Material Model Research Articles

Simulation of Uterus Active Contraction and Fetus Delivery in ls-dyna.

Vaginal childbirth is the final phase of pregnancy when one or more fetuses pass through the birth canal from the uterus, and it is a biomechanical process. The uterine active contraction, causing the pushing force on the fetus, plays a vital role in regulating the fetus delivery process. In this project, the active contraction behaviors of muscle tissue were first modeled and investigated. After that, a finite element method (FEM) model to simulate the uterine cyclic active contraction and delivery of a fetus was developed in ls-dyna. The active contraction was driven through contractile fibers modeled as one-dimensional truss elements, with the Hill material model governing their response. Fibers were assembled in the longitudinal, circumferential, and normal (transverse) directions to correspond to tissue microstructure, and they were divided into seven regions to represent the strong anisotropy of the fiber distribution and activity within the uterus. The passive portion of the uterine tissue was modeled with a Neo Hookean hyperelastic material model. Three active contraction cycles were modeled. The cyclic uterine active contraction behaviors were analyzed. Finally, the fetus delivery through the uterus was simulated. The model of the uterine active contraction presented in this paper modeled the contractile fibers in three-dimensions, considered the anisotropy of the fiber distribution, provided the uterine cyclic active contraction and propagation of the contraction waves, performed a large deformation, and caused the pushing effect on the fetus. This model will be combined with a model of pelvic structures so that a complete system simulating the second stage of the delivery process of a fetus can be established.

Modelling a Response of Complex-Phase Steel at High Strain Rates

In this article, a response of the complex-phase high-strength steel SZBS800 was modelled by considering the strain-rate influence. The material’s response was first measured with a series of standard tensile tests at lower strain rates. Higher strain rates were achieved using the unconventional test of shooting the ball into flat specimens. A viscoplastic formulation of the Cowper–Symonds material model was applied to consider the strain-rate effects. The parameters SIGY, p, and C of the material model were estimated using a step-wise procedure. First, rough estimates of the three parameters were obtained from the tensile tests using the grid search method. Then, the parameters p and C were fine-tuned using the reverse engineering approach. With the help of explicit dynamic simulations and all the experimental data, a multi-criteria cost function was defined and applied to obtain a smooth response function for the parameters p and C. Its optimum was determined by a real-valued genetic algorithm. The optimal values of the estimated parameters model the material response well, although a domain of optimum candidates spans two orders of magnitude for the parameter p and a few orders of magnitude for the parameter C.

Filled Elastomers: Mechanistic and Physics-Driven Modeling and Applications as Smart Materials

Elastomers are made of chain-like molecules to form networks that can sustain large deformation. Rubbers are thermosetting elastomers that are obtained from irreversible curing reactions. Curing reactions create permanent bonds between the molecular chains. On the other hand, thermoplastic elastomers do not need curing reactions. Incorporation of appropriated filler particles, as has been practiced for decades, can significantly enhance mechanical properties of elastomers. However, there are fundamental questions about polymer matrix composites (PMCs) that still elude complete understanding. This is because the macroscopic properties of PMCs depend not only on the overall volume fraction (ϕ) of the filler particles, but also on their spatial distribution (i.e., primary, secondary, and tertiary structure). This work aims at reviewing how the mechanical properties of PMCs are related to the microstructure of filler particles and to the interaction between filler particles and polymer matrices. Overall, soft rubbery matrices dictate the elasticity/hyperelasticity of the PMCs while the reinforcement involves polymer–particle interactions that can significantly influence the mechanical properties of the polymer matrix interface. For ϕ values higher than a threshold, percolation of the filler particles can lead to significant reinforcement. While viscoelastic behavior may be attributed to the soft rubbery component, inelastic behaviors like the Mullins and Payne effects are highly correlated to the microstructures of the polymer matrix and the filler particles, as well as that of the polymer–particle interface. Additionally, the incorporation of specific filler particles within intelligently designed polymer systems has been shown to yield a variety of functional and responsive materials, commonly termed smart materials. We review three types of smart PMCs, i.e., magnetoelastic (M-), shape-memory (SM-), and self-healing (SH-) PMCs, and discuss the constitutive models for these smart materials.

