Architected beam lattices—ultra-light truss networks fabricated by additive manufacturing—have emerged as a promising material class for protecting different structures against impact, including defending spacecraft against micro-meteoroid and orbital debris (MMOD) impacts. Their high specific strength, energy absorption and tunable mechanical properties make them ideal for various high performance applications including impact protection. Current work on lattice metamaterials has regarding their energy-absorption potentially has identified three distinct impact modes: bounce-back, which is characterized by the impactor rebounding from the lattice; capture, where the impactor becomes lodged in the lattice structure; and penetration, where the impactor breaches the lattice and continues through it. In this work the 3 regimes are reproduced using a Discontinuous Galerkin framwork that integrate Kirchoff-Love beams, Cohesive Zone Model and Penalty Contact Law to model the impact of a rigid impactor on a lattice metamaterial. The framework is used to predict large-deformation contact and fracture in high-fidelity lattice structures struck by rigid projectiles at up to 350 m/s. As a result trends are identified with regards of impact velocity, lattice density and impact regimes. Additionally, key issues are identified with the current setup that give insight into metamaterial impact simulations.

With the progressively more widespread exploitation of post-buckled skin-stiffened structures in the aerospace industry, aimed at making them more weight efficient, assessing the failure of such structures is of utmost importance. Additionally with the trend of moving towards fastener free connections, one of the potential failure modes of skin-stiffened structures is the skin-stiffener separation that leads to a sudden and drastic reduction in the load carrying capacity of the structure. Hence, it is imperative that structures are designed against such failure modes, being crucial to have predictive models for the initiation and propagation of delaminations at the connection interface.

Given this motivation, the present thesis investigates the development of a novel multi-domain semi-analytical cohesive zone approach to predict the initiation and propagation of damage along a two-dimensional interface. In the multi-domain method the main structure being analyzed is decomposed into multiple smaller domains for computational advantage, with each domain having its own set of continuous approximation functions based on the Ritz method. A penalty formulation governs the inter-connection of domains. To extend the multi-domain framework to model damage, the interface connection is modelled as a cohesive zone with the traction-separation law governing its response. The area connection from the multi-domain method is modified to include a damage-dependent penalty stiffness term, whose degradation is controlled by the traction-separation law of the cohesive zone model. Lastly, the energy dissipated to create a crack is included in the formulation. Since the framework is based on the Ritz approach, the appropriate choice of continuous approximations to model damage is investigated, presenting their limitations and the proposed methods to overcome them.

Finally, the results obtained from the multi-domain framework were verified against finite elements for a multitude of cases, which in turn had been validated by experimental data. The results were subsequently followed by discussions and the caveats of the developed framework. Results indicate that the developed multi-domain semi-analytical cohesive zone method predicts the results within 4.3% of finite element results, while taking 75% less time on average.

Finally, having somewhat successfully modelled the initiation and propagation of damage along a two-dimensional interface, this thesis establishes a robust basis for future research involving the use mixed-mode behaviours with multi-linear cohesive laws, whose formulations can just be slotted into the current framework. All of this forms a solid foundation to efficiently model damage initiation in larger structures and, more concretely, skin-stiffener separation.

Thin-walled composite structures are critical in aerospace engineering, making it essential to understand their failure mechanisms. However, manufacturing test samples and conducting high-fidelity experimental tests are both complex and expensive, making advanced modeling techniques essential. The Cohesive Zone Model (CZM) is a widely used method for modeling delaminations in composites, but it typically depends on artificial elastic compliance, which can compromise accuracy. This thesis introduces a high-fidelity finite element model that integrates the Discontinuous Galerkin method with geometrically exact shell elements to explicitly model each lamina during large deformations and buckling in multi-layered materials. By coupling the layers, this approach eliminates the need for artificial compliance. Numerical simulations show the model’s accuracy in predicting structural behavior under various loading conditions, although challenges remain in maintaining proper layer separation during deformation. This study lays the groundwork for modeling delaminations along arbitrary interfaces.

