采用动态交互作用分析的叶轮机械优化算法研究

汪祺能; 宋立明; 郭振东; 李军; 丰镇平

doi:10.7652/xjtuxb202307013

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采用动态交互作用分析的叶轮机械优化算法研究

—————————————————————— | 更新时间：2025-07-09

- 采用动态交互作用分析的叶轮机械优化算法研究
- Study on Turbomachinery Optimization Algorithm Based on Dynamic Interaction Analysis
- 西安交通大学学报 2023年57卷第7期页码：139-150
- 作者机构：
  
  西安交通大学能源与动力工程学院,西安,710049
- 作者简介：
  
  : 2022-10-07。作者简介: 汪祺能(1997—),男,博士生
  郭振东(通信作者),男,助理教授。基金项目: 国家科技重大专项资助项目(2019-Ⅱ-0008-0028)
- 基金信息：
  
  : 2022-10-07。作者简介: 汪祺能(1997—),男,博士生;郭振东(通信作者),男,助理教授。基金项目: 国家科技重大专项资助项目(2019-Ⅱ-0008-0028);中国航空发动机集团产学研合作项目(HFZL2021CXY004);秦创原引用高层次创新创业人才项目(QCYRCXM-2022-210)。
- DOI：10.7652/xjtuxb202307013
  中图分类号： TK421.4
- 网络首发：2023-07-10，
  
  纸质出版：2023
- 稿件说明：
移动端阅览
汪祺能, 宋立明, 郭振东, 等. 采用动态交互作用分析的叶轮机械优化算法研究[J]. 西安交通大学学报, 2023,57(7):139-150.

WANG Qineng, SONG Liming, GUO Zhendong, et al. Study on Turbomachinery Optimization Algorithm Based on Dynamic Interaction Analysis[J]. 2023, 57(7): 139-150.
汪祺能, 宋立明, 郭振东, 等. 采用动态交互作用分析的叶轮机械优化算法研究[J]. 西安交通大学学报, 2023,57(7):139-150. DOI： 10.7652/xjtuxb202307013.

WANG Qineng, SONG Liming, GUO Zhendong, et al. Study on Turbomachinery Optimization Algorithm Based on Dynamic Interaction Analysis[J]. 2023, 57(7): 139-150. DOI： 10.7652/xjtuxb202307013.

摘要

针对当前基于代理模型的优化算法因遭遇“维度灾难”而难以开展叶轮机械高维优化的难题

提出了自适应厘清变量间关系的动态聚合全局优化算法。该算法能在不消耗额外样本的条件下获取设计变量间的交互信息

并基于该信息一方面将高维问题分解为多个低维问题

实现高维问题的高效优化

另一方面为后续知识挖掘提供信息基础。在完成典型30维函数算例测试后

利用所提出的算法完成了包含28个设计变量的Rotor 37叶栅气动优化与设计空间知识挖掘。结果表明

优化后

Rotor 37叶栅设计工况效率相对参考设计提高了1.69%

且变工况性能相对参考设计亦有不同程度提高。在获得最优解的同时

所提出的算法还厘清了Rotor 37叶栅设计空间各变量间交互作用关系。进一步

结合算法所揭示的变量交互作用进行知识挖掘与CFD验证

证明对分离线附近型线进行微调可有效降低激波与边界层相互作用

从而提高跨声速叶栅的气动性能。由此

所提出的算法在求解高维大资源叶轮机械优化设计问题上的适用性和有效性得到验证。

Abstract

Due to the curse of dimensionality

it is difficult to perform high-dimensional optimization of turbomachinery with current surrogate-based optimization algorithm. To address the problem

a dynamic aggregation global optimization algorithm

which adaptively clarifies the variable interactions

was proposed. The proposed algorithm obtains the information on interactions between design variables without consuming additional samples. Based on this information

the high-dimensional problem is decomposed into multiple low-dimensional problems for efficient optimization. In addition

such information also serves as the basis for subsequent knowledge mining. After finishing the test on a 30-dimensional mathematical benchmark problem

the proposed algorithm is used for optimization and design space knowledge mining of the Rotor 37 compressor blade with 28 design variables. After optimization

the isentropic efficiency of Rotor 37 blade is increased by 1.69% at design condition; the aerodynamic performance of optimized design at off-design conditions is also improved. While obtaining the optimal solution

the proposed algorithm also reveals the interactions among variables in the design space of Rotor 37 blades

and knowledge mining and CFD verification are further carried out based on the variable interactions revealed. It shows that

the fine tuning of the profile near the separation line will effectively reduce the interactions between the shock waves and boundary layer

thus improving the aerodynamic performance of the transonic blade. Therefore

the applicability and effectiveness of the proposed algorithm in optimization of expensive high-dimensional turbomachinery design are well demonstrated.

