苑光明,张龙飞,景国玺,等.某型柴油机活塞销孔型线多目标优化设计[J].内燃机工程,2023,44(4):77-84.
某型柴油机活塞销孔型线多目标优化设计
Multi-Objective Optimization Design of Piston Pinhole Profile of A Diesel Engine
DOI：10.13949/j.cnki.nrjgc.2023.04.010
关键词:活塞销孔  型线设计  代理模型  多目标优化
Key Words:piston pinhole  profile design  surrogate model  multi-objective optimization
基金项目:
作者单位E-mail
苑光明* 河北工业大学 机械工程学院，天津 300130 1642436550@qq.com 
张龙飞 河北工业大学 机械工程学院，天津 300130
天津市新能源汽车动力传动与安全技术重点实验室，天津 300130 		 
景国玺* 河北工业大学 机械工程学院，天津 300130
天津市新能源汽车动力传动与安全技术重点实验室，天津 300130 		okjgx@163.com 
张伟斌 滨州东海龙活塞有限公司，滨州 256600  
刘义朋 河北工业大学 机械工程学院，天津 300130
天津市新能源汽车动力传动与安全技术重点实验室，天津 300130 		 
韩梦瑜 河北工业大学 机械工程学院，天津 300130
天津市新能源汽车动力传动与安全技术重点实验室，天津 300130 		 
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摘要:针对柴油机向高功率密度方向发展对活塞销孔承载能力提出越来越高的要求，提出了活塞销孔型线多目标优化设计方法。首先，建立1/4活塞连杆组热–机耦合模型，通过对比仿真模型的销孔接触压力分布与台架试验下活塞销孔磨损情况，验证了仿真模型的准确性。随后，选取销孔型线参数为设计变量建立活塞销孔型线参数化模型，以活塞销孔峰值接触压力和销孔、内冷油腔、燃烧室喉口的最大等效应力为优化目标，在设计空间内进行优化拉丁超立方采样，结合响应目标值建立代理模型，并使用决定系数验证其精度。最后，采用第二代非劣排序遗传算法（NSGA-Ⅱ）对销孔型线进行多目标优化，获得了销孔型线优化设计参数。结果表明：在保证活塞其他重要部位应力取得较小值的情况下，优化后的销孔峰值接触压力降低了24.5%。
Abstract:With the development of diesel engines towards high power density， the bearing capacity of piston pinhole needs higher requirements. A multi-objective optimization design for piston pinhole profile was proposed. Firstly， the thermo-mechanical coupling simulation model of 1/4 piston connecting rod group was established and the bearing characteristics of piston pinhole were analyzed. And then the validity of the simulation model was verified by comparing the pinhole contact pressure distribution of simulation model with the pinhole wear under the bench test. Subsequently， the parametric model of pinhole profile was established and the pinhole profile parameters were selected as design variables. The pinhole peak contact pressure， the pinhole maximum Mises stress， the inner chamber maximum Mises stress， and the combustion chamber lip maximum Mises stress were set as the optimization objectives. The optimized Latin hypercube sampling method was used on uniformly sampling in the design space and the Kriging surrogate model was established with the response target value. And then the decision coefficient was used to verify the accuracy of the surrogate model. Finally， the optimal design parameters of pinhole profile were obtained， by tacking the elitist non-dominated sorting genetic algorithm （NSGA-Ⅱ） to optimize the pinhole profile. The optimization results show that the pinhole peak contact pressure was reduced by 24.5%， under the condition that the stress of other important parts of the piston was kept small.
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