Metamodel-assisted design optimization of piezoelectric flex transducer for maximal bio-kinetic energy conversion

Luo, Liheng; Liu, Dianzi; Zhu, Meiling; Ye, Jianqiao

doi:10.1177/1045389x17689943

Cited by 14 publications

(9 citation statements)

References 27 publications

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“…It can be seen that the proposed multi-level surrogate modeling strategy for design optimization of piezoelectric energy- harvesting devices improves not only the accuracy of the predictions but also the performance of the device. In order to demonstrate the superiority of the proposed multi-level surrogate modeling strategy over other optimization methodologies used in the previous work (Forrester and Keane, 2009;Luo et al, 2017), the optimal designs including optimal solution of the multi-level surrogate modeling strategy, the singlesingle level surrogate modeling approach, and the conventional varying one parameter a time optimization technique are listed and compared in Table 4. Where a safety factor of 2.0 is applied to the stress constraint.…”

Section: Optimal Results and Validation By Fe Simulationsmentioning

confidence: 99%

“…The PFT was manufactured and experimentally tested, the results of which were used for validation of FE models, including the one used in this article. Luo et al (2017) successfully applied the metamodeling technique to obtain an optimal design of the PFT device by parametric optimization. The optimal design offered a power output of 6.54 mW, representing an improvement of more than 58% as compared to the power output of 4.13 mW from the original PFT design when a safety factor of 2.0 is applied.…”

Section: Coupled Piezoelectric Fe Modelmentioning

confidence: 99%

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Maximum energy conversion from human motion using piezoelectric flex transducer: A multi-level surrogate modeling strategy

Luo

Liu

Zhu

et al. 2018

Journal of Intelligent Material Systems and Structures

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Conventional engineering design optimization requires a large amount of expensive experimental tests from prototypes or computer simulations, which may result in an inefficient and unaffordable design process. In order to overcome these disadvantages, a surrogate model may be used to replace the prototype tests. To construct a surrogate model of sufficient accuracy from limited number of tests/simulations, a multi-level surrogate modeling strategy is introduced in this article. First, a chosen number of points determined by optimal Latin Hypercube Design of Experiments are used to generate global-level surrogate models with genetic programming and the fitness landscape can be explored by genetic algorithms for near-optimal solutions. Local-level surrogate models are constructed then from the extended-optimal Latin Hypercube samples in the vicinity of global optimum on the basis of a much smaller number of chosen points. As a result, an improved optimal design is achieved. The efficiency of this strategy is demonstrated by the parametric optimization design of a piezoelectric flex transducer energy harvester. The optimal design is verified by finite element simulations and the results show that the proposed multi-level surrogate modeling strategy has the advantages of faster convergence and more efficiency in comparison with the conventional single-single level surrogate modeling technique.

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Section: Optimal Results and Validation By Fe Simulationsmentioning

confidence: 99%

Section: Coupled Piezoelectric Fe Modelmentioning

confidence: 99%

Maximum energy conversion from human motion using piezoelectric flex transducer: A multi-level surrogate modeling strategy

Luo

Liu

Zhu

et al. 2018

Journal of Intelligent Material Systems and Structures

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show abstract

“…Among those introduced optimization techniques, metamodeling based algorithms show advantages of high efficiency compared to stochastic algorithms and more general applicability, given problems where the gradient information is nontrivial to calculate [16]. Metamodeling has a broad application in simulation-based optimization, where the simulation is treated as numerical experiments [1,17,18]. Meanwhile, the computational cost may be reduced dramatically [19].…”

Section: Literature Reviewmentioning

confidence: 99%

CFD-Based Optimization of Fluid Flow Product Aided by Artificial Intelligence and Design Space Validation

Cheng

Lange

2018

Mathematical Problems in Engineering

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Computational fluid dynamics (CFD) plays an important role in investigating the flow in products. With the help of optimization algorithms, CFD-based optimization is increasingly applied in product development to improve the product design. Even though this approach is becoming increasingly mature, it is faced with the problem that the CFD solver is not able to correctly respond to the design changes under the batch mode, leading to incorrect simulation and optimization results. Besides, there is no work dedicated to dealing with the design points which are physically invalid during the optimization process. In this paper, the intelligent CFD solver is employed to analyze the flow at each design point and to set up the solver with the best fit simulation models. Based on correct simulation results, the physically invalid design points are automatically removed from the design space. Metamodeling is used to process the valid design space with simulation results provided by the intelligent solver and derive the optimum. A prototype system is developed incorporating ANSYS, Python, and MATLAB. The design optimization of a steam control valve is used as the case study to demonstrate how the proposed system works. The optimization is conducted based on the metamodel built by response surface model and radial basis function to verify the effectiveness of the proposed method.

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“…So far, GP has been widely reported to be successfully applied in developing analytical expression for cold-formed steel, 15 deformation modulus of rock, 16 torsional strength of concrete beam 17 and the bond strength of carbon fibre reinforced polymers (CFRPs). 18 In addition, LHS (Latin Hypercube sampling) and GP coupled techniques were used in studies 19,20 to predict the principal stress of a piezoelectric flex transducer, where successful new design was obtained based on the developed method. In these studies, the GP method demonstrated its significant advantage in high prediction accuracy over empirical equations, conventional design codes and traditional multiregression methods.…”

Section: Introductionmentioning

confidence: 99%

Design and analysis method of nonlinear helical springs using a combining technique: Finite element analysis, constrained Latin hypercube sampling and genetic programming

Hou

2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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Helical springs have been widely used in various engineering applications for centuries. For many years, there is no significant development in the design methods of helical springs. Recently, a renewed interest is raised from the industry in exploring new designs for the helical springs with novel configurations due to the requirements of customised properties, such as specific spring stiffness and natural frequency for better performance of valve train systems. In this paper, an innovative method which combines the techniques of Finite Element Analysis (FEA), constrained Latin Hypercube sampling (cLHS) and Genetic Programming (GP) is developed to design and analyse helical springs with arbitrary shapes. cLHS method is applied to generate 300 sets of spring samples within a constrained design domain, and FE analysis is conducted on these spring samples. Two meta-models are developed from the 300 sets of FE results by using GP. They successfully describe the relationships between the design parameters and the overall mechanical performances including compression force and fundamental natural frequency of helical springs. The results show that the developed models have robust abilities on designing helical springs with arbitrary shapes, which significantly expands the design domain of the engineering design methods and potential for precise optimization of helical springs.

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Metamodel-assisted design optimization of piezoelectric flex transducer for maximal bio-kinetic energy conversion

Cited by 14 publications

References 27 publications

Maximum energy conversion from human motion using piezoelectric flex transducer: A multi-level surrogate modeling strategy

Maximum energy conversion from human motion using piezoelectric flex transducer: A multi-level surrogate modeling strategy

CFD-Based Optimization of Fluid Flow Product Aided by Artificial Intelligence and Design Space Validation

Design and analysis method of nonlinear helical springs using a combining technique: Finite element analysis, constrained Latin hypercube sampling and genetic programming

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