Computational Homogenization Method Research Articles

A high-throughput statistical homogenization technique to convert realistic microstructures into idealized periodic unit cells

Abstract Metal alloys frequently contain distributions of second-phase particles that deleteriously affect the material
behavior by acting as sites for void nucleation. These distributions are often extremely complex and
processing can induce high levels of anisotropy. The particle length-scale precludes high-fidelity microstructure
modeling in macroscale simulations, so computational homogenization methods are often employed.
These, however, involve simplifying assumptions to make the problem tractable and many rely on periodic
microstructures. Here we propose a methodology to bridge the gap between realistic microstructures
composed of anisotropic, spatially varying second-phase void morphologies and an idealized periodic microstructure
with roughly equivalent mechanical response. We create a high-throughput, parametric study
to investigate 96 unique bridging methods. We apply our proposed solution to rolled AZ31B magnesium
alloy, for which we have a rich dataset of microstructure morphology and mechanical behavior. Our methodology
converts a μ-CT scan of the realistic microstructure to idealized periodic unit cell microstructures that
are specific to the loading orientation. We recreate the unit cells for each parameter set in a commercial finite
element software, subject them to macroscopic uniaxial loading conditions, and compare our results to the
datasets for the various loading orientations. We find that certain combinations of our parameters capture
the overall stress-strain response, including anisotropy effects with some degree of success. The effect of
different parameter options are explored in detail and we find that excluding certain particle populations
from the analysis can give improved results.

Predicting Effective Transport Properties of Randomly Oriented Polycrystalline Li-Ion Battery Electrodes

Widely practiced Li-ion battery models, such as pseudo-two-dimensional (P2D) models [1-3], use representative spherical electrode particles with assumed isotropic transport properties. Electrode particles, such as NMC, are typically composites that are comprised of numerous randomly oriented crystallites. Each of the single-crystal primary particles that compose secondary particle can have strongly anisotropic transport properties that affect the intercalation process. The empirically approximated effective properties (e.g., diffusion coefficients) as reported in literature vary by as much as six orders of magnitude. In many cases, the effective diffusion coefficients are determined as a part of a parameter-fitting procedure to represent battery polarization measurements.The present approach develops both analytical and computational homogenization methods that derive effective properties based upon the intrinsic single-crystal properties of the crystallites. Homogenization techniques are computationally effective methods to predict macroscale behavior based on microscale properties.As illustrated in Fig. 1, the computational approach begins by assembling over 1000 randomly oriented single-crystal primary particles, with each crystallite having anisotropic properties associated with the crystal lattice. The dodecahedron-shaped primary particles are tightly assembled into a secondary particle that is approximately spherical. A three-dimensional finite-element model predicts transient Li-intercalation fractions throughout a secondary particle during charge or discharge processes. In graphite, the transverse and normal diffusion coefficients differ by approximately six orders of magnitude. Based on the cut plane in Fig. 1, and despite extreme anisotropy within the crystallites, the homogenized macroscopic behavior is nearly isotropic.The analytical approach extends a self-consistent method (SCM) for polycrystalline materials first presented by Ponte-Castañeda and Willis [4], which mathematically combines tensors describing diffusion pathways for every primary particle. The present study considers the concentration-dependent and anisotropic diffusion parameters collected by Persson et al. [5] and Zhou et al. [6] for graphite and NMC, respectively. Another homogenization method is the use of an orientation distribution function (ODF) [7], which predicts the effective diffusion parameters based on the statistical average of every possible primary particle orientation. Fig. 2 shows the predicted effective diffusion coefficients bounded by Hashin-Shtrikman bounds for anisotropic composites proposed by Willis [8] and the experimentally determined diffusion coefficients. The predicted effective diffusion coefficients are lower than the experimentally determined values in transverse direction but are significantly larger than the values in the normal direction. Additionally, the in-plane diffusion dominates the curvature of the predicted effective properties.[1] M. Doyle, T. F. Fuller, and J. Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. J. Electrochem. Soc., 140:1526–1533, 1993.[2] T. F. Fuller, M. Doyle, and J. Newman. Simulation and optimization of the dual lithium ion insertion cell. J. Electrochem. Soc., 141:1–10, 1994.[3] M. Doyle, J. Newman, A. S. Gozdz, C. N. Schmutz, and J.-M. Tarascon. Comparison of modeling predictions with experimental data from plastic lithium ion cells. J. Electrochem. Soc., 143:1890–1903, 1996[4] P.P.- Castañeda and J.R. Willis. The effect of spatial distribution on the effective behavior of composite materials and cracked media. J. Mech. Phys. Solids, 43:1919–1951, 1995[5] K. Persson, V. A. Sethuraman, L. J. Hardwick, Y. Hinuma, Y. S. Meng, A. van der Ven, V. Srinivasan, R. Kostecki, and G. Ceder. Lithium diffusion in graphitic carbon. J. Phys. Chem. Lett., 1:1176–1180, 2010[6] H. Zhou, F. Xin, B. Pei, and M. S. Whittingham. What limits the capacity of layered oxide cathodes in lithium batteries? ACS Energy Lett., 4:1902–1906, 2019[7] S. Nemat-Nasser, M. Hori, and J. D. Achenbach. Micromechanics: Overall properties of heterogeneous materials. In Micromechanics, volume 37. Elsevier Science I& Technology, The Netherlands, 1993[8] J.R. Willis. Bounds and self-consistent estimates for the overall properties of anisotropic composites. J. Mech. Phys. Solids, 25:185–202, 1977 Figure 1

