Dynamic sampling method for ship resistance performance optimisation based on approximated model

Chang, Haichao; Zhan, Chengsheng; Liu, Zuyuan; Cheng, Xide; Feng, Bin

doi:10.1080/17445302.2020.1730090

Cited by 6 publications

(2 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For engineering optimisation problems without a priori knowledge, one-shot sampling may result in an undersampling of the objective function or in an excessive number of training points [14]. In contrast, dynamic sampling, which does not have the limitations and deficiencies of one-shot sampling, involves sampling of additional points in regions with significant errors [15][16][17] or potential optimum regions [18][19][20][21][22][23][24], thereby enabling the construction of more accurate approximate models with less sample points.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, several dynamic sampling-based methods have been proposed for approximate modelling. Jiang et al [15] and Chang et al [24] used leave-one-out (LOO) method-based sequential sampling to improve the model prediction accuracy in the output space while considering the filling characteristics of the input space. Beck and Guillas [16] proposed a mutual information for computer experiment (MICE)-based adaptive sequential sampling algorithm that adaptively selected design values at which to run the computer simulator to maximise the expected information gain over the input space.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Information Matrix-Based Adaptive Sampling in Hull Form Optimisation

Ouyang

Chang

Feng

et al. 2021

JMSE

Self Cite

View full text Add to dashboard Cite

Hull form optimisation involves challenges such as large design spaces, numerous design variables, and high nonlinearity. Therefore, optimisation that only use global approximate models alone cannot yield desirable results. An information matrix-based method is proposed for dynamically embedded local approximate models (IM-DEAM) in this paper, which uses the Gaussian-function information matrix to extract one or more subspaces for additional sampling and a Latin hypercube design (LHD) for adaptive sampling. In addition, to prevent overfitting by global approximate models in some spaces because of the uneven distribution of the samples, local approximate models are embedded in the subspaces identified for additional sampling to enable accurate description of subspaces. The effectiveness and robustness of the method are validated and analysed by applying the proposed method to optimise mathematical functions and the hull form of the DTMB 5415. The results demonstrate that the proposed method is effective for improving the accuracies and can produce reliable optimisation results.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Information Matrix-Based Adaptive Sampling in Hull Form Optimisation

Ouyang

Chang

Feng

et al. 2021

JMSE

Self Cite

View full text Add to dashboard Cite

show abstract

A Scoping Review on Simulation-Based Design Optimization in Marine Engineering: Trends, Best Practices, and Gaps

Serani,

Scholcz,

Vanzi

2024

Arch Computat Methods Eng

View full text Add to dashboard Cite

This scoping review assesses the current use of simulation-based design optimization (SBDO) in marine engineering, focusing on identifying research trends, methodologies, and application areas. Analyzing 277 studies from Scopus and Web of Science, the review finds that SBDO is predominantly applied to optimizing marine vessel hulls, including both surface and underwater types, and extends to key components like bows, sterns, propellers, and fins. It also covers marine structures and renewable energy systems. A notable trend is the preference for deterministic single-objective optimization methods, indicating potential growth areas in multi-objective and stochastic approaches. The review points out the necessity of integrating more comprehensive multidisciplinary optimization methods to address the complex challenges in marine environments. Despite the extensive application of SBDO in marine engineering, there remains a need for enhancing the methodologies’ efficiency and robustness. This review offers a critical overview of SBDO’s role in marine engineering and highlights opportunities for future research to advance the field.

show abstract