Application of metaheuristic algorithms to the identification of nonlinear magneto-viscoelastic constitutive parameters

Pelteret, Jean-Paul; Walter, Bastian; Steinmann, Paul

doi:10.1016/j.jmmm.2018.02.094

Cited by 22 publications

(8 citation statements)

References 94 publications

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“…Multiscale-based approaches are another class of successful modeling schemes to obtain microscale information in the MRE composites [48][49][50][51][52][53][54][55]. Furthermore, continuum-based models are popular tools to understand magneto-elastic responses [52,[56][57][58][59][60][61][62][63][64][65][66], magneto-viscoelastic phenomena [67][68][69], and temperature-dependent responses of MREs [70][71][72][73][74][75][76].…”

Section: Introductionmentioning

confidence: 99%

A review on magneto-mechanical characterizations of magnetorheological elastomers

Bastola

Hossain

2020

Composites Part B: Engineering

243

122

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Section: Introductionmentioning

confidence: 99%

A review on magneto-mechanical characterizations of magnetorheological elastomers

Bastola

Hossain

2020

Composites Part B: Engineering

243

122

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“…Metaheuristic optimization algorithms generally find solutions for highly non-linear optimization tasks [26]. They follow an iterative process that directs a subordinate heuristic by automatically fusing various ideas to explore a solution search space.…”

Section: Metaheuristic Algorithmmentioning

confidence: 99%

A new evaluation function for face image enhancement in unconstrained environments using metaheuristic algorithms

Oloyede

Hancke

Myburgh

et al. 2019

J Image Video Proc.

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Image enhancement is an integral component of face recognition systems and other image processing tasks such as in medical and satellite imaging. Among a number of existing image enhancement methods, metaheuristic-based approaches have gained popularity owing to their highly effective performance rates. However, the need for improved evaluation functions is a major research concern in the study of metaheuristic-based image enhancement methods. Thus, in this paper, we present a new evaluation function for improving the performance of metaheuristic-based image enhancement methods. Essentially, we applied our new evaluation function in conjunction with metaheuristic-based optimization algorithms in order to select automatically the best enhanced face image based on a linear combination of different key quantitative measures. Furthermore, different from other existing evaluation functions, our evaluation function is finitely bounded to determine easily whether an image is either too dark or too bright. This makes it better suited to find optimal solutions (best enhanced images) during the search process. Our method was compared with existing metaheuristic-based methods and other state-of-the-art image enhancement techniques. Based on the qualitative and quantitative measures obtained, our approach is shown to enhance facial images in unconstrained environments significantly.

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“…To generalize the proposed model, it is considered that the fitting parameters process could be done in a more general way by means of metaheuristic optimization algorithms, which are adequate for solving highly non-convex and nonlinear problems [66].…”

Section: Fitting Of Model Parameters From Experimental Datamentioning

confidence: 99%

Visco-Hyperelastic Model with Damage for Simulating Cyclic Thermoplastic Elastomers Behavior Applied to an Industrial Component

et al. 2018

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Abstract:In this work a nonlinear phenomenological visco-hyperelastic model including damage consideration is developed to simulate the behavior of Santoprene 101-73 material. This type of elastomeric material is widely used in the automotive and aeronautic sectors, as it has multiple advantages. However, there are still challenges in properly analyzing the mechanical phenomena that these materials exhibit. To simulate this kind of material a lot of theories have been exposed, but none of them have been endorsed unanimously. In this paper, a new model is presented based on the literature, and on experimental data. The test samples were extracted from an air intake duct component of an automotive engine. Inelastic phenomena such as hyperelasticity, viscoelasticity and damage are considered singularly in this model, thus modifying and improving some relevant models found in the literature. Optimization algorithms were used to find out the model parameter values that lead to the best fit of the experimental curves from the tests. An adequate fitting was obtained for the experimental results of a cyclic uniaxial loading of Santoprene 101-73.

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Application of metaheuristic algorithms to the identification of nonlinear magneto-viscoelastic constitutive parameters

Cited by 22 publications

References 94 publications

A review on magneto-mechanical characterizations of magnetorheological elastomers

A review on magneto-mechanical characterizations of magnetorheological elastomers

A new evaluation function for face image enhancement in unconstrained environments using metaheuristic algorithms

Visco-Hyperelastic Model with Damage for Simulating Cyclic Thermoplastic Elastomers Behavior Applied to an Industrial Component

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