The phase-field hydraulic fracture model entails a non-convex energy functional. This renders a poor convergence behaviour for monolithic solution techniques, such as the Newton–Raphson method. Consequently, researchers have adopted alternative solution techniques such as the staggered solution technique and the Newton–Raphson method with convexification via extrapolation of the phase-field. Both methods are robust. However, the former is computationally expensive and in the latter, the extrapolation itself is questionable w.r.t regularity in time. In this work, a novel dissipation-based arc-length method is proposed as a robust and computationally efficient monolithic solution technique for the phase-field hydraulic fracture model. Similar to brittle fracture in force driven mechanical problems, constant flux driven hydraulic fracture processes are also unstable. Furthermore, due to the constant flux loading in hydraulic fracturing problems, scaling of the external force is not possible. Instead, the time step-size is considered as the additional unknown, augmenting the arc-length constraint equation. The robustness and computational efficiency of the proposed arc-length method is demonstrated using numerical experiments, where comparisons are made with the staggered solver as well as the quasi-Newton BFGS method.

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In this work, a hybrid physics-based data-driven surrogate model for the microscale analysis of heterogeneous material is investigated. The proposed model benefits from the physics-based knowledge contained in the constitutive models used in the full-order micromodel by embedding the material models in a neural network. Following previous developments, this paper extends the applicability of the physically recurrent neural network (PRNN) by introducing an architecture suitable for rate-dependent materials in a finite strain framework. In this model, the homogenized deformation gradient of the micromodel is encoded into a set of deformation gradients serving as input to the embedded constitutive models. These constitutive models compute stresses, which are combined in a decoder to predict the homogenized stress, such that the internal variables of the history-dependent constitutive models naturally provide physics-based memory for the network. To demonstrate the capabilities of the surrogate model, we consider a unidirectional composite micromodel with transversely isotropic elastic fibers and elasto-viscoplastic matrix material. The extrapolation properties of the surrogate model trained to replace such micromodel are tested on loading scenarios unseen during training, ranging from different strain-rates to cyclic loading and relaxation. Speed-ups of three orders of magnitude with respect to the runtime of the original micromodel are obtained.

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Numerical methods for delamination analysis, such as the cohesive zone method, require fracture energy as an essential input. Existing formulations rely on a phenomenological relationship that links fracture energy to the mode of fracture based on linear elastic fracture mechanics (LEFM). However, doubts exist about the applicability of LEFM. It has been demonstrated that the phenomenological relationships describing fracture energy as a function of mode-ratio are not universally valid. Computational homogenization (FE²) provides an alternative where the dissipative mechanisms can be resolved on the microscale. This paper aims to assess the suitability of a proposed discontinuous FE² framework for characterizing delamination growth under mode-II conditions by comparing it to direct numerical simulations (DNS). The impact of plasticity on effective fracture energy is evaluated for two distinct mode-II test configurations. The dissipation density from the bulk integration points within the delamination propagation zone is monitored. The findings demonstrate the FE² model's capability to accurately capture plastic energy dissipation around a growing crack. Variations in plastic dissipation are observed between the mTCT and ENF test setups, leading to differences in effective mode-II fracture energy. These nuances, unaccounted for in state-of-the-art mesoscale cohesive models, highlight the FE² framework's potential for enhancing delamination modeling.

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A numerical framework for simulating progressive failure under high-cycle fatigue loading is validated against experiments of composite quasi-isotropic open-hole laminates. Transverse matrix cracking and delamination are modeled with a mixed-mode fatigue cohesive zone model, covering crack initiation and propagation. Furthermore, XFEM is used for simulating transverse matrix cracks and splits at arbitrary locations. An adaptive cycle jump approach is employed for efficiently simulating high-cycle fatigue while accounting for local stress ratio variations in the presence of thermal residual stresses. The cycle jump scheme is integrated in the XFEM framework, where the local stress ratio is used to determine the insertion of cracks and to propagate fatigue damage. The fatigue cohesive zone model is based on S-N curves and requires static material properties and only a few fatigue parameters, calibrated on simple fracture testing specimens. The simulations demonstrate a good correspondence with experiments in terms of fatigue life and damage evolution. @en

