Topology Optimization of Capacitive MEMS Accelerometers for Seismic Applications

Najafabadi, Hossein R.; Goto, Tiago G.; Martins, Thiago C.; Barari, Ahmad; Tsuzuki, Marcos de Sales Guerra

doi:10.1007/978-3-031-13588-0_66

Cited by 6 publications

(3 citation statements)

References 32 publications

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“…Various parameters need to be initialized, including the initial temperature, the lower temperature limit, cooling schedule rate, the number of iterations, the objective function, and the initial solution (see Table ). The cooling schedule rate should be moderate, as a too high rate can cause the algorithm to converge to a local optimal solution, while a too slow rate can result in a longer search time. , After initializing the parameters, the initial solution is evaluated using the objective function, which aims to maximize the specific areal capacitance (max f ( x n )). The algorithm calculates the difference between the current solution value and the previous accepted solution value .…”

Section: Electrochemical Behavior Prediction Of Pseudocapacitor Elect...mentioning

confidence: 99%

Optimizing the Ultrashort Laser Pulses for In Situ Nanostructure Generation Technique for High-Performance Supercapacitor Electrodes Using Artificial Neural Networks and Simulated Annealing Algorithms

Khosravinia

Kiani

2023

ACS Omega

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Transition metals (TMs) are being investigated as electrodes for pseudocapacitors, where an oxide layer is necessary to allow for rapid redox reactions. In this work, we utilized an in situ, rapid, binder-free, and green method for the fast fabrication of pseudocapacitor electrodes called ultrashort laser pulses for in situ nanostructure generation (ULPING) to form oxide layers on a titanium sheet. By utilizing this fabrication technique on a titanium sheet, a specific areal capacitance of 0.3579 mF cm −2 was achieved at a current density of 0.25 mA cm −2 . However, the laser fabrication parameters were selected experimentally and resulted in low performance of pseudocapacitors. Therefore, one of the main objectives of this study was to find the optimal laser fabrication parameters to achieve the highest specific areal capacitance. A large dataset was generated to find the relationship between the laser fabrication parameters and the electrochemical behavior performance (impedance and specific areal capacitance) of the fabricated electrodes by using an artificial neural network (ANN). We used an optimization algorithm (simulated annealing-SA) to overlook the trained ANN model as a black box and try to maximize the objective function, which in our case is a specific capacitance value, to find the most optimal laser fabrication parameters. Using SA, optimal laser fabrication parameters were found, which increased the specific areal capacitance to 0.9999 mF cm −2 at a current density of 0.25 mA cm −2 . The results demonstrated that the conducted study has the potential to introduce effective techniques for utilizing ULPING to produce nanoscale structures on TMs. These structures have the potential to be employed as electrodes in pseudocapacitors. Additionally, the research underscores the significance of employing data-driven approaches in electrode design procedures.

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Section: Electrochemical Behavior Prediction Of Pseudocapacitor Elect...mentioning

confidence: 99%

Optimizing the Ultrashort Laser Pulses for In Situ Nanostructure Generation Technique for High-Performance Supercapacitor Electrodes Using Artificial Neural Networks and Simulated Annealing Algorithms

Khosravinia

Kiani

2023

ACS Omega

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“…In these complex and non-linear cases, non-gradient-based algorithms have demonstrated success in navigating the design space to reach an optimal solution. They excel at accommodating intricate trade-offs and non-linearities more effectively than gradient-based methods [27].…”

Section: Introductionmentioning

confidence: 99%

Structural Design with Self-Weight and Inertial Loading Using Simulated Annealing for Non-Gradient Topology Optimization

Rostami Najafabadi,

Martins,

Tsuzuki

et al. 2023

Machines

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This paper explores implementation of self-weight and inertial loading in topology optimization (TO) employing the Simulated Annealing (SA) algorithm as a non-gradient-based technique. This method can be applied to find optimum design of structures with no need for gradient information. To enhance the convergence of the SA algorithm, a novel approach incorporating the crystallization factor is introduced. The method is applied in a benchmark problem of a cantilever beam. The study systematically examines multiple scenarios, including cases with and without self-weight effects, as well as varying point loads. Compliance values are calculated and compared to those reported in existing literature to validate the accuracy of the optimization results. The findings highlight the versatility and effectiveness of the SA-based TO methodology in addressing complex design challenges with considerable self-weight or inertial effect. This work can contribute to structural design of systems where only the objective value is available with no gradient information to use sensitivity-based algorithms.

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“…But it is limited by the maximum displacement. It also affects the surface area which is important in increasing the capacitance between electrodes [103].…”

Section: Objective and Constraints Of Topology Optimizationmentioning

confidence: 99%

Design of surface micro-machined inertial MEMS sensor with meta-heuristic topology optimization.

Najafabadi¹

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Os sistemas microeletromecânicos (MEMS) estão se tornando populares para uma variedade de aplicações devido ao seu pequeno tamanho, baixo custo e bom desempenho. Os dispositivos MEMS são projetados combinando formas simples com base na experiência de casos anteriores. O uso de métodos de projeto matemático, como a Otimização de Topologia (TO), ajuda a encontrar o melhor projeto possível para um aplicativo específico. Em uma aplicação de monitoramento de deslizamentos de terra, acelerômetros MEMS de alta sensibilidade são necessários para prever com precisão a ocorrência de deslizamentos de terra. Mas os métodos TO disponíveis são incapazes de encontrar um projeto com sensibilidade máxima, condição de carregamento variável e sujeito a várias restrições. As restrições são devidas aos limites de fabricação na fabricação de MEMS e aos limites de desempenho do problema físico. O objetivo desta tese é desenvolver um método TO meta-heurístico usando Simulated Annealing (SA) para resolver problemas TO não convexos e multi-restrições sem informação de gradiente. Este TO utiliza fatores de cristalização para melhorar a convergência e reduzir os custos computacionais no TO. O método proposto é validado com problemas de referência na literatura e é usado com sucesso para os acelerômetros TO MEMS. A análise dos parâmetros de otimização neste projeto fornece algumas informações úteis sobre convergência e exclusividade da solução ótima. Os designs otimizados são então comparados aos designs disponíveis para vários parâmetros de desempenho. Além disso, alguns métodos de filtragem e pós-processamento são desenvolvidos para aplicar os limites de fabricação no processo de litografia.

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Topology Optimization of Capacitive MEMS Accelerometers for Seismic Applications

Cited by 6 publications

References 32 publications

Optimizing the Ultrashort Laser Pulses for In Situ Nanostructure Generation Technique for High-Performance Supercapacitor Electrodes Using Artificial Neural Networks and Simulated Annealing Algorithms

Optimizing the Ultrashort Laser Pulses for In Situ Nanostructure Generation Technique for High-Performance Supercapacitor Electrodes Using Artificial Neural Networks and Simulated Annealing Algorithms

Structural Design with Self-Weight and Inertial Loading Using Simulated Annealing for Non-Gradient Topology Optimization

Design of surface micro-machined inertial MEMS sensor with meta-heuristic topology optimization.

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