This research investigates the role of how Finite Element simulation models reproduced progressively crushed Glass Fiber Reinforced Polymer (GFRP) parts. The proposed work is motivated by the research questions concerning what are the relevant critical modeling strategies that accurately reproduce the behavior of in-plane crushed laminated composite parts with LS-Dyna, how will the simulation model be evaluated following the modeling strategies, and which are the main similarities and dissimilarities among the result of the modeling strategies considered. The research objective is to develop detailed finite element simulation models for axially crushed GFRP composite parts which can represent complex damage modes by a methodical approach in a reasonable time with LS-Dyna explicit. Previous research has shown the necessity to calibrate many parameters with no physical meaning to correctly reproduce crushing. This research addresses these gaps by benchmarking two material models that are not characterized by such effects (*MAT_261, *MAT_262) in comparison with the one characteristic of such behavior, *MAT_54. The most important contribution is to the crashworthiness industry. To date, no research studies were found in the literature covering all the aspects described. This work demonstrates how to deploy such capabilities starting from simple numerical models, to vehicle parts. To illustrate these concepts, the necessary assumptions to create the models and how the parameters were identified have been described while choosing fully integrated shell and thick shell elements. The qualitative and quantitative criteria defined were necessary to systematically evaluate more than 200 simulations in comparison with available experimental data from Daimler AG. The significant findings from the research demonstrate that *MAT_261 and *MAT_262 are not subjected to strain localization phenomena, unlike *MAT_54. The developed set of parameters resulted in a low sensitivity of the *MAT_262 models concerning element size, lay-up or impact energy. Moreover, the research demonstrates that more accurate quantitative results than the current state of the art can be produced with a 5.0 mm element length against the general belief that fine-meshed and mesh convergence studies are required. Contrary to the expectations *MAT_261 demonstrated a low mean force in crushing although sharing the input parameters with *MAT_262 and similar damage laws. The developed models and set of *MAT_262 parameters can be deployed in full vehicle crash simulations with the plug-in possibility to evaluate new design variants. It is concluded that the findings provide support fulfilling the research objective.

High performance continuous fiber-reinforced composites are becoming increasingly important in the aerospace industry. In these structures, internal damage is often created and propagated throughout the lifetime, having a negative impact on the structural properties. In order to allow for condition-based maintenance, there is a need for reliable prognostics to predict future damage states. Therefore, a model-based machine learning approach is developed in this thesis to perform prognostics of the remaining useful life (RUL) and remaining useful properties (RUP) of cross-ply composites to end-of-early-fatigue-life (EOEFL). In-situ transverse matrix crack density and dynamic stiffness data are available, as well as offline delamination ratio and damage induced stiffness degradation data. To account for the multicausality and non-linearity in stiffness degradation, the evolution of cracks and delaminations is modeled using separate phenomenological relations that are pre-trained with high variance using non-linear least squares (NLS) on a separate training set. These damage properties are then combined into a normalized stiffness prediction using NLS pre-trained phenomenological relations that express the induced stiffness degradation for each of the aforementioned properties. A particle filter (PF) trains the model parameters, initialized in a high variance uniform distribution, for the phenomenological models in real-time (i.e. online). This is done with the in-situ crack density and normalized stiffness measurements of the testing specimen only. A random walk on the model parameters, which declines towards EOEFL with a given rate of convergence, is added to allow for continuous adaptivity. By propagating each particle to future states beyond the online time step in the PF, the prognostics are obtained. This encompasses the RUP and RUL to EOEFL using a weighted sum of the particles. After applying this methodology to the case study data, it is concluded that the potential of PF to offer adaptivity required for RUP prognostics of composites is identified, definitely for damage properties showing early characteristic behavior. However, reliable RUL estimation to EOEFL with the methodology set out in this thesis remains difficult. Especially the stiffness degradation model and the failure criterion for EOEFL generate difficulties. Therefore, a promising recommendation would be to combine similar phenomenological relations to end-of-life for crack and delamination growth with an alternative stiffness model. A feasible approach would be to train a surrogate model on synthetic data generated with finite element modeling simulations. Finally, a sensitivity analysis is done on three PF hyperparameters: sample size, threshold effective sample size, and rate of convergence. The first shows that a minimum sample size can be distinguished after which no improvement occurs when the sample size increases. The second indicates a 'sweet spot' that balances the sample impoverishment and weight degeneracy drawbacks. The latter makes it plausible that a moderate rate of convergence is preferable.