A thermodynamically consistent physics-informed deep learning material model for short fiber/polymer nanocomposites

This work proposes a physics-informed deep learning (PIDL)-based constitutive model for investigating the viscoelastic–viscoplastic behavior of short fiber-reinforced nanoparticle-filled epoxies under various ambient conditions. The deep-learning model is trained to enforce thermodynamic principles, leading to a thermodynamically consistent constitutive model. To accomplish this, a long short-term memory network is combined with a feed-forward neural network to predict internal variables required for characterizing the internal dissipation of the nanocomposite materials. In addition, another feed-forward neural network is used to indicate the free-energy function, which enables defining the thermodynamic state of the entire system. The PIDL model is initially developed for the three-dimensional case by generating synthetic data from a classical constitutive model. The model is then trained by extracting the data directly from cyclic loading–unloading experimental tests. Numerical examples show that the PIDL model can accurately predict the mechanical behavior of epoxy-based nanocomposites for different volume fractions of fibers and nanoparticles under various hygrothermal conditions.

Simulation of Mechanical Properties of Injection Molded Thermoplastic Foam Structures

This article presents an approach regarding the interlinking of simulation programs to determine mechanical properties of foamed polymers. The software Moldex3D is used to simulate the heterogeneous foam structure, which serves as a database for the material modelling. This material modelling is performed with the software Digimat and evaluated using various modeling approaches. The material models are characterized on the one side by a homogeneous structure and on the other by the simulated heterogeneous foam structure. In addition, different failure indicators are used and evaluated, which were originally developed for fiber reinforced materials. The simulation of the tensile test is carried out with the software Marc‐Mentat. Here, local stresses and strains are calculated, which reach a maximum value due to the influence of the failure indicators. The results are used to show the extent to which the integration of the heterogeneous foam structure proves to be beneficial. It is additionally demonstrated that the applied failure indicators cannot be used for the prediction of maximum stress and strain simultaneously. Thus, for the time of this study, it is formulated to include both indicators in order to calculate the maximum stress and strain or to evaluate alternative approaches regarding failure prognosis.This article is protected by copyright. All rights reserved.

Prediction of rolling resistance and wheel force for a passenger car tire: A comparative study on the use of different material models and numerical approaches

In this research, the characteristics of tire-road interaction of a 185/65R14 88H passenger car tire are investigated using the Finite Element Method in Abaqus commercial software. Moreover, the effect of various material models on tire performance is studied by implementing Visco-Hyperelastic, Parallel Rheological Framework, and Mullins effect. The novelty of this research is devoted to the development of the complex material models particularly considering the Mullins effect of the rubber compounds in the tire structure for the load-displacement criteria. For this purpose, a tire finite element model was generated using Abaqus/Standard command line in two different methods including an Arbitrary Lagrangian-Eulerian formulation for steady state rolling and implementing a pure Lagrangian approach for the transient dynamic analysis carried out implicit and explicit process respectively. Rolling resistance force was computed according to ISO 28580 with 210 kPa inflation pressure and 4155 N vertical load. The footprint test results were extracted in both static and transient dynamic analyses. Additionally, the wheel reaction force was predicted using an indirect method by extracting the tire-terrain contact patch reaction force in Abaqus/Explicit to observe the effect of the material convection along with stress softening phenomena of the rubber compounds of tire structure. In the post-processing analysis, the wheel reaction was filtered by implementing SAE60 filter to reduce the numerical noise in the final response.

Basic properties of ferromagnetic vector hysteresis play models and their validation by finite element method

Purpose Magnetic hysteresis holds significant technical and physical importance in the design of electromagnetic components. Despite extensive research in this area, modeling magnetic hysteresis remains a challenging task that is yet to be fully resolved. The purpose of this paper is to study vector hysteresis play models for anisotropic ferromagnetic materials in a physical, thermodynamical approach. Design/methodology/approach In this work, hysteresis play models are implemented to interpret magnetic properties, drawing upon classical rate-independent plasticity principles derived from continuum mechanics theory. By conducting qualitative and quantitative verification and validation, various aspects of ferromagnetic vector hysteresis were thoroughly examined. By directly incorporating the hysteresis play models into the primal formulations using fixed point method, the proposed model is validated with measurements in a finite element (FE) environments. Findings The proposed vector hysteresis play model is verified with fundamental properties of hysteresis effects. Numerical analysis is performed in an FE environment. Measured data from a rotational single sheet tester (RSST) are validated to the simulated results. Originality/value The results of this work demonstrates that the essential properties of the hysteresis effects by electrical steel sheets can be represented by the proposed vector hysteresis play models. By incorporation of hysteresis play models into the weak formulations of the magnetostatic problem in the h-based magnetic scalar potential form, magnetic properties of electrical steel sheets can be locally analyzed and represented.

