考虑湍流模型不确定性量化的喷管伴随优化设计

李安娜; 孙中国; 黄柱; 席光

doi:10.7652/xjtuxb202503001

您当前的位置：

首页 >

文章列表页 >

考虑湍流模型不确定性量化的喷管伴随优化设计

更新时间：2026-01-06

- 考虑湍流模型不确定性量化的喷管伴随优化设计
- Adjoint Optimization Design of Nozzle Considering Uncertainty Quantification of Turbulence Model
- 西安交通大学学报 2025年59卷第3期页码：1-8
- 作者机构：
  
  西安交通大学能源与动力工程学院， 710049，西安
- 作者简介：
  
  李安娜（1998—），女，博士生；
  席光（通信作者），男，教授，博士生导师。
- 基金信息：
  
  国家自然科学基金资助项目(52130603;12102331)
- DOI：10.7652/xjtuxb202503001
  中图分类号： V211.3
- 收稿：2024-06-30，
  
  网络首发：2024-10-24，
  
  纸质出版：2025-03-10
- 稿件说明：
移动端阅览
李安娜, 孙中国, 黄柱, 等. 考虑湍流模型不确定性量化的喷管伴随优化设计[J]. 西安交通大学学报, 2025,59(3):1-8.

LI Anna, SUN Zhongguo, HUANG Zhu, et al. Adjoint Optimization Design of Nozzle Considering Uncertainty Quantification of Turbulence Model[J]. Journal of Xi’an Jiaotong University, 2025, 59(3): 1-8.
李安娜, 孙中国, 黄柱, 等. 考虑湍流模型不确定性量化的喷管伴随优化设计[J]. 西安交通大学学报, 2025,59(3):1-8. DOI： 10.7652/xjtuxb202503001.

LI Anna, SUN Zhongguo, HUANG Zhu, et al. Adjoint Optimization Design of Nozzle Considering Uncertainty Quantification of Turbulence Model[J]. Journal of Xi’an Jiaotong University, 2025, 59(3): 1-8. DOI： 10.7652/xjtuxb202503001.

摘要

为提高工程中广泛采用的基于雷诺平均Navier-Stokes（RANS）模型设计优化的可靠性和稳健性，针对RANS模型存在的结构不确定性，通过质心图和雷诺应力的可实现性对其进行合理量化，采用自适应非均匀扰动方法对湍流各向异性张量的特征值和特征空间施加扰动，并对模型预测的不确定区间进行数值估计。提出了一种RANS模型不确定性量化框架下的伴随设计优化方法，探索了该方法在拉瓦尔喷管优化设计中的应用，通过6次模拟获得了不同扰动下的优化几何型线，不同型线所围成的区域（置信区间）反映了模型结构不确定性引起的几何优化差异。研究结果表明：在不同扰动下，优化后的喷管总压损失降低了6.7%~19.2%，实现了喷管性能的稳健提升，获得的置信区间降低了对制造公差的敏感性，从而在一定程度上降低了精度要求和制造成本。研究结果展示了考虑RANS模型不确定性量化的优化设计在航空航天工程应用中的潜在价值和指导作用。

Abstract

In order to improve the reliability and robustness of design optimization based on the Reynolds-averaged Navier-Stokes (RANS) models

which are widely used in engineering

this paper addresses the structural uncertainties present in RANS models. By using centroidal diagrams and the feasibility of Reynolds stresses

these uncertainties are reasonably quantified. An adaptive non-uniform perturbation method is employed to apply disturbances to the eigenvalues and eigenspaces of the turbulence anisotropy tensor

and a numerical estimation of the uncertainty interval predicted by the model is conducted. This paper proposes a method for adjoint design optimization under the uncertainty quantification framework of the RANS model and explores its application in the optimization design of a Laval nozzle. Through six simulations

optimized geometric shapes under different perturbations are obtained

and the areas enclosed by different shapes (confidence intervals) reflect the geometric optimization differences caused by structural uncertainties in the model. The results indicate that after optimization under different perturbations

the total pressure loss of the nozzle is reduced by 6.7% to 19.2%

achieving a robust improvement in nozzle performance. The resulting confidence intervals reduce sensitivity to manufacturing tolerances

thus lowering precision requirements and manufacturing costs to some extent. The findings demonstrate the potential value and guiding role of optimization design considering the uncertainty quantification of the RANS model in aerospace engineering applications.

关键词

Keywords

references

SLOTNICK J , KHODADOUST A , ALONSO J , et al . CFD vision 2030 study: a path to revolutionary computational aerosciences [EB/OL ] . ( 2014-03-01 ) [ 2024-05-10 ] . https://ntrs.nasa.gov/citations/20140003093 https://ntrs.nasa.gov/citations/20140003093 .

