考虑几何设计参数不确定性影响的涡轮叶栅稳健性气动设计优化

罗佳奇; 陈泽帅; 曾先

doi:10.7527/S1000-6893.2020.23826

航空学报 >

2020 , Vol. 41 >Issue 10: 123826 - 123826

DOI: https://doi.org/10.7527/S1000-6893.2020.23826

流体力学与飞行力学

考虑几何设计参数不确定性影响的涡轮叶栅稳健性气动设计优化

罗佳奇 ,
陈泽帅 ,
曾先

展开

浙江大学航空航天学院, 杭州 310027

收稿日期: 2020-01-13

修回日期: 2020-03-16

网络出版日期: 2020-03-13

基金资助

国家自然科学基金（51676003，51976183）；中央高效基本科研业务费专项资金（2019QNA4058）

收起

Robust aerodynamic design optimization of turbine cascades considering uncertainty of geometric design parameters

LUO Jiaqi ,
CHEN Zeshuai ,
ZENG Xian

Expand

School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China

Received date: 2020-01-13

Revised date: 2020-03-16

Online published: 2020-03-13

Supported by

National Nature Science Foundation of China (51676003, 51976183); the Fundamental Research Funds for the Central Universities of China (2019QNA4058)

Fold

摘要

外形偏差是典型的叶片气动不确定性影响因素，考虑几何设计参数不确定性影响的叶片稳健性气动设计优化（RADO）有助于提高叶片平均气动性能及气动稳健性。首先，介绍RADO的基本原理及实现方法，采用基于灵敏度分析的叶片气动不确定性量化方法计算叶片目标气动函数的统计值，并实现目标函数对设计参数的梯度计算。然后，开展考虑设计参数不确定性影响的HS1A跨声速涡轮叶栅RADO研究，降低总压损失系数的统计均值及方差；通过与确定性气动设计优化（DADO）对比，揭示RADO在改善优化叶片气动稳健性方面的有效性和优越性。最后，对叶片进行流场统计分析，进一步揭示气动外形优化设计对降低总压损失系数敏感度的影响机理。

关键词： 气动设计优化; 稳健性; 不确定性; 伴随方法; 叶栅

本文引用格式

罗佳奇 , 陈泽帅 , 曾先 . 考虑几何设计参数不确定性影响的涡轮叶栅稳健性气动设计优化[J]. 航空学报, 2020 , 41(10) : 123826 -123826 . DOI: 10.7527/S1000-6893.2020.23826

Abstract

Geometric deviation is a principal source of aerodynamic uncertainty for turbomachinery blades. The aero-dynamic shape optimization considering uncertainty effects of geometric design parameters, also named Robust Aerodynamic Design Optimization (RADO), is suggested to improve both the mean aerodynamic performance and aerodynamic robustness. The basic principles and implementations of RADO are firstly introduced, followed by the evaluation of statistic mean and variance of aerodynamic performance changes by using the sensitivity-based uncertainty quantification method, from which the gradients of RADO cost function to the design parameters can be calculated. Then the study of RADO on a transonic turbine cascade, HS1A, considering the uncertainty of geometric design parameters is performed to reduce the mean total pressure loss and the corresponding variance. The optimization results compared with those of Deterministic Aerodynamic Design Optimization (DADO) demonstrate the effectiveness and superiority of RADO in improving the aerodynamic robustness. Finally, the statistical flow solutions of the original, DADO and RADO cascades are compared and presented to illustrate the mechanisms of reducing the sensitivity of total pressure loss through aerodynamic shape optimization by RADO.

