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Article(id=1325035439962337792, tenantId=1146029695717560320, journalId=1205116786560364546, issueId=1325035380357079315, articleNumber=null, orderNo=null, doi=10.16236/j.cnki.nrjxb.202505051, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1734537600000, receivedDateStr=2024-12-19, revisedDate=1746892800000, revisedDateStr=2025-05-11, acceptedDate=null, acceptedDateStr=null, onlineDate=1793842666854, onlineDateStr=2026-11-05, pubDate=1758729600000, pubDateStr=2025-09-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793842666854, onlineIssueDateStr=2026-11-05, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793842666854, creator=13701087609, updateTime=1793842666854, updator=13701087609, issue=Issue{id=1325035380357079315, tenantId=1146029695717560320, journalId=1205116786560364546, year='2025', volume='43', issue='5', pageStart='385', pageEnd='479', issueExtLink='null', onlineDate='null', pubDate='1758729600000', pubDateStr='2025-09-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793842652644, creator='13701087609', updateTime=1793959718537, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1325526390799618088, tenantId=1146029695717560320, journalId=1205116786560364546, issueId=1325035380357079315, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1325526390799618089, tenantId=1146029695717560320, journalId=1205116786560364546, issueId=1325035380357079315, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=451, endPage=459, ext={EN=ArticleExt(id=1325035441409372673, articleId=1325035439962337792, tenantId=1146029695717560320, journalId=1205116786560364546, language=EN, title=Design and optimization of electrically controlled injectors without static leakage, columnId=null, journalTitle=Transactions of CSICE, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To improve the response characteristics of a no-static-leakage injector, an electronically controlled injector was designed with a three-orifice plate structure. Its performance was optimized and evaluated through simulation modeling. In the performance analysis, the leakage ratio was introduced as a standard to assess the fuel leakage level of the injector in order to eliminate the evaluation error caused by cyclic injection volume. In the optimization phase, a fitness function was constructed based on three performance indicators: Leakage ratio, opening response time, and closing response time. The non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ)with an elitist strategy was used for multi-objective optimization. The optimization results indicate that, under a rail pressure range of 100MPa to 200MPa, compared to the original model designed for the operational conditions, the closing response time of the designed injector is reduced by up to 11.46%, the opening response time is reduced by up to 1.68%, and the leakage ratio is increased by up to 11.23%. The application of this algorithm effectively improves optimization efficiency and enhances the overall performance of the injector.

, authors=Xiuyi Lyu1, Yingdi Dai2, Jing Xu2, Liyun Fan2, authorsList=Xiuyi Lyu, Yingdi Dai, Jing Xu, Liyun Fan, authorCompany=null, correspAuthors=Yingdi Dai, Liyun Fan, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1325035448225116739, articleId=1325035439962337792, tenantId=1146029695717560320, journalId=1205116786560364546, language=CN, title=基于遗传算法的无静态泄漏电控喷油器设计与优化, columnId=null, journalTitle=内燃机学报, columnName=, runingTitle=null, highlight=null, articleAbstract=

为改善无静态泄漏喷油器的响应特性，设计了一种带有三量孔板的无静态泄漏电控喷油器，并通过仿真建模评估其性能．性能分析部分引入泄漏占比作为喷油器燃油泄漏程度的评估标准，用以消除循环喷油量对其燃油泄漏程度的评估误差．优化部分基于泄漏占比、开启响应时间和关闭响应时间3个性能指标，构建适应度函数，并采用带有精英策略的快速非支配排序遗传算法(NSGA-Ⅱ)进行多目标优化．优化结果表明：在轨压为100～200MPa的工况内，相对于满足设计工况的原始模型，喷油器关闭响应时间最多减少11.46%，开启响应时间最多减少1.68%，泄漏占比最多增加11.23%．该算法的应用有效提高了优化效率，并提升了喷油器的整体性能．

, authors=吕修颐1, 代蓥笛2, 许菁2, 范立云2, authorsList=吕修颐, 代蓥笛, 许菁, 范立云, authorCompany=null, correspAuthors=代蓥笛, 范立云, authorNote=

吕修颐，博士，讲师，.

, correspAuthorsNote=
范立云，博士，教授，
代蓥笛，博士研究生，.
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吕修颐，博士，讲师，.

