Learning-based ship design optimization approach

With the development of computer applications in ship design, optimization, as a powerful approach, has been widely used in the design and analysis process. However, the running time, which often varies from several weeks to months in the current computing environment, has been a bottleneck problem for optimization applications, particularly in the structural design of ships. To speed up the optimization process and adjust the complex design environment, ship designers usually rely on their personal experience to assist the design work. However, traditional experience, which largely depends on the designer’s personal skills, often makes the design quality very sensitive to the experience and decreases the robustness of the final design. This paper proposes a new machine-learning-based ship design optimization approach, which uses machine learning as an effective tool to give direction to optimization and improves the adaptability of optimization to the dynamic design environment. The natural human learning process is introduced into the optimization procedure to improve the efficiency of the algorithm. Q-learning, as an approach of reinforcement learning, is utilized to realize the learning function in the optimization process. The multi-objective particle swarm optimization method, multiagent system, and CAE software are used to build an integrated optimization system. A bulk carrier structural design optimization was performed as a case study to evaluate the suitability of this method for real-world application

An integrated methodology for the design of Ro-Ro passenger ships

The present paper provides a brief introduction to the holistic approach to ship design, defines the generic ship design optimization problem and demonstrates its solution by use of advanced optimization techniques

Stochastic life cycle ship design optimization

In the presented research study, a parametric model of a bulk carrier design is utilized in order to explore the technical and economic ship design features (design attributes) in the frame of a stochastic optimization procedure. The life cycle assessment of a newbuilding's investment that includes ship's acquisition and operation cost for ship's life cycle is affected by a variety of cost and other parameters have an inherent uncertainty. The ship design attributes are herein represented by six main ship parameters that define the basic characteristics of a vessel: length, breadth, depth, draft, block coefficient and speed. Among the ship characteristics that are related to high uncertainty are ship's energy consumption in terms of fuel consumption, fuel mix and fuel prices. In the present paper, an attempt is made to investigate how the uncertainty of estimations of the fuel consumption, fuel mix and prices, which are made at an early stage of ship design, can affect the outcome of the ship design optimization procedure with respect to ship's life cycle cost. Therefore, a stochastic optimization procedure is being applied, which is utilizing well established optimization algorithms and techniques in a robust and efficient manner. Sample results of this stochastic optimization are compared with solutions of a deterministic optimization and eventually lead to a rational basis for the decision making regarding the life cycle assessment of ship investments

Energy efficiency parametric design tool in the framework of holistic ship design optimization

Recent International Maritime Organization (IMO) decisions with respect to measures to reduce the emissions from maritime greenhouse gases (GHGs) suggest that the collaboration of all major stakeholders of shipbuilding and ship operations is required to address this complex techno-economical and highly political problem efficiently. This calls eventually for the development of proper design, operational knowledge, and assessment tools for the energy-efficient design and operation of ships, as suggested by the Second IMO GHG Study (2009). This type of coordination of the efforts of many maritime stakeholders, with often conflicting professional interests but ultimately commonly aiming at optimal ship design and operation solutions, has been addressed within a methodology developed in the EU-funded Logistics-Based (LOGBASED) Design Project (2004–2007). Based on the knowledge base developed within this project, a new parametric design software tool (PDT) has been developed by the National Technical University of Athens, Ship Design Laboratory (NTUA-SDL), for implementing an energy efficiency design and management procedure. The PDT is an integral part of an earlier developed holistic ship design optimization approach by NTUA-SDL that addresses the multi-objective ship design optimization problem. It provides Pareto-optimum solutions and a complete mapping of the design space in a comprehensive way for the final assessment and decision by all the involved stakeholders. The application of the tool to the design of a large oil tanker and alternatively to container ships is elaborated in the presented paper

Optimización del diseño holístico de buques: embarcaciones mercantes y navales

The present paper provides a brief introduction to a holistic approach to ship design optimization, defines the generic ship design optimization problem, and demonstrates its solution by using advanced optimization techniques for the computer-aided generation, exploration, and selection of optimal designs. It discusses proposed methods on the basis of some typical ship design optimization problems of cargo and naval ships related to multiple objectives, leading to improved and partly innovative design features with respect to ships’ economy, cargo carrying capacity, safety, survivability, comfort, required powering, environmental protection, or combat strength, as applicable.Este documento brinda una breve introducción a un enfoque holístico a la optimización del diseño de embarcaciones, define el problema genérico de la optimización del diseño de embarcaciones y demuestra su solución mediante el uso de técnicas avanzadas de optimización asistidas por computador para la generación, exploración y selección de diseños óptimos. Discute los métodos propuestos sobre la base de algunos problemas típicos de optimización de diseño de embarcación de buques de carga y navales relacionados a los objetivos múltiples, conllevando a características de diseño mejoradas y parcialmente innovadoras con respecto a la economía de la embarcación, capacidad de carga, seguridad, supervivencia, comodidad, potencia requerida, protección ambiental o fortaleza de combate, como sea aplicable

