Optimization of layer configurations for ballistic impact on light-weight armour plates

• Autor(a) principal: Reis, Isaac Bastos Correia
• Data de Publicação: 2019
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10773/28183
• topic: Ballistic Impact; Abaqus Python API; Design Optimization; Genetic Algorithm; Particle Swarm Optimization; Simulated Annealing; Multi-objective Optimization

The broad field of engineering is facing a paradigm shift where advanced optimization methods and techniques are more often used to solve complex problems. Most of these problems either require the analysis of a large amount of data or the solving of complex calculations, or even both. This dissertation aims to develop an understanding of non-linear optimization algorithms applied to a complex engineering design problem: a multi-layer plate under a ballistic impact. To solve a complex design engineering problem, the most efficient way is to combine non-linear optimization algorithms with a software capable of simulating the model and event. Accordingly, the first part of this document focuses on developing a Python script of the simulation model system using Abaqus API. The usage of an Abaqus Python script to simulate the event allows to generate specific variables and post-processing outputs essential to its posterior integration with optimization algorithms. Nevertheless, the development of a model that simulates a ballistic impact is complex and, thus, a sounding understanding on the physics and mechanics behind such an event are properly discussed. These insights are then used to validate the dynamic response and equilibrium of the simulated model. Furthermore, several modeling strategies are considered and analyzed throughout the first part of this document. The second part of this dissertation aims to acquire a comprehensive understanding of three optimization algorithms: Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Simulated Annealing (SA). The performance and efficiency of each algorithm, as well as numerous programming and optimization strategies, are tested in four different benchmarks. Each benchmark increases in complexity regarding its precedent and they all use the Abaqus Python script previously developed. This dissertation culminates in a multi-objective optimization procedure that uses the most efficient algorithm out of the three algorithms tested in the previous benchmarks. This multi-objective procedure uses every single-objective formulation, variables and constraints from the previous benchmarks which results in a highly non-linear problem. The results from this complex optimization problem are analyzed using and discussed.