ESGA algorithm optimized schemes

下载: 全尺寸图片幻灯片

表 1 GA算法与ESGA算法优化结果比较

Table 1. Comparison of optimization results between GA algorithm and ESGA algorithm

优化方法	目标值
原设计	0.003 45
GA算法	0.001 89
ESGA算法	0.001 82

下载: 导出CSV

表 2 原方案与ESGA优化方案除锈效率比较

Table 2. Comparison of the rust-removing efficiency between original scheme and ESGA optimized scheme

试验	喷头设计类型	时间/min	有效清除面积/m²	核算单位时间清除面积/（m²·h⁻¹）
1	原设计方案	20	12.1	36.2
2	原设计方案	20	12.3	37.1
3	ESGA算法优化方案	20	17.5	52.5
4	ESGA算法优化方案	20	17.2	51.6

下载: 导出CSV

参考文献(21)

[1]	ZHANG F F, SUN X R, LI Z P, et al. Influence of processing parameters on coating removal for high pressure water jet technology based on wall-climbing robot [J]. Applied Sciences, 2020, 10(5): 1862. DOI: 10.3390/app10051862.
[2]	薛胜雄. 超高压水射流自动爬壁除锈机理与成套设备技术 [D]. 杭州: 浙江大学, 2005. XUE S X. Studies on the removal rust forming by UHP waterjetting auto-robot and its unit technology [D]. Hangzhou: Zhejiang University, 2005.
[3]	衣正尧, 弓永军, 王祖温, 等. 用于搭载船舶除锈清洗器的大型爬壁机器人 [J]. 机器人, 2010, 32(4): 560–567. DOI: 10.3724/SP.J.1218.2010.00560. YI Z Y, GONG Y J, WANG Z W, et al. Large wall climbing robots for boarding ship rust removal cleaner [J]. Robot, 2010, 32(4): 560–567. DOI: 10.3724/SP.J.1218.2010.00560.
[4]	GERO M B P, GARCÍA A B, DEL COZ DÍAZ J J. A modified elitist genetic algorithm applied to the design optimization of complex steel structures [J]. Journal of Constructional Steel Research, 2005, 61(2): 265–280. DOI: 10.1016/j.jcsr.2004.07.007.
[5]	YILDIZELI A, CADIRCI S. Multi-objective optimization of multiple impinging jet system through genetic algorithm [J]. International Journal of Heat and Mass Transfer, 2020, 158: 119978. DOI: 10.1016/j.ijheatmasstransfer.2020.119978.
[6]	ALHAMAYDEH M, BARAKAT S, NASIF O. Optimization of support structures for offshore wind turbines using genetic algorithm with domain-trimming [J]. Mathematical Problems in Engineering, 2017, 2017: 5978375. DOI: 10.1155/2017/5978375.
[7]	FU X Y, LEI L, YANG G, et al. Multi-objective shape optimization of autonomous underwater glider based on fast elitist non-dominated sorting genetic algorithm [J]. Ocean Engineering, 2018, 157: 339–349. DOI: 10.1016/j.oceaneng.2018.03.055.
[8]	ZAIN A M, HARON H, SHARIF S. Genetic algorithm and simulated annealing to estimate optimal process parameters of the abrasive waterjet machining [J]. Engineering with computers, 2011, 27(3): 251–259. DOI: 10.1007/s00366-010-0195-5.
[9]	SRINIVASU D S, BABU N R. A neuro-genetic approach for selection of process parameters in abrasive waterjet cutting considering variation in diameter of focusing nozzle [J]. Applied Soft Computing, 2008, 8(1): 809–819. DOI: 10.1016/j.asoc.2007.06.007.
