Multiphysics field analysis and evolutionary optimization: Design of an electro-thermo-elastic microactuator

Barba, Paolo Di; Liu, Bo; Mognaschi, Maria Evelina; Venini, Paolo; Wiak, Sławomir

doi:10.3233/jae-160118

Cited by 12 publications

(8 citation statements)

References 25 publications

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“…Therefore, a weaklycoupled analysis problem is dealt with; to solve it, a cascade algorithm such as the successive substitution algorithm can be applied. The micro-actuator is modeled in COMSOL Multiphysics [55]. Its parametric finite-element model has a typical mesh composed of about 8,000 3-D elements and each simulation costs 25 minutes on average.…”

Section: A Examplementioning

confidence: 99%

Efficient Design Optimization of High-Performance MEMS Based on a Surrogate-Assisted Self-Adaptive Differential Evolution

et al. 2020

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High-performance microelectromechanical systems (MEMS) are playing a critical role in modern engineering systems. Due to computationally expensive numerical analysis and stringent design specifications nowadays, both the optimization efficiency and quality of design solutions become challenges for available MEMS shape optimization methods. In this paper, a new method, called self-adaptive surrogate model-assisted differential evolution for MEMS optimization (ASDEMO), is presented to address these challenges. The main innovation of ASDEMO is a hybrid differential evolution mutation strategy combination and its self-adaptive adoption mechanism, which are proposed for online surrogate model-assisted MEMS optimization. The performance of ASDEMO is demonstrated by a high-performance electro-thermoelastic micro-actuator, a high-performance corrugated membrane micro-actuator, and a highly multimodal mathematical benchmark problem. Comparisons with state-of-the-art methods verify the advantages of ASDEMO in terms of efficiency and optimization ability.INDEX TERMS MEMS design optimization, high-performance MEMS design, surrogate model assisted evolutionary algorithm, Gaussian process, differential evolution.

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Section: A Examplementioning

confidence: 99%

Efficient Design Optimization of High-Performance MEMS Based on a Surrogate-Assisted Self-Adaptive Differential Evolution

et al. 2020

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“…In this section, two real-world engineering design problems are used to demonstrate the TSMO framework. The first problem is the design and optimization of the four-variable multi-physics electro-thermo-elastic micro-actuator [18] and the second problem is the design and optimization of the seven-variable hybrid dielectric resonator antenna [19]. For both problems, the implementation of TSMO is according to Section II.…”

Section: Real-world Design Casesmentioning

confidence: 99%

“…4. It is modeled in COMSOL Multiphysics according to the forward problem in [18]. Its parametric 3-D finite-element model has a typical mesh composed of about 5,000 and 40,000 3-D elements for the low-fidelity and high-fidelity models, respectively.…”

Section: A Case 1: Multi-physics Electro-thermo-elastic Microactuatormentioning

confidence: 99%

Hybrid Single and Multiobjective optimization for Engineering Design without Exact Specifications

Liu

Akinsolu

Zhang

2020

2020 IEEE Congress on Evolutionary Computation (CEC)

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A challenge in engineering design optimization is that sufficient information may not be available to define the exact specifications beforehand. While iterative trial optimization using different specifications is widely used in industry, multiobjective optimization is attracting much attention in the academic field. However, off-the-shelf methods in both categories are timeconsuming due to the involved computationally expensive simulations. In this paper, the characteristics of the targeted problem are summarized; the gap between off-the-shelf methods and the practical need is then analyzed. A simple yet effective framework, called two-stage multi-fidelity surrogate model-assisted optimization (TMSO), is proposed to improve efficiency. TSMO is implemented by two state-of-the-art optimization algorithms and two real-world design cases demonstrate its effectiveness in practice. The research topics in multiobjective optimization and surrogate model-assisted optimization inspired by the TSMO framework is finally discussed.

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“…In fact, a multi-criteria design is usually needed in many problems of real-life engineering: from classical applications such as electrical machines [4] and [5] to the newest devices in the micro electro-mechanical systems or nano electro-mechanical systems area [6], the approach in terms of multi-objective optimization is more and more used. Moreover, according to the rationale of the no-free lunch theorem, new algorithms need to be tested on specific issues [7]- [9].…”

Section: Introductionmentioning

confidence: 99%

A Benchmark TEAM Problem for Multi-Objective Pareto Optimization of Electromagnetic Devices

et al. 2018

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This paper proposes a new benchmark for multi-objective optimization. A solution is furnished which has enabled an extensive search and reliable estimation of the shape of the Pareto front. Field uniformity and sensitivity are considered in the context of robust design. It is argued that the benchmark will provide a challenging target for new algorithms, especially those involving numerical modeling using finite-element codes where the number of objective function calls needs to be minimized for practical design processes.

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Multiphysics field analysis and evolutionary optimization: Design of an electro-thermo-elastic microactuator

Cited by 12 publications

References 25 publications

Efficient Design Optimization of High-Performance MEMS Based on a Surrogate-Assisted Self-Adaptive Differential Evolution

Efficient Design Optimization of High-Performance MEMS Based on a Surrogate-Assisted Self-Adaptive Differential Evolution

Hybrid Single and Multiobjective optimization for Engineering Design without Exact Specifications

A Benchmark TEAM Problem for Multi-Objective Pareto Optimization of Electromagnetic Devices

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