In the context of damage tolerance for aeronautical structures, substantial research has focused on simulating skin-stiffener separation in stiffened composite panels. This separation is marked by unstable crack growth at the skin-stiffener interface, which can lead to structural collapse in the post-buckled regime. Recent post-buckling tests on thermoplastic butt-joint single stiffener panels indicate the development of delamination within the skin before crack propagation occurs at the skin-stiffener interface. This delamination is likely triggered by the crack extension process prior to the buckling tests, where the skin was subjected to out-of-plane loads away from the stiffener, promoting the extension of the artificial crack at the interface. It has been hypothesized that this delamination results from crack migration from the skin-stiffener interface into the ply interfaces within the skin. Crack migration, which involves complex interactions between delamination and matrix cracks, is crucial for improving numerical models. For accurate prediction, these models must capture both matrix crack and delamination interactions.

Cohesive zone models, in conjunction with the eXtended Finite Element Method (XFEM) and cohesive elements (CE), have been employed in literature to model the interaction between matrix cracks and delamination. While previous approaches often enrich cohesive elements using user subroutines, this thesis aims to leverage ABAQUS's built-in methods to model crack migration. A series of migration test simulations were conducted to evaluate the combined XFEM and CE approach. The LaRC05 failure criterion in ABAQUS was applied to initiate inclined matrix cracks within the plies, while delamination was modeled with standard 8-node linear cohesive elements. A three-dimensional mesoscale model of the test specimen was developed to simulate the migration test. The LaRC05 criterion successfully captured the orientation changes in matrix cracks due to changes in shear stress, consistent with experimental results. However, the predicted migration distance was 2-3 times greater than observed experimentally. A parametric study revealed that lower matrix strength and fracture energy facilitated migration, although increasing these parameters did not result in a consistent delay in migration, with discrepancies arising at higher values. Despite this, the methodology demonstrated the ability to predict crack migration tendencies and is considered suitable for structural-level applications.

Simulations of the butt-joint thermoplastic skin-stiffener panel under bending were also performed using a global-local modeling approach. The 19-ply skin was meshed with shell elements, while the local model explicitly represented the outer two plies (45/-45) with solid elements and the remaining 17 plies with shell elements. Three modeling approaches were explored: (i) damage only at the skin-stiffener interface, (ii) damage at both the skin-stiffener and ply interfaces (global-local model), and (iii) matrix cracks combined with delamination at both interfaces (global-local model). The first two approaches predicted mode I crack extension at the skin-stiffener interface, with no interlaminar damage in the second approach. However, the third approach using XFEM-CE predicted significant matrix cracking in the outer ply beneath the filler material, which further initiated delamination at the 45/-45 interface. This method successfully predicted delamination migration in the stiffened panel, demonstrating its capability to capture complex damage interactions at the structural level.

The present study, which was carried out in collaboration with GKN Fokker, focuses on incorporating bird strike crashworthiness requirements within a multidisciplinary optimization (MDO) framework. During the preceding three-month internship in the same company, a pivotal contribution to this project was the development of an Abaqus interface for the Multidisciplinary Modeller, MDM, created within the Center of Competence in Design department. MDM is a Python/ParaPy-based automated generator of wings, moveables and flaps, starting from a set of user-specified parameters. The generation of ready-to-run input files thus lays the foundation for the subsequent optimization process, as any changes in materials or geometry can be easily accommodated.

The core objective of the research is to minimize the weight of an aircraft wing while taking into account additional requirements related to the extent of damage caused by bird strikes. Unfortunately, such events occur more frequently than one would be comfortable with, and stringent requirements are set in place to guarantee the safety of the passengers. Among these requirements, the aircraft must be capable of landing safely after such an event, being subject to loads associated with get-home conditions.

As a consequence, two critical constraints are formulated within the optimization framework, addressing the residual strength of the damaged front spar following a bird strike, coupled with a requirement based on a maximum penetration depth. The last constraint has also been included due to the rising popularity of the electric vertical take-off and landing aircraft, which not only fly at low altitudes, thus increasing the risk of bird strike, but may also contain battery packs in the leading edge, for instance, which can pose a significant risk if damaged. To tackle the complexity of this highly-dimensional optimization challenge, a methodology based on Bayesian optimization is proposed, employing surrogate models coupled with a preliminary variable ranking procedure.

