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基于Kriging模型的水中放电沉积能量优化分析

赵景林 王志强 王进君 张东东 李国锋

赵景林, 王志强, 王进君, 等. 基于Kriging模型的水中放电沉积能量优化分析[J]. 强激光与粒子束, 2023, 35: 035005. doi: 10.11884/HPLPB202335.220240
引用本文: 赵景林, 王志强, 王进君, 等. 基于Kriging模型的水中放电沉积能量优化分析[J]. 强激光与粒子束, 2023, 35: 035005. doi: 10.11884/HPLPB202335.220240
Zhao Jinglin, Wang Zhiqiang, Wang Jinjun, et al. Deposited energy optimization analysis of discharge in water based on Kriging model[J]. High Power Laser and Particle Beams, 2023, 35: 035005. doi: 10.11884/HPLPB202335.220240
Citation: Zhao Jinglin, Wang Zhiqiang, Wang Jinjun, et al. Deposited energy optimization analysis of discharge in water based on Kriging model[J]. High Power Laser and Particle Beams, 2023, 35: 035005. doi: 10.11884/HPLPB202335.220240

基于Kriging模型的水中放电沉积能量优化分析

doi: 10.11884/HPLPB202335.220240
基金项目: 国家自然科学基金项目(51607023);辽宁省科学技术计划项目(2021-BS-293)
详细信息
    作者简介:

    赵景林,2855806591@qq.com

    通讯作者:

    王志强,wangzq@dlut.edu.cn

  • 中图分类号: TM89

Deposited energy optimization analysis of discharge in water based on Kriging model

  • 摘要: 水中脉冲放电过程较为复杂,放电参数与放电沉积能量之间没有明确的函数关系。为了获得最佳沉积能量,明晰不同放电参数相互作用对沉积能量的影响,获得最佳放电参数组合,本文搭建了水中高压脉冲放电实验平台,结合Kriging代理模型探究了电压、电极间距和电导率三种放电参数对水中放电沉积能量的影响;利用遗传算法进行全局寻优,确定了最佳放电参数组合。研究结果表明:通过交叉验证评估该模型的均方根误差为6.95%,满足精度要求;外加电压一定时,在电极间距和电导率的协同作用下,沉积能量的变化呈现多峰值特性;在电压、电极间距和电导率分别为17 kV、2.28 mm和0.8 mS/cm的条件下产生的沉积能量最大,为最佳参数组合;通过实验验证了在最佳点的预测值和实际值相对偏差在8%以内。
  • 图  1  水中高压脉冲放电实验平台示意图

    Figure  1.  Schematic diagram of underwater high-voltage pulse discharge experiment system

    图  2  水中高压脉冲放电典型电压电流波形图

    Figure  2.  Typical voltage and current waveform of high voltage pulse discharge in water

    图  3  流注发展过程

    Figure  3.  Plasma channel development process

    图  4  沉积能量的计算结果图

    Figure  4.  Results of deposited energy calculation

    图  5  优化设计流程图

    Figure  5.  Flowchart of the optimization search analysis

    图  6  20个初始样本点的空间分布

    Figure  6.  Spatial distribution of the 20 initial sample points

    图  7  交叉验证计算过程

    Figure  7.  Cross-validation of the computational process

    图  8  均方根误差随加点次数的变化

    Figure  8.  Variation of root mean square error with the number of additions

    图  9  沉积能量的多峰值特性

    Figure  9.  Multi-peak characteristics of deposited energy

    图  10  全局寻优流程图

    Figure  10.  Global optimization search flow chart

    图  11  实验结果与最优值的对比

    Figure  11.  Comparison between experimental results and optimal deposited energy

    表  1  实验变量及其范围

    Table  1.   Experimental variables and their scope

    voltage/kVelectrode spacing/mmconductivity/(mS·cm−1)
    13−172−50.2−0.8
    下载: 导出CSV

    表  2  反归一化后的部分初始样本点及对应的实验结果

    Table  2.   Some of the initial sample points after inverse normalization and the corresponding experimental results

    voltage/kVconductivity/(mS·cm−1)electrode spacing/mmdeposited energy/J
    14.10.264.5340.46
    13.40.774.3726.00
    16.20.332.1662.3
    15.10.454.6846.87
    15.70.642.3251.81
    14.50.522.0041.74
    13.80.392.7940.09
    16.80.552.9559.76
    下载: 导出CSV

    表  3  反归一化后的部分新增点及对应的实验结果

    Table  3.   After the normalization of some of the new points and the corresponding experimental results

    voltage/kVconductivity/(mS·cm−1)electrode spacing/mmdeposited energy/J
    170.3312.2867.41
    170.425266.78
    170.22.0466.51
    170.83.565.6
    170.652.1966.93
    下载: 导出CSV

    表  4  不同电导率下,电极间距变化对沉积能量的影响

    Table  4.   Effect of electrode spacing variation on deposited energy at different conductivities

    conductivity/(mS·cm−1)spacing variation Δd/mmdeposited energy variation/J
    0.2417.59
    0.445.04
    0.648.96
    0.8413.56
    1411.4
    下载: 导出CSV

    表  5  模型的全局最优解

    Table  5.   Global optimal solution of the model

    voltage/kVconductivity/(mS·cm−1)electrode spacing/mmoptimal deposited energy/J
    170.82.2868.73
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-08-05
  • 修回日期:  2022-10-12
  • 录用日期:  2022-10-14
  • 网络出版日期:  2022-10-18
  • 刊出日期:  2023-03-01

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