A sequential single-loop reliability optimization and confidence analysis method

Hao, Peng; Yang, Hang; Yang, Hao; Zhang, Yue; Wang, Yutian; Wang, Bo

doi:10.1016/j.cma.2022.115400

Cited by 9 publications

(2 citation statements)

References 44 publications

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“…For example, Karar et al [50] presented a sensitivity/elasticity factor-driven reliability modeling technique for robust design optimization. Hao et al [51] proposed a sequential single-loop optimization and confidence analysis method for reliability-based design optimization, and its computing efficiency and accuracy were validated to be improved significantly. To simultaneously deal with the probabilistic, uncertain-but-bounded, and fuzzy parameters, Meng et al [52] proposed a unified high-performance computation method for the reliable optimization of complicated structures.…”

Section: Mcsmentioning

confidence: 99%

Random Forest-Based Fatigue Reliability-Based Design Optimization for Aeroengine Structures

Li,

Song

2024

CMES

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Fatigue reliability-based design optimization of aeroengine structures involves multiple repeated calculations of reliability degree and large-scale calls of implicit high-nonlinearity limit state function, leading to the traditional direct Monte Claro and surrogate methods prone to unacceptable computing efficiency and accuracy. In this case, by fusing the random subspace strategy and weight allocation technology into bagging ensemble theory, a random forest (RF) model is presented to enhance the computing efficiency of reliability degree; moreover, by embedding the RF model into multilevel optimization model, an efficient RF-assisted fatigue reliability-based design optimization framework is developed. Regarding the low-cycle fatigue reliability-based design optimization of aeroengine turbine disc as a case, the effectiveness of the presented framework is validated. The reliabilitybased design optimization results exhibit that the proposed framework holds high computing accuracy and computing efficiency. The current efforts shed a light on the theory/method development of reliability-based design optimization of complex engineering structures.

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Section: Mcsmentioning

confidence: 99%

Random Forest-Based Fatigue Reliability-Based Design Optimization for Aeroengine Structures

Li,

Song

2024

CMES

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“…To overcome this challenge, many scholars have applied machine learning 27,28 to improve the efficiency of structural reliability analysis. By integrating optimization design 29 and reliability analysis methodologies, 30,31 reliability metrics can be embedded into the design phase, 32,33 thus ensuring the reliability level of turbine shaft during the fatigue optimization design. 34 To enhance the life expectancy of a turbine shaft under CCF loading, this study proposes a fatigue reliability analysis and optimization design method.…”

Section: Introductionmentioning

confidence: 99%

A reliability analysis and optimization method for a turbine shaft under combined high and low cycle fatigue loading

Bai,

Zeng,

Huang

et al. 2024

Quality & Reliability Eng

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The combined high and low cycle fatigue (CCF) loading condition and random uncertainty exert a considerable impact on the design of turbine shafts. To enhance the fatigue life and reliability, this research proposes a CCF reliability analysis and optimization method for turbine shafts. A CCF fatigue reliability analysis framework is established, which focuses on the quantification of CCF loading characteristics and random uncertainty. The consideration of CCF loading characteristics contain the loading frequency ratio of high cycle fatigue (HCF) to low cycle fatigue (LCF), the stress amplitude ratio of HCF to LCF, as well as their interaction. The consideration of random uncertainty contains material, geometry and load, and a surrogate model‐based method is introduced to improve the quantification efficiency. Through the validation by comparing with experimental data and traditional methods, the proposed method is with higher accuracy and efficiency. By integrating the proposed fatigue reliability analysis method with design optimization, optimal design values for the turbine shaft were identified. This method theoretically extends the shaft's CCF life and provides practical engineering guidance for its reliability analysis and design.

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Data-driven confidence bound for structural response using segmented least squares: a mixed-integer programming approach

Kanno

2024

Japan J. Indust. Appl. Math.

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As one of data-driven approaches to computational mechanics in elasticity, this paper presents a method finding a bound for structural response, taking uncertainty in a material data set into account. For construction of an uncertainty set, we adopt the segmented least squares so that a data set that is not fitted well by the linear regression model can be dealt with. Since the obtained uncertainty set is nonconvex, the optimization problem solved for the uncertainty analysis is nonconvex. We recast this optimization problem as a mixed-integer programming problem to find a global optimal solution. This global optimality, together with a fundamental property of the order statistics, guarantees that the obtained bound for the structural response is conservative, in the sense that, at least a specified confidence level, probability that the structural response is in this bound is no smaller than a specified target value. We present numerical examples for three different types of skeletal structures.

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A sequential single-loop reliability optimization and confidence analysis method

Cited by 9 publications

References 44 publications

Random Forest-Based Fatigue Reliability-Based Design Optimization for Aeroengine Structures

Random Forest-Based Fatigue Reliability-Based Design Optimization for Aeroengine Structures

A reliability analysis and optimization method for a turbine shaft under combined high and low cycle fatigue loading

Data-driven confidence bound for structural response using segmented least squares: a mixed-integer programming approach

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