The Kriging metamodel is identified as a suitable candidate, thanks to its error prediction capabilities, which are paramount in Bayesian optimization. A variance-based dimensionality reduction method is proposed, which makes use of an initial surrogate to estimate the main and interaction effects of the variables. The quantification of the significance of a variable is expressed as its percentage contribution to the total variance, thus allowing for an intuitive selection of the most important parameters. After the screening procedure is complete, the optimization procedure is carried out in the reduced design space, which uses the constrained expected improvement as an acquisition function. The proposed methodology is then applied on a case study problem, involving a five-bay metallic wing segment subject to the constraints aforementioned, involving 19 design variables representing the thicknesses of various components.

Remarkable weight savings have been achieved, the final result being 40\% lighter than the lightest feasible design among the initial data points. A significant dimensional reduction has also been attained for the maximum depth constraint, which is expected due to the local nature of the impact. Not only did the number of variables greatly decrease from 19 to just 3, but a considerable increase in the accuracy of the corresponding metamodel has also been registered, thanks to an increase in sampling density in the reduced space. However, the variable screening procedure revealed intricate interaction effects with respect to the residual strength of the front spar, emphasizing the nuanced complexity inherent in crashworthiness considerations. Nevertheless, a moderate dimensional reduction has been achieved for this constraint as well, reducing the number of variables to 8, thus proving the efficacy of the proposed variable screening procedure.

In conclusion, the utilization of Kriging models, variable ranking procedures, and Bayesian optimization collectively contributed to the success of achieving remarkable weight savings, proving the efficiency of the proposed methodology. Moreover, it has been shown that the integration of a residual strength requirement is necessary, as many cases were uncovered where no significant penetration occurred, although the application of the considered load case, which is not from critical to an undamaged wing, resulted in high stresses to the front spar of the damaged structure.

One of the novel methodologies in computational physics research is to use mimetic discretisation techniques. Among these, the mimetic spectral element method holds special promise as it not only has the benefits of mimetic methods but also the additional benefit of higher-order discretisations using higher polynomial degrees. These methods are aided by the development of algebraic dual polynomials, resulting in a sparser system for better computational efficiency. This combination was used to develop a formulation that would result in topological relations for equilibrium of forces as well as the symmetry of the stress tensor for linear elasticity as well as the first steps for Stokes flows in an orthogonal domain. As a result, this study was extended to look at how a modified formulation would behave for an unsteady linear elastic solid, with the intention to extend this method to Fluid-Structure Interaction cases. However, the choice of both primal and nodal basis functions makes it impossible to undertake this challenge, demanding a rethink in strategy towards looking at linear elastic solids when the physical domain is not orthogonal. With the use of bundle-valued forms to represent physical quantities, a new hybrid formulation is developed where the equivalent of the physical problem is computed on a reference domain, which is orthogonal and thus can utilise the spectral bases defined before. The physical problem is defined on a skewed domain, where partial transformation of components results in a formulation that can conserve linear momentum point-wise, but not conservation of angular momentum, although angular momentum does converge on refinement of polynomial degree and mesh parameters. A change in bases with partial transformation aiming to make angular momentum conservation topological is not fruitful, although the value of the error decreases in the process. The final attempt is through full transformation, which results in a formulation with an inherent error in the formulation, owing to erroneous assumptions.

Designing and modeling compatibilized polymer blends require accurate interface model. In addition, it is possible that crazing occurs during failure of the interfaces leading to large deformation prior to complete failure and therefore must be accounted for by the interface model.

A preliminary literature review showed that existing formulations for the large
deformation account for the nonlinearity by adjusting the assumptions. For example, Van den Bosch et al. (2007) proposed redefining the local basis at each integration point. In contrast, Reinoso and Paggi (2014) argued that this did not account for geometric nonlinearity and proposed including the first derivative of rotation vector in the finite element equation. However, the mixed-mode responses of the models were not characterized and validated thoroughly. Therefore, we studied the response from standardized mixed-mode tests to compare the large displacement formulation with the commonly used small-displacement formulation of the cohesive zone model.

The standardized tests used by Moreira et al. (2020) for characterizing the mixed-mode behavior of ductile interfaces inspired the tests used in this study. When implemented with the BK criteria, the mode partitioning method used by Moreira et al. (2020) results in a wider spread of the mean predicted mode ratio and the mean predicted fracture toughness. However, the corresponding mode ratio predictions are similar when the predicted fracture toughness is close to expected. Therefore, while the power-law is better for implementing the mode partitioning method, we can use the predictions from the mode partitioning method implemented with the power-law to find the BK parameter.