A micromechanical model for simulating failure of unidirectional composites under cyclic loading has been developed and tested. To efficiently pass through the loading signal, a two-scale temporal framework with adaptive stepping is proposed, with a varying step size between macro time steps, and a fixed number of equally spaced micro time steps in between. With the focus on matrix dominated failure under off-axis loading, viscoplasticity and microcracking are included in the model for the polymer matrix, while carbon fibers are modeled as elastic. For a proper representation of viscous deformation in the matrix under cyclic loading, a two-scale version of the Eindhoven Glassy Polymer constitutive model is formulated, that is based on time homogenization with an effective time increment. The failure state of the representative volume element is reached by the initiation and damaging of cohesive microcracks. Cyclic and static degradation are represented by using Dávila's fatigue damage function, which is built on top of Turon's quasi-static cohesive model. The model results are compared with available experimental data on unidirectional carbon/PEEK composites tested at different stress levels, load ratios, frequencies and off-axis angles. Plasticity controlled and crack growth controlled failure mechanisms, characteristic of the long-term response of polymeric composites, are captured by the model, as well as their distinct frequency dependence. As a limit case, the model is able to reproduce the time to failure in creep loading, where the heterogeneous microstructure and viscoplastic flow of the matrix trigger the evolution of quasi-static damage. However, for the studied material system, the present model does not accurately reproduce the load ratio dependence and the off-axis angle dependence of the crack growth controlled failure mechanism.

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In this work, the uncertainty associated with the finite element discretization error is modeled following the Bayesian paradigm. First, a continuous formulation is derived, where a Gaussian process prior over the solution space is updated based on observations from a finite element discretization. To avoid the computation of intractable integrals, a second, finer, discretization is introduced that is assumed sufficiently dense to represent the true solution field. A prior distribution is assumed over the fine discretization, which is then updated based on observations from the coarse discretization. This yields a posterior distribution with a mean that serves as an estimate of the solution, and a covariance that models the uncertainty associated with this estimate. Two particular choices of prior are investigated: a prior defined implicitly by assigning a white noise distribution to the right-hand side term, and a prior whose covariance function is equal to the Green’s function of the partial differential equation. The former yields a posterior distribution with a mean close to the reference solution, but a covariance that contains little information regarding the finite element discretization error. The latter, on the other hand, yields posterior distribution with a mean equal to the coarse finite element solution, and a covariance with a close connection to the discretization error. For both choices of prior a contradiction arises, since the discretization error depends on the right-hand side term, but the posterior covariance does not. We demonstrate how, by rescaling the eigenvalues of the posterior covariance, this independence can be avoided.

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Simulating the mechanical response of advanced materials can be done more accurately using concurrent multiscale models than with single-scale simulations. However, the computational costs stand in the way of the practical application of this approach. The costs originate from microscale Finite Element (FE) models that must be solved at every macroscopic integration point. A plethora of surrogate modeling strategies attempt to alleviate this cost by learning to predict macroscopic stresses from macroscopic strains, completely replacing the microscale models. In this work, we introduce an alternative surrogate modeling strategy that allows for keeping the multiscale nature of the problem, allowing it to be used interchangeably with an FE solver for any time step. Our surrogate provides all microscopic quantities, which are then homogenized to obtain macroscopic quantities of interest. We achieve this for an elasto-plastic material by predicting full-field microscopic strains using a graph neural network (GNN) while retaining the microscopic constitutive material model to obtain the stresses. This hybrid data-physics graph-based approach avoids the high dimensionality originating from predicting full-field responses while allowing non-locality to arise. In addition, this approach introduces beneficial inductive bias to the model by encoding microscopic geometrical features. By training the GNN on a variety of meshes, it learns to generalize to unseen meshes, allowing a single model to be used for a range of microstructures. The embedded microscopic constitutive model in the GNN implicitly tracks history-dependent variables and leads to improved accuracy. While the microscopic stresses are fully dependent on the microscopic strains, we found it crucial to include both microscopic strains and stresses in the loss function. We demonstrate for several challenging scenarios that the surrogate can predict complex macroscopic stress–strain paths. As the computation time of our method scales favorably with the number of elements in the microstructure compared to the FE method, our method can significantly accelerate FE² simulations.