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Comparison of correlation coefficient

, figureFileSmall=null, figureFileBig=null, tableContent=
轨压/MPa控制脉宽/ms相关系数
喷油速率与时间喷油量与控制脉宽
800.80.96970.9956
1.50.9901
2.50.9735
1800.80.97430.9977
1.50.9896
2.50.9969
), ArticleFig(id=1325514984423301714, tenantId=1146029695717560320, journalId=1205116786560364546, articleId=1325035439962337792, language=CN, label=表1, caption=

相关系数对比

, figureFileSmall=null, figureFileBig=null, tableContent=
轨压/MPa控制脉宽/ms相关系数
喷油速率与时间喷油量与控制脉宽
800.80.96970.9956
1.50.9901
2.50.9735
1800.80.97430.9977
1.50.9896
2.50.9969
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Comparison of names and value ranges of each structural parameter

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参数xvxsxu
出油节流孔直径Dc/mm0.360.560.76
第三节流孔直径Ds/mm0.10.20.3
进油节流孔直径DJ/mm0.100.200.25
针阀最大升程Cz/mm0.40.50.6
喷孔直径DP/mm0.410.510.56
电磁阀弹簧预紧力FD/N100110140
控制阀回油孔直径DK/mm0.500.560.62
密封球直径DM/mm1.41.51.8
针阀柱塞直径DZ/mm4.95.05.1
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各结构参数名称及取值范围对比

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参数xvxsxu
出油节流孔直径Dc/mm0.360.560.76
第三节流孔直径Ds/mm0.10.20.3
进油节流孔直径DJ/mm0.100.200.25
针阀最大升程Cz/mm0.40.50.6
喷孔直径DP/mm0.410.510.56
电磁阀弹簧预紧力FD/N100110140
控制阀回油孔直径DK/mm0.500.560.62
密封球直径DM/mm1.41.51.8
针阀柱塞直径DZ/mm4.95.05.1
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Comparison table of the determination coefficients of each parameter approximation model

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性能指标近似模型建立策略R2 Radj2
开启响应时间SVD0.87370.8847
Kriging0.98950.9567
SS-ANOVA0.70950.7150
关闭响应时间SVD0.83510.8396
Kriging0.98660.9870
SS-ANOVA0.79710.7851
泄漏占比SVD0.75640.7651
Kriging0.98760.9936
SS-ANOVA0.82940.8620
循环喷油量SVD0.80570.8069
Kriging0.98050.9790
SS-ANOVA0.87470.8543
喷油持续时间SVD0.79230.8025
Kriging0.97180.9720
SS-ANOVA0.81760.8263
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各参数近似模型决定系数对比

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性能指标近似模型建立策略R2 Radj2
开启响应时间SVD0.87370.8847
Kriging0.98950.9567
SS-ANOVA0.70950.7150
关闭响应时间SVD0.83510.8396
Kriging0.98660.9870
SS-ANOVA0.79710.7851
泄漏占比SVD0.75640.7651
Kriging0.98760.9936
SS-ANOVA0.82940.8620
循环喷油量SVD0.80570.8069
Kriging0.98050.9790
SS-ANOVA0.87470.8543
喷油持续时间SVD0.79230.8025
Kriging0.97180.9720
SS-ANOVA0.81760.8263
), ArticleFig(id=1325514985161499223, tenantId=1146029695717560320, journalId=1205116786560364546, articleId=1325035439962337792, language=EN, label=Tab.4, caption=

Comparison of structural parameters

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参数基准值优化值
出油节流孔直径/mm0.560.57
电磁阀弹簧预紧力/N110.00114.99
进油节流孔直径/mm0.200.21
控制阀回油孔直径/mm0.560.56
密封球直径/mm1.501.45
喷孔直径/mm0.510.52
第三节流孔直径/mm0.200.21
针阀最大升程/mm0.500.51
针阀柱塞直径/mm5.004.95
), ArticleFig(id=1325514985249579608, tenantId=1146029695717560320, journalId=1205116786560364546, articleId=1325035439962337792, language=CN, label=表4, caption=