Further, simulating the mixed-mode fracture tests with properties presented by
Moreira et al. (2020) showed that bulk materials with high modulus or stiffness, such as carbon fiber reinforced plastics, do not undergo nonlinear deformation to require the large deformation formulation. However, for bulk materials with lower modulus or stiffness, the responses of the two formulations in question are different. Additionally, for a given load case, a cohesive zone with anisotropic fracture properties experiences a different mode ratio when implemented with the large displacement formulation than the small displacement formulation. Moreover, more significant influence of the stronger mode on the load case results in greater difference between the mode ratio experienced at the interface. Nevertheless, the large displacement formulation is also applicable for stronger mode-II interfaces. However, a further investigation involving physical experiments is required to compare the response of the two formulations to the behavior of real material systems.

With the increasing availability of sensor data to monitor the health of systems and components, maintenance is encountering a shift from the traditional preventive and corrective methodologies to a predictive approach. Given that maintenance, repair and overhaul are a significant operational cost to organizations, it is imperative that the adoption of predictive maintenance techniques such as prognostics comes to the forefront. Prognostics models ensures that remaining useful life (RUL) estimates are available throughout the life of the component by leveraging sensor data. The obstacle faced by prognostics though, is the lack of standardized metrics, performance evaluation and their application to different case studies. Visualization results in understanding the evolution of the performance over time rather than single point estimates, which helps in identifying performance at crucial points in the life of systems. This paper focuses on assessing and visualizing the performance of prognostics models by addressing the shortcomings of the currently available performance metrics, advancing these metrics to suit the requirements and applying these to visualize the performance of two linear regression models over time. The results show that the modification of currently available performance metrics can enhance performance visualization, leading to better interpretation of the performance of prognostics models.

The demand for composites is rising in industries for instance, in aircraft and automobile engines. In these applications, composites encounter high thermal gradients service condition, and composites exhibit material discontinuous gradient field. It is essential to study how high thermal gradients and material discontinuities influence on the composites’ behavior. Composites are usually modelled with the standard finite element method (FEM), but mesh refinement is required near material interfaces and regions with high thermal gradients to obtain accurate solutions. Enriched finite element procedures are able to solve this issue. The Generalized Finite Element Method (GFEM) can approximate high thermal gradients by adding enriched degrees of freedom (DOFs) to original mesh nodes. In addition, the Interface-enriched Generalized Finite Element Method (IGFEM) can cope with material discontinuities by creating nodes at the intersection between discontinuities and edges of elements in the mesh. Yet, GFEM and IGFEM have their own limitations when implemented: while GFEM needs extra enrichments to resolve the material interfaces in composites, and IGFEM requires mesh refinement if thermal gradients are too high in cut elements. Then we can combine the best of both methods.

In this thesis, a new Generalized Finite Element Method with spread and discrete enrichments (GFEM^sd) is developed to simulate heat transfer problems with high thermal gradients in composites. By combining GFEM and IGFEM formulations, GFEM^sd is capable of dealing with high thermal gradients and material discontinuities simultaneously in an effective manner. We show that GFEM^sd obtains accurate results when compared with analytical solutions in numerical examples. A convergence study illustrates that fewer DOFs are required in GFEM^sd for achieving the same level of accuracy comparable to that of IGFEM. We then apply GFEM^sd to simulate a twill pattern composite and derive effective heat conductivity values. Lastly, based on the composite’s minimum effective heat conductivity, fiber shape optimization is conducted to obtain the best design for a fixed fiber volume fraction.

Type IV composite pressure vessels (CPVs) are used commercially for the gaseous storage of hydrogen in fuel cell electric vehicles (FCEVs). However, their economic implementation requires material optimization and a reliable prediction of the vessel strength. In this regard, their burst when loaded under internal pressure is impacted by the combined effect of the stacking sequence design and the variability of mechanical properties resulting from the manufacturing process. This work shows a framework for analysis that accounts for some of these manufacturing-induced characteristics in the mechanical response, namely the relation between the vessel stacking sequence, its final geometry, and the material properties. Furthermore, vessel burst pressures are alternatively estimated from the failure criteria evaluation in constitutively elastic analyses and the modeling of damage progression. Predictions are reasonably accurate when the collapse occurs in the cylinder (+2.2 %), although a more considerable discrepancy exists with experimental results when vessels fail in the dome transition region (+12.3 %).

