Solid Mechanics and Vehicle Conceptual Design

Reliability algorithm of composite structure based on active learning basis-adaptive PC-Kriging model

  • Yulian GONG ,
  • Jianguo ZHANG ,
  • Zhigang WU ,
  • Guangyuan CHU ,
  • Xiaoduo FAN ,
  • Ying HUANG
Expand
  • 1.School of Reliability and Systems Engineering,Beihang University,Beijing 100191,China
    2.School of Aeronautic Science and Engineering,Beihang University,Beijing 100191,China
    3.China Academy of Launch Vehicle Technology,Beijing 100076,China
E-mail: zjg@buaa.edu.cn

Received date: 2023-05-09

  Revised date: 2023-06-12

  Accepted date: 2023-06-14

  Online published: 2023-06-16

Supported by

National Key R & D Program of China(2021YFB1715000)

Abstract

To address the complex, high dimensional, highly nonlinear, and long computing time-consuming problems of random natural frequency reliability analysis of composite wings, a reliability algorithm based on active learning basis-adaptive PC-Kriging model is proposed in this paper. A basis-adaptive strategy is used in this model to determine the orthogonal polynomial basis of the polynomial chaos expansion to approximate the global response of the numerical model, and Kriging is used for higher-order nonlinear interpolation to approximate the local response of the numerical model. In the framework of active learning reliability calculation, weighted K mean clustering is introduced, which means that K candidate sample points with greater contribution to failure probability are added in one iteration to reduce the number of iterations and accelerate the convergence rate. The effectiveness and accuracy of the proposed method are proved by a highly nonlinear numerical example. The proposed method is applied to the random natural frequency reliability analysis of composite plate and composite wing, and the accurate and efficient reliability calculation results are obtained.

Cite this article

Yulian GONG , Jianguo ZHANG , Zhigang WU , Guangyuan CHU , Xiaoduo FAN , Ying HUANG . Reliability algorithm of composite structure based on active learning basis-adaptive PC-Kriging model[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2024 , 45(8) : 228982 -228982 . DOI: 10.7527/S1000-6893.2023.28982

References

1 龚煜廉, 张建国, 李文博. 光纤传感在航天复材结构健康监测中的应用[J]. 航天器工程202231(5): 60-66.
  GONG Y L, ZHANG J G, LI W B. Application of optical fiber sensing in health monitoring of aerospace composite structure[J]. Spacecraft Engineering202231(5): 60-66 (in Chinese).
2 DEY S, MUKHOPADHYAY T, ADHIKARI S. Uncertainty quantification in laminated composites: A meta-model based approach[M]. London: CRC Press, 2017: 1-16.
3 晏良. 不确定性量化的代理模型分析及优化[D]. 长沙: 国防科技大学, 2018: 7-11.
  YAN L. Analysis and optimization of agent model for uncertainty quantization[D].Changsha: National University of Defense Technology, 2018: 7-11 (in Chinese) .
4 LEMAIRE M. Structural reliability[M]. Hoboken:John Wiley & Sons, 2013: 3.
5 SEPAHVAND K, SCHEFFLER M, MARBURG S. Uncertainty quantification in natural frequencies and radiated acoustic power of composite plates: Analytical and experimental investigation[J]. Applied Acoustics201587: 23-29.
6 DEY S, MUKHOPADHYAY T, SAHU S K, et al. Effect of cutout on stochastic natural frequency of composite curved panels[J]. Composites Part B: Engineering2016105(1): 188-202.
7 DEY S, NASKAR S, MUKHOPADHYAY T, et al. Uncertain natural frequency analysis of composite plates including effect of noise - A polynomial neural network approach[J]. Composite Structures2016143(5): 130-142.
8 MUKHOPADHYAY T, NASKAR S, KARSH P K, et al. Effect of delamination on the stochastic natural frequencies of composite laminates[J]. Composites Part B: Engineering2018154(7): 242-256.
9 CHEN X, QIU Z P. A novel uncertainty analysis method for composite structures with mixed uncertainties including random and interval variables[J]. Composite Structures2018184(11): 400-410.
10 ZHOU Y, ZHANG X F. Natural frequency analysis of functionally graded material beams with axially varying stochastic properties[J]. Applied Mathematical Modelling201967(10): 85-100.
11 ZHANG X, LIU Y, CAO X B, et al. Uncertain natural characteristics analysis of laminated composite plates considering geometric nonlinearity[J]. Composite Structures2023315(4): 117028.
12 MARELLI S, SUDRET B. UQLab: A framework for uncertainty quantification in Matlab[C]∥ Vulnerability, Uncertainty, and Risk. Reston: American Society of Civil Engineers, 2014.
13 SCHOBI R, SUDRET B, WIART J. Polynomial-chaos-based kriging[J]. International Journal for Uncertainty Quantification20155(2): 171-193.
14 MOONEY C Z. Monte Carlo simulation[M]. Addison:Sage, 1997.
15 ECHARD B, GAYTON N, LEMAIRE M. AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation[J]. Structural Safety201133(2): 145-154.
16 ZAKI M J, MEIRA W. Data mining and analysis: Fundamental concepts and algorithms [M]. New York: Cambridge University Press, 2014.
17 MOUSTAPHA M, MARELLI S, SUDRET B. UQLab user manual-active learning reliability: UQLab-V 2.0-117. Zurich: Chair of Risk, Safety and Uncertainty Quantification, 2022.
18 WANG J S, LI C F, XU G J, et al. Efficient structural reliability analysis based on adaptive Bayesian support vector regression[J]. Computer Methods in Applied Mechanics and Engineering2021387(9): 114172.
19 YI J X, ZHOU Q, CHENG Y S, et al. Efficient adaptive Kriging-based reliability analysis combining new learning function and error-based stopping criterion[J]. Structural and Multidisciplinary Optimization202062(5): 2517-2536.
20 WU H, YAN Y, LIU Y J. Reliability based optimization of composite laminates for frequency constraint[J]. Chinese Journal of Aeronautics200821(10): 320-327.
21 FENG K X, LU Z Z, CHEN Z B, et al. An innovative Bayesian updating method for laminated composite structures under evidence uncertainty[J]. Composite Structures2023304(10): 116429.
22 TOMBLIN J, MCKENNA J, NG Y, et al. Advanced general aviation transport experiments: Agate-wp3.3-033051-134 [S]. Washington,D.C.:NASA, 2002.
Outlines

/