关键词

Keywords

references

HE Wanxin, LI Gang, NIE Zhaokun. An adaptive sparse polynomial dimensional decomposition based on Bayesian compressive sensing and cross-entropy [J]. Structural and Multidisciplinary Optimization, 2022, 65(1): 26.

席光, 王志恒, 王尚锦. 叶轮机械气动优化设计中的近似模型方法及其应用 [J]. 西安交通大学学报, 2007, 41(2): 125-135, 184.

XI Guang, WANG Zhiheng, WANG Shangjin. Aerodynamic optimization design of turbomachinery with approximation model method [J]. Journal of Xi'an Jiaotong University, 2007, 41(2): 125-135, 184.

郭振东, 宋立明, 李军, 等. 基于子元模型的全局优化与设计空间知识挖掘方法 [J]. 推进技术, 2015, 36(2): 207-216.

GUO Zhendong, SONG Liming, LI Jun, et al. Metamodel-based global design optimization and exploration method [J]. Journal of Propulsion Technology, 2015, 36(2): 207-216.

黄松, 阳诚武, 韩戈, 等. 采用鲸鱼算法的可控扩散叶型优化设计 [J]. 西安交通大学学报, 2020, 54(3): 49-57.

HUANG Song, YANG Chengwu, HAN Ge, et al. Optimal design of a controlled diffusion airfoil with the whale algorithm [J]. Journal of Xi'an Jiaotong University, 2020, 54(3): 49-57.

王加浩, 刘小民, 田晨晔, 等. 多翼离心风机双圆弧叶片的参数优化设计及气动性能分析 [J]. 西安交通大学学报, 2022, 56(3): 94-104.

WANG Jiahao, LIU Xiaomin, TIAN Chenye, et al. Parametric optimization design of double-circular arc blade used in multi-blade centrifugal fan and its aerodynamic analysis [J]. Journal of Xi'an Jiaotong University, 2022, 56(3): 94-104.

王啸宸, 李雪松, 任晓栋, 等. 多级压气机通流与CFD一体化优化设计方法 [J]. 清华大学学报(自然科学版), 2022, 62(4): 774-784.

WANG Xiaochen, LI Xuesong, REN Xiaodong, et al. Integrated through-flow and CFD optimization design method of multi-stage compressors [J]. Journal of Tsinghua University(Science and Technology), 2022, 62(4): 774-784.

SHAN Songqing, WANG G G. Metamodeling for high dimensional simulation-based design problems [J]. Journal of Mechanical Design, 2010, 132(5): 051009.

CHEN Wenxiang, WEISE T, YANG Zhenyu, et al. Large-scale global optimization using cooperative coevolution with variable interaction learning[M]//SCHAEFER R, COTTA C, KOODZIEJ J, et al. Parallel Problem Solving from Nature, PPSN XI. Heidelberg, Berlin, Germany: Springer, 2010: 300-309.

FRIEDMAN J H, POPESCU B E. Predictive learning via rule ensembles [J]. The Annals of Applied Statistics, 2008, 2(3): 916-954.

OMIDVAR M N, YANG Ming, MEI Yi, et al. DG2: a faster and more accurate differential grouping for large-scale black-box optimization [J]. IEEE Transactions on Evolutionary Computation, 2017, 21(6): 929-942.

李琛玺, 王静, 宋立明, 等. 高压比离心叶轮气动强度多学科优化与知识挖掘 [J]. 西安交通大学学报, 2017, 51(5): 102-111.

LI Chenxi, WANG Jing, SONG Liming, et al.Aero-mechanical multidisciplinary optimization and knowledge discovery of high pressure ratio centrifugal impeller [J]. Journal of Xi'an Jiaotong University, 2017, 51(5): 102-111.

