Learning-based ship design optimization approach

Hao, Chunfang; Turan, Osman; Sayer, P.

doi:10.1016/j.cad.2011.06.011

Cited by 41 publications

(18 citation statements)

References 24 publications

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“…Unlike traditional optimisation based design exploration, GDTs explore large design spaces to find a variety of optimal alternatives that give users the ability to choose a design that best fits his/her needs. Literature contains many efforts from researchers in design exploration techniques for preliminary design of naval vessels [7,38,9,10]. However, these techniques are not developed in the context of generative design and therefore, can only explore a limited region of design space to generate single or Pareto designs, which are usually a slight variation of the parent shape.…”

Section: Generative Designmentioning

confidence: 99%

“…Although, some academic scholars from the maritime field have made a considerable amount of contribution to the modernisation of preliminary ship design techniques, however, their usage in the industry is still limited. Some of the recent efforts to support ship design at the preliminary stage includes the development of attribute-based design techniques [1]; parametric design systems [2]; library-based [3], sketching based [4], interactive optimisation [5] based and three-dimensional packing based [6,7] approaches for exploration of hull form variations; simulation-driven [8] and holistic approach to ship design [9] and machine learning-based ship design method to assist the optimisation towards the optimal solution [10].…”

Section: Introductionmentioning

confidence: 99%

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GenYacht: An interactive generative design system for computer-aided yacht hull design

2019

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In the present work, a new digital design system, GenYacht, is proposed for the creation of optimal and user-centred yacht hull forms. GenYacht is a hybrid system involving generative and interactive design approaches, which enables users to create a variety of design alternatives. Among them, a user can select a hull design with desirable characteristics based on its appearance and hydrostatics/hydrodynamic performance. GenYacht first explores a given design space using a generative design technique (GDT), which creates uniformly distributed designs satisfying the given design constraints. These designs are then presented to a user and single or multiple designs are selected based on the user's requirements. Afterwards, based on the selections, the design space is refined using a novel space-shrinking technique (SST). In each interaction, SST shrinks the design space, which is then fed into GDT to create new designs in the shrank space for the next interaction. This shrinkage of design space guides the exploration process and focuses the computational efforts on user-preferred regions. The interactive and generative design steps are repeated until the user reaches a satisfactory design(s). The efficiency of GenYacht is demonstrated via experimental and user studies and its performance is compared with interactive genetic algorithms.

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Section: Generative Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GenYacht: An interactive generative design system for computer-aided yacht hull design

2019

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“…Juang et al [23] proposed a design of fuzzy controllers by ant colony optimization incorporated with fuzzy Q-learning. Cui et al [24] proposed a new machine-learning-based ship design optimization approach, where the Q-learning algorithm was utilized to realize the learning function in the optimization process. In a multiagent system, the Q-learning algorithm can increase the intelligence of the system, when multiagents pursuit a common goal by communication and cooperation.…”

Section: Introductionmentioning

confidence: 99%

A Multiagent Q-Learning-Based Optimal Allocation Approach for Urban Water Resource Management System

Liu

Ren

et al. 2014

IEEE Trans. Automat. Sci. Eng.

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Water environment system is a complex system, and an agent-based model presents an effective approach that has been implemented in water resource management research. Urban water resource optimal allocation is a challenging and critical issue in water environment systems, which belongs to the resource optimal allocation problem. In this paper, a novel approach based on multiagent Q-learning is proposed to deal with this problem. In the proposed approach, water users of different regions in the city are abstracted into the agent-based model. To realize the cooperation among these stakeholder agents, a maximum mapping value function-based Q-learning algorithm is proposed in this study, which allows the agents to self-learn. In the proposed algorithm, an adaptive reward value function is used to improve the performance of the multiagent Q-learning algorithm, where the influence of multiple factors on the optimal allocation can be fully considered. The proposed approach can deal with various situations in urban water resource allocation. The experimental results show that the proposed approach is capable of allocating water resource efficiently and the objectives of all the stakeholder agents can be successfully achieved.Note to Practitioners-Water resource optimal allocation is an important decision making activity in water resource management systems. This paper sets up a water resource optimal allocation model based on multiagent modeling technology, where different optimal objectives are abstracted into various properties of agents, and a new multiagent Q-learning approach is proposed to deal with the optimal allocation problem in water resource management system. The proposed approach can be used in practical water resource management systems, with any feasible data from the statistical agencies of government and companies. The experimental results demonstrate the effectiveness and efficiency of the agent-based allocation model and the proposed approach based on the novel multiagent Q-learning algorithm.Index Terms-Complex system, multiagent Q-learning, optimal allocation, urban water resource management.

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“…Alvarez et al [16], optimized the hull geometry to reduce the total resistance and show that, the hull shape resulting from the optimization process substantially reduces (up to 25%) the estimated total resistance. Cui et al [17], performed the structural optimization of a bulk carrier with two conflicting objectives (weight and fatigue) as a case study. A JAVA-based optimization system and ABAQUS were integrated into the optimization framework.…”

Section: Introductionmentioning

confidence: 99%

Optimum Structural Design of Deep Submarine Pressure hull to achieve Minimum Weight

Fathallah¹,

Helal

2016

The International Conference on Civil and Architecture Engineer

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The pressure hull is a significant structural component of underwater vehicles, to enable them to withstand environmental loadings such as hydrostatical pressure. Geometric configurations such as hull shape, shell thickness, stiffener layout, and type of construction materials are the key factors influencing the structural performance of pressure hulls. This study aims to maximize the structural efficiency of elliptical deep-submerged pressure hull under hydrostatic pressure. Minimize the buoyancy factor of a submarine pressure hull under hydrostatic pressure was proposed as an objective function to achieve Minimum Weight, with constraints on factors such as general instability, buckling of shell between stiffeners, plate yielding, stiffeners yielding and operating depth. The shell thickness, the radii of the ellipse, the stiffeners offsets and the stiffeners dimensions are selected as design variables. Additionally, a sensitivity analysis is performed to study the influence of the design variables on the structural optimum design, the buoyancy factor ， strength and stability ． The Optimization results of this study provide a valuable reference for designers of underwater vehicles.

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Learning-based ship design optimization approach

Cited by 41 publications

References 24 publications

GenYacht: An interactive generative design system for computer-aided yacht hull design

GenYacht: An interactive generative design system for computer-aided yacht hull design

A Multiagent Q-Learning-Based Optimal Allocation Approach for Urban Water Resource Management System

Optimum Structural Design of Deep Submarine Pressure hull to achieve Minimum Weight

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