结构参数对比

, figureFileSmall=null, figureFileBig=null, tableContent=
参数基准值优化值
出油节流孔直径/mm0.560.57
电磁阀弹簧预紧力/N110.00114.99
进油节流孔直径/mm0.200.21
控制阀回油孔直径/mm0.560.56
密封球直径/mm1.501.45
喷孔直径/mm0.510.52
第三节流孔直径/mm0.200.21
针阀最大升程/mm0.500.51
针阀柱塞直径/mm5.004.95
), ArticleFig(id=1325514985480266329, tenantId=1146029695717560320, journalId=1205116786560364546, articleId=1325035439962337792, language=EN, label=Tab.5, caption=

Comparison of performance indicators

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参数轨压为100MPa，控制脉宽为1.0ms轨压为150MPa，控制脉宽为1.5ms轨压为200MPa，控制脉宽为2.0ms
优化前优化后优化前优化后优化前优化后
回油量/mm331.222832.008950.298153.327973.031476.6764
循环喷油量/mm31140.04791050.73271805.54291828.52762616.35982537.3272
泄漏占比/%2.713.052.792.912.833.02
开启响应时间/ms0.79430.78120.65280.64550.56930.5652
关闭响应时间/ms1.34671.19241.19061.07471.12691.0092
喷油持续时间/ms1.21501.22131.82241.83152.56132.5134
), ArticleFig(id=1325514987153793626, tenantId=1146029695717560320, journalId=1205116786560364546, articleId=1325035439962337792, language=CN, label=表5, caption=

性能指标对比

, figureFileSmall=null, figureFileBig=null, tableContent=
参数轨压为100MPa，控制脉宽为1.0ms轨压为150MPa，控制脉宽为1.5ms轨压为200MPa，控制脉宽为2.0ms
优化前优化后优化前优化后优化前优化后
回油量/mm331.222832.008950.298153.327973.031476.6764
循环喷油量/mm31140.04791050.73271805.54291828.52762616.35982537.3272
泄漏占比/%2.713.052.792.912.833.02
开启响应时间/ms0.79430.78120.65280.64550.56930.5652
关闭响应时间/ms1.34671.19241.19061.07471.12691.0092
喷油持续时间/ms1.21501.22131.82241.83152.56132.5134
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基于遗传算法的无静态泄漏电控喷油器设计与优化
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吕修颐 1 , 代蓥笛 2 , 许菁 2 , 范立云 2
内燃机学报 | 2025,43(5): 451-459
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内燃机学报 | 2025 , 43 (5) : 451 -459
基于遗传算法的无静态泄漏电控喷油器设计与优化
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吕修颐1 , 代蓥笛2 , 许菁2, 范立云2
作者信息
  • 1.重庆交通大学航运与船舶工程学院，重庆 400074
  • 2.哈尔滨工程大学动力与能源工程学院，哈尔滨 150006
通讯作者:
范立云，博士，教授，
代蓥笛，博士研究生，.
作者简介:

吕修颐，博士，讲师，.

Design and optimization of electrically controlled injectors without static leakage
Xiuyi Lyu1 , Yingdi Dai2 , Jing Xu2, Liyun Fan2
Affiliations
  • 1.School of Shipping and Naval Architecture，Chongqing Jiaotong University，Chongqing 400074，China
  • 2.School of Power and Energy Engineering，Harbin Engineering University，Harbin 150006，China
出版时间: 2025-09-25 doi: 10.16236/j.cnki.nrjxb.202505051
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摘要
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为改善无静态泄漏喷油器的响应特性，设计了一种带有三量孔板的无静态泄漏电控喷油器，并通过仿真建模评估其性能．性能分析部分引入泄漏占比作为喷油器燃油泄漏程度的评估标准，用以消除循环喷油量对其燃油泄漏程度的评估误差．优化部分基于泄漏占比、开启响应时间和关闭响应时间3个性能指标，构建适应度函数，并采用带有精英策略的快速非支配排序遗传算法(NSGA-Ⅱ)进行多目标优化．优化结果表明：在轨压为100～200MPa的工况内，相对于满足设计工况的原始模型，喷油器关闭响应时间最多减少11.46%，开启响应时间最多减少1.68%，泄漏占比最多增加11.23%．该算法的应用有效提高了优化效率，并提升了喷油器的整体性能．