LIU Xuan , FURRER D , KOSTERS J , et al . Vision 2040: a roadmap for integrated, multiscale modeling and simulation of materials and systems [EB/OL ] . ( 2018-03-01 ) [ 2024-05-10 ] . https://ntrs.nasa.gov/citations/20180002010 https://ntrs.nasa.gov/citations/20180002010 .

JU Yaping , LIU Yiming , JIANG Wei , et al . Aerodynamic analysis and design optimization of a centrifugal compressor impeller considering realistic manufacturing uncertainties [J ] . Aerospace Science and Technology , 2021 , 115 : 106787 .

DURAISAMY K , IACCARINO G , XIAO Heng . Turbulence modeling in the age of data [J ] . Annual Review of Fluid Mechanics , 2019 , 51 : 357 - 377 .

陈鑫 , 王刚 , 叶正寅 , 等 . CFD不确定度量化方法研究综述 [J ] . 空气动力学学报 , 2021 , 39 ( 4 ): 1 - 13 .

CHEN Xin , WANG Gang , YE Zhengyin , et al . A review of uncertainty quantification methods for computational fluid dynamics [J ] . Acta Aerodynamica Sinica , 2021 , 39 ( 4 ): 1 - 13 .

PLATTEEUW P D A , LOEVEN G J A , BIJL H . Uncertainty quantification applied to the k-epsilon model of turbulence using the probabilistic collocation method [C ] // 49th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference . Reston, VA, USA : AIAA , 2008 : AIAA 2008-2150 .

CHEUNG S H , OLIVER T A , PRUDENCIO E E , et al . Bayesian uncertainty analysis with applications to turbulence modeling [J ] . Reliability Engineering & System Safety , 2011 , 96 ( 9 ): 1137 - 1149 .

LING J , TEMPLETON J . Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier stokes uncertainty [J ] . Physics of Fluids , 2015 , 27 ( 8 ): 085103 .

BANERJEE S , KRAHL R , DURST F , et al . Presentation of anisotropy properties of turbulence, invariants versus eigenvalue approaches [J ] . Journal of Turbulence , 2007 , 8 : N32 .

CHOI K S , LUMLEY J L . The return to isotropy of homogeneous turbulence [J ] . Journal of Fluid Mechanics , 2001 , 436 : 59 - 84 .

EMORY M , PE R , IACCARINO G . Modeling structural uncertainties in Reynolds-averaged computations of shock/boundary layer interactions [C ] // 49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition . Reston, VA, USA : AIAA , 2011 : AIAA 2011-479 .

EMORY M , LARSSON J , IACCARINO G . Modeling of structural uncertainties in Reynolds-averaged Navier-Stokes closures [J ] . Physics of Fluids , 2013 , 25 ( 11 ): 110822 .

IACCARINO G , MISHRA A A , GHILI S . Eigenspace perturbations for uncertainty estimation of single-point turbulence closures [J ] . Physic Review Fluids , 2017 , 2 ( 2 ): 024605 .

HUANG Z , MISHRA A , IACCARINO G . A nonuniform perturbation to quantify RANS model uncertainties [EB/OL ] . [ 2024-05-10 ] . http://web.stanford.edu/group/ctr/ResBriefs/2020/22_Huang.pdf http://web.stanford.edu/group/ctr/ResBriefs/2020/22_Huang.pdf .

梁霄 , 王瑞利 . 爆炸波中的混合不确定度量化方法 [J ] . 计算物理 , 2017 , 34 ( 5 ): 574 - 582 .

LIANG Xiao , WANG Ruili . Mixed uncertainty quantification of blast wave problem [J ] . Chinese Journal of Computational Physics , 2017 , 34 ( 5 ): 574 - 582 .

王运涛 , 刘刚 , 陈作斌 . 第一届航空CFD可信度研讨会总结 [J ] . 空气动力学学报 , 2019 , 37 ( 2 ): 246 - 261 .

WANG Yuntao , LIU Gang , CHEN Zuobin . Summary of the first aeronautical computational fluid dynamics credibility workshop [J ] . Acta Aerodynamica Sinica , 2019 , 37 ( 2 ): 246 - 261 .

CHEN Jiangtao , ZHANG Chao , ZHAO Wei , et al . A high-fidelity polynomial chaos modified method suitable for CFD uncertainty quantification [C ] // Journal of Physics: Conference Series . Bristol, United Kingdom : IOP Publishing , 2021 : 012042 .

