DPF结构参数多目标优化  

Multi Objective Optimization of DPF Structural Parameters

作　　者：彭美春[1] 叶伟斌 李君平 黄文伟[2] PENG Mei-chun;YE Wei-bin;LI Jun-ping;HUANG Wen-wei(School of Electromechanical Engineering,Guangdong University of Technology,Guangdong Guangzhou 510006,Chi-na;School of Automotive and Transportation Engineering,Shenzhen Polytechnic,Guangdong Shenzhen 518055,China)

机构地区：[1]广东工业大学机电工程学院,广东广州510006 [2]深圳职业技术学院汽车与交通学院,广东深圳518055

出　　处：《机械设计与制造》2023年第6期206-211,共6页Machinery Design & Manufacture

基　　金：深圳市环境科研课题资助项目(2019XY1368STZF)。

摘　　要：以提高DPF捕集与再生性能为目的,进行柴油机颗粒捕集器(DPF)结构参数优化。以一款重型柴油货车为对象建立DPF性能仿真模型,基于车辆高速路行驶工况与发动机排气数据,仿真分析得出DPF载体长度、载体直径与捕集压降正相关,与到达峰值捕集效率时长负相关,孔目数、壁厚则相反;载体直径、孔目数、壁厚均与再生峰值温度负相关,载体长度、载体直径、孔目数、壁厚均与再生时长正相关。运用人工神经网络方法建立DPF结构参数与捕集再生性能参数间关系模型用于结构参数优化研究,采用多目标遗传算法寻找DPF最优结构参数。得到的优化后DPF载体长度缩短15%、孔目数减少33.3%、DPF捕集压降降低5.6%、达到峰值捕集效率时长缩短11.7%、再生时长缩短3.1%,再生峰值温度降低1.4%,DPF结构变小、捕集与再生性能优化明显。To improve the filtration and regeneration performance of DPF,the structural parameters of diesel particulate filter(DPF)were optimized.Taking a heavy diesel truck as the object,the DPF performance simulation model is established based on the vehicle highway driving conditions and engine exhaust data.The simulation results show that the carrier length and diameter of DPF are positively correlated with the capture pressure drop,negatively correlated with the time to reach the peak capture efficiency,and the number of holes and wall thickness are opposite.Carrier diameter,number of holes and wall thickness are negatively correlated with regeneration peak temperature,and carrier length,carrier diameter,number of holes and wall thickness are positively correlated with regeneration time.The relationship model between DPF structural parameters and capture regeneration performance parameters is established by using artificial neural network method for structural parameter optimization,and the multi-objective genetic algorithm is used to find the optimal structural parameters of DPF.The optimized DPF carrier length is shortened by 15%,the number of holes is reduced by 33.3%,the DPF capture pressure drop is reduced by 5.6%,the time to reach the peak capture efficiency is shortened by 11.7%,the regeneration time is shortened by 3.1%,the regeneration peak temperature is reduced by 1.4%,the DPF structure is reduced,and the capture and regeneration performance is optimized obviously.

关 键 词：柴油车颗粒捕集器(DPF) 结构参数 优化 人工神经网络 多目标遗传算法 

分 类 号：TH16[机械工程—机械制造及自动化] TK423[动力工程及工程热物理—动力机械及工程]