大弯角串列叶型形状及相对位置的耦合优化设计
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摘要
为了提高串列叶型设计的质量,建立了一套结合改进微粒群优化算法、自适应Kriging模型、非均匀有理B样条(NURBS)参数化方法的串列叶型优化设计系统。该系统可以实现叶型形状和叶型相对位置的耦合优化设计。提出了一种改进微粒群优化算法。在微粒群算法中,自适应改变微粒的惯性因子、学习因子、邻域微粒数目可以有效地平衡算法的全局和局部寻优能力。采用人工免疫算子对微粒群进行变异处理可以有效保持种群多样性。运用NURBS方法实现了串列叶型的参数化,设计了一种NURBS控制点的扰动方法,证明了改进EI(expected improvement)准则能使Kriging模型更容易跳出局部最优解。应用该系统优化某大弯角串列叶型,优化结果表明:在设计工况,优化后叶型的总压损失系数降低了40.4%,优化后的叶型在全攻角下的总压损失系数减小了,静压升增加了,在正攻角下的性能改善更明显,证明了该研究的耦合优化设计方法具有很好的实际应用价值。
To improve the design quality of tandem blade,an automatic optimization system of tandem blade was developed based on an improved particle swarm optimization algorithm(IPSO),adaptive Kriging model and non-uniform rational B-splines(NURBS)method.The optimization system can be used to realize the coupling optimization for shape and relative position of tandem blade.Given that Particle swarm optimization(PSO)algorithm has the advantage of fast convergence speed and may also fall into local optimal solution,an improved particle swarm optimization algorithm was proposed.It can effectively balance the global and local searching ability of PSO by adaptively changing the inertia factor,learning factor,and the number of neighborhood particles.The artificial immune operator can effectively maintain the population diversity of PSO.In addition,NURBS method was used to parameterize tandem blade,and a perturbation method of NURBS control points wasdesigned.It was proved that the improved expected improvement(EI)criterion can make Kriging more easily jump out of the local optimal solution.The optimization system was validated by optimizing a large-turning tandem blade.Results indicate that,as compared with original tandem blade,at design condition,the total pressure loss coefficient of the optimized blade decreased by 40.4%.Besides,the static pressure ratio of optimized blade was higher and the total pressure loss coefficient was smaller at all incidence conditions.The performance of optimized blade was largely improved at positive incidence.It also proved that the coupling optimization design method of tandem blade had a good application value.
To improve the design quality of tandem blade,an automatic optimization system of tandem blade was developed based on an improved particle swarm optimization algorithm(IPSO),adaptive Kriging model and non-uniform rational B-splines(NURBS)method.The optimization system can be used to realize the coupling optimization for shape and relative position of tandem blade.Given that Particle swarm optimization(PSO)algorithm has the advantage of fast convergence speed and may also fall into local optimal solution,an improved particle swarm optimization algorithm was proposed.It can effectively balance the global and local searching ability of PSO by adaptively changing the inertia factor,learning factor,and the number of neighborhood particles.The artificial immune operator can effectively maintain the population diversity of PSO.In addition,NURBS method was used to parameterize tandem blade,and a perturbation method of NURBS control points wasdesigned.It was proved that the improved expected improvement(EI)criterion can make Kriging more easily jump out of the local optimal solution.The optimization system was validated by optimizing a large-turning tandem blade.Results indicate that,as compared with original tandem blade,at design condition,the total pressure loss coefficient of the optimized blade decreased by 40.4%.Besides,the static pressure ratio of optimized blade was higher and the total pressure loss coefficient was smaller at all incidence conditions.The performance of optimized blade was largely improved at positive incidence.It also proved that the coupling optimization design method of tandem blade had a good application value.
引文
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[3]LI Q,HONG W,SHENG Z.Application of tandem cascade to design of fan stator with supersonic inflow[J].Chinese Journal of Aeronautics,2010,23(1):9-14.
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[7]ROY B,SAHA U K.On the application of variable camber blading in axial flow fans and compressors[R].ASME Paper 96-TA-58,1996.
[8]WU G X,ZHUANG B,GUO B.Experimental investigations of tandem blade cascades with double circular arc profiles[R].ASME Paper 85-IGT-94,1985.