Variational formulation and monolithic solution of computational homogenization methods

AbstractIn this contribution, we derive a consistent variational formulation for computational homogenization methods and show that traditional FE and IGA approaches are special discretization and solution techniques of this most general framework. This allows us to enhance dramatically the numerical analysis as well as the solution of the arising algebraic system. In particular, we expand the dimension of the continuous system, discretize the higher dimensional problem consistently and apply afterwards a discrete null‐space matrix to remove the additional dimensions. A benchmark problem, for which we can obtain an analytical solution, demonstrates the superiority of the chosen approach aiming to reduce the immense computational costs of traditional FE and IGA formulations to a fraction of the original requirements. Finally, we demonstrate a further reduction of the computational costs for the solution of general nonlinear problems.

Ultimate resistance and fatigue performance predictions of woven-based fiber reinforced polymers using a computational homogenization method

PurposeIn order to achieve the desired macroscopic mechanical properties of woven fiber reinforced polymer (FRP) materials, it is necessary to conduct a detailed analysis of their microscopic load-bearing capacity.Design/methodology/approachUtilizing the representative volume element (RVE) model, this study delves into how the material composition influences mechanical parameters and failure processes.FindingsTo study the ultimate strength of the materials, this study considers the damage situation in various parts and analyzes the stress-strain curves under uniaxial and multiaxial loading conditions. Furthermore, the study investigates the degradation of macroscopic mechanical properties of fiber and resin layers due to fatigue induced performance degradation. Additionally, the research explores the impact of fatigue damage on key material properties such as the elastic modulus, shear modulus and Poisson's ratio.Originality/valueBy studying the load-bearing mechanisms at different scales, a direct correlation is established between the macroscopic mechanical behavior of the material and the microstructure of woven FRP materials. This comprehensive analysis ultimately elucidates the material's mechanical response under conditions of fatigue damage.

Effective response and microstructure evolution for shape memory alloy laminated composites

A model for the macroscopic mechanical behavior of rank-1 laminates including two shape memory alloy (SMA) phases is presented. The model expresses the general behavior of the composite with phases undergoing rate-independent elastic and inelastic deformations. Homogenization techniques (including the rank-1 laminate model) are used to establish the effective behavior of the SMA laminated composite (SLC) based on the information about the mechanical response of the individual phases and their volume concentrations. A stress-control algorithm is put forward to implement the model. With the aid of the stress-control algorithm, an implicit expression for the effective tangent stiffness and an evolution equation for the effective inelastic strain are derived. Results are compared with the outcomes of an FE-based computational homogenization and a very good agreement is seen. By using a constitutive model with internal variables for the dense SMA, the overall response of the SLC under different mechanical loadings is evaluated. The effective response of the SLC for various volume concentrations of the phases is assessed and exclusive comparisons are illustrated. Furthermore, the influence of different temperatures on the effective superelastic behavior of the SLCs is studied. The findings have implications for the analysis and the design of more complex shape-memory-alloy laminated composites for high-end applications.

