高压比离心叶轮气动强度多学科优化与知识挖掘
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摘要
通过引入数据挖掘技术,耦合RANS方程求解技术、有限元分析方法、自适应多目标差分进化算法和非均匀B样条曲面造型方法,提出了高压比离心叶轮多学科多目标设计优化方法。该方法包括:利用研发的优化平台获取给定设计空间内性能最优的解,涉及优化算法、参数化方法、约束处理和多学科性能评估4个模块,前3个模块通过自主编程完成,最后1个模块通过编写软件接口、利用NUMECA和ANSYS软件生成;利用数据挖掘技术,如总变差分析,对设计空间进行知识挖掘,由此识别出对性能影响显著的变量。以等熵效率最高和叶轮表面最大离心应力最小为目标,开展了典型高压比离心叶轮SRV2-O的优化设计,优化后等熵效率提高了2.00%,最大应力降低了2.08%;通过对优化设计的分析和对设计空间的知识挖掘知,对类似高压比离心叶轮进行设计时,应重点控制扩压段轮盖的设计以减小叶顶泄漏流的影响,通过减小流动通道面积以减小泄漏流的损失。该结果可为类似离心叶轮设计提供借鉴。
Integrating data mining techniques,RANS solver technique,finite element method(FEM),self-adaptive multi-objective differential evolution(SMODE)algorithm and B-spline surface parameterization method,a multidisciplinary and multi-objective design optimization strategy for centrifugal impeller is proposed to obtain the optimal properties in given design space on an optimization platform.This strategy includes modules optimization algorithm,parameterization method,constraint handling and multidisciplinary assessment.The first three modules are established by self-coding program,and the last module is implemented by the software NUMECA and ANSYS.Data mining techniques,like total variation analysis,are introduced to discover the significant variables in design space,the design optimization is carried out for maximizing isentropic efficiency and minimizing the maximum stress in impeller surface of a typical high pressure ratio centrifugal impeller SRV2-O.After optimization,the efficiency increases by 2.0% and the maximum stress lowers by 2.08%.Knowledge discovery of design space is carried out,and the effects of design variables on aerodynamic and mechanical performances of impeller are also analyzed.It is indicated that for centrifugal impeller withsimilar high pressure ratio,the shroud of diffuser section ought to be mainly controlled in design to weaken tip leakage flow,and flow passage area ought to be reduced to decrease leakage flow loss.
Integrating data mining techniques,RANS solver technique,finite element method(FEM),self-adaptive multi-objective differential evolution(SMODE)algorithm and B-spline surface parameterization method,a multidisciplinary and multi-objective design optimization strategy for centrifugal impeller is proposed to obtain the optimal properties in given design space on an optimization platform.This strategy includes modules optimization algorithm,parameterization method,constraint handling and multidisciplinary assessment.The first three modules are established by self-coding program,and the last module is implemented by the software NUMECA and ANSYS.Data mining techniques,like total variation analysis,are introduced to discover the significant variables in design space,the design optimization is carried out for maximizing isentropic efficiency and minimizing the maximum stress in impeller surface of a typical high pressure ratio centrifugal impeller SRV2-O.After optimization,the efficiency increases by 2.0% and the maximum stress lowers by 2.08%.Knowledge discovery of design space is carried out,and the effects of design variables on aerodynamic and mechanical performances of impeller are also analyzed.It is indicated that for centrifugal impeller withsimilar high pressure ratio,the shroud of diffuser section ought to be mainly controlled in design to weaken tip leakage flow,and flow passage area ought to be reduced to decrease leakage flow loss.
引文
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[3]刘波,杨晰琼.带分流叶片离心压气机优化设计[J].推进技术,2014,35(11):1461-1468LIU Bo,YANG Xiqiong.Optimization design of a centrifugal compressor with splitters[J].Journal of Propulsion Technology,2014,35(11):1461-1468.
[4]SILLER U,VOC,NICKE E.Automated multidisciplinary optimization of a transonic axial compressor[C]∥47th AIAA Aerospace Sciences Meeting.Reston,VA,USA:AIAA,2009:94765.
[5]VERSTRAETE T,ALSALIHI Z,BRAEM-BUSSCHE R A V D,et al.Multidisciplinary optimization of a radial compressor for micro gas turbine application[J].ASME Journal of Turbomachinery,2010,132(2),1291-1299.
[6]CHIBA K,OYAMA A,OBAYASHI S,et al.Multidisciplinary design optimization and data mining for transonic regional-jet wing[J].Journal of Aircraft,2007,44(4):1100-1112.
[7]OYAMA A,NONOMURA T,FUJII K.Data mining of Pareto-optimal transonic airfoil shapes using proper orthogonal decomposition[J].Journal of Aircraft,2010,47(5):1756-1762.
[8]OYAMA A,KAWAKATSU Y.Data mining of Pareto-optimal solutions of a solar observatory trajectory design problem[C]∥AIAA Infotech at Aerospace Conference and Exhibit.Reston,VA,USA:AIAA,2012:2442.
[9]KIPOUROS T,MLECZKO M,SAVILL A M.Use of parallel coordinates for post-analyses of multi-objective aerodynamic design optimisation in turbomachinery[C]∥4th AIAA Multidisciplinary Design Optimization Specialist Conference.Reston,VA,USA:AIAA,2008:2138.
[10]SLAWOMIR K,TESFAHUNEGN Y A,AMRIT A,et al.Rapid multi-objective aerodynamic design using co-Kriging and space mapping[C]∥57th AIAA/ASCE/AHS/ASC Structures,Structural Dynamics,and Materials Conference.Reston,VA,USA:AIAA,2015:0418.
[11]KRAIN H.Swirling impeller flow[J].ASME Journal of Turbomachinery,1988,110(1):122-128.
[12]KANG S.Numerical investigation of a high speed centrifugal compressor impeller[C]∥Proceedings of ASME Turbo Expo 2005.New York,USA:ASME,2005:813-821.
[13]徐秉业,刘信声.应用弹塑性力学[M].北京:清华大学出版社,1995:99-108.
[14]宋立明.基于进化算法的轴流式叶轮机械叶栅气动优化设计系统的研究[D].西安:西安交通大学,2006.
[15]SRINIVAS N,DEB K.Multiobjective optimization using nondominated sorting in genetic algorithms[J].Evolutionary Computation,1993,2(3):221-248.
[16]ROOSEN C B.Visualization and exploration of highdimensional functions using the functional ANOVA decomposition[D].Stanford,CA,USA:Standford University,1995.
[17]SHIMOYAMA K,SUGIMURA K,JEONG S,et al.Performance map construction for a centrifugal diffuser with data mining techniques[J].Journal of Computational Science and Technology,2010,4(1):36-50.
[18]KUMANO T.Multidisciplinary analysis and optimization for regional jet design[D].Sendai,Miyagi,Japan:Tohoku University,2009.
[19]GUO Z,SONG L,LI J,et al.Research on meta-model based global design optimization and data mining methods[C]∥ASME Turbo Expo 2015.New York,USA:ASME,2015:42554.
[20]SCHONLAU M,WELCH W J.Screening the input variables to a computer model via analysis of variance and visualization screening[M].Berlin,Germany:Springer,2006:308-327.
[21]JONES M S D R,WELCH W J.Efficient global optimization of expensive black-box functions[J].Journal of Global Optimization,1998,13:455-492.