振动与冲击
振動與遲擊
진동여충격
JOURNAL OF VIBRATION AND SHOCK
2014年
3期
191-196,202
,共7页
段绪伟%李以农%郑玲%邓召学
段緒偉%李以農%鄭玲%鄧召學
단서위%리이농%정령%산소학
磁流变悬置%集总参数%优化%倍程区间灵敏度%NSGA-II算法
磁流變懸置%集總參數%優化%倍程區間靈敏度%NSGA-II算法
자류변현치%집총삼수%우화%배정구간령민도%NSGA-II산법
MR mount%lumped parameters%optimization%multiple interval sensitivity%NSGA-II algorithm
磁流变悬置集总参数优化是设计高性能发动机悬置的关键。为克服以往悬置优化中优化目标单一、优化目标选取不合理、未考虑实际加工可行性等问题,建立单自由度磁流变悬置隔振系统数学模型,提出倍程区间灵敏度分析法,对各集总参数灵敏度进行分析,并以此为依据选取优化变量。以发动机常用转速激振频率段的力传递率积分为优化目标,采用改进型非支配排序遗传算法(NSGA-II)进行多目标优化。在一定范围内将结构尺寸进行离散化处理,计算各组离散尺寸对应的集总参数值,以离散集总参数与集总参数Pareto非劣解之间的综合距离为准则筛选最优解。
磁流變懸置集總參數優化是設計高性能髮動機懸置的關鍵。為剋服以往懸置優化中優化目標單一、優化目標選取不閤理、未攷慮實際加工可行性等問題,建立單自由度磁流變懸置隔振繫統數學模型,提齣倍程區間靈敏度分析法,對各集總參數靈敏度進行分析,併以此為依據選取優化變量。以髮動機常用轉速激振頻率段的力傳遞率積分為優化目標,採用改進型非支配排序遺傳算法(NSGA-II)進行多目標優化。在一定範圍內將結構呎吋進行離散化處理,計算各組離散呎吋對應的集總參數值,以離散集總參數與集總參數Pareto非劣解之間的綜閤距離為準則篩選最優解。
자류변현치집총삼수우화시설계고성능발동궤현치적관건。위극복이왕현치우화중우화목표단일、우화목표선취불합리、미고필실제가공가행성등문제,건립단자유도자류변현치격진계통수학모형,제출배정구간령민도분석법,대각집총삼수령민도진행분석,병이차위의거선취우화변량。이발동궤상용전속격진빈솔단적력전체솔적분위우화목표,채용개진형비지배배서유전산법(NSGA-II)진행다목표우화。재일정범위내장결구척촌진행리산화처리,계산각조리산척촌대응적집총삼수치,이리산집총삼수여집총삼수Pareto비렬해지간적종합거리위준칙사선최우해。
To achieve a high performance,the design optimization of lumped parameters for a magneto-rheological (MR)engine mount is essential.Mathematical model of a single DOF vibration isolation system was established and the multiple interval sensitivity method was proposed to overcome drawbacks of conventional optimization designs,such as, single objective optimization,improper optimization objective,unfeasible machining and so on.Optimization variables in a MR engine mount were selected with a lumped parameter multiple interval sensitivity analysis.The integral of force transmissibility within normal frequency ranges of an engine was assigned as an objective function,the non-dominated sorting genetic algorithm (NSGA-II)was improved and used to optimize design variables.The synthesized distances between Pareto lumped parameters and discontinuous lumped parameters matching along with physical discretization dimensions were calculated to select the most appropriate solution from Pareto lumped parameters.