| Authors | Mohammadreza Salarkia- Sa’id Golabi- Behzad Amirsalari |
|---|---|
| Journal | Journal of Applied and Computational Mechanics |
| Presented by | University of Kashan |
| Serial number | 6 |
| Volume number | 4 |
| Paper Type | Full Paper |
| Published At | 2019-10-18 |
| Journal Grade | ISI |
| Journal Type | Electronic |
| Journal Country | Iran, Islamic Republic Of |
Abstract
A comprehensive set of ten artificial neural networks is developed to suggest optimal dimensions of type ‘C’ Bi-lobe tanks used in the shipping of liquefied natural gas. Multi-objective optimization technique considering the maximum capacity and minimum cost of vessels are implemented for determining optimum vessel dimensions. Generated populations from a genetic algorithm are used by Finite Element Analysis to develop new models and find primary membrane and local stresses to be compared with their permissible ranges using PYTHON coding. The optimum design space is mathematically modeled by training ten artificial neural networks with design variables generated by the Taguchi method. The predicted results are compared with actual design data and the 93% achieved accuracy shows the precision of the developed design system.
tags: Liquefied Natural Gas, Bi-lobe tank, Finite Element Method, Genetic algorithm, Artificial Neural Network, Taguchi method.