电控喷油器  /  无静态泄漏  /  多目标优化

To improve the response characteristics of a no-static-leakage injector, an electronically controlled injector was designed with a three-orifice plate structure. Its performance was optimized and evaluated through simulation modeling. In the performance analysis, the leakage ratio was introduced as a standard to assess the fuel leakage level of the injector in order to eliminate the evaluation error caused by cyclic injection volume. In the optimization phase, a fitness function was constructed based on three performance indicators: Leakage ratio, opening response time, and closing response time. The non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ)with an elitist strategy was used for multi-objective optimization. The optimization results indicate that, under a rail pressure range of 100MPa to 200MPa, compared to the original model designed for the operational conditions, the closing response time of the designed injector is reduced by up to 11.46%, the opening response time is reduced by up to 1.68%, and the leakage ratio is increased by up to 11.23%. The application of this algorithm effectively improves optimization efficiency and enhances the overall performance of the injector.

electronically controlled injectors  /  no-static-leakage  /  multi-objective optimization
吕修颐, 代蓥笛, 许菁, 范立云. 基于遗传算法的无静态泄漏电控喷油器设计与优化. 内燃机学报, 2025 , 43 (5) : 451 -459 . DOI: 10.16236/j.cnki.nrjxb.202505051
Xiuyi Lyu, Yingdi Dai, Jing Xu, Liyun Fan. Design and optimization of electrically controlled injectors without static leakage[J]. Transactions of CSICE, 2025 , 43 (5) : 451 -459 . DOI: 10.16236/j.cnki.nrjxb.202505051
正文
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低速二冲程柴油机以高燃油效率、高可靠性和动力输出强等优势，成为海洋装备和大型舰船不可替代的主动力设备．喷油器作为低速机完成做功过程的核心部件之一，其喷射效果会直接决定低速机工作性能的优劣．相较于机械喷油器，高压共轨电控喷油器可针对低速机服役工况迅速调整喷油策略，以提供最佳的燃油供应条件．但因其结构的特殊性，电控喷油器在工作过程中会不可避免地出现燃油泄漏，并随着高压技术的不断迭代更新，使得当前燃油喷射系统喷射压力大幅提高，导致燃油泄漏现象愈发严重．无静态泄漏喷油器也因此出现，虽然解决了燃油泄漏，但采用该种结构的喷油器存在着针阀落座缓慢的问题．因此，拟在传统无静态泄漏喷油器结构基础上，通过引入三量孔板结构改善其相关性能指标，并基于遗传算法对该款喷油器的结构参数进行多目标优化设计．