[9]REHAN S,ROY B.Gap optimization for tandem blades in axial flow compressor/fan using computational tools[R].AIAA-2007-5024,2007.
[10]BAMMERT K,STAUDE R.Optimization for rotor blades of tandem design for axial flow compressors[R].ASME Paper 79-GT-125,1979.
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[14]赵斌,刘宝杰.跨声串列转子及前后排叶片匹配特性分析[J].航空学报,2011,32(6):978-987.ZHAO Bin,LIU Baojie.Analysis of transonic tandem rotor and matching characteristic of forward and aft blades[J].Acta Aeronautica et Astronautica Sinica,2011,32(6):978-987.(in Chinese)
[15]赵斌,刘宝杰.前、后排叶片相对位置对串列转子性能的影响[J].推进技术,2012,33(1):26-36.ZHAO Bin,LIU Baojie.Effects of relative geometry position of forward and aft blades on performance of tandem rotor[J].Journal of Propulsion Technology,2012,33(1):26-36.(in Chinese)
[16]宋召运,刘波,程昊,等.基于改进粒子群算法的串列叶型优化设计[J].推进技术,2016,37(8):1469-1476SONG Zhaoyun,LIU Bo,CHENG Hao,et al.Optimization of tandem blade based on modified particle swarm algorithm[J].Journal of Propulsion Technology,2016,37(8):1469-1476.(in Chinese)
[17]薛亮,韩万金.基于遗传算法与近似模型的全局气动优化方法[J].推进技术,2008,29(3):360-366.XUE Liang,HAN Wanjin.Global aerodynamic optimization method using genetic algorithms and surrogate model[J].Journal of Propulsion Technology,2008,29(3):360-366.(in Chinese)
[18]SINKYU J,MITSUHIRO M,KAZUOMI Y.Efficient optimization design method using Kriging model[R].AIAA-2004-118,2004.
[19]LIU J,SONG W P,HAN Z H,et al.Efficient aerodynamic shape optimization of transonic wings using aparallel infilling strategy and surrogate models[J].Structural and Multidisciplinary Optimization,2017,55(3):925-943.
[20]KENNEDY J,EBERHART R.Particle swarm optimization[R].Washington,US:IEEE International Conference on Neural Networks,2002.
[21]LIANG J J,SUGANTHAN P N.Dynamic multi-swarm particle swarm optimizer[R].Washington,US:IEEE Swarm Intelligence Symposium,2005.
[22]MENDES R,KENNEDY J,NEVES J.The fully informed particle swarm:simpler,maybe better[J].IEEE Transactions on Evolutionary Computation,2004,8(3):204-210.
[23]DEOCA M A M,STUTZLE T,BIRATTARI M,et al.Frankenstein's PSO:a composite particle swarm optimization algorithm[J].IEEE Transactions on Evolutionary Computation,2009,13(5):1120-1132.
[24]ZHAN Z H,ZHANG J,LI Y,et al.Adaptive particle swarm optimization[J].IEEE Transactions on Systems,Man,and Cybernetics,Part B:Cybernetics,2009,39(6):1362-1380.
[25]JONES D R,SCHONLAU M,WELCH W J.Efficient global optimization of expensive blackbox funcitons[J].Journal of Global Optimization,1998,13(4):455-482.
[2]MUELLER L,KOZULOVIC D,WULFF D,et al.High turning compressor tandem cascade for high subsonic flows:Part 2 numerical and experimental investigations[R].AIAA-2011-5602,2011.
[3]LI Q,HONG W,SHENG Z.Application of tandem cascade to design of fan stator with supersonic inflow[J].Chinese Journal of Aeronautics,2010,23(1):9-14.
[4]周正贵,吴国钏.串列叶栅尾迹特性的实验研究[J].南京航空航天大学学报,1994(4):555-559.ZHOU Zhenggui,WU Guoxun.Experimental investigation on wake characteristics of tandem cascades[J].Journal of Nanjing University of Aeronautics and Astronautics,1994(4):555-559.(in Chinese)
[5]周正贵,吴国钏.自由流湍流度对串列叶栅性能的影响[J].航空动力学报,1996,11(1):1-3.ZHOU Zhenggui,WU Guoxun.Influence of the free flow turbulence on the performance of cascade cascades[J].Journal of Aerospace Power,1996,11(1):1-3.(in Chinese)
[6]ROY B,SAHA U K.Experimental analysis of controlled diffusion compressor cascades with single and tandem airfoils[R].ASME Paper 95-CTP-41,1995.
