Optimization of Structures and Material Processes and Applications in Energy Storage, Topology Design, and Cold Spray Additive Manufacturing

Abstract

Optimization is widely utilized for various engineering problems, mostly with the ultimate aim of minimizing the effort and required cost and/or maximizing the desired benefit. However, not all optimization problems can be effectively tackled using a single method, and problem-specific schemes need to be applied. In this dissertation, the central objective is to employ computational methods and propose/apply optimization techniques to achieve optimal solutions for the state-of-the-art problems in structural engineering and material processes, including energy storage, topology design, and cold spray additive manufacturing. Regarding energy storage, design optimization of compressed air energy storage using filament wound carbon fiber reinforced plastic pressure vessels is proposed and carried out to attain the most cost-effective option. As for topology design, a novel partition and microstructure-based method for topology optimization of large-scale structures is presented to maximize any metric, and, here, in particular, mechanical stiffness. Eventually, the most influential process variables for achieving high-quality cold spray depositions are identified and optimized by means of computational simulation of cold spray process and employment of machine learning algorithms.