Multi-fidelity Co-Kriging surrogate model for ship hull form optimization

Liu, Xinwang; Zhao, Weiwen; Wan, Decheng

doi:10.1016/j.oceaneng.2021.110239

Cited by 52 publications

(22 citation statements)

References 39 publications

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“…Although performed under compressible flow conditions, the same techniques are interesting to incompressible flows. Liu et al [23] uses viscous and potential flow calculations as the high-and low-fidelity respectively and shows the multi-fidelity Kriging approach can obtain a more optimal hydrodynamic hull form than the single-fidelity model alone.…”

Section: R Wenink Et Al / Multi-fidelity Kriging For a Falling Lifeboatmentioning

confidence: 99%

Multi-fidelity Kriging extrapolation together with CFD for the design of the cross-section of a falling lifeboat

Wenink,

van der Eijk,

Yorke-Smith

et al. 2023

ISP

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Surrogate modelling techniques such as Kriging are a popular means for cheaply emulating the response of expensive Computational Fluid Dynamics (CFD) simulations. These surrogate models are often used for exploring a parameterised design space and identifying optimal designs. Multi-fidelity Kriging extends the methodology to incorporate data of variable accuracy and costs to create a more effective surrogate. This work recognises that the grid convergence property of CFD solvers is currently an unused source of information and presents a novel method that, by leveraging the data structure implied by grid convergence, could further improve the performance of the surrogate model and the corresponding optimisation process. Grid convergence states that the simulation solution converges to the true simulation solution as the numerical grid is refined. The proposed method is tested with realistic multi-fidelity data acquired with CFD simulations. The performance of the surrogate model is comparable to an existing method, and likely more robust. More research is needed to explore the full potential of the proposed method. Code has been made available online at https://github.com/robertwenink/MFK-Extrapolation.

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Section: R Wenink Et Al / Multi-fidelity Kriging For a Falling Lifeboatmentioning

confidence: 99%

Multi-fidelity Kriging extrapolation together with CFD for the design of the cross-section of a falling lifeboat

Wenink,

van der Eijk,

Yorke-Smith

et al. 2023

ISP

View full text Add to dashboard Cite

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“…Although numerical simulation methods such as MCS have high calculation accuracy, they require many simulation sample points (Yuan et al, 2017). In recent years, the surrogate-assisted RBMDO method, which can fit the performance function according to the relationship between input and output variables, has become a research hotspot in the RBMDO field (Wang et al, 2020;Tang et al, 2020;Slot et al, 2020;Liu et al, 2022). For small-probability failure problems, it is usually necessary to use the variance reduction method and a high-precision surrogate model to reduce computational cost.…”

Section: High-precision Surrogate Modelmentioning

confidence: 99%

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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“…To reduce the computational cost of optimization, researchers often replace an accurate but computationally expensive physical model with a quickly computable model for approximation of objective function -the so-called surrogate model [6]. Surrogate modeling is actively used to solve problems from different fields: simulating oil reservoirs for maximizing the total production of oil value and forecasting the most profitable oilfields [7], optimizing of the heatgenerating components in small electronic devices for control of temperature field [8], obtaining the hydrodynamic performance indexes of ship hull form for increasing its strength [9]. Researchers consider a wide variety of models as surrogates: from classical methods (polynomial regression, kriging, support vector regression) to complex ensemble models, deep neural networks, long-short term memory networks, etc [10].…”

Section: Related Workmentioning

confidence: 99%

Surrogate-Assisted Evolutionary Generative Design Of Breakwaters Using Deep Convolutional Networks

Starodubcev¹,

Nikitin²,

Kalyuzhnaya³

2022

Preprint

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In the paper, a multi-objective evolutionary surrogate-assisted approach for the fast and effective generative design of coastal breakwaters is proposed. To approximate the computationally expensive objective functions, the deep convolutional neural network is used as a surrogate model. This model allows optimizing a configuration of breakwaters with a different number of structures and segments. In addition to the surrogate, an assistant model was developed to estimate the confidence of predictions. The proposed approach was tested on the synthetic water area, the SWAN model was used to calculate the wave heights. The experimental results confirm that the proposed approach allows obtaining more effective (less expensive with better protective properties) solutions than non-surrogate approaches for the same time.

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Multi-fidelity Co-Kriging surrogate model for ship hull form optimization

Cited by 52 publications

References 39 publications

Multi-fidelity Kriging extrapolation together with CFD for the design of the cross-section of a falling lifeboat

Multi-fidelity Kriging extrapolation together with CFD for the design of the cross-section of a falling lifeboat

Multidisciplinary design optimization of engineering systems under uncertainty: a review

Surrogate-Assisted Evolutionary Generative Design Of Breakwaters Using Deep Convolutional Networks

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