Reliability analysis of composite sandwich structure for fuselage skin based on surrogate model

被引:0
作者
Ding Z. [1 ]
Li H. [1 ]
Guan X. [2 ]
机构
[1] College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing
[2] Beijing Institute of Power Machinery, China Aerospace Science and Industry Corporation, Beijing
来源
Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University | 2022年 / 40卷 / 02期
关键词
Kriging; Parametric modeling; Reliability; Sandwich structure; Subset simulation;
D O I
10.1051/jnwpu/20224020360
中图分类号
学科分类号
摘要
Composite structures are characterized by multiple source uncertainties in mechanical properties, geometric dimension, etc., and multiple failure modes, which bring challenges to their design and application. In order to explore the reliability analysis method of composite sandwich structures with multi-source uncertainties and multi-failure modes, a parametric finite element model and an acoustic boundary element model were established for deterministic analysis of a fuselage skin sandwich structure. Considering the randomness of the mechanical properties of composite materials and the laminate thickness, the structural reliability models were established for three typical failure modes of sandwich structures: static strength failure, global buckling and vibration noise. In order to reduce the computational effort, the Kriging surrogate models of failure modes were constructed, and the subset simulation method was used to predict the system failure probability of the sandwich structure of fuselage skin. The proposed computational framework provides support and tool for the fine design of composite sandwich structure. © 2022 Journal of Northwestern Polytechnical University.
引用
收藏
页码:360 / 368
页数:8
相关论文
共 15 条
[1]  
CHOI S K, CANFIELD R A, GRANDHI R., Reliability-based structural design, (2007)
[2]  
WANG Z, JR J, ST-PIERRE L, Et al., Reliability-based buckling optimization with an accelerated Kriging metamodel for filament-wound variable angle tow composite cylinders, Composite Structures, 254, (2020)
[3]  
MATHEW T V, PRAJITH P, RUIZ R O, Et al., Adaptive importance sampling based neural network framework for reliability and sensitivity prediction for variable stiffness composite laminates with hybrid uncertainties, Composite Structures, 245, (2020)
[4]  
LIN Sen, Reliability analysis of composite pressure vessel based on response surface method, (2020)
[5]  
SUN Z, WANG J, LI R, Et al., LIF: a new Kriging based learning function and its application to structural reliability analysis, Reliability Engineering and System Safety, 157, pp. 152-165, (2017)
[6]  
ZHOU Chunping, LIU Fuchao, ZHOU Changcong, Et al., Reliability calculation and global sensitivity analysis of static strength of quartz fiber/epoxy resin composites, Journal of Composite Materials, 37, 7, pp. 1611-1618, (2020)
[7]  
GASPAR B, TEIXEIRA A P, SOARES C G., Adaptive surrogate model with active refinement combining Kriging and a trust region method, Reliability Engineering & System Safety, 165, pp. 277-291, (2017)
[8]  
ZHANG X, WANG L, SORENSEN J D., REIF: a novel active-learning function towards adaptive Kriging surrogate models for structural reliability analysis, Reliability Engineering and System Safety, 185, pp. 440-454, (2019)
[9]  
ZUO Kongcheng, CHEN Peng, WANG Zheng, Et al., Research status of aircraft cabin noise, Acta Aeronautica et Astronautica Sinica, 37, 8, pp. 2370-2384, (2016)
[10]  
GIBSON R F., Principles of composite material mechanics, (2007)