As the demand on new lightweight materials increases due to economic and environmental reasons, fiber reinforced plastics seem an interesting solution. In order to maximize their potential, ways have to be found to introduce damage tolerant designs. To this end, in the present exercise, the damage caused by cyclic loading is investigated. Cyclic loading causes loss of stiffness and strength of composite structures. In the present work a progressive damage analysis model has been built. Damage is tracked by a macroscopic failure criterion. Failure occurring at levels below the macroscopic level are captured by a degradation theory which requires S-N curves as input. An attempt is made to use S-N curves derived from static tests only. Stress solutions are obtained using a finite element package. In order to bring the macroscopic damage, degradation and stress solution parts together, a cycle jumping algorithm has been developed which minimizes the amount of FEM stress solutions required and the required amount of function evaluations. In its current form, the model is able to properly predict the trend of stiffness and strength behavior. Showing that a model like this has potential. For the proper predictions of mag- nitudes, additional failure modes should be modeled and the usage of S-N curves obtained from large specimens for applying on smaller geometries (elements) should be reconsidered.

This research experimentally investigates the effects of an impact event during fatigue loading of a CFRP structure. Open-hole specimens were impacted in-situ during a short interruption of a tension-tension cyclic loading program. The impact was aimed directly below the central hole, with the critical fatigue damage path being left and right of this hole. A combination of digital image correlation (DIC), acoustic emission (AE) and C-scan measurements was used to measure the material response and damage patterns. The results indicate that while an impact caused the total amount of damage to increase as one would expect, its effect on the damage along the critical path depends on the timing of the impact. Only an impact before fatigue clearly accelerated fatigue damage accumulation leading to a shorter fatigue life. In contrast, impacting specimens at a later moment during fatigue loading had no effect on this critical damage accumulation.

Induction welding is an effective technique for joining unidirectional carbon fiber reinforced thermoplastic composites and L-joints can be produced through quick and cost-effective processing steps. However, due to high localized stresses in the skin-stiffener interface, these L-joints are often avoided in primary aircraft structures. Also, no international testing standards have been developed for testing of such joints. A method was developed for the implementation of a neat thermoplastic resin fillet between the L-joint skin and stiffener web using the induction welding process in an attempt to remove the high stress concentration at this location. A 35.4% increase in quasi-static pull-off strength was measured with a weight penalty of less than 0.5%. This result was compared with a similar autoclave co-consolidated joint, which showed an 80.9% improvement. An ANSYS Parametric Design Language finite element model was developed based on the virtual crack closure technique and it showed that the joint pull-off performance is strongly dependent on geometric parameters such as the skin and stiffener thickness. Also, a new test setup was developed, which reduced internal stresses created by the setup compared to those commonly used in literature. By further improving the method through which the fillet is joined to the induction welded L-joint, a performance increase similar to that of the co-consolidated joint should be achievable. Test results have shown that the use of this type of fillet can lead to the skin-stiffener interface no longer being the critical failure point for realistic joints in primary aircraft structures.

The reduction of the Levelised Cost of Electricity (LCoE) in wind energy, and offshore in particular, is among the main objectives of research in the academia nowadays, and a demanding task as well. Offshore wind farms are capital intensive investments and lowering uncertainties and costs throughout design, planning, installation and operational phases will offer cheaper electricity production and a more competitive nature against fossil fuel powered energy. Of course, a big part of the total cost of a wind farm is the wind turbines themselves. Innovations in the design and manufacturing processes are always welcome in order to further advance within the learning curve of the technology and achieve efficient economies of scale. Furthermore, the blades of the wind turbine rotor are its most important component because they express the most basic operation of the turbine, aerodynamics and energy capture. Yet they pose structural challenges. There are efforts to maximise energy output while keeping their mass low, which should subsequently drive down secondary costs. Additionally, another important factor is the increase of energy output in the farm level that can lead to a larger cost reduction. This thesis aims to investigate the performance and feasibility of lightweight, low power density rotors. This investigation is made by acquiring a performance baseline with the reference rotor of the 10 MW INNWIND machine and comparing it with the proposed alternative rotor designs by means of parametrical modifications. The reference machine rotor radius is 89.166 m, with a blade mass of 41.7 tn and a rated power output of 10 MW operating at an optimal lambda of 7.5. All the new designs include an increase in rotor radius to 103 m. They also include differences in operational parameters such as rated rotational speed, blade slenderness and tip speed, while rated power stays the same. These designs result in different blade masses and allocation of energy production...