Modern material systems with properly designed microstructures offer new avenues for engineering materials with advantageous mechanical properties and functionalities in various applications. With the increasing desire to design structures that have complex shapes or that are simply cheaper and more efficient, the demand for complex numerical simulations intensifies. Capturing the behaviour of composites relies on the interaction between the macroscopic and microscopic components, which can make direct numerical simulation too computationally expensive. Concurrent finite element analysis (FE²) links the different length scales in a multiscale approach. In each integration point of the macroscale a representative volume element (RVE) is embedded to address the microscopic heterogeneity. However, the need to evaluate the micromodel many times will come with the drawback of high computational costs. Various surrogate models have been proposed to accelerate computational models. One possible approach is employing machine learning techniques to substitute the evaluation of the RVE. Currently, artificial neural networks are being used in a wide variety of fields, due to their exceptional ability to recognize patterns. Although these techniques are able to construct models for complex input–output relations, their applications to mechanics of materials are still limited. These techniques are usually problem-dependent and may suffer from the danger of extrapolation beyond the original sampling space, e.g. different loading paths. This is not naturally resolved, mainly due to the loss of physics in the current machine learning models. The newly proposed Deep Material Network (DMN) by Liu et al. is a data-driven multiscale material modelling method that aims to resolve this issue by composing a network constructed from an assembly of mechanistic building blocks, where each building block resembles an analytical homogenization. Because the fitting parameters of this network are interpretable with physical meaning and because sampling the DMN still involves evaluation of classical constitutive relations, the loss of essential physics in minimized. The material network can effectively be trained using stochastic gradient descent with backpropagation, based on linear elastic data of an RVE. The extrapolation capabilities to unknown loading paths are investigated with a matrix-inclusion composite, with the focus on the accuracy of the model for loading-unloading scenarios, and an extension to the training process is proposed where the fidelity of a trained DMN is assessed by generating and grading a yield stress envelope. The results show that a well trained DMN is capable of predicting the response for unseen loading paths involving unloading exceptionally well. The complete learning and extrapolation procedures of the DMN establish a reliable data-driven framework for concurrent multiscale material modelling and design.

Today's leading projections of climate change predicate on Atmospheric General Circulation Models (GCMs). Since the atmosphere consists of a staggering range of scales that impact global trends, but computational constraints prevent many of these scales from being directly represented in numerical simulations, GCMs require "parameterisations'' - models for the influence of unresolved processes on the resolved scales. State-of-the-art parameterisations are commonly based on combinations of phenomenological arguments and physics, and are of considerably lower fidelity than the resolved simulation. In particular, the parameterisation of low-altitude stratocumulus clouds that result from small-scale processes in sub-tropical marine boundary layers is widely considered the largest source of uncertainty that remains in contemporary GCMs' prediction of the temperature response to a global increase in CO2. Improvements in the capacity of machine learning algorithms and the increasing availability of high-fidelity datasets from global satellite data and local Large Eddy Simulations (LES) have identified data-driven parameterisations as a high-potential option to break the deadlock. However, early contributions in this field still rely on inconsistent multiscale model formulations and are plagued by instability. To sketch a clearer picture on the sources of the accuracy and instability of data-driven parameterisations, this work proposes a framework in which no assumptions on the model form are made, building on recent work at the TU Delft. It uses Artificial Neural Networks (ANNs) to infer exact projections of the unresolved scales processes on the resolved degrees of freedom. These ``interaction terms'' naturally arise from Variational Multiscale (VMS) model formulations. The resulting VMM-ANN framework limits error to the data-driven interaction term approximations, offering explicit insight into their functioning.
The model is assessed in the context of a statistically stationary convective boundary layer turbulence problem, which is further reduced to a one-dimensional, forced inviscid Burgers' equation. Simple, feedforward ANNs with relatively local input stencils that are trained on error-free data a priori to inserting them in forward simulations (offline) are capable of predicting the interaction terms of this problem much better than traditional, algebraic VMS closures in offline settings at various levels of discretisation; they also generalise well to uncorrelated instances of the turbulence. However, this performance does not translate to simulations of forward problems. It is shown that the model suffers from instability due to i) ill-posed nonlinear solution procedures and ii) self-inflicted error accumulation. These correspond to two dimensions of forward simulations that are not accounted for by offline training on error-free data. The first instability mode can be dealt with by reformulating the VMM-ANN model architecture; the second is conjectured to demand training strategies that account for the self-induced errors. Finally, despite scaling well, the framework is still found to be comparatively computationally expensive. In all, appreciable challenges therefore remain in order to capitalise on the promise offered by ANNs to improve the parameterisation of clouds in GCMs in particular and turbulence in general.

