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Defining a Reduced Volume Zone for the Simulation of Burst Test on a Composite Pressure Vessels
(2018)
The random nature of fibre break in composite materials has to be modelled in all part of the structure as it may contain millions of fibres. The reduced volume method was introduced to determine a smaller zone where we could characterise the strength based on the accumulation of fibre break, hence increasing the computation time
Defining a reduced volume zone for the simulation of burst test on composite pressure vessels
(2018)
The developed model uses a Monte-Carlo simulation to evaluate the accumulation of fibre breaks on composite structures. As the micromechanical aspects affecting the accumulation process is evaluated within a particular finite element size (0.1 mm x 0.1 mm x 8 mm), the industries would not be able to use the model on a real-size structure. Therefore, the reduced volume method is used to provide the statistical information between the required number of elements and simulations.
The aims of the Research Unit „Acting Principles of Nano-Scaled Matrix Additives for Composite Structures“ (DFG FOR 2021) are based on different synergetic pathways. Challenges are to achieve an improved damage tolerance combined with unchanged processability and a proof of the nano-based effect from molecular scale up to structural level. First of all, a comprehensive understanding of the acting mechanisms of nano-scaled ceramic additives onto polymer matrices of continuous fibre reinforced polymer composites with respect to improved matrix dominated properties is in focus. To proof of the nanoscopic and microscopic effects up to structural level; experimental investigations start on the functional correlation between the particle properties and the resulting properties of the epoxy as suspension and in the solid state. This includes tests for the resulting composite structures as well. Along the entire process chain different multi-scale simulations are performed from molecular modelling up to the macroscopic, structural level. The combination of experimental investigations and simulation methods enables a holistic understanding of the acting principles and basic mechanisms.
Specialized techniques based on Scanning Force Microscopy are the basis of our analysis of physicochemical properties of the boehmite nanoparticles and their polymer environment. A surface map of mechanical properties as an input for simulations facilitate a deeper understanding of such composites across all scales. This enables us to understand the macroscopic structure-property relationship and to predict failure mechanisms as well as routes for optimization.
Understanding on how a machine learning model interprets data is a crucial step to verify its reliability and avoid overfitting. While the focus of the scientific community is nowadays orientated towards deep learning approaches, which are considered as black box approaches, this work presents a toolbox that is based on complementary methods of feature extraction and selection, where the classification decisions of the model are transparent and can be physically interpreted. On the example of guided wave benchmark data from the open guided waves platform, where delamination defects were simulated at multiple positions on a carbon fiber reinforced plastic plate under varying temperature conditions, the authors could identify suitable frequencies for further investigations and experiments. Furthermore, the authors presented a realistic validation scenario which ensures that the machine learning model learns global damage characteristics rather than position specific characteristics.
Understanding on how a machine learning model interprets data is a crucial step to verify its reliability and avoid overfitting. While the focus of the scientific community is nowadays orientated towards deep learning approaches, which are considered as black box approaches, this work presents a toolbox that is based on complementary methods of feature extraction and selection, where the classification decisions of the model are transparent and can be physically interpreted. On the example of guided wave benchmark data from the open guided waves platform, where delamination defects were simulated at multiple positions on a carbon fiber reinforced plastic plate under varying temperature conditions, the authors could identify suitable frequencies for further investigations and experiments. Furthermore, the authors presented a realistic validation scenario which ensures that the machine learning model learns global damage characteristics rather than position specific characteristics.
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.
Structural Health Monitoring of composite materials gains in importance with the increasing use of Composite structures for safety-related applications, e.g. in aerospace. Because of the material properties of composites, defects often occur inside the structures and raise the demand for integrated sensors. The advantages of optical fibres predestine them not only for the surface-application on composite structures but also for the Integration into composite structures. Even without quasi-distributed sensors such as FBGs or LPGs, the optical fibre itself can be used to measure the structure and occurring events over the entire fibre length by distributed sensing techniques. This paper presents the surface-application of polyimide coated silica optical fibres onto PEEK specimens and the integration of copper and polyimide coated silica optical fibres into an AS-4/PEEK Composite to be used as distributed fibre optic sensors for SHM. Results from distributed measurements by optical backscatter reflectometry based on Rayleigh backscattering are shown.
