Adaptive sequential strategy for risk estimation of engineering systems using Gaussian process regression active learning

Azizsoltani, Hamoon; Sadeghi, Elham

doi:10.1016/j.engappai.2018.06.007

Cited by 22 publications

(8 citation statements)

References 29 publications

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“…Under workbench environment of ANSYS (15.0 version), blisk radial deformation is deterministically investigated in [0 s, 215 s], by FE modeling and fluid-thermal-structural coupling. In fluid-thermal-structural coupling analysis, the closecoupling analysis method [33] is employed to implement the radial deformation analyses of turbine blisk in [0 s, 215 s], by Multiphysics Simulation module 3 [47], [48]. We decompose the fluid-thermal-structural system into three subsystems such as fluid, thermal and structural system.…”

Section: A Dynamic Deterministic Analysis Of Turbine Bliskmentioning

confidence: 99%

“…We decompose the fluid-thermal-structural system into three subsystems such as fluid, thermal and structural system. In other words, the dynamic deterministic analysis of blisk radial deformation is carried out by operating three subsystems separately with the standard k-ε turbulence model, employ the law of energy conservation to complete thermal analysis, 3 https://www.ansys.com/products/platform/multiphysics-simulation and apply FE method to achieve turbine blisk analyses with tetrahedron shape equation and geometric equation. Regarding the deterministic analysis, therefore, the variations of blade and disk radial deformations with time are drawn in Fig.…”

Section: A Dynamic Deterministic Analysis Of Turbine Bliskmentioning

confidence: 99%

“…With the development of structural probabilistic failure analysis, various approaches have emerged. Azizsoltani, et al, discussed the structural reliability estimation of engineering systems by adopting Monte Carlo (MC) simulation and Gaussian process regression active learning [3]. Zhan, et al, presented continuum damage mechanics based approach for the fatigue life evaluation of scarf bolted joints [4], [5].…”

Section: Introductionmentioning

confidence: 99%

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Decomposed-Coordinated Framework With Enhanced Extremum Kriging for Multicomponent Dynamic Probabilistic Failure Analyses

Feng

Fei

et al. 2019

IEEE Access

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For multicomponent structures enduring dynamic workloads coming from multi-physical fields, safety assessment is significant to guarantee the normal operation of entire structure system. In this paper, an enhanced extremum Kriging-based decomposed coordinated framework (E2K-DCF) is proposed to improve the dynamic probabilistic failure analyses of multicomponent structures. In this method, extremum Kriging model (EKM) is developed by introducing Kriging model into extremum response surface method (ERSM) to process the transient response problem and shorten computational burden in dynamic probabilistic failure analyses. Multiple population genetic algorithm (MPGA) is employed to solve maximum likelihood equation (MLE) and find the optimal hyperparameter θ in the EKM, which is promising to enhance approximate accuracy; decomposed-coordinated (DC) strategy is used to handle the coordinated relationship of multiple analytical objectives. To validate the proposed E2K-DCF, the probabilistic failure analysis of turbine blisk radial deformation is conducted by comparing with different methods within time domain [0 s, 215 s], considering fluid-thermal-structural interaction. It is revealed that the failure probability of blisk radial deformation is only 0.0022 when the allowable value is 2.5702 × 10 −3 m acquired from real world practice. Compared to the other approaches, this E2K-DCF has obvious advantages in fitting time and accuracy as well as simulation efficiency and accuracy. The results illustrate that the E2K-DCF is effective and applicable in dynamic probabilistic failure analysis. The efforts of this paper provide a novel viewpoint for the transient reliability evaluation of multicomponent structures, which is likely to enrich mechanical reliability theory. INDEX TERMS E2K-DCF, multicomponent structure, multiple population genetic algorithm, probabilistic failure, turbine blisk.

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Section: A Dynamic Deterministic Analysis Of Turbine Bliskmentioning

confidence: 99%

Section: A Dynamic Deterministic Analysis Of Turbine Bliskmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Decomposed-Coordinated Framework With Enhanced Extremum Kriging for Multicomponent Dynamic Probabilistic Failure Analyses

Feng

Fei

et al. 2019

IEEE Access

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“…The problem was solved by researchers using MCS, Markov chain Monte Carlo–based Subset Simulation (which is named SubSim/MCMC), and IS‐based subset simulation (which is named SubSim/IS). The results were reported by previous studies . According to the references, MCS computes the RBS results using 262 350 samples while at least 54 000 samples are requiring to obtain results by SubSim/MCMC and SubSim/IS .…”

Section: Numerical Examplesmentioning

confidence: 99%

“…In simulation methods, the basic concept is to generate samples from random variables to estimate failure probability . In majority of these methods, parameter sensitivities could be estimated with application of Score function approach:…”

Section: Introductionmentioning

confidence: 99%

System‐level reliability sensitivity analysis by using weighted average simulation method

Rashki

Moghaddam

Miri

2019

Quality & Reliability Eng

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In addition to reliability analysis, investigation of the uncertainties' effect on the safety of structures can be regarded as one of the great concerns in engineering fields. The present study provides an efficient perturbation-based reliability sensitivity analysis approach based on weighted average simulation method (WASM) to attain uncertainties effects on the structures safety. Without asking additional samplings and/or requiring to function derivation (that is necessary in score function method), the proposed approach simultaneously uses the finite difference and weight flexibility feature of WASM to estimate the parameter sensitivities of a reliability problem. The proposed method has also been successfully applied to the improved version of WASM to obtain reliability based sensitivity results with very few samples. The accuracy and efficiency of the proposed method is examined by solving five analytical and engineering examples with highly nonlinear performance functions and system-level reliability problems. For each example, results are compared with those obtained by Monte Carlo simulation and common reliability methods. It is shown that the method is capable of solving real world system-level engineering problems efficiently and accurately.

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Hierarchical Reinforcement Learning for Pedagogical Policy Induction

Zhou

Azizsoltani

Ausin

et al. 2019

Lecture Notes in Computer Science

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Adaptive sequential strategy for risk estimation of engineering systems using Gaussian process regression active learning

Cited by 22 publications

References 29 publications

Decomposed-Coordinated Framework With Enhanced Extremum Kriging for Multicomponent Dynamic Probabilistic Failure Analyses

Decomposed-Coordinated Framework With Enhanced Extremum Kriging for Multicomponent Dynamic Probabilistic Failure Analyses

System‐level reliability sensitivity analysis by using weighted average simulation method

Hierarchical Reinforcement Learning for Pedagogical Policy Induction

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