This thesis presents a model that is able to predict fatigue crack growth and damage directionality in non-conventional Fibre Metal Laminates (FMLs) in CentreCracked Tension (CCT) specimens. Non-conventional FMLs encompass all FMLs other than standardised ones such as GLARE. FMLs can be made non-conventional by using multiple fibre types, any fibre orientation, multiple alloy types or thicknesses, or a combination thereof. These characteristics provide much more tailorability than standardised FMLs and thereby extend the applicability of FMLs to, for example, door corner reinforcements and wing structures. Contrary to standardised FMLs, the damage in non-conventional FMLs is non-uniform, necessitating the ability to compute the crack growth rate in the metal layers and the delamination at the metal-fibre interfaces separately.

Mines and improvised explosive devices are the cause of many mortalities of vehicle occupants. Both experimental and numerical research in this field aims to improve the safety of military vehicles. TNO has advanced Finite Element (FE) models to simulate such events. The numerical research is done to better understand mine blasts. Another aim is to reduce experimental costs for Defence Material Organization (DMO). For full vehicle simulations the empirical Westine model is the basis of the in-house TNO mine blast model. The model consists of a triangular pressure pulse consistent with the Westine impulse which is calibrated using the a test rig developed by TNO and DMO. Experiments are compared with numerical results using the TNO mine blast model and the jump height, representative for total impulse transferred, shows scattered results for different vehicle tests. A possible cause can be accuracy of the current mine blast model. This research will study a new technique to validate mine blast models. When this new technique results in improvements in validation of mine blast models it can be used to study and improve the current TNO model. The aim of the new validation method is to calculate the interaction pressure of an plate excited by a mine blast loading. The obtained pressure can be compared with existing mine blast models for validation of the mine blast model. The methodology is based on solving the inverse problem which requires transient Digital Image Correlation (DIC) measurements of the deforming plate for the duration of the mine blast loading. Such measurements are done with the state of the art test setup from TNO and DMO. Several methods to solve the inverse problem will be studied. The proposed best way to solve the problem at hand is by solving the corresponding optimization problem using an iterative gradient of descent algorithm. The main difficulty in this approach will be the calculation of the gradient of the objective function. To do this the corresponding transient adjoint problem has to be solved. First an algorithm to solve the inverse problem is implemented for a linear Kirchhoff Love plate model to study the behaviour of the inverse problem for a relatively simple test case. For this algorithm the transient adjoint problem is derived. The forward and adjoint problem are numerically solved. The implementation is verified using the method of manufactured solutions and a convergence study. The algorithm is verified using three different bench mark test pressures. It was found that without any exception the displacement of the algorithm converged with great accuracy to the applied displacement. The corresponding pressure converged good for smooth pressure distributions. Non smooth pressure distributions representative for mine blast interaction pressure did not converge. This shows the non-uniqueness of the solution of the inverse problem. It was realized after these tests that the forward problem acts as a low pass filter for time and spatial oscillations of the pressure distribution. This implies that the inverse problem in non-unique and that noise in the displacement data will be amplified in the obtained pressure from the inverse solution. The total force and radial position as function of time, obtained after integration over the spatial domain, for a localized load are accurately captured back. These parameters could be useful to validate a mine blast model such that research was continued for a continuum model. A non-linear elastic material model is proposed to model the deforming plate assuming monotonically increas- ing strain. This model is used and calibrated against the Johnson-Cook plasticity model. One plate simulation excited by the TNO mine blast model verifies that the non-linear elastic and Johnson-Cook material model result in almost the same displacement. The adjoint problem for a general continuum with elastic material model is derived. The same optimization algorithm is implemented for the continuum model using the calibrated non-linear elastic material model. The forward and adjoint problem are solved using the author’s transient FEM implementation which is verified using commercial software. From the benchmark tests it was verified that the displacement converged quite well however less compared to the linear problem studied earlier. The corresponding pressures behaved similar. It was concluded that the total force and centroid position of a localized load are not always in agreement for the benchmark and solved pressure. This research shows that the limitations of the inverse problem shed light on the limitations in the validation methods employed by many researchers in the field.

