Complex design and analysis of filament wound composite pressure vessels using data driven evolutionary algorithms

Abstract

The typical composite pressure vessel manufactured by means of filament winding consists of three elementary construction parts, that is, cylindrical part, end dome and the junction between its cylindrical part and the end dome which are subject of this article. The pressure vessel may fail in each of these three parts. Therefore, all of them must be considered during the design process. This study shows the possible way how to design an optimal pressure vessel related to chosen parameters (which can be mass of the pressure vessel, depth of the end dome, magnitude of internal pressure etc.), so that it does not fail in any construction parts. Any decision, whether the pressure vessel comply or not in studied places, is based on Hoffman's failure index analysis. The finding of an optimal solution requires performing a multi-objective optimization task, which was solved here using data-driven evolutionary algorithms. From the input data, which were generated through analytical approaches, surrogate models were created using the Evolutionary Deep Neural Nets (EvoDN2). These surrogate models were further used as an input to the optimization module which is based upon Constrained Reference Vector Evolutionary Algorithm (cRVEA). The results show that the pressure vessel designed in this way comply in each analyzed location. Moreover, it also finds the most stressed location, which can be useful for the designers.