Comparison and Validation of Numerical Homogenization Based on Asymptotic Method and Representative Volume Element Method in Thermal Composites

Lee, Dohun; Lee, Jae‐Wook

doi:10.1007/s42493-021-00067-4

“…In comparison, the representative volume element (RVE) method has a wide range of applications because it allows relatively easy determination of nonlinear as well as linear constitutive properties of composite materials, as long as the governing equations and periodic boundary conditions are known. Furthermore, despite the simplicity in deriving the formulation, the effective material properties calculated by the RVE method is comparable to those obtained using the AHM (Lee and Lee. 2021).…”

Section: Introductionsupporting

confidence: 56%

Topological optimal design of composite magnetic actuators to improve driving force and thermal conductivity

Oh,

Yoo

2024

Preprint

0

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The aim of this study is to introduce a topology optimization approach to improve the driving force of magnetic actuators along with their thermal conductivity considering the nonlinearity of composite materials. The anisotropic magnetic composite is composed of two parts, taking into account differences in magnetic saturation effect and thermal conductivity. The first part has low magnetic reluctivity and high conductivity, while the other part has high reluctivity and low conductivity. The representative volume element (RVE) method and deep neural network (DNN) were used to obtain a dataset of effective composite material properties and generate a machine learning (ML) module for material property determination used in the optimization process. To optimize and verify both performances, a multi-objective function was established. By employing gradually changing preferences with an initial and utopia points-based adaptive weighting method, design processes were performed to obtain Pareto-optimal solution sets evenly distributed in the objective space. Numerical examples are presented for both symmetric and asymmetric magnetic actuator models, aiming to validate the effectiveness of the proposed design process. To investigate the effects of nonlinearity in magnetic material properties, design results are compared when subjected to high and low currents.

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“…The boundary value problems of partial differential equations have been studied for application in many diverse engineering fields. The AH method is mathematically rigorous, but it requires a complex and lengthy derivation procedure to obtain effective material properties [ 51 ]. AH assumes that any field quantity, such as the displacement, can be described as an asymptotic expansion, which, once replaced in the governing equations of equilibrium, allows the effective properties of the composite material to be derived.…”

Section: Introductionmentioning

confidence: 99%

Simulation of a Composite with a Polyhydroxybutyrate (PHB) Matrix Reinforced with Cylindrical Inclusions: Prediction of Mechanical Properties

Gómez-Gast,

Rivera-Santana,

Otero

et al. 2023

Polymers

1

0

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Biocomposite development, as a sustainable alternative to fossil-derived materials with diverse industrial applications, requires expediting the design process and reducing production costs. Simulation methods offer a solution to these challenges. The main aspects to consider in simulating composite materials successfully include accurately representing microstructure geometry, carefully selecting mesh elements, establishing appropriate boundary conditions representing system forces, utilizing an efficient numerical method to accelerate simulations, and incorporating statistical tools like experimental designs and re-regression models. This study proposes a comprehensive methodology encompassing these aspects. We present the simulation using a numerical homogenization technique based on FEM to analyze the mechanical behavior of a composite material of a polyhydroxybutyrate (PHB) biodegradable matrix reinforced with cylindrical inclusions of flax and kenab. Here, the representative volume element (RVE) considered the geometry, and the numerical homogenization method (NHM) calculated the macro-mechanical behavior of composites. The results were validated using the asymptotic homogenization method (AHM) and experimental data, with error estimations of 0.0019% and 7%, respectively. This model is valuable for predicting longitudinal and transverse elastic moduli, shear modulus, and Poisson’s coefficient, emphasizing its significance in composite materials research.

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Topological optimal design of composite magnetic actuators to improve driving force and thermal conductivity

Oh,

Yoo

2024

Struct Multidisc Optim

0

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The aim of this study is to introduce a topology optimization approach to improve the driving force of magnetic actuators along with their thermal conductivity considering the nonlinearity of composite materials. The anisotropic magnetic composite is composed of two parts, taking into account differences in magnetic saturation effect and thermal conductivity. The first part has low magnetic reluctivity and high conductivity, while the other part has high reluctivity and low conductivity. The representative volume element (RVE) method and deep neural network (DNN) were used to obtain a dataset of effective composite material properties and generate a machine learning (ML) module for material property determination used in the optimization process. To optimize and verify both performances, a multi-objective function was established. By employing gradually changing preferences with an initial and utopia points-based adaptive weighting method, design processes were performed to obtain Pareto-optimal solution sets evenly distributed in the objective space. Numerical examples are presented for both symmetric and asymmetric magnetic actuator models, aiming to validate the effectiveness of the proposed design process. To investigate the effects of nonlinearity in magnetic material properties, design results are compared when subjected to high and low currents.

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Comparison and Validation of Numerical Homogenization Based on Asymptotic Method and Representative Volume Element Method in Thermal Composites

Cited by 7 publications

References 20 publications

Topological optimal design of composite magnetic actuators to improve driving force and thermal conductivity

Topological optimal design of composite magnetic actuators to improve driving force and thermal conductivity

Simulation of a Composite with a Polyhydroxybutyrate (PHB) Matrix Reinforced with Cylindrical Inclusions: Prediction of Mechanical Properties

Topological optimal design of composite magnetic actuators to improve driving force and thermal conductivity

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