Key words： aerodynamic design optimization; robust; uncertainty; adjoint method; cascades

参考文献

[1] BAMMERT K, STOBBE H. Results of experiments for determining the influence of blade profile changes and manufacturing tolerances on the efficiency, the enthalpy drop, and the mass flow of multi-stage axial turbines:ASME Paper No. 70 WA/GT-4[R]. New York:ASME, 1970.
[2] BAMMERT K, SANDSTEDE H. Influence of manufacturing tolerances and surface roughness of blades on the performance of turbines[J].Journal of Engineering for Power, 1976, 98(1):29-36.
[3] GARZON V, DARMOFAL D. Impact of geometric variability on axial compressor performance[J].Journal of Turbomachinery, 2003, 125(4):692-703.
[4] EDWARDS R, ASGHAR A, WOODASON R, et al. Numerical investigation of the influence of real world blade profile variation on the aerodynamic performance of transonic nozzle guide vanes[J].Journal of Turbomachinery, 2012, 134(2):021014.
[5] LANGE A, VOIGT M, VOGELER K, et al. Impact of manufacturing variability on multistage high-pressure compressor performance[J].Journal of Engineering for Gas Turbines and Power, 2012, 134(11):112601.
[6] SCHNELL R, LENGYEL-KAMPMANN T, NICKE E. On the impact of geometric variability on fan aerodynamic performance, unsteady blade row interaction, and its mechanical characteristics[J].Journal of Turbomachinery, 2014, 136(9):091005.
[7] YANG J, XIONG J, MCBEAN I, et al. Performance impact of manufacturing variations for multi-stage steam turbines[J].Journal of Propulsion and Power, 2017, 33(4):1031-1036.
[8] 蔡宇桐, 高丽敏, 马驰, 等. 基于NIPC的压气机叶片加工误差不确定性分析[J].工程热物理学报, 2017, 38(3):490-497. CAI Y T, GAO L M, MA C, et al. Uncertainty quantification on compressor blade considering manufacturing error based on NIPC method[J].Journal of Engineering Thermophysics, 2017, 38(3):490-497(in Chinese).
[9] 罗佳奇, 朱亚路, 刘锋. 基于伴随方法的叶片加工偏差气动灵敏度分析[J].工程热物理学报, 2017, 38(3):498-504. LUO J Q, ZHU Y L, LIU F. Aerodynamic sensitivity analysis for manufacturing variations of a turbine blade by an adjoint method[J].Journal of Engineering Thermophysics, 2017, 38(3):498-504(in Chinese).
[10] LUO J, LIU F. Statistical evaluation of performance impact of manufacturing variability by an adjoint method[J].Aerospace Science and Technology, 2018, 77:471-484.
[11] LI H, MA C. Hybrid dimension-reduction method for robust design optimization[J].AIAA Journal, 2013, 51(1):138-144.
[12] PAIVA R, CRAWFORD C, SULEMAN A. Robust and reliability-based design optimization framework for wing design[J].AIAA Journal, 2014, 52(4):711-724.
[13] RYAN K, LEWIS M, YU K. Comparison of robust optimization methods applied to hypersonic vehicle design[J].Journal of Aircraft, 2015, 52(5):1510-1523.
[14] KEANE A J. Comparison of several optimization strategies for robust turbine blade design[J].Journal of Propulsion and Power, 2009, 25(5):1092-1099.
[15] GHISU T, PARKS G, JARRETT J, et al. Robust design optimization of gas turbine compression systems[J].Journal of Propulsion and Power, 2011, 27(2):282-295.
[16] WANG X, HIRSCH C, LIU Z, et al. Uncertainty-based robust aerodynamic optimization of rotor blades[J].International Journal for Numerical Methods in Engineering, 2013, 94:111-127.
[17] VINOGRADOV K, KRETININ G, OTRYAHINA K, et al. Robust optimization of the hpt blade cooling and aerodynamic efficiency:ASME Paper No. GT2016-56195[R]. New York:ASME, 2016.
