Multidisciplinary Design Optimization of a Naval Surface Combatant

2003 ◽  
Vol 47 (01) ◽  
pp. 1-12 ◽  
Author(s):  
Daniele Peri ◽  
Emilio F. Campana
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Optimization Methods ◽  
Ship Design ◽  
Multidisciplinary Design ◽  
Construction Costs ◽  
Gradient Based ◽  
Numerical Tools ◽  
Surface Combatant ◽  
Hydrodynamic Optimization

Whereas shape optimal design has received considerable attention in many industrial contexts, the application of automatic optimization procedures to hydrodynamic ship design has not yet reached the same maturity. Nevertheless, numerical tools, combining together modern computational fluid dynamics and optimization methods, can aid in the ship design, enhancing the operational performances and reducing development and construction costs. This paper represents an attempt of applying a multidisciplinary design optimization (MDO) procedure to the enhancement of the performances of an existing ship. At the present stage the work involves modeling, development, and implementation of algorithms only for the hydrodynamic optimization. For a naval surface combatant, the David Taylor Model Basin (DTMB) model ship 5415, a three-objective functions optimization for a two-discipline design problem is devised and solved in the framework of the MDO approach. A simple decision maker is used to order the Pareto optimal solutions, and a gradient-based refinement is performed on the selected design.

Support Vector Regression-Based Multidisciplinary Design Optimization for Ship Design

2012 ◽  
Author(s):  
Dongqin Li ◽  
Yifeng Guan ◽  
Qingfeng Wang ◽  
Zhitong Chen
Keyword(s):  
Design Optimization ◽  
Support Vector Regression ◽  
Design Process ◽  
Multidisciplinary Design Optimization ◽  
Initial Point ◽  
Ship Design ◽  
Collaborative Optimization ◽  
Multidisciplinary Design ◽  
Support Vector ◽  
Independent Design

The design of ship is related to several disciplines such as hydrostatic, resistance, propulsion and economic. The traditional design process of ship only involves independent design optimization within each discipline. With such an approach, there is no guarantee to achieve the optimum design. And at the same time improving the efficiency of ship optimization is also crucial for modem ship design. In this paper, an introduction of both the traditional ship design process and the fundamentals of Multidisciplinary Design Optimization (MDO) theory are presented and a comparison between the two methods is carried out. As one of the most frequently applied MDO methods, Collaborative Optimization (CO) promotes autonomy of disciplines while providing a coordinating mechanism guaranteeing progress toward an optimum and maintaining interdisciplinary compatibility. However there are some difficulties in applying the conventional CO method, such as difficulties in choosing an initial point and tremendous computational requirements. For the purpose of overcoming these problems, Design Of Experiment (DOE) and a new support vector regression algorithm are applied to CO to construct statistical approximation model in this paper. The support vector regression algorithm approximates the optimization model and is updated during the optimization process to improve accuracy. It is shown by examples that the computing efficiency and robustness of this CO method are higher than with the conventional CO method. Then this new Collaborative Optimization (CO) method using approximate technology is discussed in detail and applied in ship design which considers hydrostatic, propulsion, weight and volume, performance and cost. It indicates that CO method combined with approximate technology can effectively solve complex engineering design optimization problem. Finally, some suggestions on the future improvements are proposed.

Design and Optimization of Injector Structure in Diesel Engine Based on ISIGHT Platform DOE Methods

2011 ◽  
Vol 335-336 ◽  
pp. 1376-1380
Author(s):  
Xin Ying Wu ◽  
Guang Yao Ouyang ◽  
Yu Xue Li
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Optimization Design ◽  
Design Method ◽  
Optimization Methods ◽  
Multidisciplinary Design ◽  
Design And Optimization ◽  
Traditional Design ◽  
Improved Performance

The traditional design method of injector structure cannot meet the demand of farther improved performance,the change of modern environment demand not only the optimization of one performance but also the optimization of various comprehensive performance.iSIGHT is a multidisciplinary design optimization platform that offer a integrated designenvironment and advanced design optimization methods. The optimization design of injector structure based on design of experiment of iSIGHT platform to improve the spray quality of injector is implemented.

Multidisciplinary Design Optimization for an Earth Observation Satellite

2012 ◽  
Vol 591-593 ◽  
pp. 132-135
Author(s):  
Xiao Hui Wang ◽  
Yan Xu ◽  
Ren Wei Xia
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Numerical Models ◽  
Optimization Methods ◽  
Earth Observation ◽  
Multidisciplinary Optimization ◽  
Multidisciplinary Design ◽  
State Variables ◽  
Software Environment ◽  
Earth Observation Satellite

This paper presents the multidisciplinary design optimization (MDO) for an earth observation satellite. The aim of the paper is to use various multidisciplinary optimization methods to optimize the numerical models of an earth observation satellite under iSIGHTTM software environment. Based on the best earth observation criteria, a mathematical model of the earth observation satellite is proposed, which considers the design variables, the state variables and constraints of the payload system, the attitude system, the control system, the power system, the structure system, and the propulsion system. This paper conducts the optimization by using the above three methods, and compare the efficiency and applicability among the methods.

Reliability-Based Multidisciplinary Design Optimization Using Probabilistic Gradient-Based Transformation Method

2012 ◽  
Vol 135 (2) ◽  
Author(s):  
Po Ting Lin ◽  
Hae Chang Gea
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Transformation Method ◽  
Performance Measure ◽  
Probabilistic Constraints ◽  
Multidisciplinary Design ◽  
Sensitivity Analyses ◽  
Chance Constrained Programming ◽  
Complex Design ◽  
Gradient Based

Recently, solving the complex design optimization problems with design uncertainties has become an important but very challenging task in the communities of reliability-based design optimization (RBDO) and multidisciplinary design optimization (MDO). The MDO algorithms decompose the complex design problem into the hierarchical or nonhierarchical optimization structure and distribute the workloads to each discipline (or subproblem) in the decomposed structure. The coordination of the local responses is crucial for the success of finding the optimal design point. The problem complexity increases dramatically when the existence of the design uncertainties is not negligible. The RBDO algorithms perform the reliability analyses to evaluate the probabilities that the random variables violate the constraints. However, the required reliability analyses build up the degree of complexity. In this paper, the gradient-based transformation method (GTM) is utilized to reduce the complexity of the MDO problems by transforming the design space to multiple single-variate monotonic coordinates along the directions of the constraint gradients. The subsystem responses are found using the monotonicity principles (MP) and then coordinated for the new design points based on two general principles. To consider the design uncertainties, the probabilistic gradient-based transformation method (PGTM) is proposed to adapt the first-order probabilistic constraints from three different RBDO algorithms, including the chance constrained programming (CCP), reliability index approach (RIA), and performance measure approach (PMA), to the framework of the GTM. PGTM is efficient because only the sensitivity analyses and the reliability analyses require function evaluations (FE). The optimization processes of monotonicity analyses and the coordination procedures are free of function evaluations. Several mathematical and engineering examples show the PGTM is capable of finding the optimal solutions with desirable reliability levels.

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