现阶段，无静态泄漏喷油器结构设计和性能研究逐渐成为学者们的研究热点[1-3]．在喷油器结构优化设计方面，吴小勇等[4]针对某中大型柴油机的共轨喷油器泄漏，通过对其密封球等零部件进行优化设计，有效改善高压力工况下的静态泄漏量和总泄漏量．喻曹丰等[5]提出一种超磁致伸缩驱动的喷油器设计方案，有效缩短了轨压为160MPa工况下的最低控制脉宽．胥奇等[6]针对无静态泄漏喷油器关闭响应时间长于同类型传统喷油器问题，对针阀挺杆各结构参数进行了单因素变化分析，优化后针阀总运动时间缩短了34.88%．Yin等[7]以汽油缸内直喷(GDI)喷油器电磁阀的尺寸和响应特性为优化目标，应用MOSA算法开展喷油器磁路配置优化，优化后电磁阀总体积和响应时间分别缩短了15.700%和9.375%．以上研究在针对无静态泄漏喷油器的结构优化上相对局限，仅针对现有结构进行优化改进，未加入新结构以改善喷油器相关性能问题．并且在无静态泄漏电控喷油器多目标优化的目标性能参数选取上，未引入直接对燃油泄漏程度进行评估的指标，主要优化目标不在缓解喷油器的燃油泄漏，而循环喷油量的提高对评估喷油器泄漏程度带来的误差也有所欠缺．
因此，笔者首先对无静态泄漏喷油器进行结构设计，对其建立仿真模型并选择参照喷油器验证了仿真模型准确度．在仿真模型的基础上，以开、闭响应时间和泄漏占比为指定性能指标，分析轨压和控制脉宽对其的影响，再进行单结构参数的量化分析，筛选出影响程度较大的结构参数．最后以筛选出的结构参数为自变量构建数学模型，以目标参数为因变量构建近似模型，再基于遗传算法进行多目标优化设计，探索该款喷油器结构设计的最优解．
1　无静态泄漏喷油器结构设计及仿真模型
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1.1　结构设计
在传统电控喷油器设计中，高压燃油和低压伺服油分别从不同的油路通至针阀腔和盛油腔．两腔室因为针阀中段与腔壁的缝隙联通，导致盛油腔处的高压燃油泄漏至针阀腔，从而发生静态泄漏现象．无静态泄漏方案的出现解决了该问题，但相比于传统方案，其针阀落座时间显著增长．三量孔板可有效缩短喷油器关闭响应时间[8]，因而在设计喷油器时引入了该结构，喷油器整体结构示意如图1所示．
所设计的无静态泄漏喷油器采用了三量孔板结构以优化响应特性，如图2所示．基于上述结构，在针阀抬起阶段，第三节流孔与出油孔联通，形成进油通道，该通道与进油孔同时向控制腔补油，达到稳定泄油速率的效果；在针阀落座阶段，该通道与进油孔同时进油又可以加快控制腔建压速率，并缩短关闭响应时间．
1.2　模型建立和试验验证
该仿真模型主要分为电磁阀、针阀和量孔板3个部分，如图3所示．其中，电磁阀部分主要包括位移传感器、复位弹簧、集中质量、球阀和流量传感器等；三量孔板部分共有5条油路，将进油节流孔、出油节流孔、第三节流孔、回油孔和控制腔相连；喷孔部分对喷油嘴的控制腔、中间腔、盛油腔和针阀腔分别采用了容积元件进行仿真．
仿真模型的准确程度会直接影响研究结论，为了验证其准确性，基于高压共轨系统性能测试平台对参考喷油器进行了喷油特性和喷油量的测量．图4示出参照电控喷油器的仿真模型．相比于参照的喷油器，增加了三量孔板以改善喷油器响应特性．所参照喷油器的进油节流孔直径为0.3mm，设计喷油器的进油节流孔孔径为0.2mm、第三节流孔孔径为0.2mm．
参照喷油器试验值与仿真结果对比如图5所示．通过皮尔森相关系数法计算，得到不同工况下，喷油速率与时间及喷油量与控制脉宽的相关系数，如表1所示．各工况下所得仿真的喷油速率与时间曲线以及喷油量与控制脉宽曲线的相关性较好，其相关系数都大于要求的0.9500，认为所建立仿真模型具备较好的复现能力．由于输入喷油器的高压燃油由恒压油源模拟，所以未考虑到共轨系统产生的轨压波动对燃油喷射过程的影响，导致最大喷油速率、喷油速率波动相位及喷油持续时间等方面存在一定差异，从而使得喷油量略有不同．
2　无静态泄漏喷油器性能影响分析