Computational homogenization of the elastic properties of polycrystalline fcc metals within Mindlin’s second strain-gradient theory

The lack of interest in Mindlin’s second strain-gradient theory, despite its success in characterizing various phenomena for which there is no explanation in the classical elasticity, is predominantly due to the numerous higher-order elastic constants involved therein. As an attempt to overcome this shortcoming, a computational homogenization method for determining such elastic constants of polycrystalline face-centered cubic (fcc) metals is proposed in the present study. In this homogenization method, using the Voigt-type averaging scheme, a polycrystalline material is modeled by an isotropic aggregate of randomly oriented single crystals without accounting for the grain size effect and the complexities due to grain boundaries and junctions. Subsequently, analytical expressions for the strain energy due to certain modes of loading are determined, and molecular simulations of a few fcc metals under such modes of loading are performed. Then, by fitting the corresponding analytical expressions to the results obtained from these simulations, the effective elastic constants and consequently the effective characteristic lengths of the fcc metals in their polycrystalline form are determined. Moreover, the free-surface-induced reconstruction in a thin layer of a solid is addressed both analytically and by molecular simulations, as a result of which the effective moduli of cohesion of the fcc metals in their polycrystalline form are calculated. In addition, the complete set of conditions for the positive-definiteness of the strain-energy-density function of isotropic materials within the adopted theory is derived, and subsequently a discussion of whether or not the numerical values obtained for the effective elastic moduli of the polycrystalline fcc metals satisfy such conditions is provided.

A reduced order variational spectral method for efficient construction of eigenstrain‐based reduced order homogenization models

AbstractReduced order models (ROMs) are often coupled with concurrent multiscale simulations to mitigate the computational cost of nonlinear computational homogenization methods. Construction (or training) of ROMs typically requires evaluation of a series of linear or nonlinear equilibrium problems, which itself could be a computationally very expensive process. In the eigenstrain‐based reduced order homogenization method (EHM), a series of linear elastic microscale equilibrium problems are solved to compute the localization and interaction tensors that are in turn used in the evaluation of the reduced order multiscale system. These microscale equilibrium problems are typically solved using either the finite element method or semi‐analytical methods. In the present study, a reduced order variational spectral method is developed for efficient computation of the localization and interaction tensors. The proposed method leads to a small stiffness matrix that scales with the order of the reduced basis rather than the number of degrees of freedom in the finite element mesh. The reduced order variational spectral method maintains high accuracy in the computed response fields. A speedup higher than an order of magnitude can be achieved compared to the finite element method in polycrystalline microstructures. The accuracy and scalability of the method for large polycrystals and increasing phase property contrast are investigated.

Imposing Dirichlet boundary conditions directly for FFT-based computational micromechanics

We discuss how Dirichlet boundary conditions can be directly imposed for the Moulinec–Suquet discretization on the boundary of rectangular domains in iterative schemes based on the fast Fourier transform (FFT) and computational homogenization problems in mechanics. Classically, computational homogenization methods based on the fast Fourier transform work with periodic boundary conditions. There are applications, however, when Dirichlet (or Neumann) boundary conditions are required. For thermal homogenization problems, it is straightforward to impose such boundary conditions by using discrete sine (and cosine) transforms instead of the FFT. This approach, however, is not readily extended to mechanical problems due to the appearance of mixed derivatives in the Lamé operator of elasticity. Thus, Dirichlet boundary conditions are typically imposed either by using Lagrange multipliers or a “buffer zone” with a high stiffness. Both strategies lead to formulations which do not share the computational advantages of the original FFT-based schemes. The work at hand introduces a technique for imposing Dirichlet boundary conditions directly without the need for indefinite systems. We use a formulation on the deformation gradient—also at small strains—and employ the Green’s operator associated to the vector Laplacian. Then, we develop the Moulinec–Suquet discretization for Dirichlet boundary conditions—requiring carefully selected weights at boundary points—and discuss the seamless integration into existing FFT-based computational homogenization codes based on dedicated discrete sine/cosine transforms. The article culminates with a series of well-chosen numerical examples demonstrating the capabilities of the introduced technology.