The high specific strength and stiffness of composite materials have resulted in a widespread introduction of composite structures in the commercial aviation industry. Although composite materials offer the possibility to design lightweight structures, they also suffer from complex failure modes of which damage progression is badly understood. This thesis research is about Structural Health Monitoring (SHM) for composite aircraft structures. By permanently attaching a sensor network to a structure, maintenance activities can be improved by reducing costs and by optimizing maintenance scheduling. SHM could furthermore be used to increase the understanding of composite behavior. This research focuses on investigating an image reconstruction algorithm which can be used to visualize impact damage on a composite plate using piezoelectric (PZT) sensors. The relationship between the reconstructed images and damage growth is analyzed using different excitation frequencies and different formulations of a damage index while also assessing the influence of sensor failure on the algorithm. Moreover, an experimental campaign is performed during which composite plates are subjected to Quasi-Static Indentation (QSI) testing in order to create impact damage and monitored to obtain data.

New methods for aircraft movables introduce oblique- and out-of-plane loads on lug joints. With the introduction of these loads on lugs the need arises for a more detailed fatigue life prediction method.

Therefore, the thesis research focused on creating a methodology for predicting the fatigue life for lug joints subjected to combined in- and out-of-plane loading in co-operation with GKN Fokker Aerostructures. Current prediction methods are all based on axially loaded lugs. In concept, this method applies the Larsson relation.

From Finite Element Analyses (FEA) of different in- and out-of-plane loaded lugs, the influence of eccentricity, taper angle, load angle, and lateral loads on the Larsson relation became apparent. Resulting in the addition of correction factors K_ecc, K_α,and K_L in Larsson. Although the method is verified, experimental test have to be performed for validation. Recommended is the expansion of the out-of-plane FEA and the inclusion of transverse loaded lugs.

The dutch government plans to build a vast amount of offshore wind turbines. Therefore, it will be necessary to decrease the costs of the transportation, installation and maintenance of these wind turbines. Jules Dock tries to achieve this by replacing the steel design of the next generation 10MW wind turbine tower by a flexible composite design.
Damping is an important parameter of flexible structures, therefore a research thesis has been set up to determine the damping properties of the composite tower. In order to determine the material damping of the tower, DMA measurements have been conducted and a viscoelastic damping model has been created. According to this model, the damping loss factors are approximately 71% higher than the damping loss factors of a steel tower with the same dimensions, which results in an increase of the fatigue life. Also the tower’s material damping can be considered temperature and frequency independent.

Crack initiation and propagation in composite structures exists as a prominent knowledge gap in computational fracture mechanics. Prior to the development and implementation of fracture modules in FEM solvers, most studies were constrained to simple geometries and load cases. The set of parametric studies that constitute this thesis aims to partially fill this knowledge gap by studying the fracture behavior of air-plasma sprayed thermal barrier coatings (APS-TBCs) using cohesive zone modelling (CZM) in conjunction with FEM. Two-dimensional projections of the TBC microstructures were developed and subjected to a thermomechanical analysis involving thermal strains between the different components of the TBC system. The parametric study was split into three sections. The preliminary study, set O, was executed to discern the most appropriate set of boundary conditions that would produce realistic crack patterns in an expedient manner. Set A and Set B studied the fracture behaviour of conventional TBCs and the recently developed self-healing TBC composite, comprised of lamellae and pores and for Set B, self-healing particles. Qualitative observations reveal the influence of feature dimensions and relative placement to one another. The results appear to indicate that TBC top-coats that are comprised of finer lamellae exhibit higher fracture resistance on both conventional and self-healing systems, and that smaller particles in self-healing systems mitigate damage caused by pores less than larger particles.