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完成无静态泄漏喷油器结构设计与模型仿真工作后，将基于仿真模型展开控制脉宽、轨压和喷油器结构参数对泄漏占比和开、闭响应时间的单参数变化研究工作．
2.1　泄漏占比影响分析
通常喷油器回油量大小直接反映了其燃油泄漏的程度．随着循环喷油量的增加，在既定的喷油持续时间内，油路所供给高压燃油的体积会随之增多，针阀和油路的尺寸会随之增大，控制腔的体积也会增大，单次喷射过程中产生的回油量也因此增多．但对于船用大喷量柴油机而言，对回油量进行单一评价，难以说明电控喷油器对高压燃油的利用率．
综上所述，引入“泄漏占比”作为喷油器燃油泄漏程度的评价指标，其本质是回油量和循环喷油量的比值．该指标可消除循环喷油量增加引起的评价误差．其值越大表示电控喷油器工作过程浪费的高压燃油越多，液压效率越低；反之则电控喷油器对高压燃油的利用率高，液压效率高．其公式为
K=Qh/Qcyc
式中：K为泄漏占比；Qcyc为循环喷油量；Qh为回油量．
探究控制脉宽和轨压对泄漏占比的综合影响规律，发现影响趋势较为复杂，如图6(a)所示．单独分析发现，随着控制脉宽的增大，其在不同轨压下对泄漏占比的影响趋势相同，在当前控制脉宽取值区间，泄漏占比的最大值与最小值都分别位于区间最值处．分段分析发现，当控制脉宽位于1.0～1.2ms时，泄漏占比在不同轨压下上升趋势明显，而其他取值范围内泄漏占比的上升趋势相对稳定，如图6(b)所示．随着轨压的增大，其在不同控制脉宽梯度下对泄漏占比的影响趋势相同，泄漏占比都随轨压的增加而增大，但增长率不相同．尤其当控制脉宽为1.0ms时，其增长率远大于其他几组控制脉宽的泄漏占比变化曲线，如图6(c)所示．
对于该现象单独探究回油量变化规律发现，其增长率随控制脉宽稳定不变；相反，循环喷油量增长率随控制脉宽变化而波动，这是导致泄漏占比突变的直接原因．进一步分析认为，该现象形成的主要原因是控制脉宽线性增加情况下，喷油持续时间却呈非线性增长，如图7(a)所示．在较低控制脉宽为1.0～1.2ms时，其增长率高于1.2～2.0ms，这与图6(b)中泄漏占比的变化规律相吻合，如图7(b)所示．在轨压较高时，喷油持续时间的增长率明显高于低轨压工况，并且控制脉宽在1.0～1.2ms时的增长率明显大于其他取值区间，这与图6(c)中泄漏占比的变化规律相吻合，如图7(c)所示．
结果表明，泄漏占比在当前轨压、控制脉宽的设定区间，其变化趋势不规律，其最大值位于轨压为200MPa、控制脉宽为2.0ms，此时泄漏占比为2.834%；最小值位于轨压为100MPa、控制脉宽为1.0ms，此时泄漏占比为2.709%．
2.2　响应特性影响分析
不同于轨压和控制脉宽对泄漏占比不规律的影响趋势，控制脉宽与轨压对该款喷油器响应特性的影响均表现得相对稳定．基于仿真模型计算得到轨压与控制脉宽对开、闭响应时间的综合影响趋势，其表现为开启响应时间与轨压呈正相关，控制脉宽对其的影响可忽略不计，如图8(a)所示；关闭响应时间与轨压呈正相关，其与控制脉宽也呈正相关关系(图8(b))．
上述计算结果表明，在该工况区间，开、闭响应时间的最大值都位于轨压为200MPa、控制脉宽为2.0ms，此时开启响应时间为0.7165ms，关闭响应时间为1.5800ms；最小值都位于轨压为100MPa、控制脉宽为1.0ms，此时开启响应时间为0.5400ms，关闭响应时间为0.3950ms．
2.3　单结构因素影响分析
根据当前学者们对喷油器结构因素的研究成果及进展，笔者选取了13个对所设计无静态泄漏喷油器性能有较大影响的结构参数[9-11]．
进一步筛选喷油器泄漏占比和响应特性的主要影响因素．由于船舶中低速巡航至高速巡航占比完整航程的80%及以上，中低速巡航时轨压为1.0MPa、控制脉宽为1.0ms，高速巡航对应轨压为2.0MPa、控制脉宽为2.0ms，所以笔者以该范围对应的喷油器工况为优化范围，设定在控制脉宽为1.0、1.5和2.0ms时，分别对轨压为100、150和200MPa进行对比．为了消除各结构参数之间因上、下水平值差异带来的影响，通过对比各结构参数对开、闭响应时间和泄漏占比的影响因子，对比各结构参数对其影响程度的大小．影响因子公式为
A=(ΔB/B)[(x1−x2)/x3]−1
式中：A为结构参数x对目标量的影响因子；x1为结构参数上水平；x2表示结构参数中水平；x3为结构参数下水平；ΔB为结构参数取值变化引起目标量的变化量；B为结构参数中水平时对应的值．