Computational homogenization method for the bending analysis of submarine power cables

Computational homogenization method for the bending analysis of submarine power cables

Pre-trained transformer model as a surrogate in multiscale computational homogenization framework for elastoplastic composite materials subjected to generic loading paths

A composite material typically exhibits complex behavior at the engineer scale, arising from the interactions between its underlying constituent phases, as well as the competitions between micro-processes. It is generally a daunting task to develop an engineering model to adequately capture the essential micro mechanisms that propagate onto the macro scale. To this end, the multiscale computational homogenization (FE2) method enables a consistent coupling across length scales, to give results that compare well with direct numerical simulations having the full micro-structural details, without the need for any constitutive assumptions nor calibrations at the macro scale. Despite its predictive capabilities, the typical computational homogenization method is still computationally too expensive for most practical problems, as the coupling between micro and macro scales are solved simultaneously during its numerical implementation. In this presentation, focusing on the elastoplastic behavior of fiber-reinforced composite, we address this bottleneck with an offline development of a microscopic surrogate model for a given micro-structure, to be incorporated into a standard nonlinear FE framework, for rapid online implementations at the macro scale. For the offline training phase, we adopt the transformer-based architecture within a pre-training and fine-tuning framework. The proposed pre-trained transformer model is capable of parallelizing computations to effectively capture global dependencies within the strain-stress data sequences. To reduce the data generation cost, a constructed source representative volume element (RVE) having a single central heterogeneity with an identical volume fraction with the target RVE is utilized, to rapidly generate a huge source dataset for a pre-training process. The performance of the surrogate model is first demonstrated by comparing its predictions under random loading paths against the reference homogenized RVE responses. Next, the surrogate model is incorporated into a macro FE framework, and its predictive capabilities illustrated via the generic loading of two specimens with different microstructures, each having a different loading–unloading path. Finally, a chunking method is discussed as a potential remedy for managing very long load sequences.

Data-driven inverse design of composite triangular lattice structures

In this work, we introduce a class of novel bi-material composite triangular lattice structures. The inverse design of these structures is achieved by using a data-driven method. They exhibit a broad range of tunable effective elastic properties, i.e., the effective Young’s and shear moduli span a few orders of magnitude, and the effective Poisson’s ratio can be both negative and positive. We exploit the computational homogenization method to calculate the effective elastic constants of these structures with varying structural features to generate a representative dataset. Subsequently, we harness the dataset to train artificial neural network models for both forward prediction and inverse design. The forward model predicts the effective properties of a given structure, while the inverse model generates a structure design for the specified target properties. We validate the performance of these models by several examples such as optimizing the isotropic auxetic properties. The data-driven surrogate models greatly facilitate the practical application of these novel lattice structures for various structural and/or functional purposes.

Solver‐free classical computational homogenization for nonlinear periodic heterogeneous media

AbstractModeling the behavior of composite materials is an important application of computational homogenization methods. Classical computational homogenization (CCH), based on asymptotic analysis, is such a method. In CCH, equilibrium equations are separated into two length scales and solved numerically. Solving the fine‐scale equilibrium equations at every coarse‐scale Gauss point, in every iteration of a Newton–Raphson loop, is often too computationally expensive for real engineering applications. In this study, we propose a modified CCH approach that avoids solving the fine‐scale equilibrium equations. The proposed method, which we call solver‐free CCH, works by pre‐computing a set of eigenstrain influence function tensors based on data from a small number of numerical experiments. Then, during the online stage of the computation, these eigenstrain influence tensors are used in the fine‐scale problem to evaluate and homogenize strains and stresses. This article begins by formulating the solver‐free CCH approach for small‐deformation problems involving composites with nonlinear constituent phase material models, including computation of the eigenstrain influence tensors and their application within the online stage. To verify the proposed approach, we consider loading cases outside the training set used to derive the eigenstrain influence tensors. The combinational efficiency and accuracy of the solver‐free CCH in comparison to the conventional CCH is studied on a multilayer composite plate in three point bending (3pt‐bend) and open hole tension.

Multiscale modeling of woven composites by deep learning neural networks and its application in design optimization

Due to complex yarn geometries and multiscale nature, it is very challenging to predict mechanical properties and strength constants of woven composites in an accuracy and efficient way. In this paper, mechanics of structure genome (MSG) is employed to predict effective material properties and strength constants of woven composites. The microscale analysis is performed to compute the effective properties and strength constants of yarns based on a hexagonal packed microstructure. Then, the mesoscale analysis is performed to compute the effective material properties and strength constants of woven composites. The accuracy and efficiency of the MSG method are compared with the computational homogenization method. In addition, simulation data using the MSG model is generated to train deep learning neural networks (DNN) models. The trained models provide ultra-efficient surrogate models with high accuracy. The DNN models are further applied to a design optimization problem to tailor geometry parameters to enhance the mechanical performance of plain woven composites, and the optimization can be efficiently solved with gradients from the DNN models. The developed computational framework can be applied to general woven composites featuring complex microscale and mesoscale features.