Ever since the attacks on the World Trade Center, airport security has been a topic of interest. The United States was caught by surprise and the attacks triggered a renewed interest in aviation security. It was well recognized that airport security was one step behind on intelligent attackers and this created the need to developed better risk assessment methods. Unfortunately, none of the risk assessment methods developed has the possibility to quantify the vulnerabilities in an airport checkpoint. One of the main challenges in developing a method which can do this, is to find a technique that can account for the complexity of the airport environment from which the vulnerabilities emerge. Furthermore empirical research has shown that security operators do not necessarily follow protocol, but behave as autonomous agents which regularly bend or break the rules. A method which can potentially identify vulnerabilities in such a complex environment is agent based modeling. This modeling technique has proven to be very powerful in modeling complex systems that emerge from the behaviour of autonomous agents. Some work has been done in this area, but until now this technique has not been used to quantify vulnerabilities in an airport checkpoint. Therefore, the aim of this project is to develop an agent based model of an airport checkpoint, quantify the vulnerabilities emerging from this checkpoint and analyze the effect of employee behaviour on these vulnerabilities. To do this, the behaviour of the security operators is modeled using models that are rooted in behavioural psychology and have strong empirical backing. The employees decision making is modeled using Decision Field Theory and the employee performance is modeled using the Functional State Model. With the model developed, a set of experiments is performed to calibrate the model and analyze the vulnerabilities. These experiments result in the quantification of vulnerabilities for a predefined set of threat scenarios. The analysis of these threat scenarios shows that employee behaviour mainly impact threats scenarios in which a weapon is hidden in the carry-on baggage. The reason that security operators have a large influence on the outcome of this screening process is that it is the process in which employees have to perform multiple activities and make multiple decisions. It is found that the vulnerabilities in this screening process are mainly dependent on the speed/accuracy trade-off as made by the employees. The perceived risk of the detected prohibited items plays a limited role, since most prohibited items are only seen as a small risk. The performance of the employees played a less important role on the outcome of the screening process and differences in performance are mainly caused by the personality type of the agents. The personality type that put more effort into a task, outperformed the other agents. The skill level of the operators however, did not significantly effect the outcome the simulation. This suggests that the effort an operator puts in is more important than the skill level of the operator. Finally it is found that it is beneficial to minimize the number of steps in the screening process. This benefits overall performance, since adding steps to the screening process means adding opportunities to make mistakes.

A lot of studies have been done on Acoustic Emission (AE) covering a wide range of materials and applications. Within a structure that is under loading, AE has proven to be useful in determining the type, location and accumulation of damage within a material. The general goal is to use AE for inservice monitoring of structurally loaded parts. However, more research is necessary before the method can be employed in real-time applications. The objective of this project is to contribute to the development of algorithms that classify failure modes real time in composites using AE. Composite material specimens are loaded under tension while recording AE, after which the signals that are recorded are used to create an independent damage mode signature. This leads to the ability to classify damage modes per time increment. Mechanical tests are performed on specimens while recording AE signals. Carbon fiber reinforced polymer specimen with unidirectional 90∘layup are loaded in tension up to failure to create a fingerprint of the matrix cracking damage mechanism. The same material with a crossply layup is loaded in tension under quasi static and fatigue loading. The fingerprint is then used to separate signals coming from this type of damage and other damage mechanisms. Next to the basic parameters of these signals, additional features are generated by processing the waveform of each signal. The wavelet transform is used to find frequency related features that characterize each signal. Machine learning algorithms are developed and used to cluster and classify the AE Signals. Overall, the real-time clustering algorithms have proven to be successful in clustering and classifying incoming AE signals in an efficient way. The system was able to correlate incoming data to matrix cracking data, within a very limited time frame. With room for optimization the proposed methods seem fit to be applied in real-time applications. Before this can be done however, more testing and validation of these methods is required. The most valuable addition to this study would be a proper validation of the clustering results. Regarding improvement of the system, the main bottleneck of the proposed algorithms is loading large datafiles. This can be solved by reading data in batches, but requires further development of the algorithms.