图9直观地反映了各结构参数在对应工况下对泄漏占比、开启响应时间以及关闭响应时间等性能指标的影响程度．而针阀柱塞直径对开、闭响应时间和泄漏占比的影响都较为显著，尤其对开启响应时间的影响权重达到了34.09%；三量孔板结构中的第三节流孔在各工况下对开、闭响应时间和泄漏占比的影响权重分别达到了9.61%、6.33%和17.92%，且影响程度稳定，认为是有效的影响结构．
因此，可选取进油节流孔直径、电磁阀弹簧预紧力、出油节流孔直径、控制阀回油孔直径、密封球直径、喷孔直径、第三节流孔直径、针阀最大升程和针阀柱塞直径这9个结构参数，作为对指定性能指标影响较大的结构参数进行多目标优化分析．
3　多目标优化分析
收起
通过上述影响分析，选取了9个对开、闭响应时间和泄漏占比影响程度较大的结构参数，可以用作多目标优化分析的输入条件．并将建立开、闭响应时间和泄漏占比的近似模型，再基于遗传算法寻得最优结构参数组合．
3.1　多目标优化数学模型
多目标优化问题的数学模型可以描述为
minF(x)=min[Top(x) , Ted(x) , λxl(x)]x=(DC , DJ , DZ , DM , DK , FD , DP , CZ , DS)s.t.{Tdur≥1.8  ms  Qcyc≥1 800  mm3xiu≤xis≤xiv  (i=1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9)
式中：Top为开启响应时间；Ted为关闭响应时间；Tdur为喷油持续时间；λxl为泄漏占比；x为各结构参数；xu为结构参数上水平；xs为结构参数中水平；xv为结构参数下水平；DC为出油节流孔直径；DJ为进油节流孔直径；DZ为针阀柱塞直径；DM为密封球直径；DK为控制阀回油孔直径；FD为电磁阀弹簧预紧力；DP为喷孔直径；CZ为针阀最大升程；DS为第三节流孔直径．表2所示各结构参数x对应的参数上水平xu和下水平xv．
基于数学原理模型，在成熟的多目标优化寻优软件modeFRONTIER中建立了该问题的优化模型，如图10所示．在变量模块中，分别对喷油器电磁阀部分、控制腔部分和针阀部分参与优化的参数变化区间进行设置；在初始化模块中，设置算法类型及其迭代次数；在约束模块中，对喷油持续时间和循环喷油量的可行区间进行设置，保证结果有效；在优化模块中，将开启响应时间、关闭响应时间和泄漏占比的变化趋势设置为最小化．
3.2　近似模型建立方法选取与验证
在类似问题的研究中，学者们开展近似模型构造工作主要基于Ploynomial SVD、Kriging及Smoothing Spiline ANOVA等近似模型建立策略[12-14]，近似模型的精确性会对优化过程产生较大误差．为了降低误差，通过考察近似模型的决定系数R2和校正决定系数Radj2，以选择合理的近似模型建立策略，如式(4)、式(5)所示．
R2=1−[∑im(y^i−yi)2/∑im(y¯i−yi)2]
Radj2=1−(1−R2)(n−1)n−p−1
式中：yi为真实值；y¯i为真实值的平均值；ŷi为预测值；n为样本数量；p为特征个数，既模型中的自变量数量.．
对于决定系数R2其越接近于1，说明模型拟合程度越高．而校正决定系数Radj2则能消除样本数量对决定系数的影响表明，Kriging近似模型建立策略更加契合近似模型的建立，如表3所示．
以泄漏占比为例，选用Kriging模型的相关模型Anistropic Kriging构建其适应度函数，如式(6)所示．
y=6×10−4−2×10−4DC+2×10−4DJ+10−4DS+  10−4CZ+10−4DP−10−4DK+10−4DZ−  10−4DCDJ−3.573×10−8DCFD+4.229×  10−6DCDM+3.672×10−6DJCZ+7.369×  10−6DJFD−9.699×10−6DJDM+9.069×  10−6DSCZ−9.624×10−6DSDM+7.839×  10−6DSDZ+5.907×10−6CZDP−1.794×  10−6CZDK−3.178×10−6CZDK+1.192×  10−7CZDM+3.864×10−6CZDZ+1.937×  10−6DPFD−4.542×10−6DPDM+9.715×  10−6DPDZ−7.565×10−7FDDK−2.628×  10−6FDDM+4.876×10−7FDDZ−6.325×  10−6DKDM−4.484×10−6DMDZ+10−4DC2+  1.767×10−6CZ2+8.596×10−6DP2+9.302×  10−6FD2+9.779×10−6DM2+7.713×10−6DZ2
