Optimization is a decision making process to solve problems in a number of fields including engineering mechanics. Bio-inspired optimization algorithms, including genetic algorithm (GA), have been studied for many years. There is a large literature on applying the GA to mechanics problems. However, disadvantages of the GA include the high computational cost and the inability to get the global optimal solution that can be found by using a honeybee-inspired optimization algorithm, called the New Nest-Site Selection (NeSS). We use the NeSS to find optimal parameters for three mechanics problems by following the three processes: screening, identifying relationships, and optimization. The screening process identifies significant parameters from a set of input parameters of interest. Then, relationships between the significant input parameters and responses are established. Finally, the optimization process searches for an optimal solution to achieve objectives of a problem.
For the first two problems, we use the NeSS algorithm in conjunction with a third order shear and normal deformable plate theory (TSNDT), the finite element method (FEM), a one-step stress recovery scheme (SRS) and the Tsai-Wu failure criterion to find the stacking sequence of composite laminates and the topology and materials for doubly curved sandwich shells to maximize the first failure load. It is followed by the progressive failure analysis to determine the ultimate failure load. For the sandwich shell, we use the maximum transverse shear stress criterion for delineating failure of the core, and also study simultaneously maximizing the first failure load and minimizing the mass subject to certain constraints. For composite laminates, it is found that the first failure load for an optimally designed stacking sequence exceeds that for the typical [0°/90°]₅ laminate by about 36%. Moreover, the design for the optimal first failure load need not have the maximum ultimate load. For clamped laminates and sandwich shells, the ultimate load is about 50% higher than the first failure load. However, for simply supported edges the ultimate load is generally only about 10% higher than the first failure load.
For the atmospheric spray process, we employ the NeSS algorithm to find optimal values of four process input parameters, namely the argon flow rate, the hydrogen flow rate, the powder feed rate and the current, that result in the desired mean particle temperature and the mean particle velocity when they reach the substrate. These optimal values give the desired mean particle temperature and the mean particle velocity within 5% of their target values. / Ph. D.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/81978 |
| Date | 31 January 2018 |
| Creators | Taetragool, Unchalisa |
| Contributors | Engineering Science and Mechanics, Batra, Romesh C., Yue, Pengtao, Thangjitham, Surot, Cramer, Mark S., Hendricks, Scott L. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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