Determination of the effective conductivities of solid oxide fuel cell electrodes using the first‐order homogenization method

AbstractApproximately 80% of the world's primary energy supply consists of fossil fuels. In order to reduce the CO2 consumption in the world, fuel cells will be indispensable for the environmentally friendly generation of electrical energy. Many experimental and numerical studies show that the composition and the microstructure morphology of porous electrodes have a great influence on the durability and conversion efficiency of fuel cells. In order to establish a relationship between the specific microstructure of the electrodes and the performance of the overall fuel cell, the macroscopic physical quantities need to be determined. In this work, the first‐order computational homogenization method capable of capturing anisotropic effects is applied in order to estimate different types of homogenized conductivities. The effective conductivities can be used to conduct numerical simulations on the fuel cell level in order to obtain correlations between the microstructure characteristics (e.g., volume fraction of pores, morphology) and the performance.

Machine learning aided multiscale magnetostatics

Computational material modeling using advanced numerical techniques speeds up the design process and reduces the costs of developing new engineering products. In the field of multiscale modeling, huge computation efforts are expected for modeling heterogeneous materials while trying to reach high accuracy levels. In this work, a machine learning approach, namely the convolutional neural network (CNN), is developed as a solution providing a high level of accuracy while being computationally efficient. The input for the CNN model consists of two/three-dimensional images of artificial periodic and biphasic microstructures in the form of nonoverlapping and overlapping, mono- and polydisperse circular/spherical inclusion systems, which are generated by a random sequential inhibition process. These correspond to Statistical Volume Elements (SVE). Considering linear magnetostatics at the microscale, the output is the apparent permeability of the SVE. Training and testing data for the apparent properties is produced with finite element method-based two-scale asymptotic homogenization. The model efficiency is revealed by employing some representative examples in two and three-dimensional settings. In this regard, the performance of the CNN model is assessed with the applied computational homogenization method relating to the accuracy and computational efficiency. The results with the CNN model show high accuracy in predicting the homogenized permeability and a significant decrease in computation time.

A multiscale framework for atomistic–continuum transition in nano-powder compaction process using a cap plasticity model

In this paper, a computational atomistic-continuum multiscale framework is presented for the simulation of the nano-powder compaction process using a cone-cap plasticity model. The atomistic representative volume element (RVE) comprises of nano-powder particles that is used to perform the molecular dynamics analysis in order to capture the mechanical behavior of nano-powder material. The periodic boundary condition is applied over the atomistic RVE to satisfy the inter-scale kinematic compatibility, and the stress tensor is derived according to the inter-scale energetic consistency principals from the nanoscale at each material point of the powder component. A multiscale framework is performed using the constitutive law of the coarse-scale domain enhanced by the molecular dynamics results of the fine-scale domain. The coarse-scale constitutive law is conformed to the cone-cap plasticity model as a robust and capable constitutive relation in modeling the powder compaction process, in which the cap plasticity parameters are determined using the data derived from the molecular dynamics simulations. The mechanical response of nano-powder particles within the RVE is obtained from the confining pressure test and true-triaxial test, where a sensitivity analysis is accomplished on the computed parameters of the cone-cap plasticity model. The enhanced cap plasticity model is then employed to simulate various industrial nano-powder components that illustrates the performance and efficiency of the proposed multiscale computational homogenization method.

Challenges in two‐scale computational homogenization of mechanical metamaterials

AbstractThanks to the advancement of additive manufacturing technologies, mechanical metamaterials have attracted a great deal of attention in recent years. With the employment of such technologies, materials with exceptional or tailored mechanical properties can be easily manufactured mainly by 3D printing of different microstructures rather than by changing the material composition. These lattice materials can provide remarkable material properties in spite of being significantly lighter than typical bulk materials. Due to the large number of degrees of freedom for engineering structures, single‐scale numerical simulation of such cellular materials is computationally demanding. Therefore, two‐scale computational homogenization approaches, such as FE2 and FE‐FFT, can perform a key role in the cost‐effective numerical modeling of metamaterials. Two‐scale computational homogenization methods rely on solving a boundary value problem (BVP) for each of the macroscopic and microscopic scales in a nested procedure. Although representative homogenization techniques have been widely used to study materials with heterogeneous microstructures, there still exist some challenges in their employment for lattice materials. This study addresses main challenges in two‐scale‐based computational homogenization methods for numerical modeling of mechanical metamaterials. High dependence of convergence rate and accuracy on phase contrast for fast Fourier transform (FFT) solvers and comparable macro and micro characteristic lengths in metamaterials (i.e. the applicability of the principle of scale separation) are some examples of such challenges.