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A numerical framework for simulating progressive failure under high-cycle fatigue loading is validated against experiments of composite quasi-isotropic open-hole laminates. Transverse matrix cracking and delamination are modeled with a mixed-mode fatigue cohesive zone model, covering crack initiation and propagation. Furthermore, XFEM is used for simulating transverse matrix cracks and splits at arbitrary locations. An adaptive cycle jump approach is employed for efficiently simulating high-cycle fatigue while accounting for local stress ratio variations in the presence of thermal residual stresses. The cycle jump scheme is integrated in the XFEM framework, where the local stress ratio is used to determine the insertion of cracks and to propagate fatigue damage. The fatigue cohesive zone model is based on S-N curves and requires static material properties and only a few fatigue parameters, calibrated on simple fracture testing specimens. The simulations demonstrate a good correspondence with experiments in terms of fatigue life and damage evolution.@en

A microscale numerical framework for modeling creep rupture in unidirectional composites under off-axis loading is presented, building on recent work on imposing off-axis loading on a representative volume element. Creep deformation of the thermoplastic polymer matrix is accounted for by means of the Eindhoven Glassy Polymer material model. Creep rupture is represented with cohesive cracks, combining an energy-based initiation criterion with a time-dependent cohesive law and a global failure criterion based on the minimum in homogenized creep strain-rate. The model is compared against experiments on carbon/PEEK composite material tested at different off-axis angles, stress levels and temperatures. Creep deformation is accurately reproduced by the model, except for small off-axis angles, where the observed difference is ascribed to macroscopic variations in the experiment. Trends in rupture time are also reproduced although quantitative rupture time predictions are not for all test cases accurate.@en

In this work, a recently proposed high-cycle fatigue cohesive zone model, which covers crack initiation and propagation with limited input parameters, is embedded in a robust and efficient numerical framework for simulating progressive failure in composite laminates under fatigue loading. The fatigue cohesive zone model is enhanced with an implicit time integration scheme of the fatigue damage variable which allows for larger cycle increments and more efficient analyses. The method is combined with an adaptive strategy for determining the cycle increment based on global convergence rates. Moreover, a consistent material tangent stiffness matrix has been derived by fully linearizing the underlying mixed-mode quasi-static model and the fatigue damage update. The enhanced fatigue cohesive zone model is used to describe matrix cracking and delamination in laminates. In order to allow for matrix cracks to initiate at arbitrary locations and to avoid complex and costly mesh generation, the phantom node version of the eXtended finite element method (XFEM) is employed. For the insertion of new crack segments, an XFEM fatigue crack insertion criterion is presented, which is consistent with the fatigue cohesive zone formulation. It is shown with numerical examples that the improved fatigue damage update enhances the accuracy, efficiency and robustness of the numerical simulations significantly. The numerical framework is applied to the simulation of progressive fatigue failure in an open-hole [±45]-laminate. It is demonstrated that the numerical model is capable of accurately and efficiently simulating the complete failure process from distributed damage to localized failure.

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Driven by the need to accelerate numerical simulations, the use of machine learning techniques is rapidly growing in the field of computational solid mechanics. Their application is especially advantageous in concurrent multiscale finite element analysis (FE²) due to the exceedingly high computational costs often associated with it and the high number of similar micromechanical analyses involved. To tackle the issue, using surrogate models to approximate the microscopic behavior and accelerate the simulations is a promising and increasingly popular strategy. However, several challenges related to their data-driven nature compromise the reliability of surrogate models in material modeling. The alternative explored in this work is to reintroduce some of the physics-based knowledge of classical constitutive modeling into a neural network by employing the actual material models used in the full-order micromodel to introduce non-linearity. Thus, path-dependency arises naturally since every material model in the layer keeps track of its own internal variables. For the numerical examples, a composite Representative Volume Element with elastic fibers and elasto-plastic matrix material is used as the microscopic model. The network is tested in a series of challenging scenarios and its performance is compared to that of a state-of-the-art Recurrent Neural Network (RNN). A remarkable outcome of the novel framework is the ability to naturally predict unloading/reloading behavior without ever seeing it during training, a stark contrast with popular but data-hungry models such as RNNs. Finally, the proposed network is applied to FE² examples to assess its robustness for application in nonlinear finite element analysis.