[18] REIS C, MANZANARES-FILHO N, DE LIMA A. Robust optimization of turbomachinery cascades using inverse methods[J].Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2016, 38(1):297-305.
[19] MA C, GAO L, CAI Y, et al. Robust optimization design of compressor blade considering machining error:ASME Paper GT2017-63157[R]. New York:ASME, 2017.
[20] GIEBMANNS A, BACKHAUS J, FREY C, et al. Compressor leading edge sensitivities and analysis with an adjoint flow solver:ASME Paper GT2013-94427[R]. New York:ASME, 2013.
[21] ZAMBONI G, BANKS G, BATHER S. Gradient-based adjoint and design of experiment CFD methodologies to improve the manufacturability of high pressure turbine blades:ASME Paper GT2016-56042[R]. New York:ASME, 2016.
[22] JOUINI D. Experimental investigation of two transonic linear turbine cascades at off-design conditions[D]. Ottawa:Carleton University, 2000.
[23] HICKS R, HENNE P. Wing design by numerical optimization[J].Journal of Aircraft, 1978, 15(7):407-412.
[24] JAMESON A. Aerodynamic design via control theory[J].Journal of Scientific Computing, 1988, 3(3):233-260.
[25] NADARAJAH S, JAMESON A. Optimum shape design for unsteady flows with time-accurate continuous and discrete adjoint methods[J].AIAA Journal, 2007, 45(7):1478-1491.
[26] 熊俊涛, 乔志德, 杨旭东, 等. 基于黏性伴随方法的跨声速机翼气动优化设计[J].航空学报, 2007, 28(2):281-285. XIONG J T, QIAO Z D, YANG X D, et al. Optimum aerodynamic design of transonic wing based on viscous adjoint method[J].Acta Aeronautica et Astronautica Sinica, 2007, 28(2):281-285(in Chinese).
[27] MARTINS J, LAMBE A. Multidisciplinary design optimization:A survey of architectures[J].AIAA Journal, 2013, 51(9):2049-2075.
[28] 黄江涛, 周铸, 刘刚, 等. 飞行器气动/结构多学科延迟耦合伴随系统数值研究[J].航空学报, 2018, 39(5):121731. HUANG J T, ZHOU Z, LIU G, et al. Numerical study of aero-structural multidisciplinary lagged coupled adjoint system for aircraft[J].Acta Aeronautica et Astronautica Sinica, 2018, 39(5):121731(in Chinese).
[29] YANG S, WU H, LIU F, et al. Aerodynamic design of cascades by using an adjoint equation method:AIAA-2003-1068[R]. Reston:AIAA, 2003.
[30] WANG D, HE L. Adjoint aerodynamic design optimization for blades in multi-stage turbo-machines:part i-methodology and verification[J].Journal of Turbomachinery, 2010, 132(2):021011.
[31] LUO J, XIONG J, LIU F, et al. Three-dimensional aerodynamic design optimization of a turbine blade by using an adjoint method[J].Journal of Turbomachinery, 2011, 133(1):011026.
[32] WALTHER B, NADARAJAH S. Constrained adjoint-based aerodynamic shape optimization of a single-stage transonic compressor[J].Journal of Turbomachinery, 2013, 135(2):021017.
[33] LUO J, LIU F, MCBEAN I. Turbine blade row optimization through endwall contouring by an adjoint method[J].Journal of Propulsion and Power, 2015, 31(2):505-518.
[34] MA C, SU X, YUAN X. An efficient unsteady adjoint optimization system for multistage turbomachinery[J].Journal of Turbomachinery, 2016, 139(1):011003.
[35] PAPADIMITRIOU D, GIANNAKOGLOU K. Comput-ation of Hessian matrix in aerodynamic inverse design using continuous adjoint formulations[J].Computers & Fluids, 2008, 37:1029-1039.
[36] PAPADIMITRIOU D, GIANNAKOGLOU K. The continuous adjoint approach for second order sensitivities in viscous aerodynamic inverse design problems[J].Computers & Fluids, 2009, 38:1539-1548.

Options

文章导航

模态框（Modal）标题

摘要

本文引用格式

Abstract

参考文献