Accelerated offline setup of homogenized microscopic model for multi‐scale analyses using neural network with knowledge transfer

AbstractThe FE computational homogenization method is a predictive multi‐scale method without the need for constitutive assumptions and/or potential function postulates at the macro engineering scale. Instead, the effective micro‐structural responses are extracted directly from a representative volume element (RVE) underlying each macro point. However, the FE method is still computationally too expensive for most practical uses, since the micro‐macro FE coupling is done at each loading step/iteration for the entire domain. To this end, the machine learning method has been utilized in the literature for the offline training of a surrogate model to predict the RVE homogenized response for general loading conditions. In this contribution, the neural network (NN) is incorporated into the macro finite element framework in a non‐intrusive manner. This is termed as the FE‐NN framework, in analogy to the FE method. In general, online simulations in the FE‐NN method is very efficient, with predictions matching closely to those obtained from reference direct numerical simulations (DNS). A bottleneck with the FE‐NN framework, however, is the high computational cost associated with the data generation for offline NN model setup. In this paper, focusing on the FE‐NN multi‐scale framework for non‐linear elastic deformation of heterogeneous materials, a sequential training strategy with knowledge transfer is proposed, to enable an efficient offline microscopic NN model setup. For a given target RVE, we first consider a simplified source RVE, where data can be generated rapidly, for the NN pre‐training of surrogate model. The pre‐trained network parameters are next downloaded to initialize the target NN surrogate model, followed by a fine‐tuning training process, using only a small dataset generated by the computationally expensive high‐fidelity RVE. The efficiency of the proposed sequential learning method over the conventional NN training, as well as, its excellent predictive capability for multi‐scale analyses, are demonstrated for a multi‐phase composite material. The proposed FE‐NN‐KT approach can be implemented easily without complicated pre‐processing procedures, since the data generation only involves the standard extraction of effective responses from RVEs, together with a standard NN architecture.

Factors influencing the dynamic stiffness in short‐fiber reinforced polymers

AbstractIn short‐fiber reinforced polymers, fatigue damage is typically characterized by measuring the dynamic stiffness and its degradation under cyclic loading. Computational homogenization methods may be used to characterize the fatigue behavior of the composite via numerical predictions. Such an approach may reduce the experimental effort significantly. In the previous works, the authors proposed an elastic fatigue damage model for predicting the relative stiffness degradation of short‐fiber reinforced materials. However, the absolute value of the dynamic stiffness within the first cycle showed deviations from the expected elastic material behavior. Thus, the effect of viscoelastic polymer behavior as well as different microstructure descriptors on the dynamic stiffness is studied in the work at hand.

Nonlinear finite element free and forced vibrations of cellular plates having lattice-type metamaterial cores: a strain gradient plate model approach

The main objective of this paper is to develop a theoretically and numerically reliable and efficient methodology based on combining a finite element method and a strain gradient shear deformation plate model accounting for the nonlinear free and forced vibrations of cellular plates having lattice-type metamaterial cores. The proposed model provides a modelling framework computationally more efficient than framework of the conventional three-dimensional elasticity for the simulation of the underlying nonlinear dynamics of cellular plates with microarchitectures. The corresponding governing equations follow Mindlin’s strain gradient elasticity theory including the micro-inertia effect applied to the first-order shear deformation plate theory along with the nonlinear von Kármán kinematics. Standard and higher-order computational homogenization methods determine the classical and strain gradient material constants, respectively. A higher-order C1-continuous 6-node finite element is adopted for the discretization in the spatial domain, and an arc-length continuation technique along with time-periodic discretization is implemented to solve the resulting nonlinear time-dependent problem. Through a set of comparative studies with 3D full-field models as reliable validation references for triangularly prismatic example cores, the accuracy and efficacy of the proposed dimension reduction methodology are demonstrated for a diverse range of problem parameters for analyzing the large-amplitude dynamic structural response. The results show that the proposed strain gradient plate model can accurately capture the microarchitecture effects of cellular plates with low computational costs, which signifies a relevant contribution particularly to the computational analysis of nonlinear dynamics.