QIN Ruihong, JU Yaping, SPENCE S, et al. Metamodel-driven data mining model to support three-dimensional design of centrifugal compressor stage [J]. Journal of Turbomachinery, 2021, 143(12): 121013.

BAERT L, DUMONT C, BEAUTHIER C, et al. Multidisciplinary design of a low-noise propeller: part Ⅱ efficient aero-acoustic-mechanical design methodology exploiting surrogate models in an adaptive design space [C]//ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. New York, NY, USA: ASME, 2020: V02DT38A023.

WIENER N. The homogeneous chaos [J]. American Journal of Mathematics, 1938, 60(4): 897-936.

MARELLI S, SUDRET B. UQLab: a framework for uncertainty quantification in Matlab [C]//Vulnerability, Uncertainty, and Risk: Quantification, Mitigation, and Management. Reston, VA, USA: ASCE, 2014: 2554-2563.

韩忠华. Kriging模型及代理优化算法研究进展 [J]. 航空学报, 2016, 37(11): 3197-3225.

HAN Zhonghua. Kriging surrogate model and its application to design optimization: a review of recent progress [J]. Acta Aeronautica et Astronautica Sinica, 2016, 37(11): 3197-3225.

KANG K, LEE I. Efficient high-dimensional metamodeling strategy using recursive decomposition coupled with sequential sampling method [J]. Structural and Multidisciplinary Optimization, 2021, 63(1): 375-390.

PICKETT B, TURNER C J. A review and evaluation of existing adaptive sampling criteria and methods for the creation of NURBs-based metamodels [C]//ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. New York, NY, USA: ASME, 2011: 609-618.

ZHAN Dawei, XING Huanlai. A fast kriging-assisted evolutionary algorithm based on incremental learning [J]. IEEE Transactions on Evolutionary Computation, 2021, 25(5): 941-955.

STORN R, PRICE K. Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces [J]. Journal of Global Optimization, 1997, 11(4): 341-359.

DEB K. An introduction to genetic algorithms [J]. Sadhana, 1999, 24(4): 293-315.

TANG Ke, LI Xiaodong, SUGANTHAN P N, et al. Benchmark functions for the CEC'2010 special session and competition on large-scale global optimization [EB/OL]. [2022-08-12]. http://al-roomi.org/multimedia/CEC_Database/CEC2010/LargeScaleGlobalOptimization/CEC2010_LargeScaleGO_TechnicalReport.pdf.

宋立明, 李军, 丰镇平, 等. 三维跨声速压气机叶栅多目标气动优化设计 [J]. 工程热物理学报, 2007, 28(2): 223-225.

SONG Liming, LI Jun, FENG Zhenping, et al.Multi-objective optimization design of the 3D transonic compressor cascade [J]. Journal of Engineering Thermophysics, 2007, 28(2): 223-225.

陈玲, 琚亚平, 李震, 等. 多级轴流压气机末级串列静子三维优化设计与分析 [J]. 西安交通大学学报, 2023, 57(1): 164-174.

CHEN Ling, JU Yaping, LI Zhen, et al.Three-dimensional design optimization and analysis for last stator of multi-stage axial flow compressor [J]. Journal of Xi'an Jiaotong University, 2023, 57(1): 164-174.

SUDER K L. Experimental investigation of the flow field in a transonic, axial flow compressor with respect to the development of blockage and loss [EB/OL].(1996-10-01)[2022-08-12]. https://ntrs.nasa.gov/citations/19970001675.

周岳琨, 王建新, 管继伟, 等. 汽轮机叶片设计和几何成型方法综述 [J]. 汽轮机技术, 2001, 43(4): 198-202.

ZHOU Yuekun, WANG Jianxin, GUAN Jiwei, et al. A summary about turbine blade design and molding method [J]. Turbine Technology, 2001, 43(4): 198-202.

AMERI A A. NASA rotor 37 CFD code validation glenn-HT code [C]//47th AIAA Aerospace Sciences Meeting including The New Horizons Forum and Aerospace Exposition. Reston, VA, USA: AIAA, 2009: AIAA 2009-1060.

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