Hybrid enhanced Monte Carlo simulation coupled with advanced machine learning approach for accurate and efficient structural reliability analysis

Luo, Changqi; Keshtegar, Behrooz; Zhu, Shibai; Taylan, Osman; Niu, Xiaopeng

doi:10.1016/j.cma.2021.114218

Cited by 122 publications

(40 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Rbmdo-sora Considering Aleatory Uncertaintymentioning

confidence: 99%

“…However, in practical engineering, it is not very easy to obtain enough sample points. Especially when assessing the reliability of small-probability failure events, this problem is more significant (Luo et al, 2022;Liao et al, 2020;Wu et al, 2021;Zhu et al, 2022;. Au and Beck (2001) proposed a Subset Simulation (SS) method for evaluating smallprobability failure events considering aleatory uncertainty.…”

Section: Design Variable Reliability Constraintmentioning

confidence: 99%

See 1 more Smart Citation

Multidisciplinary design optimization of engineering systems under uncertainty: a review

Meng

Yang

et al. 2022

IJSI

View full text Add to dashboard Cite

PurposeAs an advanced calculation methodology, reliability-based multidisciplinary design optimization (RBMDO) has been widely acknowledged for the design problems of modern complex engineering systems, not only because of the accurate evaluation of the impact of uncertain factors but also the relatively good balance between economy and safety of performance. However, with the increasing complexity of engineering technology, the proposed RBMDO method gradually cannot effectively solve the higher nonlinear coupled multidisciplinary uncertainty design optimization problems, which limits the engineering application of RBMDO. Many valuable works have been done in the RBMDO field in recent decades to tackle the above challenges. This study is to review these studies systematically, highlight the research opportunities and challenges, and attempt to guide future research efforts.Design/methodology/approachThis study presents a comprehensive review of the RBMDO theory, mainly including the reliability analysis methods of different uncertainties and the decoupling strategies of RBMDO.FindingsFirst, the multidisciplinary design optimization (MDO) preliminaries are given. The basic MDO concepts and the corresponding mathematical formulas are illustrated. Then, the procedures of three RBMDO methods with different reliability analysis strategies are introduced in detail. These RBMDO methods were proposed for the design optimization problems under different uncertainty types. Furtherly, an optimization problem for a certain operating condition of a turbine runner blade is introduced to illustrate the engineering application of the above method. Finally, three aspects of future challenges for RBMDO, namely, time-varying uncertainty analysis; high-precision surrogate models, and verification, validation and accreditation (VVA) for the model, are discussed followed by the conclusion.Originality/valueThe scope of this study is to introduce the RBMDO theory systematically. Three commonly used RBMDO-SORA methods are reviewed comprehensively, including the methods' general procedures and mathematical models.

show abstract

Section: Rbmdo-sora Considering Aleatory Uncertaintymentioning

confidence: 99%

Section: Design Variable Reliability Constraintmentioning

confidence: 99%

Multidisciplinary design optimization of engineering systems under uncertainty: a review

Meng

Yang

et al. 2022

IJSI

View full text Add to dashboard Cite

show abstract

“…(2021a, b) introduced the multilevel MC to dynamic reliability analysis, which reduces the computational complexity of MC simulation. Luo et al. (2022) explored the enhanced MC simulation for improving accurate and efficient structural reliability analysis.…”

Section: Dynamic Reliability Analysis Of Single-objective Structurementioning

confidence: 99%

Structural dynamic reliability analysis: review and prospects

Teng

Feng

Chen

et al. 2022

IJSI

View full text Add to dashboard Cite

PurposeThe purpose of this paper is to briefly summarize and review the theories and methods of complex structures’ dynamic reliability. Complex structures are usually assembled from multiple components and subjected to time-varying loads of aerodynamic, structural, thermal and other physical fields; its reliability analysis is of great significance to ensure the safe operation of large-scale equipment such as aviation and machinery.Design/methodology/approachIn this paper for the single-objective dynamic reliability analysis of complex structures, the calculation can be categorized into Monte Carlo (MC), outcrossing rate, envelope functions and extreme value methods. The series-parallel and expansion methods, multi-extremum surrogate models and decomposed-coordinated surrogate models are summarized for the multiobjective dynamic reliability analysis of complex structures.FindingsThe numerical complex compound function and turbine blisk are used as examples to illustrate the performance of single-objective and multiobjective dynamic reliability analysis methods. Then the future development direction of dynamic reliability analysis of complex structures is prospected.Originality/valueThe paper provides a useful reference for further theoretical research and engineering application.

show abstract

“…The improved global optimization algorithm performs well in numerical optimization problems and can accurately find the optimal value of function in complex mathematical examples [11][12][13]. At the same time, many scholars apply the global optimization algorithm based on the surrogate model to engineering problems such as fluid analysis and have been effectively verified, which provides an effective method for the field of engineering optimization [14][15][16]. Yi et al [17] establishes two surrogate models of spray angle and liquid film thickness based on Kriging's global optimization algorithm, and carry out multi-objective parameter optimization for the swirl nozzle of aero-engine at low-pressure start-up stage.…”

Section: Introductionmentioning

confidence: 99%

Fluid Analysis and Structure Optimization of Impeller Based on Surrogate Model

Xu¹,

Wei²,

He³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

The surrogate model technology has a good performance in solving black-box optimization problems, which is widely used in multi-domain engineering optimization problems. The adaptive surrogate model is the mainstream research direction of surrogate model technology, which can realize model fitting and global optimization of engineering problems by infilling criteria. Based on the idea of the adaptive surrogate model, this paper proposes an efficient global optimization algorithm based on the local remodeling method (EGO-LR), which aims at improving the accuracy and optimization efficiency of the model. The proposed algorithm firstly constructs the expectation improvement (EI) function in the local area and optimizes it to get the update points. Secondly, the obtained update points are added to the global region until the global accuracy of the model meets the requirements. Then the differential evolution algorithm is used for global optimization. Sixteen benchmark functions are used to compare the EGO-LR algorithm with the existing algorithms. The results show that the EGO-LR algorithm can quickly converge to the accuracy requirements of the model and find the optimal value efficiently when facing complex problems with many local extrema and large variable spaces. The proposed algorithm is applied to the optimization design of the structural parameter of the impeller, and the outflow field analysis of the impeller is realized through finite element analysis. The optimization with the maximum fluid pressure (MP value) of the impeller as the objective function is completed, which effectively reduces the pressure value of the impeller under load.

show abstract

Hybrid enhanced Monte Carlo simulation coupled with advanced machine learning approach for accurate and efficient structural reliability analysis

Cited by 122 publications

References 66 publications

Multidisciplinary design optimization of engineering systems under uncertainty: a review

Multidisciplinary design optimization of engineering systems under uncertainty: a review

Structural dynamic reliability analysis: review and prospects

Fluid Analysis and Structure Optimization of Impeller Based on Surrogate Model

Contact Info

Product

Resources

About