RBMDO Using Gaussian Mixture Model-Based Second-Order Mean-Value Saddlepoint Approximation

Meng, Debiao; Yang, Shiyuan; Lin, Tao; Wang, Jiapeng; Yang, Hengfei; Lv, Zhiyuan

doi:10.32604/cmes.2022.020756

Cited by 34 publications

(33 citation statements)

References 47 publications

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“…, 2018b; Fang and Wen, 2019; Gholinezhad and Torabi, 2022; Meng et al. , 2021c, 2022; Yao et al. , 2011).…”

Section: Introductionmentioning

confidence: 99%

“…To accurately quantify the impact of uncertain factors and take advantage of MDO simultaneously, the Reliability-based MDO (RBMDO) has received extensive attention and has become a research hotspot (Wang and Xiong, 2019;Zhang et al, 2020a, b;Li et al, 2019;Zhu et al, 2021a, b;Fu et al, 2020;Wang et al, 2018b;Fang and Wen, 2019;Gholinezhad and Torabi, 2022;Meng et al, 2021cMeng et al, , 2022Yao et al, 2011). If one RBMDO problem is solved directly, the reliability analysis, the multidisciplinary analysis, and the design optimization will be nested and coupled during the entire iteration process (As shown in Figure 3).…”

Section: Introductionmentioning

confidence: 99%

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Multidisciplinary design optimization of engineering systems under uncertainty: a review

Meng

Yang

et al. 2022

IJSI

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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.

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“…, 2018b; Fang and Wen, 2019; Gholinezhad and Torabi, 2022; Meng et al. , 2021c, 2022; Yao et al. , 2011).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multidisciplinary design optimization of engineering systems under uncertainty: a review

Meng

Yang

et al. 2022

IJSI

Self Cite

View full text Add to dashboard Cite

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“…In this context, prognostics and health management (PHM) is playing an increasingly important role. PHM can be combined with multiple disciplines, such as machine learning [4,5] sensor technology [6,7], fault diagnosis [8], statistics [9][10][11][12] and reliability engineering [13][14][15][16], to realize an online evaluation of system health status and to predict the state of the system based on the current information. PHM converts signals and data detected by sensors into health status information about the system, and it alerts users so that the system can be maintained in a timely manner.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Maintenance Strategy for Multi-Component Systems Using a Genetic Algorithm

Shi¹,

Ma²,

Ma³

2023

Computer Modeling in Engineering &Amp; Sciences

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In multi-component systems, the components are dependent, rather than degenerating independently, leading to changes in maintenance schedules. In this situation, this study proposes a grouping dynamic maintenance strategy. Considering the structure of multi-component systems, the maintenance strategy is determined according to the importance of the components. The strategy can minimize the expected depreciation cost of the system and divide the system into optimal groups that meet economic requirements. First, multi-component models are grouped. Then, a failure probability model of multi-component systems is established. The maintenance parameters in each maintenance cycle are updated according to the failure probability of the components. Second, the component importance indicator is introduced into the grouping model, and the optimization model, which aimed at a maximum economic profit, is established. A genetic algorithm is used to solve the non-deterministic polynomial (NP)-complete problem in the optimization model, and the optimal grouping is obtained through the initial grouping determined by random allocation. An 11-component series and parallel system is used to illustrate the effectiveness of the proposed strategy, and the influence of the system structure and the parameters on the maintenance strategy is discussed.

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“…Now scholars have begun to use a variety of methods to optimize the parameters of the structure [14][15][16]. Among them, approximate methods have been widely used [17]. For the railway vehicle structure, the structure is complex and the boundary conditions are numerous, which often requires a lot of time in the simulation calculation.…”

Section: Introductionmentioning

confidence: 99%

Research on Optimization of Structural Parameters of Equipment Cabin Bottom Cover

Zou

Zhang

2022

Mathematical Problems in Engineering

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Since the railway vehicle structure has lots of parameters and several complex constraints, this study establishes a method for structural parameter optimization based on sensitivity analysis and surrogate models. Fatigue crack problem of the equipment cabin bottom cover of the EMU is taken as an example to optimize its structural parameters. First, establish the finite element (FE) model of the bottom cover and compare it with the bench test results to verify the accuracy of the load and restraint conditions. The sensitivity analysis method is used to determine the main parameters. The input samples are obtained by Latin hypercube sampling method, and the output samples are obtained by the method jointly developed by ABAQUS+Python and the surrogate model between the input and output samples is obtained by fitting, and its accuracy is verified. According to the design requirements, the optimization objective function and constraint conditions are established, and the optimization result is obtained by optimization algorithm. The results were substituted into the FE model for verification. The results show that the maximum equivalent stress of the bottom cover is reduced from 126.7 MPa to 78.9 MPa under a cyclic aerodynamic load of ±4 kPa, which is 37.7% optimized, and the effect is significant. This method avoids the iterative optimization of the FE model and improves the optimization efficiency.

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RBMDO Using Gaussian Mixture Model-Based Second-Order Mean-Value Saddlepoint Approximation

Cited by 34 publications

References 47 publications

Multidisciplinary design optimization of engineering systems under uncertainty: a review

Multidisciplinary design optimization of engineering systems under uncertainty: a review

A Dynamic Maintenance Strategy for Multi-Component Systems Using a Genetic Algorithm

Research on Optimization of Structural Parameters of Equipment Cabin Bottom Cover

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