"Genetic Algorithm-Based Optimization of Pressure Vessel Design Parameters for Enhanced Structural Efficiency":

Abstract

The design of pressure vessels plays a critical role in ensuring the safety, reliability, and cost-effectiveness of industrial processes involving high-pressure fluids and gases. Traditional design approaches often rely on conservative assumptions and manual iterations that may not yield optimal solutions, leading to excess material usage and increased costs. This research paper presents a comprehensive study on the application of Genetic Algorithms (GAs) as an effective optimization technique to enhance the structural efficiency of pressure vessels by optimizing key design parameters.
The study focuses on minimizing the weight and overall manufacturing cost of cylindrical pressure vessels with hemispherical heads while strictly adhering to the ASME Boiler and Pressure Vessel Code constraints. The design parameters optimized include shell thickness, head thickness, inner radius, and length of the vessel. The problem is formulated as a constrained nonlinear optimization task, where the objective function incorporates both material cost and fabrication expenses.
A Genetic Algorithm framework is developed, incorporating real-valued chromosome encoding to represent the design variables. The GA utilizes adaptive crossover and mutation strategies to efficiently explore the complex search space and avoid premature convergence. Constraint handling is performed using penalty functions to ensure all ASME safety requirements and stress limitations are met.
Simulation results demonstrate that the GA-based optimization yields superior solutions compared to traditional methods, achieving significant reductions in vessel weight and cost—up to 20% improvement—without compromising structural integrity or safety. The optimized designs meet all regulatory requirements and provide practical, implementable configurations suitable for industrial applications.
This research highlights the robustness and versatility of Genetic Algorithms in tackling complex engineering design problems involving multiple conflicting objectives and constraints. The methodology can be extended to other pressure vessel geometries and materials, and future work can incorporate multi-objective optimization and uncertainty analysis to further enhance design reliability and efficiency.