[10]	屈长龙, 王喜顺. 基于FLUENT的高压水射流除锈的流场仿真及射流参数优化 [J]. 机械与电子, 2016, 34(2): 24–27. DOI: 10.3969/j.issn.1001-2257.2016.02.006. QU C L, WANG X S. Jet flow simulation and parameters optimization of high pressure water jet for derusting based on FLUENT [J]. Machinery & Electronics, 2016, 34(2): 24–27. DOI: 10.3969/j.issn.1001-2257.2016.02.006.
[11]	CAI C, WANG X C, YUAN X H, et al. Experimental investigation on perforation of shale with ultra-high pressure abrasive water jet: shape, mechanism and sensitivity [J]. Journal of Natural Gas Science and Engineering, 2019, 67: 196–213. DOI: 10.1016/j.jngse.2019.05.002.
[12]	孙玲, 弓永军, 王祖温, 等. 超高压旋转清洗盘的设计及密封分析 [J]. 中国机械工程, 2014, 25(13): 1715–1718. DOI: 10.3969/j.issn.1004-132X.2014.13.003. SUN L, GONG Y J, WANG Z W, et al. Design and sealing analysis of ultra-high pressure water cleaning rotary device [J]. China Mechanical Engineering, 2014, 25(13): 1715–1718. DOI: 10.3969/j.issn.1004-132X.2014.13.003.
[13]	陈正寿, 黄璐云, 杜炳鑫, 等. 超高压水射流喷头水动力特性研究 [J]. 爆炸与冲击, 2022, 42(5): 053303. DOI: 10.11883/bzycj-2021-0310. CHEN Z S, HUANG L Y, DU B X, et al. Insight of hydrodynamic characteristics related to ultra-high pressure water jet rust removal sprayers [J]. Explosion and Shock Waves, 2022, 42(5): 053303. DOI: 10.11883/bzycj-2021-0310.
[14]	HUANG F, MI J Y, LI D, et al. Impinging performance of high-pressure water jets emitting from different nozzle orifice shapes [J]. Geofluids, 2020, 2020: 8831544. DOI: 10.1155/2020/8831544.
[15]	HUANG H C, LI D H, XUE Z, et al. Design and performance analysis of a tracked wall-climbing robot for ship inspection in shipbuilding [J]. Ocean Engineering, 2017, 131: 224–230. DOI: 10.1016/j.oceaneng.2017.01.003.
[16]	ZHANG D, WANG H L, LIU J H, et al. Flow characteristics of oblique submerged impinging jet at various impinging heights [J]. Journal of Marine Science and Engineering, 2022, 10(3): 399. DOI: 10.3390/jmse10030399.
[17]	李安贵, 刘庭成, 丁宇. 影响双喷嘴旋转速度的参数研究 [J]. 中国安全科学学报, 1999, 9(S1): 20–23. DOI: 10.3969/j.issn.1003-3033.1999.z1.005. LI A G, LIU T C, DING Y. Study on parameters of swirl speed affecting the twin water jet nozzle [J]. China Safety Science Journal, 1999, 9(S1): 20–23. DOI: 10.3969/j.issn.1003-3033.1999.z1.005.
[18]	XUE Y Z, SI H, CHEN G H. The fragmentation mechanism of coal impacted by water jets and abrasive jets [J]. Powder Technology, 2020, 361: 849–859. DOI: 10.1016/j.powtec.2019.11.018.
[19]	HOLLAND J H. Genetic algorithms [J]. Scientific American, 1992, 267(1): 66–73. DOI: 10.1038/scientificamerican0792-66.
[20]	MOTLAGH A A, SHABAKHTY N, KAVEH A. Design optimization of jacket offshore platform considering fatigue damage using Genetic Algorithm [J]. Ocean Engineering, 2021, 227: 108869. DOI: 10.1016/j.oceaneng.2021.108869.
[21]	GENTILS T, WANG L, KOLIOS A. Integrated structural optimisation of offshore wind turbine support structures based on finite element analysis and genetic algorithm [J]. Applied Energy, 2017, 199: 187–204. DOI: 10.1016/j.apenergy.2017.05.009.