COOK L W , MISHRA A A , JARRETT J P , et al . Optimization under turbulence model uncertainty for aerospace design [J ] . Physics of Fluids , 2019 , 31 ( 10 ): 105111 .

MISHRA A A , MUKHOPADHAYA J , ALONSO J , et al . Design exploration and optimization under uncertainty [J ] . Physics of Fluids , 2020 , 32 ( 8 ): 085106 .

朱玉杰 , 琚亚平 , 戴韧 , 等 . 叶栅气动反问题的伴随优化解法及应用 [J ] . 西安交通大学学报 , 2017 , 51 ( 9 ): 138 - 144 .

ZHU Yujie , JU Yaping , DAI Ren , et al . Adjoint optimization method for aerodynamic inverse problem of cascades [J ] . Journal of Xi'an Jiaotong University , 2017 , 51 ( 9 ): 138 - 144 .

白俊强 , 雷锐午 , 杨体浩 , 等 . 基于伴随理论的大型客机气动优化设计研究进展 [J ] . 航空学报 , 2019 , 40 ( 1 ): 522642 .

BAI Junqiang , LEI Ruiwu , YANG Tihao , et al . Progress of adjoint-based aerodynamic optimization design for large civil aircraft [J ] . Acta Aeronautica et Astronautica Sinica , 2019 , 40 ( 1 ): 522642 .

刘晓冬 , 张沛良 , 何光洪 , 等 . 基于伴随方法的飞翼布局多目标气动优化设计 [J ] . 西北工业大学学报 , 2021 , 39 ( 4 ): 753 - 760 .

LIU Xiaodong , ZHANG Peiliang , HE Guanghong , et al . Multi-objective aerodynamic optimization of flying-wing configuration based on adjoint method [J ] . Journal of Northwestern Polytechnical University , 2021 , 39 ( 4 ): 753 - 760 .

XU Shenren , LI Yi , HUANG Xiuquan , et al . Robust Newton-Krylov adjoint solver for the sensitivity analysis of turbomachinery aerodynamics [J ] . AIAA Journal , 2021 , 59 ( 10 ): 4014 - 4030 .

LI Anna , WANG Tongsheng , CHEN Jianan , et al . Adjoint design optimization under the uncertainty quantification of Reynolds-averaged Navier-stokes turbulence model [J ] . AIAA Journal , 2024 , 62 ( 7 ): 2589 - 2600 .

LAUNDER B E , TSELEPIDAKIS D P , YOUNIS B A . A second-moment closure study of rotating channel flow [J ] . Journal of Fluid Mechanics , 1987 , 183 : 63 - 75 .

SPEZIALE C G , GATSKI T B , MAC GIOLLA MHUIRISN . A critical comparison of turbulence models for homogeneous shear flows in a rotating frame [J ] . Physics of Fluids: A Fluid Dynamics , 1990 , 2 ( 9 ): 1678 - 1684 .

MISHRA A A , MUKHOPADHAYA J , IACCARINO G , et al . Uncertainty estimation module for turbulence model predictions in SU2 [J ] . AIAA Journal , 2019 , 57 ( 3 ): 1066 - 1077 .

NAIR P P , SURYAN A , KIM H D . Computational study on reducing flow asymmetry in over-expanded planar nozzle by incorporating double divergence [J ] . Aerospace Science and Technology , 2020 , 100 : 105790 .

VERMA S B , MANISANKAR C . Origin of flow asymmetry in planar nozzles with separation [J ] . Shock Waves , 2014 , 24 ( 2 ): 191 - 209 .

NAIR P P , NARAYANAN V , SURYAN A , et al . Prediction and visualization of supersonic nozzle flows using OpenFOAM [J ] . Journal of Visualization , 2022 , 25 ( 6 ): 1227 - 1247 .

ANSYS, Inc . Chapter 40: displaying graphics [EB/OL ] . [ 2024-05-10 ] . https://ansyshelp.ansys.com/public/account/secured?returnurl=/Views/Secured/corp/v242/en/flu_ug/flu_ug_chp_graphics.html https://ansyshelp.ansys.com/public/account/secured?returnurl=/Views/Secured/corp/v242/en/flu_ug/flu_ug_chp_graphics.html .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

气膜孔缺陷对冷却性能影响不确定性量化研究

跨声速压气机转子通用k-ω湍流模型的机器学习辅助优化

喷嘴内云空化瞬态特性的湍流模型适用性研究

离心压缩机性能预测中雷诺平均湍流模型的不确定度量化与分析

燃气轮机涡轮排气扩压器气动性能的不确定性量化和敏感性分析