Glass-fibre-reinforced plastics (GFRP) are commonly used for the construction of pressure vessels and tanks for example in automotive and aerospace industries. Especially for pressure vessels used for the storage of natural gas or hydrogen with operation pressures up to 700 bar, an early material fatigue detection is of great interest. Measuring the distributed strain profile of GFRP structures helps to understand and detect material fatigue. In this article, we demonstrate the great potential of swept wavelength interferometry (SWI) based distributed strain sensing for the monitoring of pressure vessels made from GFRP. A resin transfer molding (RTM) process was used to manufacture GFRP-tube specimens. Commercial optical fibres with polyimid coating were glued to the surface externally in circumferential and axial direction. A cyclic load of up to 150 bar was applied to the samples using a servo-hydraulic test bench. Comparing the loaded and unloaded test conditions, we determined up to 2 % elongation in circumferential direction. We demonstrate reliable distributed strain measurements with sub-centimetre spatial resolution. By monitoring these high-resolution strain profiles, we were able to detect local material degradation that manifested itself as localized strain changes. Crucially, the material Degradation could be detected already after 75 % of dat the fatigue life before a crack appeared that led to leakage.
Fibre-reinforced plastics (FRP) especially carbon-fibre-reinforced polymer (CFRP) and glass-fibre-reinforced polymer (GFRP) are commonly used materials in high pressure vessels and storage units for automotive and aerospace purposes. Optical fibres are suitable to be integrated or directly applied to the surface of FRP components. Using optical fibres it is possible to monitor the distributed strain profiles and changes within the fatigue life of a pressure vessel to ensure the operational safety. Within artificial ageing experiments we used swept wavelength interferometry (SWI) based distributed strain sensing for the monitoring of commercial high-pressure composite cylinder. This artificial ageing was performed using test conditions of 503bar pressure load (service pressure 300 bar) and 89 °C for 100 h. The polyimide coated optical fibres were glued to the surface externally in circumferential and axial direction. Using distributed strain sensing (DSS) material expansion of over 0.5% were monitored with sub-centimetre spatial resolution. Within the circumferential direction we observed up to 10 % local fluctuation compared to the median strain caused by inhomogeneous material expansion, which could cause local material fatigue. In addition, we determined material degradation manifested itself as localized remaining material expansion and/or contraction. Results have been validated by other non-destructive methods like digital strip projection.
Glass-fibre-reinforced plastics (GFRP) are commonly used for the construction of pressure vessels and tanks for example in automotive and aerospace industries. Especially for pressure vessels used for the storage of natural gas or hydrogen with operation pressures up to 700 bar, an early material fatigue detection is of great interest. Measuring the distributed strain profile of GFRP structures helps to understand and detect material fatigue. In this article, we demonstrate the great potential of swept wavelength interferometry (SWI) based distributed strain sensing for the monitoring of pressure vessels made from GFRP. A resin transfer molding (RTM) process was used to manufacture GFRP-tube specimens. Commercial optical fibres with polyimid coating were glued to the surface externally in circumferential and axial direction. A cyclic load of up to 150 bar was applied to the samples using a servo-hydraulic test bench. Comparing the loaded and unloaded test conditions, we determined up to 2 % elongation in circumferential direction. We demonstrate reliable distributed strain measurements with sub-centimetre spatial resolution. By monitoring these high-resolution strain profiles, we were able to detect local material degradation that manifested itself as localized strain changes. Crucially, the material Degradation could be detected already after 75 % of dat the fatigue life before a crack appeared that led to leakage.