HYSTERESIS OF IMPLICIT EQUATIONS IN HYPOPLASTICITY FOR SOIL MATERIALS WITH GRANULAR HARDNESS DEGRADATION

AbstractWe study a hypoplastic model for soil and granular materials stemming from geomechanical engineering which further incorporates effects of degradation of the granular hardness, therefore allowing for the description of environmental weathering. The governing system is described by a nonlinear system of transcendental-differential equations for stress and strain rate, which is investigated with respect to its long-time dynamic. Under deviatoric stress control, two different solutions of the underlying, implicit differential equations are constructed analytically. The spherical components of stress and strain rate converge asymptotically to an attractor and lead to the sparsification of material states. Whereas under cyclic loading-unloading carried out in a numerical simulation, finite ratcheting of the deviatoric strain rate is observed in the form of a square spiral.

Review and Assessment of Material, Method, and Predictive Modeling for Fiber-Reinforced Polymer (FRP) Partially Confined Concrete Columns

The repairing and strengthening of concrete structures using external and internal partial confinements are inevitable in the construction industry due to the new standards and rapid developments. The conventional materials and methods of confinement are unable to meet modern safety and functional standards. The fiber-reinforced polymer (FRP) enhances the strength and ductility of deteriorating and new concrete columns by reducing lateral confinement pressure and resistance against seismic shocks. The precise methods of partial confinement are inevitable for effective FRP-concrete bonding, durability, and cost-effectiveness under different loading conditions and to cope with external environmental factors. Predictive modeling and simulation techniques are pivotal for the optimization of confinement materials and methods by investigating the FRP-concrete novel confinement configurations, stress–strain responses, and failure modes. The novel materials and methods for concrete columns’ partial confinement lack high compressive strength, ductility, chemical attack resistivity, and different fiber orientation impacts. This review provides an overview of recent confinement materials, novel methods, and advanced modeling and simulation techniques with a critical analysis of the research gaps for partial FRP confinement of concrete columns. The current challenges and future prospects are also presented.

Development and Calibration of a Phenomenological Material Model for Steel-Fiber-Reinforced High-Performance Concrete Based on Unit Cell Calculations

A phenomenological material model has been developed to facilitate the efficient numerical analysis of fiber-reinforced high-performance concrete (HPC). The formulation integrates an elasto-plastic phase-field model for simulating fractures within the HPC matrix, along with a superimposed one-dimensional elasto-plasticity model that represents the behavior of the embedded fibers. The Drucker–Prager plasticity and one-dimensional von-Mises plasticity formulations are incorporated to describe the nonlinear material behavior of both the HPC matrix and the fibers, respectively. Specific steps are undertaken during the development and calibration of the phenomenological material model. In the initial step, an experimental and numerical analysis of the pullout test of steel fibers embedded in an HPC matrix is conducted. This process is used to calibrate the micro-mechanical model based on the elasto-plastic phase-field formulation for fracture. In the subsequent step, virtual experiments based on an ellipsoidal unit cell, also with the resolution of fibers (used as a representative volume element, RVE), are simulated to analyze the impact of fiber–matrix interactions and their physical properties on the effective material behavior of fiber-reinforced HPC. In the final step, macroscopic boundary value problems (BVPs) based on a cuboid are simulated on a single scale using the developed phenomenological material model. The resulting macroscopic stress–strain characteristics obtained from both types of simulations, namely simulations of virtual experiments and macroscopic BVPs, are compared. This comparison is utilized for the calibration of material parameters to obtain a regularized solution and to assess the effectiveness of the presented phenomenological material model.

