利用数值模拟和多目标遗传算法优化熔喷狭槽流场  

Optimization Air Flow Field of the Melt Blowing Slot Die via Numerical Simulation and Multi-objective Genetic Algorithm

作　　者：刘博文[1] 曾泳春[1] 王云侠 易建设 

机构地区：[1]东华大学纺织学院,上海201620 [2]奉化市双盾纺织帆布实业有限公司,浙江奉化315505

出　　处：《东华大学学报（自然科学版）》2011年第6期683-688,707,共7页Journal of Donghua University(Natural Science)

基　　金：国家自然科学基金资助项目(10972052);高等学校全国优秀博士学位论文作者专项资金资助项目(2007B54);新世纪优秀人才支持计划资助项目(NCET-09-0285);上海市曙光计划资助项目(10SG33)

摘　　要：熔喷流场中速度和温度的变化直接影响纤维的拉伸.采用数值模拟和多目标遗传算法结合的方法,对狭槽模头结构参数进行优化,获得速度和温度衰减最小的熔喷流场.优化的数据是基于数值模拟的结果.优化的熔喷狭槽模头结构参数包括:气槽宽度、气槽角度、气槽间距和喷嘴深度.遗传算法在第50代时终止运算,得到最优模头结构参数.The velocity and temperature of air flow field in the melt blowing play direct role in fiber drawing. The combination of numerical simulation and multi-objective genetic algorithm is proposed to optimize structure parameters of slot die in order to obtain the lowest velocity decay and temperature decay in the air flow field. The data of optimization is based on the result of numerical simulation. Four main structure parameters including slot width, slot angle, head width, and setback are studied. The genetic algorithm operation ends at the 50th generation with the optimal structure parameters.

关 键 词：熔喷 数值模拟 狭槽模头 多目标遗传算法 

分 类 号：TS174.1[轻工技术与工程—纺织材料与纺织品设计]