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In this work we present a hybrid physics-based and data-driven learning approach to construct surrogate models for concurrent multiscale simulations of complex material behavior. We start from robust but inflexible physics-based constitutive models and increase their expressivity by allowing a subset of their material parameters to change in time according to an evolution operator learned from data. This leads to a flexible hybrid model combining a data-driven encoder and a physics-based decoder. Apart from introducing physics-motivated bias to the resulting surrogate, the internal variables of the decoder act as a memory mechanism that allows path dependency to arise naturally. We demonstrate the capabilities of the approach by combining an FNN encoder with several plasticity decoders and training the model to reproduce the macroscopic behavior of fiber-reinforced composites. The hybrid models are able to provide reasonable predictions of unloading/reloading behavior while being trained exclusively on monotonic data. Furthermore, in contrast to traditional surrogates mapping strains to stresses, the specific architecture of the hybrid model allows for lossless dimensionality reduction and straightforward enforcement of frame invariance by using strain invariants as the feature space of the encoder.

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A common choice for multiscale modeling of the mechanical response of composites is to use periodic boundary conditions (PBCs) on square and cubical representative volume elements (RVEs). However, when strain localization occurs in the micromodel, these PBCs are unable to reproduce the transverse isotropy of composite materials with a random microstructure. Existing remedies to alleviate this issue have been proposed in literature by either rotating or shifting the periodicity constraints. However, this results in a mismatch of the microstructure on opposing edges which may prevent cracks to cross the boundary and consequently limit the supported localization angles. Furthermore, in absence of a strategy that ensures a single localization band to arise in a fracturing RVE, it is difficult to formulate a generic expression for the length scale parameter that is used to regulate the energy dissipation, which plays an important role in obtaining RVE-size objective results. As an alternative to square (or cubical) RVEs, circular (or spherical) RVEs have been proposed in literature since they provide a response which is independent of the orientation due to shape of the RVE. However, it is shown in this work that the existing formulation with straightforward application of PBCs on a circular RVE fails to predict the correct softening behavior, due to over-constraining when cracks reach the boundary. Therefore, a new formulation of PBCs on a circular RVE is proposed, which allows for a single fully developed localization band under arbitrary angle. The performance of the new formulation is tested with a series of simulations where macroscopic strains are imposed under varying orientations. It is demonstrated that the circular RVE with the new formulation of PBCs successfully predicts a transversely isotropic response with full softening without the issue of mismatching microstructure as with previously developed remedies for the square RVE. In addition, it is shown that the length scale parameter is well-defined and independent of the orientation of the circular RVE.

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The Thick Level Set method (TLS) is an approach for non-local damage modeling in which the damage evolution is linked to the movement of a damage front described with the level set method. More recently, a new version of the TLS, designated as the TLSV2, has been proposed as a new concept for coupling continuum damage modeling and discrete cohesive crack modeling for failure analysis in solids. The main objective of this new framework is to profit from both modeling approaches. The continuum part allows for handling crack initiation, branching and merging, whereas the cohesive part brings the capability to handle discrete cracks with large crack opening or sliding without heavily distorted elements, and with the possibility to model stiffness recovery upon contact. In this paper, a generalized framework for the TLSV2 is introduced. Two major issues with the TLSV2 method that have not been dealt with since its inception are addressed in this study, and solutions are proposed. Firstly, the method depends on the location of skeleton curve of the level set field, on which the discontinuity in the displacement field is evaluated. The problem of locating the skeleton curve can be a complicated task, mainly because topological events may emerge as the analysis progresses, such as crack branching. The skeleton curve is determined through a combination of ball-shrinking and graph-based algorithms and then mapped onto the finite element mesh. Secondly, the cohesive forces and displacement discontinuity of the TLSV2 are modeled using the phantom node method. Furthermore, a new approach to compute the averaged values of local quantities is introduced, and model calibration is discussed. The degree of stiffness recovery under compression that is still needed for the continuum part is investigated. Numerical experiments demonstrate the accuracy and ability of the proposed model to handle simulation of failure analysis presenting complex topological crack patterns.