Developing linear-elastic material models for pressure measurement films in steel-steel and wheel-rail contact

Abstract The contact between two steel components can be identified by using multiple methods, for example, by experiment using pressure measurement films or by computational simulations with finite element analysis (FEA). For FEA material models of all components are required. When the experimental setup of an implemented pressure measurement film is reproduced simulatively, material models for the films are also necessary. Here, we developed linear-elastic material models for pressure measurement films which allow to simulate steel-film-steel contact situations. The material models were developed for Hertzian contact situations and afterward validated for the non-Hertzian wheel-rail contact. It was possible to develop material models for two different pressure measurement film types. In the case of a third film type, the developed model could not be validated successfully.

Development of smart materials with versatile bending capabilities using microcellular foaming process: influence of foaming and desorption time

Abstract Herein, a smart material with versatile bending capability is developed using a microcellular foaming process (MCPs). In contrast to previous hydrogel-based approaches, the bi-layered smart material is fabricated using typical thermoplastics, polyethylene terephthalate glycol (PETG) and polymethyl methacrylate (PMMA), to achieve shape deformation in response to thermal stimuli. Further, the theoretical model for bi-layered smart materials based on the modified Timoshenko’s model is employed to predict and comprehend this thermal response phenomenon. Due to the distinct foaming characteristics of the two polymers, a reversal in the bending direction is achieved by manipulating the foaming and desorption time. The length variation after foaming differs depending on the desorption time for each polymer. PMMA decreases in length after foaming, measuring 56.25 mm at a desorption time of 40 min and 53.16 mm at 80 min. On the other hand, PETG shows an increase in length after foaming, measuring 53.33 mm at 40 min and 58.25 mm at 80 min. Consequently, when the two polymers are bonded and foamed, bending occurs depending on the desorption time, and a reversal in the bending direction is observed at the critical desorption time of around 60 min. Based on this result, the folding direction of a five-leafed flower-shaped object is successfully altered under thermal stimuli. This innovative approach extends the category of smart materials beyond the hydrogels and showcases the potential of the MCPs for the creation of smart materials for various applications that require versatile shape changes in response to temperature.

An efficient multi-field dynamic model for 3D wave propagation in saturated anisotropic porous media

An efficient multi–field coupled dynamic model for saturated anisotropic porous materials is proposed here. The mathematical formulation of the dynamic deformation–diffusion problem is developed starting from the mixture theory and the definition of the effective stress for anisotropic poro–elasticity, taking into account also the fluid phase compressibility and anisotropic permeability. The effective stress principle is properly extended through the Biot tensor to predict the coupling between the shear stress of the solid skeleton and the pore fluid pressure. Numerical solution of the coupled problem is obtained by inf-sup stable Finite Element spaces. A fundamental issue for the computational efficiency of the coupled model is the numerical solution of the resulting large–size and non–symmetric discrete problem. In this work, we develop a fully implicit monolithic solver based on the Bi–Conjugate Gradient Stabilized (Bi-CGStab) algorithm accelerated via an ad–hoc Multi–Physics Reduction (MPR) preconditioning technique. The proposed advances are implemented in the 3D GeoMatFem research code and several numerical analyses are performed to test the potential and computational efficiency of the proposed tool. In particular, we focus on 3D wave propagation applications in fully saturated single and multi-layered anisotropic media. Numerical results show that our implementation is able to identify the shear wave splitting phenomenon due to the different degree of material symmetry between the solid phase and the whole porous material.

Weak-formulated physics-informed modeling and optimization for heterogeneous digital materials

Abstract Numerical solutions to partial differential equations (PDEs) are instrumental for material structural design where extensive data screening is needed. However, traditional numerical methods demand significant computational resources, highlighting the need for innovative optimization algorithms to streamline design exploration. Direct gradient-based optimization algorithms, while effective, rely on design initialization and require complex, problem-specific sensitivity derivations. The advent of machine learning offers a promising alternative to handling large parameter spaces. To further mitigate data dependency, researchers have developed physics-informed neural networks (PINNs) to learn directly from PDEs. However, the intrinsic continuity requirement of PINNs restricts their application in structural mechanics problems, especially for composite materials. Our work addresses this discontinuity issue by substituting the PDE residual with a weak formulation in the physics-informed training process. The proposed approach is exemplified in modeling digital materials, which are mathematical representations of complex composites that possess extreme structural discontinuity. This paper also introduces an interactive process that integrates physics-informed loss with design objectives, eliminating the need for pre-trained surrogate models or analytical sensitivity derivations. The results demonstrate that our approach can preserve the physical accuracy in data-free material surrogate modeling but also accelerates the direct optimization process without model pre-training.

