Stochastic modelling and numerical simulation of fatigue damage

In continuum damage mechanics, fatigue is a phenomenon associated with a continuous material stiffness reduction. Numerically, it can be simulated as an accumulation of damage process. Since the resistance of concrete material reduces drastically after the initiation of macroscopic cracks, fatigue life can be approximated using damage models as the number of cycles by which the material continuity vanishes. The fatigue scatter is an interpretation of material heterogeneity and uncertain external influences. It can be reproduced by treating the damage evolution as a stochastic process. Inspired by the application of the stochastic process in molecular physics, the deterministic damage evolution rate of the Lemaitre model is modified as a stochastic differential equation to characterise the random damage increment. The implicit Euler scheme associated with Monte-Carlo simulation is demonstrated as a practical approach to solve the stochastic integration problem. The stochastic damage model is designed carefully to obey the thermodynamic principles and the deterministic damage law. Particular efforts are addressed to determine suitable random distributions, avoiding negative random damage increments in individual realisations, to have a statistically unbiased mean. To adequately approximate the high-cycle fatigue damage with random noise, the "jumping-cycle" algorithms with different extrapolation strategies are investigated. This damage model is further implemented in the simulation of four-point flexural fatigue of concrete beam, solved by the finite element method. The numerically reproduced fatigue data closely fit the published experimental results and the empirical solution, both in the mean and standard deviation. Compared to the Gaussian white noise, the Weibull random variable has broad applicability to simulate random fatigue damage and other physical processes.Um die Streuung der Messdaten in der Materialermüdung zu beschreiben, wird basierend auf Zufallsprozessen ein phenomenologische Modellierung vorgestellt. Erprobt wird die Modellierung an einem Betonbalken mit ebener Finite Element Diskretisierung, wobei die stochastischen Ermüdungsgleichungen mit der Monte Carlo Methode gelöst werden. Die simulierten Ermüdungsprozesse unter Biegebeanspruchung des quasi-spröden Materialswerden mit experimentellen Daten und etablierten empirischen Gleichungen vergleichen. Um hochzyklische Beanspruchungen zu behandeln, wird ein „jumping-cycle“ Algorithmus angewendet, mit dem die Rechenzeiten stark reduziert werden. Dieser Modellansatz ermöglicht die Simulation von Ermüdungsprozessen mit probabilistischen Information in einem sehr langen Zeitintervall. In derKontinuums-Modellierung geht der Prozess der Materialermüdung mit einer Degeneration der materiellen Integrität einher, die sich z.B. in der Abnahme des elastischen Moduls niederschlägt. Numerisch wird dies als ein kumulativer Schädigungsprozess modelliert. Weil der Materialwiderstand von Beton nach der Entstehung makroskopischer Risse drastisch abnimmt, kann die Ermüdungslebensdauer unter zyklischer Beanspruchung durch ein Schädigungsmodell praktisch sehr gut abgeschätzt werden, sobald das Auftreten makroskopischer Risse prognostiziert wird. Die Streuung in experimentell ermittelten Ermüdungskurven kann durch die mikro-Heterogenität der Materialien und Unsicherheiten in weiteren externen Faktoren verstanden werden, mittels einer Modellierung der Schädigungsentwicklung als stochastische Prozessgleichungen kann diese gut reproduziert werden. In Anlehnung an die Beschreibung stochastischer Prozesse in der theoretischen Physik werden die volutionsgleichungen für die Schädigungsentwicklung des Lemaitre-Modells als stochastische Differentialgleichungen dargestellt. Diese werden mittels impliziter Euler-Verfahren und Monte-Carlo Methoden effizient gelöst. Um die thermodynamische Konsistenz sicherzustellen, insbesondere negative Inkremente der Schädigungsentwicklung zu vermeiden, und unverzerrte statistische Mittel-werte zu erhalten, werden klassische Gaußsche Prozesse durch Weibull-Verteilungen substituiert. Für hochzyklische Belastungen werden „jumping-cycle“ Algorithmen hinsichtlich der Extrapolations-strategien systematisch untersucht. Am Beispiel eines Betonträgers unter Biegebeanspruchung wird das Ermüdungsverhalten simuliert und mit experimentellen Ergebnissen aus der Literatur und empirischen Formeln vergleichen. Der vorgeschlagene Modellierungsansatz zeigt eine gute Übereinstimmung der Mittelwerte und Standardabweichungen mit den publizierten Erkenntnissen. Wenngleich die hier verwendeteWeibull-Statistik im strengen mathematischen Sinne nicht konsistent sein sollte, hat sich diese jedoch als physikalisch konsistent erwiesen, um streuende Ermüdungsschädigung effizient zu beschreiben

A damage detection and location scheme for offshore wind turbine jacket structures based on global modal properties

Abstract Structural failures of offshore wind substructures might be less likely than failures of other equipments of the offshore wind turbines, but they pose a high risk due to the possibility of catastrophic consequences. Significant costs are linked to offshore operations, like inspections and maintenance activities, thus remote monitoring shows promise for a cost-efficient structural integrity management. This work aims to investigate the feasibility of a two-level detection, in terms of anomaly identification and location, in the jacket support structure of an offshore wind turbine. A monitoring scheme is suggested by basing the detection on a database of simulated modal properties of the structure for different failure scenarios. The detection model identifies the correct anomaly based on three types of modal indicators, namely, natural frequency, the modal assurance criterion between mode shapes, and the modal flexibility variation. The supervised Fisher's linear discriminant analysis is applied to transform the modal indicators to maximize the separability of several scenarios. A fuzzy clustering algorithm is then trained to predict the membership of new data to each of the scenarios in the database. In a case study, extreme scour phenomena and jacket members' integrity loss are simulated, together with variations of the structural dynamics for environmental and operating conditions. Cross-validation is used to select the best hyperparameters, and the effectiveness of the clustering is validated with slight variations of the environmental conditions. The results prove that it is feasible to detect and locate the simulated scenarios via the global monitoring of an offshore wind jacket structure

