Response Surfaces in the Numerical Homogenization of Non-Linear Porous Materials

Beluch, Witold; Hatłas, Marcin

doi:10.24423/engtrans.1012.20190502

Cited by 3 publications

(5 citation statements)

References 3 publications

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“…According to the standard method, the response function (functions) computation begins with a limited number of points, and the accuracy of the resulting approximation is checked at a few chosen verification points. If the quality is inadequate, refinement points are added to change the response surface . The Design Expert program offers three alternative RSM designs: central composite, Box–Behnken, and optimal designs.…”

Section: Response Surface Methodologymentioning

confidence: 99%

“…If the quality is inadequate, refinement points are added to change the response surface. 32 The Design Expert program offers three alternative RSM designs: central composite, Box–Behnken, and optimal designs. The optimal (custom) design is used in this study.…”

Section: Response Surface Methodologymentioning

confidence: 99%

“…Many analytical techniques, such as the finite element (FE) analysis for calculating the elastic modulus of a two-phase composite and for computing effective electrical and thermal conductivities, may be utilized to understand better the physical behavior of materials at the atomic and subatomic levels. − It has been demonstrated that the most popular of these models are in good accord with the experimental data. , Besides, the additional use of the response surface approach enables a substantial decrease in the processing effort while still delivering accurate approximations. To design an approximate numerical homogenization process for inhomogeneous nonlinear porous materials, Beluch and Hatłas, for instance, use response surfaces in the numerical homogenization …”

Section: Introductionmentioning

confidence: 99%

“… 30 , 31 Besides, the additional use of the response surface approach enables a substantial decrease in the processing effort while still delivering accurate approximations. To design an approximate numerical homogenization process for inhomogeneous nonlinear porous materials, Beluch and Hatłas, 32 for instance, use response surfaces in the numerical homogenization. 32 …”

Section: Introductionmentioning

confidence: 99%

“…To design an approximate numerical homogenization process for inhomogeneous nonlinear porous materials, Beluch and Hatłas, 32 for instance, use response surfaces in the numerical homogenization. 32 …”

Section: Introductionmentioning

confidence: 99%

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Numerical Investigation and Response Surface Optimization of the Effective Modulus and Electrical and Thermal Conductivities of the Borophene Nanoplatelet-Reinforced PEDOT:PSS Nanocomposite for Energy Storage Application

Adekoya

Sadiku

et al. 2022

ACS Omega

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Conductive organic nanocomposites have been widely employed to achieve a variety of purposes, particularly for energy storage applications, making it necessary to investigate transport properties such as electron and heat transport qualities based on geometric shapes and component materials. Due to the solid B–B bonds, unique atomic structure, and energy storage potential, borophene has received significant attention due to its reported ultrahigh mechanical modulus and metallic conduction. Herein, we investigated the effect and interaction of content materials (volume fraction) and geometric parameters such as the aspect ratio and orientation of borophene nanoplatelet (BNP) inclusions on the mechanical integrity and transport features (electrical and thermal conductivities) of a poly(3,4-ethylene dioxythiophene):poly(4-styrene sulfonate) (PEDOT:PSS) electrode. The boundary condition is crucial in developing the predictive models for the optimized mechanical and transport properties of the composites. The effective modulus, electrical conductivity, and thermal conductivity of the BNP-reinforced PEDOT:PSS-based nanocomposite are evaluated using the periodic boundary condition, the representative volume element-based finite element homogenization, and statistical analysis response surface techniques. The optimal parameters for the PEDOT:PSS/BNP nanocomposite for energy storage application are predicted based on the desirability function to have a 13.96% volume fraction of BNPs, having an aspect ratio of 0.04 at 45° inclination. The desirability value achieved for the material hinges was 0.78 with a predicted Young’s modulus of 6.73 GPa, the electrical conductivity was 633.85 S/cm, and the thermal conductivity was 1.96 W/m K at a generally high predictive performance of <0.03 error. The effective thermal conductivity of the nanocomposite was determined by considering Kapitsa nanoeffects, which exhibit an interfacial thermal resistance of 2.42 × 10–9 m2 K/W. Based on these improved findings, the enhanced PEDOT:PSS/BNP nanocomposite electrode would be a promising material for metal-ion batteries.

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Section: Response Surface Methodologymentioning

confidence: 99%

Section: Response Surface Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Numerical Investigation and Response Surface Optimization of the Effective Modulus and Electrical and Thermal Conductivities of the Borophene Nanoplatelet-Reinforced PEDOT:PSS Nanocomposite for Energy Storage Application

Adekoya

Sadiku

et al. 2022

ACS Omega

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Surrogate modeling by multifidelity cokriging for the ductile failure of random microstructures

Cadet,

Flouriot,

Forest

et al. 2024

Comput Mech

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Modeling Macroscopic Material Behavior With Machine Learning Algorithms Trained by Micromechanical Simulations

et al. 2019

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Micromechanical modeling of material behavior has become an accepted approach to describe the macroscopic mechanical properties of polycrystalline materials in a microstructure-sensitive way. The microstructure is modeled by a representative volume element (RVE), and the anisotropic mechanical behavior of individual grains is described by a crystal plasticity model. Such micromechanical models are subjected to mechanical loads in a finite element (FE) simulation and their macroscopic behavior is obtained from a homogenization procedure. However, such micromechanical simulations with a discrete representation of the material microstructure are computationally very expensive, in particular when conducted for 3D models, such that it is prohibitive to apply them for process simulations of macroscopic components. In this work, we suggest a new approach to develop microstructure-sensitive, yet flexible and numerically efficient macroscopic material models by using micromechanical simulations for training Machine Learning (ML) algorithms to capture the mechanical response of various microstructures under different loads. In this way, the trained ML algorithms represent a new macroscopic constitutive relation, which is demonstrated here for the case of damage modeling. In a second application of the combination of ML algorithms and micromechanical modeling, a proof of concept is presented for the application of trained ML algorithms for microstructure design with respect to desired mechanical properties. The input data consist of different stress-strain curves obtained from micromechanical simulations of uniaxial testing of a wide range of microstructures. The trained ML algorithm is then used to suggest grain size distributions, grain morphologies and crystallographic textures, which yield the desired mechanical response for a given application. For validation purposes, the resulting grain microstructure parameters are used to generate RVEs, accordingly and the macroscopic stress-strain curves for those microstructures are calculated and compared with the target quantities. The two examples presented in this work, demonstrate clearly that ML methods can be trained by micromechanical simulations, which capture material behavior and its relation to Reimann et al. Application of Micromechanical Modeling on Machine Learningmicrostructural mechanisms in a physically sound way. Since the quality of the ML algorithms is only as good as that of the micromechanical model, it is essential to validate these models properly. Furthermore, this approach allows a hybridization of experimental and numerical data.

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Response Surfaces in the Numerical Homogenization of Non-Linear Porous Materials

Cited by 3 publications

References 3 publications

Numerical Investigation and Response Surface Optimization of the Effective Modulus and Electrical and Thermal Conductivities of the Borophene Nanoplatelet-Reinforced PEDOT:PSS Nanocomposite for Energy Storage Application

Numerical Investigation and Response Surface Optimization of the Effective Modulus and Electrical and Thermal Conductivities of the Borophene Nanoplatelet-Reinforced PEDOT:PSS Nanocomposite for Energy Storage Application

Surrogate modeling by multifidelity cokriging for the ductile failure of random microstructures

Modeling Macroscopic Material Behavior With Machine Learning Algorithms Trained by Micromechanical Simulations

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