Modeling open-hole failure of composites is a complex task, consisting of a highly nonlinear response with interacting failure modes. Numerical modeling of this phenomenon has traditionally been based on the finite element method, but requires to tradeoff between high fidelity and computational cost. To mitigate this shortcoming, recent work has leveraged machine learning to predict the strength of open-hole composite specimens. Here, we also propose using data-based models to tackle open-hole composite failure from a classification point of view. More specifically, we show how to train surrogate models to learn the ultimate failure envelope of an open-hole composite plate under in-plane loading. To achieve this, we solve the classification problem via support vector machine (SVM) and test different classifiers by changing the SVM kernel function. The flexibility of kernel-based SVM also allows us to integrate the recently developed quantum kernels in our algorithm and compare them with the standard radial basis function kernel. Finally, thanks to kernel-target alignment optimization, we tune the free parameters of all kernels to best separate safe and failure-inducing loading states. The results show classification accuracies higher than 90% for RBF, especially after alignment, followed closely by the quantum kernel classifiers.

Delamination is a critical mode of failure that occurs between plies in a composite laminate. The cohesive element, developed based on the cohesive zone model, is widely used for modelling delamination. However, standard cohesive elements suffer from a well-known limit on the mesh density—the element size must be much smaller than the cohesive zone size. This work extends the line of research on structural cohesive elements onto 3D mixed-mode problems. A new triangular Kirchhoff–Love shell element is developed for orthotropic materials to model the plies. A new structural cohesive element, conforming to the shell elements of the plies, is developed to model the interface delamination. The proposed method is verified and validated on the classical benchmark problems of Mode I, Mode II, and mixed-mode delamination of unidirectional laminates, a recent unidirectional benchmark problem with curved delamination front, as well as the single-leg bending problem of a multi-directional laminate, significantly increasing the range and complexity of applicable problems as compared to the previous works. All the results show that the element size in the proposed models can be ten times larger than that in the standard cohesive element models, with more than 90% reduction in CPU time, while retaining prediction accuracy. This would then allow more effective and efficient modelling of delamination in composites without worrying about the cohesive zone limit on the mesh density.

Damage pattern predictions of open-hole laminates under different loading conditions are ubiquitous in the finite element modelling of composite structures. This work investigated the applicability of artificial neural networks for the fast and accurate generation of damage patterns for a composite plate with a cut-out under a variety of loading conditions. The purpose is to explore the neural networks as surrogate models capable of returning damage pattern predictions on par with a finite element model, but requiring less computational effort at run time. Data for training and evaluating these neural networks was generated through nonlinear finite element models. Different neural networks, such as a standard Feedforward Neural Network and a Hybrid Neural Network that combines a Feedforward Neural Network with a convolutional decoder, have been tested for this task. To quantify the resemblance between the predicted and actual outputs in terms of colours and contours, different performance metrics have been explored. The use of the Structural Similarity Index (SSIM), in addition to the standard Mean Square Error (MSE), was explored to improve the visual quality of outputs from the neural network. With an average test MSE of 0.0014, SSIM of 0.9814, and computational speedup factor of 34, the Hybrid Neural Network has been shown to accurately and efficiently predict the damage patterns of the open-hole laminate, thereby constituting a promising candidate for a surrogate model of open-hole composite panels.

When simulating pressure-driven fracture with the Finite Element Method (FEM), significant difficulties can arise upon representing newly formed complex damage surfaces and their concurrent crack face loading. Application of this loading can also be required when additional physics is involved as in the case of hydraulic fracture where fluid physics inside a damage need to be considered. This paper presents a new Finite Element based practical numerical framework which can model pressure-driven fractures as they form on-the-fly without remeshing. The exact location of physical discontinuities passing through the element domain can be represented in the numerical model. The numerical framework can be implemented as a user-defined element and can be integrated into any FE package. A new element (called pressure element) is formulated with the capability to apply pressure and associated forces onto the crack surfaces in an adaptive manner. This element is verified using relevant examples from literature. The framework can also be configured for multi-physics problems where crack face loading is dictated by an additional physics. The element formulation is then extended for multi-physics problems involving fluid–solid interaction. The formulation provides the capability for multi-physics coupling adaptively as the crack propagates. The element is used to successfully simulate a test case from literature using different solution procedures (iterative and simultaneous). This element is also used to model failure in different pressure vessel problems to demonstrate its potential use in structural applications. A new higher-scale vessel element is developed which can represent different size, partitioning and failure states of composite vessel systems at element level. Composite vessel failure involving high number of pressurized cracks and delaminations as well as their interaction is modelled, and burst pressures are predicted for different vessel systems. The proposed numerical framework can be used towards designing more damage-tolerant vessels critical for the sustainable propulsion technologies.