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In a concurrent (FE2) multiscale modeling is an increasingly popular approach for modeling complex materials. As such, it is especially suited for modeling composites, as their complex microstructure can be explicitly modeled and nested to each integration point of the macroscale. However, this generality is often associated with exceedingly high computational costs for real-scale applications. One way to tackle the issue is to employ a cheaper-to-evaluate surrogate model for the microstructure based on few observations of the high-fidelity solution. On this note, Neural Networks (NN) are by far the most popular technique in building constitutive surrogates. However, conventional NNs assume a unique mapping between strains and stresses, limiting their ability to reproduce path-dependent behavior. Moreover, their data-driven nature severely limits their ability to extrapolate away from their training spaces. To circumvent these drawbacks, the alternative explored in this work is to reintroduce some of the physics-based knowledge of the problem into the NN. This is done by employing actual material models used in the full-order micromodel as the activation function of one of the layers of the network. Thus, path-dependency arises naturally since every material model in the layer has its own internal variables. To assess its capabilities, the network is employed as the surrogate model for a composite Representative Volume Element with elastic fibers and elasto-plastic matrix material. First, for a single micromodel, the performance of the network is compared to that of a state-of-the-art Recurrent Neural Network (RNN) in a number of challenging scenarios for data-driven models. Then, the proposed framework is applied to an FE2 example and the results are compared to the full-order solution in terms of accuracy and computational cost. An important outcome of the physics-infused network is the ability to naturally predict unloading/reloading behavior without ever seeing it during training, a stark contrast with highly popular but data-hungry models such as RNN. @en

In this paper we develop a finite deformation micromechanical framework for modeling rate-dependent failure in unidirectional composites under off-axis loading. The model performance is compared with original experiments on thermoplastic carbon/PEEK composites tested at different strain-rates and off-axis angles. To achieve quantitative agreement with the experiments, a microcrack initiation criterion based on the local stress and the local rate of deformation state in the polymer matrix is proposed. Microcracking is represented by a cohesive zone model, with special attention to the inclusion of geometric nonlinearity in the formulation. In this regard, the cohesive geometric nonlinearity is based on extension of an existing formulation to three-dimensional space. Beside microcracking, the Representative Volume Element (RVE) also accounts for viscoplasticity in the polymer matrix. A recently introduced dedicated arclength control method is utilized to impose a strain-rate on the micromodel. Accordingly, kinematic relations governing the RVE deformation allow for the change in orientation of the micromodel in the loading process. This change in orientation of the microstructure has an important implication on the apparent material strength.

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Due to the complex underlying microscopic damage processes, the failure behavior of composite materials is challenging to understand and predict. Fracture mechanics approaches [1] and cohesive zone models [2] that simulate delamination generally consider the fracture energy as a material characteristic that exclusively depends on the fracture mode. However, it is not properly understood whether this mode-dependence completely characterizes the fracture energy or even what are the underlying mechanisms of the fracture energy variation. Computational homogenization [3] is a promising approach to provide more insight into the dissipative processes that contribute to the delamination fracture energy. Processes such as matrix plasticity and fibre/matrix interface debonding are best described on the microscale. Through computational homogenization, the microscale energy dissipation can be upscaled to the mesoscale where cohesive tractions are computed on-the-fly without prior knowledge of fracture energy. At the same time the effect of material geometry and lay-up on the microscopic mechanisms can be captured by the model. In this work, mode-II delamination of unidirectional laminated composites is simulated by a computational homogenization model which is designed to tackle fracture on both scales. On the mesoscale XFEM is adopted while on the microscale matrix plasticity and cohesive cracking are considered. The model is first applied to a material with simplified micro-structure, in comparison to a layered embedded cell model (DNS) to prove its validity. After that, a micromodel representing composites with random fibre distribution is considered. The contribution of different processes to the fracture energy is analysed by separately tracking energy dissipation due to plastic deformation, fibre/matrix debonding and matrix cracking. The effect of geometry and boundary conditions on the delamination fracture energy is investigated. @en