Asymmetric fin shape changes swimming dynamics of ancient marine reptiles’ soft robophysical models

Animals have evolved highly effective locomotion capabilities in terrestrial, aerial, and aquatic environments. Over life’s history, mass extinctions have wiped out unique animal species with specialized adaptations, leaving paleontologists to reconstruct their locomotion through fossil analysis. Despite advancements, little is known about how extinct megafauna, such as the Ichthyosauria one of the most successful lineages of marine reptiles, utilized their varied morphologies for swimming. Traditional robotics struggle to mimic extinct locomotion effectively, but the emerging soft robotics field offers a promising alternative to overcome this challenge. This paper aims to bridge this gap by studying Mixosaurus locomotion with soft robotics, combining material modeling and biomechanics in physical experimental validation. Combining a soft body with soft pneumatic actuators, the soft robotic platform described in this study investigates the correlation between asymmetrical fins and buoyancy by recreating the pitch torque generated by extinct swimming animals. We performed a comparative analysis of thrust and torque generated by Carthorhyncus, Utatsusaurus, Mixosaurus, Guizhouichthyosaurus, and Ophthalmosaurus tail fins in a flow tank. Experimental results suggest that the pitch torque on the torso generated by hypocercal fin shapes such as found in model systems of Guizhouichthyosaurus, Mixosaurus and Utatsusaurus produce distinct ventral body pitch effects able to mitigate the animal’s non-neutral buoyancy. This body pitch control effect is particularly pronounced in Guizhouichthyosaurus, which results suggest would have been able to generate high ventral pitch torque on the torso to compensate for its positive buoyancy. By contrast, homocercal fin shapes may not have been conducive for such buoyancy compensation, leaving torso pitch control to pectoral fins, for example. Across the range of the actuation frequencies of the caudal fins tested, resulted in oscillatory modes arising, which in turn can affect the for-aft thrust generated.

The fracture energy of concrete reinforced with double-helix macro basalt-fibre-reinforced polymer fibre

A novel type of fibre – double-helix macro basalt-fibre-reinforced polymer (DHM BFRP) fibre – is proposed to increase the cracking, tensile strength and ductility of concrete. The novel fibre is made from micro basalt fibres and resin, twisted together in a double helix. BFRP offers high tensile strength, good corrosion resistance and low cost, while the double-helix geometry provides excellent bond–slip performance between the fibre and the concrete matrix. Three-point bending (3PB) tests were conducted to measure the fracture energy of concrete reinforced with DHM BFRP fibres. The influences of fibre orientation (aligned and random) on cracking load, peak load, flexural strength and fracture energy were analysed. Compared with random DHM BFRP fibres, aligned fibres resulted in a 26.4% higher fracture energy and a 29.7% increase in flexural strength. Finite-element models of the 3PB tests were established using LS-Dyna. The Karagozian & Case concrete model was calibrated based on the fracture energy results to obtain a material model of fibre-reinforced concrete (FRC) considering fibre orientation. The errors between the simulated and tested maximum loads for 3PB tests of plain concrete, FRC and fibre-reinforced self-compacting concrete were 8.3%, 4.0% and 11.4%, respectively, indicating that the simulated and test results were in good agreement. This study provides a theoretical foundation and technical support for practical engineering applications of DHM BFRP fibres.

Exploring the effect of ELM and code-coupling frequencies on plasma and material modeling of dynamic recycling in divertors

