Title page for ETD etd-05102002-121003


Type of Document Master's Thesis
Author Ganguly, Sandipan
URN etd-05102002-121003
Title Algorithmic Modifications to a Multidisciplinary Design Optimization Model of Containerships
Degree Master of Science
Department Aerospace and Ocean Engineering
Advisory Committee
Advisor Name Title
Neu, Wayne L. Committee Chair
Brown, Alan J. Committee Member
Mason, William H. Committee Member
Keywords
  • Modified Method of Feasible Directions
  • Design
  • Gradient Based Algorithms
  • Optimization
Date of Defense 2004-05-06
Availability unrestricted
Abstract
When designing a ship, a designer often begins with “an idea” of what the ship might look like and what specifications the ship should meet. The multidisciplinary design optimization model is a tool that combines an analysis and an optimization process and uses a measure of merit to obtain what it infers to be the best design. All that the designer has to know is the range of values of certain design variables that confine the design within a lower and an upper bound. The designer then feeds the MDO model with any arbitrary design within the bounds and the model searches for the best design that minimizes or maximizes a measure of merit and also meets a set of structural and stability requirements.

The model is multidisciplinary because the analysis process, which calculates the measure of merit and other performance parameters, can be a combination of sub-processes used in various fields of engineering. The optimization process can also be a variety of mathematical programming techniques depending on the type of the design problem. The container ship design problem is a combination of discreet and continuous sub-problems. But to avail the advantages of gradient-based optimization algorithms, the design problem is molded into a fully continuous problem.

The efficiency and effectiveness with which an optimization process achieves the best design depends on how well the design problem is posed for the optimizer and how well that particular optimization algorithm tackles the type of design problems posed before it. This led the author to investigate the details of the analysis and the optimization process within the MDO model and make modifications to each of the processes, so that the two become more compatible towards achieving a better final design. Modifications made within the optimization algorithm were then used to develop a generalized modification method that can be used to improve any gradient-based optimization algorithm.

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