Multidisciplinary optimization of an offshore aquaculture vessel hull form based on the support vector regression surrogate model

Feng, Yanhui; Chen, Zuogang; Dai, Yi; Wang, Fei; Cai, Ji-Qiang; Shen, Zhixin

doi:10.1016/j.oceaneng.2018.07.062

Cited by 38 publications

(20 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For a computationally expensive problem where each function evaluation could require minutes, hours, or days and occupy massive parallel resources, requiring 20,000 function evaluations would be prohibitive. For computationally expensive problems, evolutionary MOAs are often combined with the RSM to reduce computational expense, as described in [34] and [52].…”

Section: Multiobjective Optimization Techniques and Algorithmsmentioning

confidence: 99%

Algorithm 1028: VTMOP: Solver for Blackbox Multiobjective Optimization Problems

Chang

Watson

Larson

et al. 2022

ACM Trans. Math. Softw.

View full text Add to dashboard Cite

VTMOP is a Fortran 2008 software package containing two Fortran modules for solving computationally expensive bound-constrained blackbox multiobjective optimization problems. VTMOP implements the algorithm of [32], which handles two or more objectives, does not require any derivatives, and produces well-distributed points over the Pareto front. The first module contains a general framework for solving multiobjective optimization problems by combining response surface methodology, trust region methodology, and an adaptive weighting scheme. The second module features a driver subroutine that implements this framework when the objective functions can be wrapped as a Fortran subroutine. Support is provided for both serial and parallel execution paradigms, and VTMOP is demonstrated on several test problems as well as one real-world problem in the area of particle accelerator optimization.

show abstract

Section: Multiobjective Optimization Techniques and Algorithmsmentioning

confidence: 99%

Algorithm 1028: VTMOP: Solver for Blackbox Multiobjective Optimization Problems

Chang

Watson

Larson

et al. 2022

ACM Trans. Math. Softw.

View full text Add to dashboard Cite

show abstract

“…A variety of data-driven reduced-order modeling techniques exist in literature like Kriging [25] (also known as Gaussian process regression) and its variants [43,5], polynomial chaos expansions [3], and support vector machines [11]. Some reviews have elaborated the applications of these methods for shape optimization applications [24,16,56].…”

Section: Introductionmentioning

confidence: 99%

Deep convolutional neural network for shape optimization using level-set approach

Mallik¹,

Farvolden²,

Jelovica³

et al. 2022

Preprint

View full text Add to dashboard Cite

This article presents a reduced-order modeling methodology via deep convolutional neural networks (CNNs) for shape optimization applications. The CNN provides a nonlinear mapping between the shapes and their associated attributes while conserving the equivariance of these attributes to the shape translations. To implicitly represent complex shapes via a CNN-applicable Cartesian structured grid, a level-set method is employed. The CNN-based reduced-order model (ROM) is constructed in a completely data-driven manner thus well suited for non-intrusive applications. We demonstrate our ROM-based shape optimization framework on a gradient-based three-dimensional shape optimization problem to minimize the induced drag of a wing in low-fidelity potential flow. We show a good agreement between ROM-based optimal aerodynamic coefficients and their counterparts obtained via a potential flow solver. The predicted behavior of the optimized shape is consistent with theoretical predictions. We also present the learning mechanism of the deep CNN model in a physically interpretable manner. The CNN-ROM-based shape optimization algorithm exhibits significant computational efficiency compared to the full-order model-based online optimization applications. The proposed algorithm promises to develop a tractable DL-ROM driven framework for shape optimization and adaptive morphing structures.

show abstract

“…The surrogated model or metamodel reduces the computational efforts and prohibits direct simulation for every generated design. Feng et al 14 optimized hullform of an offshore aquaculture vessel by using a surrogated model. Lu et al 15 developed a B spline optimization method for modifying bow and stern region of KCS ship hull.…”

Section: Introductionmentioning

confidence: 99%

Bow shape modification through multi-objective hydrodynamic optimization: Methodology comparison between CAD-based FreeForm Deformation and Mesh-based Radial Basis Function approach

Ghadimi

Nazemian

2022

Proceedings of the Institution of Mechanical Engineers, Part M:

View full text Add to dashboard Cite

Marine industrial engineering face crucial challenges because of environmental footprint of vehicles, global recession, construction, and operation cost. Meanwhile, Shape optimization is the key feature to improve ship efficiency and ascertain better design. Accordingly, the present paper proposes an automated optimization framework for ship hullform modification to reduce total resistance at two cruise and sprint speeds. The case study is a bow shape of a wave-piercing bow trimaran hull. To this end, a multi-objective hydrodynamic problem needs to be solved. A combined optimization strategy using CFD hullform optimization is presented using the software tools STAR-CCM+ and SHERPA algorithm as optimizer. Furthermore, a comparison is made between CAD-based and Mesh-based parametrization techniques. Comparison between geometry regeneration methods is performed to present a practical and efficient parametrization tool. Design variables are control points of FreeForm Deformation (FFD) for CAD-based method and Radial Basis Function (RBF) for Mesh-based method. The optimization results show a 4.77% and 2.47% reduction in the total resistance at cruise and sprint speed, respectively.

show abstract

Multidisciplinary optimization of an offshore aquaculture vessel hull form based on the support vector regression surrogate model

Cited by 38 publications

References 11 publications

Algorithm 1028: VTMOP: Solver for Blackbox Multiobjective Optimization Problems

Algorithm 1028: VTMOP: Solver for Blackbox Multiobjective Optimization Problems

Deep convolutional neural network for shape optimization using level-set approach

Bow shape modification through multi-objective hydrodynamic optimization: Methodology comparison between CAD-based FreeForm Deformation and Mesh-based Radial Basis Function approach

Contact Info

Product

Resources

About