This report, which from now and onwards will be called the main body of the thesis, covers the core of the problem that has to be solved. Generally, the aim of this thesis is to investigate a laminated composite’s structural response, made of FRP, when its material properties are assumed stochastic instead of deterministic. The materials shown in this thesis are investigated as part of a bigger project that concerns the construction of several Mine Counter Measure Vessels. In this document, the main focus is given on the results of experiments and/or analytical calculations whereas the theories/methods used to extract these results are presented in the supporting document. As a result, readers that are familiar with statistics, classical lamination theory, FEA modeling among others, can easily read this report without coming across knowledge that they already have. In general, this document covers the writer’s contribution to academics whereas the supporting document gives an extensive explanation on what was used in this document.

An increase in apparent mode II fracture toughness in Fiber Reinforced Polymers has been found from modified Transverse Crack Tension (mTCT) tests with compressive stresses added orthogonal to the delamination plane. Numerical reproduction of these results has not yet led to a good understanding of the reasons for this behavior. Currently, an analytical approach is used for determining the mode II fracture toughness from an mTCT test. In this research, the validity of the analytical relation is investigated for the case of an added compressive stress. The behavior of the material in the test is therefore numerically investigated in a qualitative manner. A mesh, using a material model for plasticity and a cohesive law for interface elements to form the crack, which have been proven to give good results, has been build. A refinement on mesh and geometrical dimensions, focused on the pressure zone of the specimen, ensures the degree of detail required to investigate the local behavior. A parameter study on the inlfuence of geometrical and material parameters is performed and linked to results from previous conducted experiments. A standard mTCT test without compressive stress shows a linear load displacement relation until crack initiation and then a constant load at which the crack grows through the specimen. This crack growth load is used for the analytical approach. The results from this research show a change from a constant crack growth load to a linear crack growth load range in the zone of the applied compressive stress. After crack initiation, the crack growth load through the compressed zone has an increasing, nearly linear relation, based on the compressive stress and length of the pressure zone. The analytical approach, in which the highest reached load on the specimen before failure is used as input, no longer applies for this case. The failure load is now dependent on the length of the pressure zone and pressure magnitude.

Airlines experience schedule disruptions on a daily basis. Poor weather conditions, unscheduled aircraft maintenance and congested air spaces are just a few of the causes that prevent airlines from operating their flight schedules as planned. In the third quarter of 2017 over 20% of all scheduled flights in Europe suffered from delays. Operation Research based decision support systems (DSS) help airlines with their disruption management processes and provide suggestions for recovery options. For large hub-and-spoke carriers, with an extensive network and a large number of aircraft and flights, the computation time required to find the optimal recovery solution after a disruption increases rapidly. As airlines require fast recovery solutions when disruptions occur, there is an ongoing trade-off between computation time and system sophistication. In the majority of disruption cases, no or a limited number of undisrupted aircraft are required to find the optimal recovery solution. By selecting a limited number of aircraft, flights and airports used to find a recovery solution, the computation time is reduced exponentially. The challenge is determining which aircraft and flights should be selected. This research aims to develop a decision support system for the schedule and aircraft recovery process that is able to present a feasible solution to a disruption in less than 120 seconds. An aircraft recovery model will be developed based on the integer linear programming model that was created by Vink et al. (2019) and Vos et al. (2015). Crew and passenger recovery are not considered. To recover disruptions, the optimization model can delay and cancel flights as well as perform tails swaps, where the flights from two aircraft are switched. The novelty of the work is that machine learning is used to predict which undisrupted aircraft will help recover a disruption. Based on those predictions a sub-network selection algorithm will select the subset of aircraft to be included in the optimization instead of the entire aircraft fleet. The performance of the system is tested on a case study for the domestic hub-and-spoke network of Delta Airlines. The dataset for the study consists of 2200 daily flights, 147 airports and 827 aircraft in 8 aircraft families. The results of the system are compared with the optimal solution, where no aircraft selections were made. The case study shows that the system is able to make an aircraft selection where 50% of the fleet is discarded, while still finding the optimal solution in 98.9% of the 556 disruptions tested. Furthermore, the system reduces the computation time by 45%, resulting in an average time of 48 seconds. For the disruptions, the computation time varied between 9 and 180 seconds.