The objective of this work is to develop a microstructure-based simulation approach to assess the fatigue life of solder joints that are used by the microelectronics industry. The developed approach can generate solder joints with random grain morphologies by means of 3D Voronoi tessellation. The anisotropic material behavior of each grain is described by the Garofalo creep equation combined with Hill's definition of the equivalent stress for anisotropic materials. Grain boundaries are implemented as interface elements, with an isotropic creep constitutive model. The stochastic variability in the creep response of solder joints was qualitatively estimated by generating 100 unique solder joints containing 5 to 9 grains, each having a random material orientation. These joints were independently loaded with a realistic stress level for microelectronic products during thermal cycling. The volume-averaged creep strain energy density in the solder joints was used to predict the fatigue life of the solder joints. The results showed a factor of ~4 difference in expected lifetime of the individual solder joints. Next, nine randomly picked solder joints from the above-mentioned pool of 100 were sandwiched between a silicon die and a printed circuit board to form a simulation model of a Wafer-Level Chip-Scale package (WLCSP). The creep strain energy density in the joints was computed for 34 unique cases of the WLCSP. A factor of ~2.5 between the highest and lowest estimate for the solder joint life was found. The slope of the corresponding Weibull distribution equals ~6, which falls within the slopes typical reported for solder joint reliability of WLCSPs.

The wide adoption of composite structures in the aerospace industry requires reliable numerical methods to account for the effects of various damage mechanisms, including delamination. Cohesive elements are a versatile and physically representative way of modelling delamination. However, using their standard form which conforms to solid substrate elements, multiple elements are required in the narrow cohesive zone, thereby requiring an excessively fine mesh and hindering the applicability in practical scenarios. The present work focuses on the implementation and testing of triangular thin plate substrate elements and compatible cohesive elements, which satisfy C¹-continuity in the domain. The improved regularity meets the continuity requirement coming from the Kirchhoff Plate Theory and the triangular shape allows for conformity to complex geometries. The overall model is validated for mode I delamination, the case with the smallest cohesive zone. Very accurate predictions of the limit load and crack propagation phase are achieved, using elements as large as 11 times the cohesive zone.

As with many tasks in engineering, structural design frequently involves navigating complex and computationally expensive problems. A prime example is the weight optimization of laminated composite materials, which to this day remains a formidable task, due to an exponentially large configuration space and non-linear constraints. The rapidly developing field of quantum computation may offer novel approaches for addressing these intricate problems. However, before applying any quantum algorithm to a given problem, it must be translated into a form that is compatible with the underlying operations on a quantum computer. Our work specifically targets stacking sequence retrieval with lamination parameters, which is typically the second phase in a common bi-level optimization procedure for minimizing the weight of composite structures. To adapt stacking sequence retrieval for quantum computational methods, we map the possible stacking sequences onto a quantum state space. We further derive a linear operator, the Hamiltonian, within this state space that encapsulates the loss function inherent to the stacking sequence retrieval problem. Additionally, we demonstrate the incorporation of manufacturing constraints on stacking sequences as penalty terms in the Hamiltonian. This quantum representation is suitable for a variety of classical and quantum algorithms for finding the ground state of a quantum Hamiltonian. For a practical demonstration, we performed numerical state-vector simulations of two variational quantum algorithms and additionally chose a classical tensor network algorithm, the DMRG algorithm, to numerically validate our approach. For the DMRG algorithm, we derived a matrix product operator representation of the loss function Hamiltonian and the penalty terms. Although this work primarily concentrates on quantum computation, the application of tensor network algorithms presents a novel quantum-inspired approach for stacking sequence retrieval.

This study aims to develop a model to predict the burst pressure of a dry filament wound cord-rubber composite pressure vessel under hydrostatic internal pressurization using a submodelling based global–local FEA model. The model links the global displacements of a rebar-based model to obtain the local deformation state in a single rhomboidal representative volume. Emphasis is placed on capturing the local stress concentrations in the fibers due to the unique filament winding mosaic pattern. Fiber damage is included in the local model using a maximum principle strain criteria. Verification of the created model is done experimentally on industrially manufactured burst-test specimens. Measurements for displacement during the experiments are taken photographically, while the burst pressure is captured using a pressure transducer. The final error between the burst pressure of the samples and the experimental demonstrators is approximately 6.5%, a marked improvement over conventional models with truss and rebar elements as fibers.

A novel and efficient numerical formulation for the modelling of multiple delaminations growth in laminated composite materials subjected to quasi-static loading is presented. The proposed formulation alleviates the high computational cost associated with models featuring cohesive elements by using a novel Adaptive Refinement Scheme and an Adaptive Floating Node Method Element to refine the model effectively during the analysis without modifying the global finite element connectivity. The formulation has been implemented in a MATLAB finite element code and validated with single and multiple delamination numerical models with varying mode mixities. The new formulation provides accurate results comparable to standard fully refined finite element models while drastically lowering the computational time of the analysis.

