无人机活塞式发动机进排气系统优化

胡春明; 郝蒙蒙

doi:10.13224/j.cnki.jasp.2018.04.028

无人机活塞式发动机进排气系统优化

doi: 10.13224/j.cnki.jasp.2018.04.028

胡春明¹,
郝蒙蒙²

1.
天津大学天津内燃机研究所，天津 300072；天津大学机械工程学院内燃机燃烧学国家重点实验室，天津 300072
2.
天津大学天津内燃机研究所，天津 300072

基金项目: 国家自然科学基金（51476112）

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出版历程
- 收稿日期: 2016-10-10
- 刊出日期: 2018-04-28

Intake and exhaust system optimization of UAV piston engine

摘要

摘要: 针对一款无人机(UAV)用活塞发动机在飞行转速为6500r/min时扭矩较低以及燃油消耗率较高的问题，提出了一种基于自适应遗传算法（GA）的发动机进排气系统优化方法，进行进排气系统改进设计。使用GT-Power软件搭建了该发动机一维仿真模型，并通过台架试验数据验证模型；基于该模型进行了进排气系统结构参数对扭矩和燃油消耗率的敏感性分析，将进气管长度、直径、空滤器后腔容积和排气管长度作为优化变量，使用Matlab进行自适应遗传算法优化，使用Simulink/GT-Power接口实现数据采集和优化结果反馈。通过台架试验验证了优化结果的准确性。结果表明:在飞行转速为6500r/min时,经过优化后的发动机扭矩和燃油消耗率都得到明显改善，扭矩最大可以提高5.51%，燃油消耗率最大降低6.31%。
- 无人机(UAV) /
- 活塞式发动机 /
- 自适应遗传算法 /
- 敏感性分析 /
- 进排气系统优化
Abstract: To solve the problem of torque decrease and high fuel consumption at 6500r/min flying speed of an unmanned aerial vehicle for plant protection, a multi-parameter optimization method using adaptive genetic algorithm(GA) based on 1-D thermodynamic simulation model was proposed to optimize the intake and exhaust system of single cylinder gasoline engine. GT-Power model of the UAV engine was built to simulate the engine working condition. The model was calibrated by experimental bench data to ensure the accurate simulation. Influence of structure parameters of intake and exhaust system on torque and fuel consumption was analyzed through design of experiment, and length of intake pipe and exhaust pipe, diameter of intake pipe and second cavity volume of air filter were chosen as optimization variables. The model was used to calculate the objective optimization function and limit conditions. Adaptive genetic algorithm was executed by Matlab which exchanged data by interface of Simulink/GT-Power to receive and feedback results. According to the optimization result of adaptive GA, new intake and exhaust system was mounted on the engine to verify optimization result. Result demonstrated that torque and fuel consumption at flying speed of 6500r/min of the engine was obviously improved by adaptive GA, and torque was raised by 5.51% and fuel consumption was improved by 6.31%.
- unmanned aerial vehicle(UAV) /
- piston engine /
- adaptive genetic algorithm (GA) /
- sensitivity analyze /
- intake and exhaust system optimization

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参考文献(17)

[1]	HEYWOOD J B.Internal combustion engine fundamentals［M］.New York:McGraw-Hill Book Company,1988.
[2]	徐斌,刘波,杨世春.某型航空活塞发动机进排气系统优化分析［J］.航空动力学报,2014,29(3):624-630.XU Bin,LIU Bo,YANG Shichun.Optimization analysis of a aircraft piston engine intake and exhaust system［J］.Journal of Aerospace Power,2014,29(3):624-630.(in Chinese)
[3]	KUTLAR O A,ARSLAN H,CALIK A T.Methods to improve efficiency of four stroke,spark ignition engines at part load［J］.Energy Conversion and Management,2005,46(20):3202-3220.
[4]	WU B,PRUCKA R G,PHILIPI Z S.Cam phasing optimization using artificial neural network as surrogate models-maximizing torque output［R］.Society of Automotive Engineers,SAE Paper No.2005-01-3757,2005.
[5]	NEWLYN H A.An analysis of the probable effects of engine geometry on the inlet pipe pressure,flow and engine volumetric efficiency［R］.Society of Automotive Engineers,SAE Paper No.1994-12-01,1994.
[6]	牛有城,李国岫.基于遗传算法的直喷式柴油机燃烧系统参数优化分析［J］.内燃机工程,2010,31(2):37-40.NIU Youcheng,LI Guoxiu.Optimization of combustion system parameters in direct injection diesel engine based on genetic algorithm［J］.Chinese Internal Combustion Engine Engineering,2010,31(2):37-40.(in Chinese)
[7]	MARTNEZ-MORALES J,PALACIOS-HERNNDEZ E R,VELZQUEZ-CARRILLO G A.Modeling and multi-objective optimization of a gasoline engine using neural networks and evolutionary algorithms［J］.Journal of Zhejiang University:A,2013,14(9):657-670.
[8]	TSOULOS I G.Solving constrained optimization problems using a novel genetic algorithm［J］.Applied Mathematics and Computation,2009,208:273-283.
[9]	DERRICO G,CERRI T,PERTUSI G.Multi-objective optimization of internal combustion engine by means of 1D fluid-dynamic models［J］.Applied Energy,2011,88(3):767-777.
[10]	SUN Y,WANG T,LU Z,et al.The optimization of intake port using genetic algorithm and artificial neural network for gasoline engines［R］.Society of Automotive Engineers,SAE Paper No.2015-01-1353,2015.
[11]	ZHAO J,XU M,ZHAO J,et al.Fuel economy optimization of an Atkinson cycle engine using genetic algorithm［J］.Applied Energy,2013,105(2):348-357.
[12]	YANG X,CHENG L,LIU J.Harmonic analysis and optimization of the intake system of a gasoline engine using GT-power［J］.Energy Procedia,2012,14(1):756-762.
[13]	周龙保.内燃机学［M］.北京:机械工业出版社,2010.
[14]	王勇,刘敬平,王正球,等.基于过程模拟技术和先进统计学方法的多参数发动机性能优化［J］.燃烧科学与技术,2015,21(2):141-149.WANG Yong,LIU Jingping,WANG Zhengqiu,et al.Optimization of multiple parameters engine performance based on process simulations and advanced statistics methods［J］.Journal of Combustion Science and Technology,2015,21(2):141-149.(in Chinese)
[15]	蔡海鸾,郭学萍.一种新的自适应惩罚函数在遗传算法中的应用［J］.华东师范大学学报(自然科学版),2015(6):36-45.CAI Hailuan,GUO Xueping.A new adaptive penalty function in the application of genetic algorithm［J］.Journal of East China Normal University (Natural Science),2015(6):36-45.(in Chinese)
[16]	MARCZYK A. Genetic algorithms and evolutionary computation［EB/OL］.［2004-04-23］.http://www.talkorigins.org/faqs/genalg/genalg.html.
[17]	赵金星,许敏,李冕,等.基于模型的Atkinson循环发动机燃油经济性遗传算法优化［J］.内燃机工程,2014,35(5):58-63.ZHAO Jinxing,XU Min,LI Mian,et al.Model-based genetic algorithm optimization for fuel economy of Atkinson cycle engine［J］.Chinese Internal Combustion Engine Engineering,2014,35(5):58-63.(in Chinese)