Separating sensitivity analysis of aleatory and epistemic uncertainties in non-parametric probability-box

  • Muchen WU ,
  • Jiangtao CHEN ,
  • Tangfan XIAHOU ,
  • Wei ZHAO ,
  • Yu LIU
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  • 1.School of Mechanical and Electrical Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China
    2.Computational Aerodynamics Institute,China Aerodynamics Research and Development Center,Mianyang 621000,China
    3.Center for System Reliability and Safety,University of Electronic Science and Technology of China,Chengdu 611731,China
E-mail: yuliu@uestc.edu.cn

Received date: 2021-11-15

  Revised date: 2021-12-08

  Accepted date: 2021-12-20

  Online published: 2021-12-24

Supported by

National Numerical Windtunnel Project(NNW2020ZT7-B32);National Natural Science Foundation of China(71922006)

Abstract

Sensitivity Analysis (SA) can identify the most important parameters affecting the complex system output to support robust design of a system. Non-parametric probability-box (P-box), as a typical imprecise probabilistic model, can effectively quantify both aleatory and epistemic uncertainties, therefore extensively used in engineering practices. As aleatory and epistemic uncertainties are coupled in P-boxes, sensitivity analysis under the P-box framework is essential to evaluate their contributions in input P-box variables to output. This study develops a Separating Sensitivity Analysis (SSA) method for aleatory and epistemic uncertainties of non-parametric p-boxes. Two methods, i.e., grid point method and expectation method, are introduced to separate the input aleatory and epistemic uncertainties in input P-box variables, respectively. A double loop procedure is utilized to propagate the input uncertainties and build the output P-box. Two uncertainty measures, namely, maximum variance metric and area metric, are proposed to evaluate the effects of input aleatory and epistemic uncertainties on the output aleatory and epistemic uncertainties, respectively. The lift-to-drag ratio prediction of the NACA0012 airfoil is exemplified to analyze the contributions of aleatory and epistemic uncertainties of income flow parameters and turbulence model parameters to those of the lift-to-drag ratio prediction result.

Cite this article

Muchen WU , Jiangtao CHEN , Tangfan XIAHOU , Wei ZHAO , Yu LIU . Separating sensitivity analysis of aleatory and epistemic uncertainties in non-parametric probability-box[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023 , 44(1) : 226658 -226658 . DOI: 10.7527/S1000-6893.2021.26658