Ship design optimization in the multimodal logistics framework

The significance of multimodal transportation systems has worldwide increased significantly over the past two decades. European Union, United States and Japan have increased their efforts in studying the dynamics of multimodal transportation networks. In the paper about recent research work on the optimization problem of the design of ferries and Ro-Ro cargo ships using pattern recognition techniques (Artificial Neural Networks), Multi-objective Genetic Algorithms (MOGA) and the know-how created through the EU FP6 Research Project LOGBASED. A case study on the transportation of agricultural goods from the Greek Island of Crete to the markets of Munich is presented. The results of this study prove useful for the assessment of viable solutions serving in a balanced way for the interests of shipyards, ship operators, cargo owners, banking and financial institutions, investors and government administrations in the framework of optimized transportation scenarios

Ship design optimization framework

This study intends to develop a Computational Fluid Dynamics space exploration frame-work, which creates a bridge between NAPA software and OpenFOAM 9 simulation soft-ware. With the use of Free Form Deformation function implemented in NAPA, iterations of KCS hull were automatically generated by changing the length of the bulb. The newly created versions of the model were further analyzed with numerical investigations to de-termine the ship resistance simulation using RANS equations. The optimization process and the data transfer between the two software packages is monitored by the Dakota op-timization software

A Drag Estimate for Concept-Stage Ship Design Optimization

During the initial phases of ship design, the naval architect would like to have as much information as possible about the design space. This information not only helps determine a good set of initial characteristics, it allows for informed design changes when reacting to evolving requirements. One of the most difficult performance measures to evaluate is the ship wave drag. This estimate is important in an optimization, because wetted surface and wave drag must be balanced. Multi-parameter optimization algorithms exist, but need a very fast and inexpensive fitness evaluation for them to be effective. Even though linear theory does capture some of the physics of the problem, it has long been out of favor due to its tendency to grossly over-estimate the wave drag. The other options available are parametric drag estimates and state-of-the-art boundary element codes. Here we present an intermediate method that makes a parametric correction to the linear theory using an artificial neural network. The method starts with a training set consisting of a large number of panel code evaluations for a systematic hull series, and then uses two approaches to the parametric correction. The first method uses the ratio of linear theory to panel code data as targets for an artificial neural network with parametric inputs. In the second method, we re-derive the linear theory with a new boundary condition, leading to a waterline integral term with unknown coefficients. The linear theory error is then used in a constrained minimization problem to solve for the unknown coefficients, which again provides targets for a neural network. Coupled with a mathematical hull form that can approximate realistic hull shapes, the results show promise for an intermediate wave drag estimation method that is fast enough to be used as a fitness evaluation for a multi-parameter optimization routine such as a genetic algorithm

An improved support vector regression and its modelling of manoeuvring performance in multidisciplinary ship design optimization

In this paper, the combination of the Laplace loss function and Support Vector Regression (SVR) are presented for the estimation of manoeuvring performance in multidisciplinary ship design optimization, and a new SVR algorithm was proposed, which has only one parameter to control the errors and automatically minimized with v, and adds b2/2 b to the item of confidence interval. It is shown that the proposed SVR algorithm in conjunction with the Laplace loss function can estimate the ship manoeuvring performance appropriately compared to the simulation results with Napa software and other approximation methods such as Artificial Neural Network (ANN) and classic SVR. In this article, we also gather enough ship information about the offshore support vessel; the Latin Hypercube Design is employed to explore the design space. Instead of requiring the evaluation of expensive simulation codes, we establish the metamedels of ship manoeuvring performance; all the numerical results show the effectiveness and practicability of the new approximation algorithms

Establishment of effective metamodels for seakeeping performance in multidisciplinary ship design optimation

Ship design is a complex multidisciplinary optimization process to determine configuration variables that satisfy a set of mission requirements. Unfortunately, high fidelity commercial software for the ship performance estimation such as Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA) are computationally expensive and time consuming to execute and deter the ship designer’s ability to explore larger range of optimization solutions. In this paper, the Latin Hypercube Design was used to select the sample data for covering the design space. A comprehensive seakeeping evaluation index, The percentage of downtime, a comprehensive seakeeping evaluation index, was also used to evaluate the seakeeping performance within the short-term and long-term wave distribution in the Multidisciplinary Design Optimization (MDO) process. The five motions of ship seakeeping performance contained roll, pitch, yaw, sway and heave. Particularly, a new effective approximation modelling technique—Single-Parameter Lagrangian support vector regression ?SPL-SVR? was investigated to construct ship seakeeping metamodels to facilitate the application of MDO. By considering the effects of two ship speeds, the established metamedels of ship seakeeping performance for the short-term percentage downtime are satisfactory for seakeeping predictions during the conceptual design stage; thus, the new approximation algorithm provides an optimal and cost-effective solution for constructing the metamodels using the MDO process