A micromechanical framework for modeling failure in unidirectional (UD) thermoplastic composites under rate-dependent off-axis loading is presented, with the aim to predict and analyze transverse matrix cracking under various load conditions. The onset of global softening in the micromodel corresponds to macroscopic matrix crack initiation. The problem addressed in this study is to include matrix plasticity and microcracking in the failure analysis of UD composites. A thin slice representative volume element (RVE) with periodic boundary conditions is used, which enables representation of 3D stress states. The testing conditions of a constant prescribed strain-rate and an off-axis uniaxial stress state are reproduced in the model with a dedicated arclength control method. The studied material system is carbon/PEEK composite material, where plasticity in the matrix is represented with the Eindhoven Glassy Polymer (EGP) constitutive law, while the fibers are modeled as transversely isotropic elastic material. In order to account for microcracking in the matrix, a cohesive surface methodology is applied. Cohesive elements are added on the fly with a stress-based initiation criterion. For this purpose, a power law microcrack initiation criterion is proposed. After initiation, the microcracking process is governed by a mixed-mode damage cohesive law. Geometric nonlinear effects are also included in the cohesive model, such that cohesive forces include material as well as geometric contributions. The model is validated with experimental data from tensile tests on UD material at different off-axis angles and strain-rates. The obtained maximum stress levels are used to generate Tsai-Hill failure envelopes for macroscopic transverse crack initiation. Additional capabilities of the model are demonstrated through examples with different fiber-volume ratios and temperature conditions. @en

In this paper, a micromechanical framework for modeling the rate-dependent response of unidirectional composites subjected to off-axis loading is introduced. The model is intended for a thin slice representative volume element that is oriented perpendicular to the reinforcement of the composite material. The testing conditions from a uniaxial off-axis test are achieved by a dedicated strain-rate based arclength formulation. The constraint equation of the arclength model is constructed such that the deformation state of the micromodel, as imposed in its local coordinate system, corresponds to the strain-rate applied on the material in global frame of reference. The kinematic description allows for finite strains in the material, meaning that the micromodel changes orientation during the deformation process. This geometric nonlinear effect is also included in the evaluation of external loading, ensuring that the external forces are equivalent to the applied off-axis stress in global coordinate system. Several examples are considered in order to show that the model resolves rate-dependency of the material, accounts for different off-axis loading, and captures finite strains exactly. Additionally, a small strain version of the model is derived from the general nonlinear framework. Results obtained with this simplified approach are compared to results of the large deformation framework.@en

The mode-I dynamic fracture energy and failure mechanisms of glass fiber-reinforced polymer composites are investigated with an embedded cell model of the single-edge-notched-tension (SENT) geometry. Under an applied dynamic loading, a crack may propagate in the embedded microstructure, accompanied by the development of a fracture process zone in which fiber/matrix debonding, matrix cracking and ductile matrix tearing are observed. Reaching a maximum nominal strain rate of 250/s, a series of SENT tests are performed for different loading velocities and specimen sizes while the dynamic energy release rate is evaluated using the dynamic version of the J-integral. The influence and interaction of loading rate, time-dependent material nonlinearity, structural inertia and matrix ligament bridging on the fracture toughness and failure mechanisms of composites are evaluated. It is found that with the given material parameters and studied loading rate range, the failure type is brittle with many microcracks but limited plasticity in the fracture process zone and a trend of increasing brittleness for larger strain rates is observed. The inertia effect is evident for larger strain rates but it is not dominating. An R-curve in the average sense is found to be strain-rate independent before the fracture process zone is fully developed and afterwards a velocity–toughness mechanism is dictating the crack growth.

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