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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Smart Topology Optimization Using Adaptive Neighborhood Simulated Annealing

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Autor(es):
R. Najafabadi, Hossein [1, 2] ; G. Goto, Tiago [1, 3] ; Falheiro, Mizael S. [1] ; C. Martins, Thiago [1] ; Barari, Ahmad [2] ; S. G. Tsuzuki, Marcos [1]
Número total de Autores: 6
Afiliação do(s) autor(es):
[1] Univ Sao Paulo, Escola Politecn, Lab Computat Geometry, BR-05508030 Sao Paulo - Brazil
[2] Univ Ontario Inst Technol Ontario Tech, Fac Engn & Appl Sci, Oshawa, ON - Canada
[3] Univ Fed Rondonopolis, Inst Ciencias Agr & Tecnol, BR-78736900 Rondonopolis - Brazil
Número total de Afiliações: 3
Tipo de documento: Artigo Científico
Fonte: APPLIED SCIENCES-BASEL; v. 11, n. 11 JUN 2021.
Citações Web of Science: 0
Resumo

Topology optimization (TO) of engineering products is an important design task to maximize performance and efficiency, which can be divided into two main categories of gradient-based and non-gradient-based methods. In recent years, significant attention has been brought to the non-gradient-based methods, mainly because they do not demand access to the derivatives of the objective functions. This property makes them well compatible to the structure of knowledge in the digital design and simulation domains, particularly in Computer Aided Design and Engineering (CAD/CAE) environments. These methods allow for the generation and evaluation of new evolutionary solutions without using the sensitivity information. In this work, a new non-gradient TO methodology using a variation of Simulated Annealing (SA) is presented. This methodology adaptively adjusts newly-generated candidates based on the history of the current solutions and uses the crystallization heuristic to smartly control the convergence of the TO problem. If the changes in the previous solutions of an element and its neighborhood improve the results, the crystallization factor increases the changes in the newly random generated solutions. Otherwise, it decreases the value of changes in the recently generated solutions. This methodology wisely improves the random exploration and convergence of the solutions in TO. In order to study the role of the various parameters in the algorithm, a variety of experiments are conducted and results are analyzed. In multiple case studies, it is shown that the final results are well comparable to the results obtained from the classic gradient-based methods. As an additional feature, a density filter is added to the algorithm to remove discontinuities and gray areas in the final solution resulting in robust outcomes in adjustable resolutions. (AU)

Processo FAPESP: 19/03453-2 - Sensor inercial micro-eletro-mecânico para cidades inteligentes
Beneficiário:Hossein Rostami Najafabadi
Linha de fomento: Bolsas no Brasil - Doutorado