Optimum Design of Liquified Natural Gas Bi-lobe Tanks using Finite Element, Genetic Algorithm and Neural Network

A comprehensive set of ten artificial neural networks <strong>i</strong>s 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 <strong>a</strong>re implemented for determining optimum vessel dimensions. Generated populations from a genetic algorithm <strong>a</strong>re used by Finite Element Analysis to develop new models and find primary membrane and local stresses to compare with their permissible ranges using PYTHON coding. The optimum design space <strong>i</strong>s mathematically modeled by training ten artificial neural networks with design variables generated by the Taguchi method. The results <strong>a</strong>re compared with actual design data and the 93% achieved accuracy shows the precision of the developed design system.

Abstract licence: CC BY-NC

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.