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Composite pressure vessels are important components in the storage of gases under high pressure. Among others, a common type of pressure vessel is made of a metal liner overwrapped with a fibre-reinforced plastic material. Conventional hydrostatic tests, used to assess the integrity of pressure vessels, may overstress the material and thus reduce the remaining lifetime of the tested component. Therefore, a truly non-destructive structural health monitoring (SHM) system would not only ensure a safer usage and extended lifetime, but also remove the necessity for periodic inspection and the testing of pressure vessels. The authors propose the use of guided ultrasonic waves, which have the potential to detect the main damage types, such as cracking in the metal liner, fibre breaks and composite matrix delamination. For the design of such an SHM system, multimodal ultrasonic wave propagation and defect-mode interaction must be fully understood. In this paper, simulation results obtained by means of finite element modelling (FEM) are presented. Based on the findings, suggestions are made regarding appropriate wave modes and their interaction with different flaw types, as well as the necessary excitation and suitable sensor configurations. Finally, a first approach for a reliable SHM system for composite pressure vessels is suggested.
Die Scaled Boundary Finite Elemente Methode (SBFEM) ist eine semi-analytische Methode, die speziell für Modellierung von geführten Wellen weiterentwickelt und optimiert wurde. Da nur den Rand der Rechendomäne diskretisiert wird, hat die SBFEM einen geringen Rechenaufwand. In diesem Beitrag wird die SBFEM benutzt, um die Ausbreitung geführter Wellen in einer Metall-Faserverbund-Werkstoffstruktur zu analysieren. Mittels der SBFEM ist es möglich, verschiede Fehlertypen, z.B. Ermüdungsrisse, Poren, Delaminationen, Korrosion, in das numerische Modell zu integrieren und damit Defekt-Mode-Wechselwirkung zu analysieren. Die Ergebnisse wurden für die Entwicklung einer Methode zur Zustandsüberwachung von Composite-Druckbehältern verwendet.
Guided waves cover comparably long distances and thus allow for online structural health monitoring of safety relevant components, e.g. lightweight composite overwrapped pressure vessels (COPV) as used for the transportation of pressurised gases. Reliable non-destructive assessment of COPVs’ condition is not available yet due to their complex composite structure comprising a thin metal liner and a fibre reinforced plastics (FRP) overwrap. The conventional overload hydrostatic pressure testing used for the metal vessels is not suitable for the composite vessels, because it may damage the FRP overwrap reducing the service life of the COPV. Therefore, ISO and CEN defined a maximum service life of composite pressure vessels as of 15 to 20 years. To extend the COPVs’ service life and to ensure a safer usage a structural health monitoring system based on guided ultrasonic waves is to be developed.
In this contribution first results of guided waves propagation in a flat composite plate consisting of an aluminium layer firmly bonded to a carbon fibre reinforced plastic laminate are presented. Based on experimental results material properties of FRP are reconstructed by means of the Scaled Boundary Finite Element Method (SBFEM).
The Scaled Boundary Finite Element Method (SBFEM) is a semi-analytical method that shows promising results in modelling of guided ultrasonic waves. Efficiency and low computational cost of the method are achieved by a discretisation of the boundary of a computational domain only, whereas for the domain itself the analytical solution is used. By means of the SBFEM different types of defects, e.g. cracks, pores, delamination, corrosion, integrated into a structure consisting of anisotropic and isotropic materials can be modelled.
In this contribution, the SBFEM is used to analyse the propagation of guided waves in a structure consisting of an isotropic metal bonded to anisotropic carbon fibre reinforced material. The method allows appropriate wave types (modes) to be identified and to analyse their interaction with different defects. Results obtained are used to develop a structural health monitoring system for composite pressure vessels used in automotive and aerospace industries.
Composite pressure vessels are important components for storing gases under high pressure. Beside others, a common type of pressure vessel is made of a metal liner overwrapped with a fibre reinforced plastic material. Conventional hydrostatic tests, used to assess the integrity of pressure vessels, may overstress the material, and thus, may reduce the remaining life-time of the tested component. Therefore, a truly nondestructive structural health monitoring (SHM) system would not only allow to ensure a safer usage and extended life-time, but also to exclude the necessity of the periodic inspection and testing of pressure vessels.
We propose to use guided ultrasonic waves which have a potential to detect the main damage types such as cracking in the metal liner, fibre breaks and composite Matrix delamination. For designing such a SHM system, the multimodal ultrasonic wave propagation and the defect-mode interaction must be fully understood.
In this contribution, we present simulation results obtained by means of finite element modelling. Based on the findings, suggestions about appropriate wave modes, their interaction with different flaw types as well as the necessary excitation and suitable sensor configuration are made. Finally, we suggest a first approach of a reliable SHM system for composite pressure vessels.
Lamb waves are widely used for non-destructive evaluation of material parameters as well as for detection of defects. Another application of Lamb waves is quality control of adhesive joints.