Abstract Integrated modeling of plasma-surface interactions (PSI) provides a comprehensive and self-consistent description of the system, moving the field closer to developing predictive and design capabilities for plasma facing components. One such workflow, including descriptions for the scrape-off-layer plasma, ion-surface interactions and the sub-surface evolution, was previously used to address steady-state scenarios and has recently been extended to incorporate time-dependence and two-way information flow. The new model can address dynamic recycling in transient scenarios, such as the application presented in this paper: the evolution of W samples pre-damaged by helium and exposed to ELMy H-mode plasmas in the DIII-D DiMES. A first set of simulations explored the effect of ELM frequency. This study was discussed in detail in this conference’s proceedings and is summarized here. The 2nd set of simulations, which is the focus of this paper, explores the effect of code-coupling frequency. These simulations include initial SOLPS solutions converged to the inter-ELM state, ion impact energy (Ein) and angles (Ain) calculated by hPIC2, and an improved heat transfer description in Xolotl. The model predicts increases in particle fluxes and decreases in heat fluxes by 10-20% with the coupling time-step. Compared with the first set of simulations, the less shallow impact angle leads to smaller reflection rates and significant D implantation. The higher fraction of implanted flux (and deeper), in particular during ELMs, increases the accumulated D content in the W near-surface region. Future expansion of the workflow includes coupling to hPIC2 and GITR to ensure accurate descriptions of Ein and Ain, and W impurity transport.

Development of Neural Networks to Study Flow Behavior of Medium Carbon Microalloyed Steel during Hot Forming

In the present article, the application of an artificial neural network (ANN) model whose function is the development of plastic instability maps of a medium carbon microalloyed steel during the hot forming process is studied. Secondly, we proceed to create another ANN capable of providing the recrystallized grain size in the steady state resulting from forming deformation. We start from the experimental data of a medium carbon microalloyed steel obtained by hot compression tests with strain rates that vary between 10−4 s−1 and 3 s−1 and in a range of temperatures between 900 °C and 1150 °C. These experimental data are used to train the proposed ANN and obtain flow curves. Finally, the processing maps are developed by applying the dynamic materials model (DMM), according to which the safe hot forming domains and the plastic instability domains of the studied material are delineated. The comparison between the ANN and the experimental maps is carried out. It is ascertained that the optimal regions of forging in the ANN maps coincide with those obtained in the experimental maps. In addition, a study of the influence of the microstructure on the behavior of the studied steel during hot forming is carried out.

Determining the Drucker-Prager cap model constants using experimental, numerical and optimization for compacted Mg powders at different strain rates

Abstract This article investigates an inverse approach to determine the coefficients of the Drucker-Prager model for magnesium powder. The approach involves conducting finite element simulations of the powder compression process within LS-DYNA software, employing the Drucker-Prager material model. The goal is to minimize the disparity between force-displacement outcomes derived from simulations and experimental data using a surrogate optimization method. Experimental data were obtained through a uniaxial compression test and served as a basis for adjusting the Cap model coefficients. A random selection of coefficients was made using the Latin cube method and simulations were performed based on the initial coefficients. The optimization was then performed using the particle swarm algorithm over 20 iterations. The optimized coefficients were validated against experimental data, demonstrating close agreement. By utilizing the extracted coefficients, the relative density of the samples was calculated at three different compaction speeds, i.e., 15.5 m/s (using a Hopkinson bar), 8 m/s (using a drop weight), and 1 mm/min (using an Instron machine). The analysis revealed the highest relative density and stress in the densified sample via the Hopkinson bar method, reaching 99.83% and 1.1 GPa, respectively.

Numerical and experimental investigations into the ballistic mechanism of 3D through-the-thickness angle interlock woven fabric

High-performance 3D fabrics have great potential for ballistic protection. This study investigated the ballistic behavior of 3D through-the-thickness angle-interlock woven fabrics (3DTAWF) under the impact of Full Metal Jacket (FMJ) projectiles. A finite element analysis (FEA) of the interaction process of para-aramid Twaron 3DTAWF against a 9 mm bullet was performed in LS-DYNA by considering actual geometries and deformability of the interacting parts. A full-size mesoscale fabric structure model was constructed to capture the ballistic behavior of 3DTAWF accurately. The strain rate effect material model was used to improve computational accuracy. The impact performance was evaluated by analyzing the energy loss of the projectile, back face deformation, and residual velocity for penetrating impact tests. The results revealed the impact damage evolution, energy absorption mechanism, and stress wave distribution in the 3DTAWF. The main goal of this study was to develop a full-size mesoscale fabric model suitable for the simulation of the ballistic interaction of 3DTAWF of actual dimensions using moderate computer resources and satisfactory adequacy. The model is instructive for the 3D fabric design and development for ballistic protection.