Adhesively bonded joints have proven to outperform their mechanically fastened joint counterparts, as they present a more structurally efficient method of load transfer, lower stress concentrations and better fatigue performance at reduced weight. In the specific case of the Adhesively Bonded Single Lap Joint (ABSLJ), bending-induced stresses that result from the load path eccentricity add up to the adherend inplane stresses. Moreover, significant peak peel and shear stresses develop at the lap ends of the adhesive and associated adherend interlaminar tensile stresses have a detrimental effect on the joint’s strength. Such joints made of Fiber Reinforced Polymer (FRP) adherends bonded with an epoxy adhesive layer sustain a substantial amount of damage, from failure onset to ultimate failure. With the purpose of design structurally efficient and damage tolerant composite joints, it is essential to understand the stress distribution and to accurately predict the damage initiation and propagation events in such joints made of composite materials. A well-established set of Damage Progression Models (DPMs) in the framework on the Continuum Damage Models (CDMs) were developed as a tool to predict the global response,damage initiation load and ultimate load of the specimens. Hashin 3D, Puck and LaRC05 werethe implemented failure criteria to detect the initiation of damage in the adherends. After thispoint, the elastic properties of the detected damage elements were reduced according to sudden and gradual material degradation models. As for the adhesive, the von Mises criterion was used to detect the damage onset and a linear softening law modeled the material degradation. For the validation of the DPMs, the numerical results were compared against the data of an already published experimental study. Four different adherend layup sequences: [45/90/ − 45/0]2푠, [90/−45/0/45]2푠, [0/45/90/−45]2푠 and [45/0/−45/0]2푠 were studied based on data extracted from the mechanical testing, Digital Image Correlation (DIC) and Acoustic Emission (AE). Good correlations between numerical predictions and averaged experimental linear stiffnesses were found, particularly for the two configurations with the outmost ply at 45∘, for which the difference was lower than 5%. The initial non-linear stage of the global response seems to be governed by the longitudinal bending stiffness, while the subsequent linear behavior is controlled by the longitudinal membrane stiffness of the adherends. Regarding damage initiation, numerical predictions showed to be 11.5%, 7.5%, 29.9% and 6.1%, respectively, more conservative for the four analysed configurations, when compared to the AE results, whose established criterion should be further developed. With respect to the ultimate load, the relative differences between predictions and tests showed significant variability among the tested configurations; specifically the deviations were of: 33.2%, 37.4%, -0.4% and -13.71%. Despite the encouraging results, an inherent shortcoming of CDMs is the representation of damage in a smeared manner due to the homogenization of the anisotropic material in the modeling process. A blended framework using CDMs to model intralaminar failure and discrete crack models to model interlaminar failure and matrix cracking might lead to more realistic damage patterns.

The failure of composite laminates is most commonly evaluated through deterministic approaches. However, even though these traditional methods predict the failure well on average, they cannot take into account the inherent probabilistic aspect of failure mechanics. Thus, reliability methods allow for a better structural assessment and improve the efficiency of the design. One of the most used methods, FORM, is shown to be limited and unreliable for applications in composites. Monte Carlo methods are therefore further used in this research. Furthermore, current reliability research in composites uses very simple models of damage and failure. Based on Monte Carlo simulations, this research was able to include a failure mode-dependent criterion and proposes two methods to model reliability in terms of last ply failure rather than first ply failure.

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.