[7]ROY B,SAHA U K.On the application of variable camber blading in axial flow fans and compressors[R].ASME Paper 96-TA-58,1996.
[8]WU G X,ZHUANG B,GUO B.Experimental investigations of tandem blade cascades with double circular arc profiles[R].ASME Paper 85-IGT-94,1985.
[9]REHAN S,ROY B.Gap optimization for tandem blades in axial flow compressor/fan using computational tools[R].AIAA-2007-5024,2007.
[10]BAMMERT K,STAUDE R.Optimization for rotor blades of tandem design for axial flow compressors[R].ASME Paper 79-GT-125,1979.
[11]SANGER N L.Analytical study of the effects of geometric changes on the flow characteristics of tandem-bladed compressor stators[R].NASA TN-D-6264,1971.
[12]HAUT R C.Experimental study of tandem blades for rotor blade usage in a single stage axial flow compressor[D].Knoxville:University of Tennessee,1975.
[13]SACHMANN J,FOTTNER L.Highly loaded tandem compressor cascade with variable camber and stagger[R].ASME Paper 93-GT-235,1993.
[14]赵斌,刘宝杰.跨声串列转子及前后排叶片匹配特性分析[J].航空学报,2011,32(6):978-987.ZHAO Bin,LIU Baojie.Analysis of transonic tandem rotor and matching characteristic of forward and aft blades[J].Acta Aeronautica et Astronautica Sinica,2011,32(6):978-987.(in Chinese)
[15]赵斌,刘宝杰.前、后排叶片相对位置对串列转子性能的影响[J].推进技术,2012,33(1):26-36.ZHAO Bin,LIU Baojie.Effects of relative geometry position of forward and aft blades on performance of tandem rotor[J].Journal of Propulsion Technology,2012,33(1):26-36.(in Chinese)
[16]宋召运,刘波,程昊,等.基于改进粒子群算法的串列叶型优化设计[J].推进技术,2016,37(8):1469-1476SONG Zhaoyun,LIU Bo,CHENG Hao,et al.Optimization of tandem blade based on modified particle swarm algorithm[J].Journal of Propulsion Technology,2016,37(8):1469-1476.(in Chinese)
[17]薛亮,韩万金.基于遗传算法与近似模型的全局气动优化方法[J].推进技术,2008,29(3):360-366.XUE Liang,HAN Wanjin.Global aerodynamic optimization method using genetic algorithms and surrogate model[J].Journal of Propulsion Technology,2008,29(3):360-366.(in Chinese)
[18]SINKYU J,MITSUHIRO M,KAZUOMI Y.Efficient optimization design method using Kriging model[R].AIAA-2004-118,2004.
[19]LIU J,SONG W P,HAN Z H,et al.Efficient aerodynamic shape optimization of transonic wings using aparallel infilling strategy and surrogate models[J].Structural and Multidisciplinary Optimization,2017,55(3):925-943.
[20]KENNEDY J,EBERHART R.Particle swarm optimization[R].Washington,US:IEEE International Conference on Neural Networks,2002.
[21]LIANG J J,SUGANTHAN P N.Dynamic multi-swarm particle swarm optimizer[R].Washington,US:IEEE Swarm Intelligence Symposium,2005.
[22]MENDES R,KENNEDY J,NEVES J.The fully informed particle swarm:simpler,maybe better[J].IEEE Transactions on Evolutionary Computation,2004,8(3):204-210.
[23]DEOCA M A M,STUTZLE T,BIRATTARI M,et al.Frankenstein's PSO:a composite particle swarm optimization algorithm[J].IEEE Transactions on Evolutionary Computation,2009,13(5):1120-1132.
[24]ZHAN Z H,ZHANG J,LI Y,et al.Adaptive particle swarm optimization[J].IEEE Transactions on Systems,Man,and Cybernetics,Part B:Cybernetics,2009,39(6):1362-1380.
[25]JONES D R,SCHONLAU M,WELCH W J.Efficient global optimization of expensive blackbox funcitons[J].Journal of Global Optimization,1998,13(4):455-482.