Tese: Structural topology optimization with many load cases: stochastic approximation and singular value decomposition approaches
Aluno(a) : Lucas do Nascimento SagriloOrientador(a): Anderson Pereira
Área de Concentração: Mecânica Aplicada
Data: 07/10/2022
Link para tese/dissertação: https://doi.org/10.17771/PUCRio.acad.61264
Resumo: It is known that most real structures are subject to different loading scenarios, related to different structural solicitations and the action of natural forces, such as winds and sea waves. In this context, it is important to consider the effect of the largest number of possible scenarios that can act on a structure when performing a topology optimization study. The traditional way of solving this type of problem involves a case-by-case analysis of the scenarios, which in the context of a structural optimization algorithm requires the solution of one finite element problem for each scenario and at each step of the algorithm, being limited by the high associated computational cost. This limitation leave room for approaches based on dimension reduction such as stochastic approximation and decomposition into singular values. This work verifies the feasibility of using these two approaches to solve structural topology optimization problems with many load cases. Two applications are presented, robust optimization and the problem of dynamic loads using the equivalent static loading method. Thus, situations involving more complex loads can be studied through efficient topology optimization algorithms. For both cases, comparisons are established between the results obtained through the methodology developed in this work and the ones from the literature.