The hybrid uncertain neural network method for mechanical reliability analysis

Peng, Wensheng; Zhang, Jianguo; You, Lingfei

doi:10.5139/ijass.2015.16.4.510

Cited by 10 publications

(2 citation statements)

References 24 publications

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“…In other applications as well, for example, [32][33][34][35][36][37][38][39], Xia and Yu proposed the change-of-variable interval stochastic perturbation method to predict the interval of the response probability density function and the response confidence interval of a hybrid uncertain structural-acoustic system with random and interval variables. Meanwhile, Chen et al presented a hybrid stochastic interval perturbation method for the unified energy flow analysis of coupled vibrating systems.…”

Section: Introductionmentioning

confidence: 99%

Application of Probabilistic and Nonprobabilistic Hybrid Reliability Analysis Based on Dynamic Substructural Extremum Response Surface Decoupling Method for a Blisk of the Aeroengine

Bai

Zhang

et al. 2017

International Journal of Aerospace Engineering

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For the nondeterministic factors of an aeroengine blisk, including both factors with sufficient and insufficient statistical data, based on the dynamic substructural method of determinate analysis, the extremum response surface method of probabilistic analysis, and the interval method of nonprobabilistic analysis, a methodology called the probabilistic and nonprobabilistic hybrid reliability analysis based on dynamic substructural extremum response surface decoupling method (P-NP-HRA-DS-ERSDM) is proposed. The model includes random variables and interval variables to determine the interval failure probability and the interval reliability index. The extremum response surface function and its flow chart of mixed reliability analysis are given. The interval analysis is embedded in the most likely failure point in the iterative process. The probabilistic analysis and nonprobabilistic analysis are investigated alternately. Tuned and mistuned blisks are studied in a complicated environment, and the results are compared with the Monte Carlo method (MCM) and the multilevel nested algorithm (MLNA) to verify that the hybrid model can better handle reliability problems concurrently containing random variables and interval variables; meanwhile, it manifests that the computational efficiency of this method is superior and more reasonable for analysing and designing a mistuned blisk. Therefore, this methodology has very important practical significance.

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Section: Introductionmentioning

confidence: 99%

Application of Probabilistic and Nonprobabilistic Hybrid Reliability Analysis Based on Dynamic Substructural Extremum Response Surface Decoupling Method for a Blisk of the Aeroengine

Bai

Zhang

et al. 2017

International Journal of Aerospace Engineering

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“…An artificial neural network involves the imitation of human cerebral opera many artificial neurons [17]. An artificial neuron is a form of mathematical mo the operation of a human neuron, which typically has multiple inputs.…”

Section: Bp Modelmentioning

confidence: 99%

A Highly Efficient Aeroelastic Optimization Method Based on a Surrogate Model

Wan¹,

Wang²,

Chao³

2016

International Journal of Aeronautical and Space Sciences

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This paper presents a highly efficient aeroelastic optimization method based on a surrogate model; the model is verified by considering the case of a high-aspect-ratio composite wing. Optimization frameworks using the Kriging model and genetic algorithm (GA), the Kriging model and improved particle swarm optimization (IPSO), and the back propagation neural network model (BP) and IPSO are presented. The feasibility of the method is verified, as the model can improve the optimization efficiency while also satisfying the engineering requirements. Moreover, the effects of the number of design variables and number of constraints on the optimization efficiency and objective function are analysed in detail. The accuracy of two surrogate models in aeroelastic optimization is also compared. The Kriging model is constructed more conveniently, and its predictive accuracy of the aeroelastic responses also satisfies the engineering requirements. According to the case of a high-aspect-ratio composite wing, the GA is better at global optimization.

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