The Second Joint NASA/FAA/DOD Conference on Aging Aircraft

The purpose of the Conference was to bring together world leaders in aviation safety research, aircraft design and manufacturing, fleet operation and aviation maintenance to disseminate information on current practices and advanced technologies that will assure the continued airworthiness of the aging aircraft in the military and commercial fleets. The Conference included reviews of current industry practices, assessments of future technology requirements, and status of aviation safety research. The Conference provided an opportunity for interactions among the key personnel in the research and technology development community, the original equipment manufacturers, commercial airline operators, military fleet operators, aviation maintenance, and aircraft certification and regulatory authorities. Conference participation was unrestricted and open to the international aviation community

Stochastic analysis of guided wave structural health monitoring for aeronautical composites

This thesis presents new methods developed for improvement of the reliability of Guided Wave Structural Health Monitoring (GWSHM) systems for aeronautical composite. Particular attention is devoted to the detection and localisation of barely visible impact damage (BVID) in Carbon-Fibre Reinforced Polymer (CFRP) structures. A novel sensor installation method is developed that offers ease of application and replacement as well as excellent durability. Electromechanical Impedance (EMI) is used to assess the durability of the sensor installation methods in simulated aircraft operational conditions, including thermal cycles, fatigue loading and hot-wet conditions. The superiority of the developed method over existing installation methods is demonstrated through extensive tests. Damage characterisation using GWSHM is investigated in different CFRP structures. Key issues in guided wave based damage identification are addressed, including wave mode /frequency selection, the influence of dynamic load, the validity of simulated damage, sensitivity of guided wave to impact damage in different CFRP materials. Identification of barely visible impact damage (BVID) are investigated on three simple CFRP panels and two stiffened CFRP panels. BVID is detected using three different damage index and located using RAPID, Delay-and-sum, Rayleigh maximum likelihood estimation (RMLE) and Bayesian inference (BI). The influence of temperature on guided wave propagation in anisotropic CFRP structures is addressed and a novel baseline reconstruction approach for temperature compensation is proposed. The proposed temperature compensation method accommodates various sensor placement and can be established using coupon level structures for the application in larger scale structures. Finally, a multi-level hierarchical approach is proposed for the quantification of ultrasonic guided wave based structural health monitoring (GWSHM) system. The hierarchical approach provides a systemic and practical way of establishing GWSHM systems for different structures under uncertainties and assessing system performance. The proposed approach is demonstrated in aircraft CFRP structures from coupon level to sub-component level.Open Acces

Verification and validation of physics-based models for structural health monitoring

Structural health monitoring (SHM) refers to a group of methods through which engineers aim to infer the health state of a piece of engineering infrastructure given some measured data from that structure. These inferences are then used to inform ongoing maintenance strategies to ensure that the structure remains operating in its optimal condition, without compromising performance or safety. Given the age of much of the existing infrastructure around the world, life-extending technology such as SHM is clearly of significant potential benefit, and the use of SHM when developing new infrastructure offers the potential for maximising the return on the investment in that infrastructure. SHM strategies can generally be divided into two types. Data-driven SHM uses data taken from the structure of interest to train a statistical model, such that the trained model will be able to label new data from the structure as being indicative of a particular health state. Model-driven SHM traditionally uses a physics-based model that can be adjusted to fit its predictions to live data taken from the structure, such that the adjustments to the model inputs give some indication of the structure's health state. Data-driven SHM methods are reliant on large sets of training data on which to develop statistical models; this is often difficult to acquire in practice, particularly when data are required from a structure in its damaged states. This issue can be mitigated by using a physics-based model to simulate the training data for the statistical model; however, in order for the physics-based model predictions to be considered trustworthy, the model must be validated against experimental data. This means that damage-state data are still required for the implementation of physics-based models in SHM in order to ensure the accuracy of their predictions. The acquisition of damage-state data from structures for this purpose therefore presents a significant set of problems for SHM methods. It is possible to reduce the difficulties associated with model validation for SHM by carrying out validation on models of the subassemblies and components that make up a larger assembly structure. The data required for this hierarchical validation task should be easier to acquire cheaply compared to validation data drawn from the full structure. If the submodels representing these individual substructures can each be validated then it should be possible to recover an assembly-level model which, given the validation tasks carried out, could be used to make confident predictions regarding the structure in a range of health states. A framework is presented in this thesis which summarises the activities required to carry out this hierarchical validation strategy, which would then enable a model to be developed with demonstrable accuracy and quantifiable uncertainty in its predictions. This framework is applied to a target structure { a truss bridge { and the model is used to carry out a series of SHM tasks on test data drawn from the structure. These tasks are carried out using the validated model in a forward manner to generate training data for statistical damage recognition models, which are then compared { in terms of performance { to traditional data-driven methods. Based on the research presented in this thesis, it is shown that model uncertainty can be accurately quantified through the hierarchical validation process by comparing the model predictions to the experimental test data before and after the validation process. After this it is shown that it is possible to develop accurate damage classification algorithms using validated model predictions, with the SHM methods developed via the hierarchical validation framework performing favourably compared to traditional data-driven methods when exposed to the test data. Further research areas that would advance the methodologies presented in the thesis are then outlined following discussions of the results

Practical reliability. Volume 3 - Testing

Application of testing to hardware program