Filament wound glass/epoxy and carbon/epoxy pressure vessels were designed using a data-driven evolutionary approach along with a classical mechanics-based analysis. Analytical solutions for three known end domes (a spherical shell, a geodesic-isotensoid shell and a shell based on minimizing of the Tsai-Hill’s criterion) and end dome based on Hoffman’s criterion were used to create surrogate models through two different evolutionary optimization algorithms: Evolutionary Neural Net (EvoNN) and Bi-objective Genetic Programming (BioGP) and bi-objective evolutionary optimization studies were carried out using them. Composite pressure vessels are manufactured by means of filament (helical) winding. The stresses in the end domes were evaluated analytically and compared with each other. Hoffman strength criterion was chosen for the evaluation of the critical areas of the shells. Moreover, the best dome shape for given material and polar hole/equator ratio was selected for various types of failure.

The junction between cylindrical part and end dome is the most critical place of the filament wound composite pressure vessels because the shear forces and bending moments occur in this area. The strength analyses of junction between cylindrical part and different types of known end domes based on influence coefficients method were performed for filament wound composite pressure vessels manufactured using glass-epoxy and carbon-epoxy composites. Four known end domes were considered that is, a spherical shell, a geodesic-isotensoid shell, a shell based on minimizing of the Tsai-Hill’s strength criterion and shell designed in authors’ previous work (Hoffman 1 shell). Moreover, the study is also dealing with design of optimal end dome shape which takes account to the junction area. Finding of optimal end dome shape was based on data-driven evolutionary algorithms. Using input dataset from the analytical simulation surrogate models were created using Evolutionary Deep Neural Nets (EvoDN2) algorithm. The multicriterial optimization procedure was performed using Constrained Reference Vector Algorithm (cRVEA) optimization algorithm. Results indicates that the optimal end dome shape highly depend on material properties.

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.

This study presents an innovative approach to designing filament-wound conical pressure vessels loaded by internal pressure, combining analytical computations based on classical lamination theory with data-driven evolutionary algorithms. The most critical area of the whole cone is the balanced layer at the lower base, in which the safety level is evaluated using the failure index based on Hoffman’s strength criterion. The designed mathematical model related the geometrical parameters of the cone to the magnitude of internal pressure, representing the performance of the structure and safety expressed by the failure index. The optimization loop runs on two modules –Evolutionary Deep Neural Networks EvoDN2 module for machine learning and creating a lighter surrogate model, together with Constrained Reference Vectors Evolutionary Algorithms (cRVEA) for multicriteria optimization. This was applied to three different material configurations: E-glass/DA4518U (glass-epoxy), T300/N5208 (carbon-epoxy) and AS4/3501-6 (carbon-epoxy). The results show that the critical area is invariant to the material configuration. In addition, it was shown that the material configurations with higher anisotropy achieve a more uniform distribution of the load in the layers of the conical three-layered wall.

This study focuses on the analysis and optimization of filament-wound composite pressure vessels loaded with a combination of mechanical and thermal loading, which occurs during the curing process and subsequent cooling to the ambient temperature. The state of stress in the pressure vessel is determined using the analytical description based on the classical lamination theory supplemented by netting theory. The analytical description is used for generating robust input data for the data-driven deep learning evolutionary algorithm EvoDN2, to carry out machine learning followed by many-objective optimization, to compute the optimal tradeoff between the nominal pressure and weight of the pressure vessel, maintaining a high level of safety. The influence of material configurations on the design of pressure vessels is also studied, where four material configurations of E-glass/DA 4518U, T300/N5208, AS4/3501-6, and Kevlar 49/CYCOM 919 are considered. The results indicate that the usage of aramid fiber in the design has its limits, whereas the materials based on carbon-epoxy show an impressive performance and great strength-to-weight ratio.