Reliability-based robust design optimization of gap size of annular nuclear fuels using kriging and inverse distance weighting methods

Doh, Jaehyeok; Kim, Young-Hoon; Lee, Jong-Soo

doi:10.1080/0305215x.2018.1428316

Cited by 8 publications

(6 citation statements)

References 26 publications

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“…After solving the deterministic problem, Gholaminezhad et al 15 employed a Monte Carlo simulation to obtain a number of points in the vicinity of the deterministic optimum, in order to evaluate reliability and robustness. Doh et al (2018) presented a reliability-based robust design problem applied to a nuclear fuel (for a pressurized water reactor, a nuclear reactor). The authors presented the deterministic optimization problem, as well as the reliability-based formulation and the reliability-based robust formulation.…”

Section: Introductionmentioning

confidence: 99%

“…The authors presented the deterministic optimization problem, as well as the reliability-based formulation and the reliability-based robust formulation. Doh et al 16 employed arbitrary weights to balance the mean and the standard deviation terms in the objectivefunction. Essentially, the reliability-based robust design problem approached by Doh et al 16 is solved as a single objective optimization problem with the choice of arbitrary weights.…”

Section: Introductionmentioning

confidence: 99%

“…Doh et al 16 employed arbitrary weights to balance the mean and the standard deviation terms in the objectivefunction. Essentially, the reliability-based robust design problem approached by Doh et al 16 is solved as a single objective optimization problem with the choice of arbitrary weights. The essential difference between all these approaches and our proposal lies in the fact that our formulation considers the reliability index as an objective in the optimization problem.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Reliability-Based Robust Design Multiobjective Optimization Formulation Applied in Chemical Engineering

Libotte

Lobato

Neto

et al. 2022

Ind. Eng. Chem. Res.

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Engineering systems are often subject to variations and uncertainties associated with external factors, environmental changes, equipment inaccuracies, and other aspects. Our fundamental objective is to formulate a multiobjective optimization problem that is capable of handling robust and reliability-based optimizations, to obtain solutions that satisfy prescribed reliability levels and are least sensitive to external noise. In project optimization, these models play a fundamental role, allowing us to obtain parameters and attributes capable of enhancing product performance, reducing costs, and operating time. To accomplish this task, we consider two different approaches capable of quantifying uncertainties during the optimization of mathematical models. In the first, robust optimization, the sensitivity of decision variables in relation to deviations caused by external factors is evaluated. The second approach, reliability-based optimization, measures the probability of system failure and obtains model parameters that ensure an established level of reliability. We tested our novel approach on benchmark and chemical engineering problems, usually treated as deterministic problems. The proposed methodology provides a systematic way to evaluate uncertainties, in order to achieve more realistic results, considering external factors.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Reliability-Based Robust Design Multiobjective Optimization Formulation Applied in Chemical Engineering

Libotte

Lobato

Neto

et al. 2022

Ind. Eng. Chem. Res.

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show abstract

“…Then, for the finite‐element simulation experiment, Abebe et al 25 developed a hybrid model to solve RBRDO problems. Doh et al 26 improved transfer efficiency and reliability of the process by using the weighting models. The above research mainly used GP models to estimate the performance functions to improve prediction accuracy and reduce calculation load.…”

Section: Introductionmentioning

confidence: 99%

Life‐cycle reliability‐based robust design optimization for GP model with response uncertainty

Feng

Wang

et al. 2021

Quality & Reliability Eng

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Reliability-based robust design optimization (RBRDO) is a crucial tool for lifecycle quality improvement. Gaussian process (GP) model is an effective alternative modeling technique that is widely used in robust parameter design. However, there are few studies to deal with reliability-based design problems by using GP model. This article proposes a novel life-cycle RBRDO approach concerning response uncertainty under the framework of GP modeling technique. First, the hyperparameters of GP model are estimated by using the Gibbs sampling procedure. Second, the expected partial derivative expression is derived based on GP modeling technique. Moreover, a novel failure risk cost function is constructed to assess the life-cycle reliability. Then, the quality loss function and confidence interval are constructed by simulated outputs to evaluate the robustness of optimal settings and response uncertainty, respectively. Finally, an optimization model integrating failure risk cost function, quality loss function, and confidence interval analysis approach is constructed to find reasonable optimal input settings. Two case studies are given to illustrate the performance of the proposed approach. The results show that the proposed approach can make better trade-offs between the quality characteristics and reliability requirements by considering response uncertainty.

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“…Zhu (Zhu et al, 2009) et al found SVR performs very well in highly nonlinear vehicle crash problems and used the surrogate model in the robust optimization process. Jaehyeok et al built the thermoelastic-plasticity-creep model of pressurized water reactors annular fuels and combined the optimal Latin hypercube design and the kriging and inverse distance weighting method to obtain the surrogate model of the thermoelastic-plasticity-creep model, which greatly reduced the computation cost in the robust design optimization of the gap size of annular nuclear fuel (Doh et al, 2018). Gao et al established the explicit mapping relationship between the casting process parameters and the casting quality by using the double-layer Kriging method and used the explicit model for relieving the computation when solving the robust optimal process parameters (Gao et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

An Efficient Integrated Framework of Reliability-Based Robust Design Optimization for Computation-Intensive Structures

Lai

Huang

Wang

et al. 2021

Preprint

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When carrying out robust design optimization for complex engineering structures, they are computed by finite element software and are always computation-intensive. Aim at this problem, the paper proposes an efficient integrated framework of Reliability-based Robust Design Optimization (RBRDO). Firstly, the conventional RBRDO problem is changed as percentile form, that is, the improved percentile formulation of computing the objective robustness and probabilistic constraints is presented by resorting to the employment of Performance Measure Approach (PMA). Secondly, the above improved RBRDO problem is simplified by a series of new approximation methods due to the need of reducing computation. An efficient approximation method is proposed to estimate PMA functions of the RBRDO formulation. Based on it, the above improved RBRDO problem can be transformed into a sequence of approximate deterministic sub-optimization problems, whose objective function and constraints are expressed as the approximate explicit form only in relation to the design variables. Furthermore, use the trust region method to solve the above sequence of sub-optimization. Lastly, several examples are used to demonstrate the effectiveness and efficiency of the proposed method.

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Reliability-based robust design optimization of gap size of annular nuclear fuels using kriging and inverse distance weighting methods

Cited by 8 publications

References 26 publications

A Novel Reliability-Based Robust Design Multiobjective Optimization Formulation Applied in Chemical Engineering

A Novel Reliability-Based Robust Design Multiobjective Optimization Formulation Applied in Chemical Engineering

Life‐cycle reliability‐based robust design optimization for GP model with response uncertainty

An Efficient Integrated Framework of Reliability-Based Robust Design Optimization for Computation-Intensive Structures

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