References

1 阮文斌, 刘洋, 熊磊. 基于全局灵敏度分析的侧向气动导数不确定性对侧向飞行载荷的影响[J]. 航空学报201637(6): 1827-1832.
  RUAN W B, LIU Y, XIONG L. Influence of side aerodynamic derivative uncertainty on side flight load based on global sensitivity analysis[J]. Acta Aeronautica et Astronautica Sinica201637(6): 1827-1832 (in Chinese).
2 BORGONOVO E, PLISCHKE E. Sensitivity analysis: A review of recent advances[J]. European Journal of Operational Research2016248(3): 869-887.
3 冯凯旋, 吕震宙, 蒋献. 基于偏导数的全局灵敏度指标的高效求解方法[J]. 航空学报201839(3): 221699.
  FENG K X, LYU Z Z, JIANG X. Efficient algorithm for estimating derivative-based global sensitivity index[J]. Acta Aeronautica et Astronautica Sinica201839(3): 221699 (in Chinese).
4 肖思男, 吕震宙, 王薇. 不确定性结构全局灵敏度分析方法概述[J]. 中国科学: 物理学 力学 天文学201848(1): 8-25.
  XIAO S N, LV Z Z, WANG W. A review of global sensitivity analysis for uncertainty structure[J]. Scientia Sinica (Physica, Mechanica & Astronomica), 201848(1): 8-25 (in Chinese).
5 LIU L J, MA Y Z, PARK C, et al. Robust sequential bifurcation for simulation factor screening under data contamination[J]. Computers & Industrial Engineering2019129: 102-112.
6 CONSTANTINE P G, DIAZ P. Global sensitivity metrics from active subspaces[J]. Reliability Engineering & System Safety2017162: 1-13.
7 BALLESTER-RIPOLL R, PAREDES E G, PAJAROLA R. Sobol tensor trains for global sensitivity analysis[J]. Reliability Engineering & System Safety2019183: 311-322.
8 BORGONOVO E, TARANTOLA S, PLISCHKE E, et al. Transformations and invariance in the sensitivity analysis of computer experiments[J]. Journal of the Royal Statistical Society: Series B (Statistical Methodology)201476(5): 925-947.
9 STRAUB D. Value of information analysis with structural reliability methods[J]. Structural Safety201449: 75-85.
10 夏侯唐凡, 陈江涛, 邵志栋, 等. 随机和认知不确定性框架下的CFD模型确认度量综述[J]. 航空学报202243(8): 025716.
  XIAHOU T F, CHEN J T, SHAO Z D, et al. Model validation metrics for CFD numerical simulation under aleatory and epistemic uncertainty[J]. Acta Aeronautica et Astronautica Sinica202243(8): 025716 (in Chinese).
11 XIAHOU T F, ZENG Z G, LIU Y, et al. Measuring conflicts of multisource imprecise information in multistate system reliability assessment[J]. IEEE Transactions on Reliability7531, PP(99): 1-18.
12 LI J W, JIANG C, NI B Y. An efficient uncertainty propagation analysis method for problems involving non-parameterized probability-boxes[J]. Journal of Mechanical Design2021143(10): 101704.
13 LIU J, MENG X H, XU C, et al. Forward and inverse structural uncertainty propagations under stochastic variables with arbitrary probability distributions[J]. Computer Methods in Applied Mechanics and Engineering2018342: 287-320.
14 MENG X H, LIU J, CAO L X, et al. A general frame for uncertainty propagation under multimodally distributed random variables[J]. Computer Methods in Applied Mechanics and Engineering2020367: 113109.
15 ZHANG H, MULLEN R L, MUHANNA R L. Interval Monte Carlo methods for structural reliability[J]. Structural Safety201032(3): 183-190.
16 JOHNSON N L. Systems of frequency curves generated by methods of translation[J]. Biometrika194936(1-2): 149-176.
17 SCHUMACHER C, SCHWARZENBERGER F, VESELI? I. A Glivenko-Cantelli theorem for almost additive functions on lattices[J]. Stochastic Processes and Their Applications2017127(1): 179-208.
18 FERSON S, TUCKER W T. Sensitivity analysis using probability bounding[J]. Reliability Engineering & System Safety200691(10-11): 1435-1442.
19 FAES M G R, DAUB M, MARELLI S, et al. Engineering analysis with probability boxes: A review on computational methods[J]. Structural Safety202193: 102092.
20 BI S F, BROGGI M, WEI P F, et al. The Bhattacharyya distance: Enriching the P-box in stochastic sensitivity analysis[J]. Mechanical Systems and Signal Processing2019129: 265-281.
21 FERRARI R, FROIO D, RIZZI E, et al. Model updating of a historic concrete bridge by sensitivity- and global optimization-based Latin Hypercube Sampling[J]. Engineering Structures2019179: 139-160.
22 BICKEL P J, KUR G, NADLER B. Projection pursuit in high dimensions[J]. Proceedings of the National Academy of Sciences of the United States of America2018115(37): 9151-9156.
23 胡政文, 张保强, 邓振鸿. 概率盒全局灵敏度和活跃子空间跨层降维[J]. 航空学报202142(9): 224582.
  HU Z W, ZHANG B Q, DENG Z H. Cross-layer dimension reduction based on probability box global sensitivity analysis and active subspace method[J]. Acta Aeronautica et Astronautica Sinica202142(9): 224582 (in Chinese).
24 QIAN W W, CHAI J R, XU Z G, et al. Differential evolution algorithm with multiple mutation strategies based on roulette wheel selection[J]. Applied Intelligence201848(10): 3612-3629.
25 SPALART P, ALLMARAS S. A one-equation turbulence model for aerodynamic flows[C]∥30th Aerospace Sciences Meeting and Exhibit. Reston: AIAA, 1992.
26 ZHU L Y, ZHANG W W, SUN X X, et al. Turbulence closure for high Reynolds number airfoil flows by deep neural networks[J]. Aerospace Science and Technology2021110: 106452.
27 HARRIS C D. Two-dimensional aerodynamic characteristics of the NACA 0012 airfoil in the Langley 8 foot transonic pressure tunnel[R].Washington,D.C.: NASA, 1981.
28 张伟伟, 寇家庆, 刘溢浪. 智能赋能流体力学展望[J]. 航空学报202142(4): 524689.
  ZHANG W W, KOU J Q, LIU Y L. Prospect of artificial intelligence empowered fluid mechanics[J]. Acta Aeronautica et Astronautica Sinica202142(4): 524689 (in Chinese).
29 李秋彦, 李刚, 魏洋天, 等. 先进战斗机气动弹性设计综述[J]. 航空学报202041(6): 523430.
  LI Q Y, LI G, WEI Y T, et al. Review of aeroelasticity design for advanced fighter[J]. Acta Aeronautica et Astronautica Sinica202041(6): 523430 (in Chinese).
30 赵辉, 胡星志, 张健, 等. 湍流模型系数不确定度对翼型绕流模拟的影响[J]. 航空学报201940(6): 122581.
  ZHAO H, HU X Z, ZHANG J, et al. Effects of uncertainty in turbulence model coefficients on flow over airfoil simulation[J]. Acta Aeronautica et Astronautica Sinica201940(6): 122581 (in Chinese).
31 KENNEDY M C, O'HAGAN A. Bayesian calibration of computer models[J]. Journal of the Royal Statistical Society: Series B (Statistical Methodology)200163(3): 425-464.
32 SCHAEFER J, HOSDER S, WEST T, et al. Uncertainty quantification of turbulence model closure coefficients for transonic wall-bounded flows[J]. AIAA Journal201655(1): 195-213.
33 LIU H B, JIANG C, LIU J, et al. Uncertainty propagation analysis using sparse grid technique and saddlepoint approximation based on parameterized p-box representation[J]. Structural and Multidisciplinary Optimization201959(1): 61-74.
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