The authors would like to point out three writing mistakes that have been found after the publication of the original paper: Equation (13) should be written as: 1 (Formula presented.) to correctly represent the intended column vector of the element's degrees of freedom. Equation (49) and (50) should be written as: 2 (Formula presented.) to keep the vector format consistent across the terms. The derivations afterwards are not affected by this change. Equation (63) should have no minus sign on the second term of the second row, that was a typographical error. The correct Equation (63) should be written as: 4 (Formula presented.) The above mistakes appear only in the writing of the manuscript, not in the actual implementation of the method. Hence, the results and conclusions in the original paper remain unchanged. ACKNOWLEDGMENT The authors would like to thank Mr. Zhe Han from Nanjing University of Aeronautics and Astronautics for pointing out some of the above mistakes.

In this work, Floating Node Method (FNM), first developed for fracture modelling of laminate composites, is coupled with cell-wise strain Smoothed Finite Element Method (SFEM) for modelling 2D linear elastic fracture mechanics problems. The proposed method is termed as Smoothed Floating Node Method (SFNM). In this framework, FNM is used to represent the kinematics of crack and the crack front inside the domain without the requirement of remeshing and discontinuous enrichment functions during crack growth. For smoothing, a constant smoothing function is considered over the smoothing domains through which classical domain integration changes to line integration along each boundary of the smoothing cell, hence derivative of shape functions are not required in the computation of the field gradients. The values of stress intensity factor are obtained from the SFNM solution using domain based interaction integral approach. Few standard fracture mechanics problems are considered to check the accuracy and effectiveness of the proposed method. The predictions obtained with the proposed framework improves the convergence and accuracy of the results in terms of the stress intensity factors and energy norms.

A novel efficient numerical formulation for the analysis of multiple fatigue-driven delamination cracks is presented. A cohesive zone model is used in combination with an Adaptive Refinement Scheme (ARS) and an Adaptive Floating Node Method (A-FNM) element that refine the model effectively during the analysis. Novel techniques are proposed to track the positions of multiple crack tips and calculate the mode decomposed energy release rates for the individual crack tips using the J-integral. The method has been implemented in a Matlab finite element code and validated with single and multiple delamination cases with varying mode mixities. Comparisons with theoretically based predictions and available experimental data showcase the high accuracy of the method. The presented method lowers the computational time compared to standard, fully refined finite element models by a factor of 4–5.

A numerical study on toughening laminated composite materials against delamination by initiating multiple interlaminar cracks is presented. Different configurations of interface toughening and weakening patches, that modify the interface properties at selected locations, are investigated as a way to trigger multiple delaminations. Both interface toughening and weakening patches can be configured to toughen the laminated material by initiating multiple delaminations. The initiation of multiple delaminations and the increase in toughness depend on the interface strengths and toughness of the patches. The main mechanisms behind the initiation of multiple delaminations for both cases are presented. An adaptive refinement method implemented within a Matlab Finite Element Analysis code that models the interfaces of the laminate with cohesive elements is used for the analyses. The adaptive refinement framework allows efficient analysis of multiple delaminations with very fine meshes at the wake of the crack tips. A discussion on the overall performance of the toughening concept, and the main parameters affecting the results, i.e. the length of the interface toughening or weakening patches, the distance of the substrate between the affected interfaces, and the material's mechanical properties, is carried out. The results presented in the paper show that a toughening effect against delamination can be achieved using interface toughening or weakening patches to onset multiple delaminations.

In practical applications, the use of computational modeling has been industry-wide adopted to speed up product development as well as reduce physical testing costs. Such models of complex or large systems are, however, often computationally expensive, hence solution times of hours or more are not uncommon. Additionally, as these models are typically evaluated using blackbox solvers, the direct study of relations between design parameters renders demanding in terms of computational time and provides poor engineering insight and understanding. To address this, a modular framework integrating computation automation with the use of surrogate-based modeling, optimization and visualization techniques is presented. The framework is built in the Python programming language. Its use is illustrated on a study of the side impact response of a car body using an artificial neural network as a surrogate together with the NSGA-III genetic algorithm for optimization.

Tremendous efforts have been put into the study of structural integrity and the understanding of failure mechanisms in composites. Geometric non-linearity, receiving few attention in coupon-level simulations, can play an important role in the design and analysis of larger structures. This paper aims at extending the recently-developed Floating Node Method for damage analysis of laminated composites subjected to large deformations. The kinematics of strong discontinuities including interfacial delamination and matrix cracks are explicitly described in a geometrically nonlinear framework. Interactions between these two kinds of failure patterns are enabled through enriched elements equipped with floating nodes. To verify this proposed method, buckling-induced delamination and low-velocity impact damage are modelled, the results of which show good agreement with results from literature.