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
- Gradient Based Algorithms
Date of Defense 2004-05-06 Availability unrestricted AbstractWhen 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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