Researchers are currently investigating shear horizontal and zero-group velocity modes for characterisation of the adhesive bonding strength. In a new approach, Lamb wave mode repulsion is used to obtain the coupling strength between different layers to characterise the adhesive bonding strength. The modes of the individual layers become coupled in the multilayered systems forming particular regions, the so-called mode repulsion regions. This study investigates these modes and their interaction in two-layered plate-like structures with varying coupling strength both numerically, with the Scaled Boundary FEM, and experimentally
Guided waves (GW) are of great interest for non-destructive testing (NDT) and structural health monitoring (SHM) of engineering structures such as for oil and gas pipelines, rails, aircraft components, adhesive bonds and possibly much more. Development of a technique based on GWs requires careful understanding obtained through modelling and analysis of wave propagation and mode-damage interaction due to the dispersion and multimodal character of GWs. The Scaled Boundary Finite Element Method (SBFEM) is a suitable numerical approach for this purpose allowing calculation of dispersion curves, mode shapes and GW propagation analysis. In this article, the SBFEM is used to analyse wave propagation in a plate consisting of an isotropic aluminium layer bonded as a hybrid to an anisotropic carbon fibre reinforced plastics layer. This hybrid Composite corresponds to one of those considered in a Type III composite pressure vessel used for storing gases, e.g., hydrogen in automotive and aerospace applications. The results show that most of the wave energy can be concentrated in a certain layer depending on the mode used, and by that damage present in this layer can be detected. The results obtained help to understand the wave propagation in multi-layered structures and are important for further development of NDT and SHM for Engineering structures consisting of multiple layers.
Issues that prevent Structural Health Monitoring (SHM) based on Guided Waves (GW) from being a part of today’s monitoring solutions in industry are not all obvious to the scientific community. To uncover and overcome these issues, scientists working on SHM and GW problems joined in an expert committee under the patronage of the German Society for Non-Destructive Testing. An initiated online survey among more than 700 experts and users reveals the hurdles hindering the practical application of GWbased SHM. Firstly, methods for proof of reliability of SHM approaches are missing.
Secondly, detailed understanding of phenomenological described wave-damage interactions is needed. Additionally, there are significant unsolved implementation issues and unsolved problems of signal processing including handling of environmental influences.
To enable substantial proof of reliability without unaffordable experimental effort also efficient simulation tools including realistic damage interaction are needed, enabling the joint use of experimental and simulated data to predict the capabilities of the Monitoring system. Considering these issues, the committee focusses on simulation, signal processing, as well as probability of detection and standardization. In the presented work, recent activities of the expert committee starting with survey results are summarized. An open access data basis of life-like measurements is presented to allow testing and comparison of signal processing and simulation algorithms. Finally, a strategy for efficient proof of reliability increasing the acceptance of SHM in industry and for successful Integration of SHM into real-world engineering structures is proposed.
Data-driven analysis for damage assessment has a large potential in structural health monitoring (SHM) systems, where sensors are permanently attached to the structure, enabling continuous and frequent measurements. In this contribution, we propose a machine learning (ML) approach for automated damage detection, based on an ML toolbox for industrial condition monitoring. The toolbox combines multiple complementary algorithms for feature extraction and selection and automatically chooses the best combination of methods for the dataset at hand. Here, this toolbox is applied to a guided wave-based SHM dataset for varying temperatures and damage locations, which is freely available on the Open Guided Waves platform. A classification rate of 96.2% is achieved, demonstrating reliable and automated damage detection. Moreover, the ability of the ML model to identify a damaged structure at untrained damage locations and temperatures is demonstrated.
Damit eine Simulationsrechnung, beispielsweise mit einer FEM-Software, eine ausreichend hohe Genauigkeit erreicht, muss vorausgesetzt werden, dass die Modellparameter eine sehr hohe Güte aufweisen. Die genaue Kenntnis der Materialparameter ist dabei von besonderer Bedeutung. Um diese Parameter bestimmen zu können, müssen die verwendeten Werkstoffe messtechnisch charakterisiert werden. Neben anderen Ansätzen sind dafür akustische Verfahren im Ultraschallbereich geeignet. Für dünnwandige und plattenförmige Materialien können aus den sich ausbreitenden geführten Wellen messtechnisch Dispersionskurven bestimmt und aus diesen die Materialparameter abgeleitet werden.
Da für die Signalverarbeitung und für Optimierungsaufgaben aktuell zunehmend Machine Learning Tools zum Einsatz kommen, stellt sich die Frage, ob diese Werkzeuge auch für die Ermittlung der Materialparameter aus den gemessenen Dispersionskurven eingesetzt werden können.
In der vorgestellten Untersuchung soll ein Convolutional Neural Network aufgestellt werden, welches aus Dispersionsbildern Muster extrahiert und aus diesen eine Schätzung für die Materialparameter ermittelt. Um die Machbarkeit dieses Ansatzes zu prüfen, werden zunächst nur isotrope Materialien betrachtet. Für das Netz werden mit der Scaled-Boundary-Finite-Element-Methode synthetische Daten für das Trainieren und Validieren generiert. Zusätzlich werden die Hyperparameter des neuronalen Netzes variiert, um ein optimales Model für die Schätzung zu finden. Anschließend kann das Netz mit experimentellen Daten getestet und das Ergebnis hinsichtlich der Genauigkeit bewertet werden.