式中：y为泄漏占比适应度函数．
3.3　多目标优化寻优算法
遗传算法、蚁群算法、粒子群优化算法和模拟退火算法都是当前解决多目标优化问题常用方法．遗传算法具有高普适性，不容易受到局部最优解的影响，其中带有精英策略的快速非支配排序遗传算法NSGA-Ⅱ提高了算法的运算速度和鲁棒性[15]，故采用该方法对问题进行求解．NSGA-Ⅱ算法流程示意如图11所示．基于建立的近似模型，采用遗传算法进行寻优求解．在寻优求解过程中，泄漏占比适应度函数的适应度曲线迭代至2986步时收敛，如图12所示．其他参数曲线的收敛迭代数都小于4000步，故在modeFRONTIER初始化化模块中设置总迭代数目为4000．
最终求得符合条件的解为2358组，进一步筛选数据，求得Pareto前沿示意如图13所示．结果表明，开、闭响应时间和泄漏占比三者之间存在着矛盾关系，在其中某一个参数达到最小值时总会伴随着其他某参数数值的提升．
通过最小距离法求取最优解[16]，优化后模型性能参数值如表4和表5所示．对比原始模型，在控制脉宽为100MPa、轨压为1.0ms的下限工况下，泄漏占比上升了11.23%，开启响应时间下降了1.68%，关闭响应时间下降了11.46%；优化模型在轨压为150MPa、控制脉宽为1.5ms的中间工况下，泄漏占比上升了8.67%，开启响应时间下降了0.73%，关闭响应时间下降了10.44%；在控制脉宽为200MPa、轨压为2.0ms的上限工况下，泄漏占比上升了8.26%，开启响应时间下降了0.73%，关闭响应时间下降了10.44%．
4　结论
收起
(1) 设计了一种带有三量孔板的无静态泄漏高压共轨电控喷油器，并将其喷油速率和循环喷油量的仿真数据与参照喷油器的试验数据进行对比显示，各工况下仿真结果与试验结果的相关性均大于0.95，验证了仿真模型的可靠性以及笔者所设计喷油器的可行性．
(2) 引入“泄漏占比”以客观评价喷油器的燃油泄漏程度，并结合开启和关闭响应时间作为喷油器的性能指标，开展结构参数的单因素影响分析；各参数的综合影响程度排序依次为：针阀柱塞直径、出油节流孔直径、进油节流孔直径、控制阀回油孔直径、第三节流孔直径、针阀最大升程、喷孔直径、密封球直径、电磁阀弹簧预紧力、针阀弹簧刚度、针阀弹簧预紧力、电磁阀升程和电磁阀弹簧刚度．
(3) 基于快速非支配排序遗传算法(NSGA-Ⅱ)，以影响程度较大的9个结构参数作为决策变量，泄漏占比、开启响应时间和关闭响应时间作为目标变量，进行了多目标优化表明，与原始模型相比，关闭响应时间减少了11.46%，开启响应时间减少了1.68%，但泄漏占比增加了11.23%．
基金
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  • 重庆市教育委员会科技资助项目(KJQN202300704)
  • 重庆市自然科学基金面上资助项目(CSTB2024NSCQ-MSX0927)
文献
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2025年第43卷第5期
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doi: 10.16236/j.cnki.nrjxb.202505051
  • 接收时间：2024-12-19
  • 首发时间：2026-11-05
  • 出版时间：2025-09-25
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  • 收稿日期：2024-12-19
  • 修回日期：2025-05-11
基金
重庆市教育委员会科技资助项目(KJQN202300704)
重庆市自然科学基金面上资助项目(CSTB2024NSCQ-MSX0927)
作者信息
    1.重庆交通大学航运与船舶工程学院，重庆 400074
    2.哈尔滨工程大学动力与能源工程学院，哈尔滨 150006

通讯作者:

范立云，博士，教授，
代蓥笛，博士研究生，.
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