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基于Atkinson循环发动机的燃油消耗率优化方法对比研究
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Comparative Study on Optimization Methods Based on FuelConsumption Rate of Atkinson Cycle Engine
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    针对利用GT-Power详细模型耦合遗传算法(方案1)优化Atkinson循环发动机燃油消耗率时,存在公认的不易收敛且计算缓慢的问题,提出了神经网络简化模型耦合遗传算法(方案2)进行全局优化并与方案1进行比对.方案1利用GT-Power搭建某Atkinson循环发动机详细仿真模型,应用Heywood公式建立爆震预测模型,并耦合遗传算法对Atkinson循环发动机燃油消耗率进行优化;方案2则利用拉丁超立方算法采集4 500个实验点,将GT-Power详细模型及爆震模型简化为神经网络模型,通过简化模型耦合遗传算法进行优化.研究结果表明:利用方案2可以将Atkinson循环发动机的实际燃油消耗率最多降低4.6%,且仿真优化结果相对实际优化结果的最大误差率仅为7.3%,同时相对于方案1仿真优化时间最大可节省322倍.因此,采用方案2替代方案1用于Atkinson循环发动机燃油消耗率的快速全局优化是切实可行的方法.

    Abstract:

    The fuel consumption rate of Atkinson cycle engine has always been optimized by GT-Power detail model with Genetic Algorithm (GA) (scheme 1),but this method is hard to converge and its calculation is very slow,so a simplified model coupling GA based on Artificial Neural Network (ANN) (scheme 2) for optimization and comparison was proposed.A detailed simulation model based on GT-Power software for Atkinson cycle engine had been carefully built by scheme 1,and the Knock prediction model was built based on Heywood formula.The fuel consumption was then optimized by scheme 1.Scheme 2 used the Latin Hypercube Sample (LHS) to collect 4 500 experimental points,and simplified the GT-Power model and Knock model into the ANN model,which was optimized by the simplified coupled GA model.The results show that by using scheme 2 for the optimization of Atkinson cycle engine,the actual fuel consumption rate is reduced by 4.6%,and the maximum error rate related to the measured optimization results is 7.3%,while the maximum simulation optimization time was saved 322 times that for scheme 1.As a result,the fast global optimization of the fuel consumption rate for the Atkinson cycle engine is feasible by using scheme 2 rather than scheme 1.

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杨靖,薛川,刘凯敏,江武,张纯标,冯仁华.基于Atkinson循环发动机的燃油消耗率优化方法对比研究[J].湖南大学学报:自然科学版,2017,44(8):15~22

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  • 在线发布日期: 2017-09-20
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