Wasserstoff
Filtern
Dokumenttyp
- Zeitschriftenartikel (11) (entfernen)
Sprache
- Englisch (11)
Schlagworte
- Neutron imaging (3)
- Fuel cell (2)
- Machine learning (2)
- Structural health monitoring (2)
- Artificial neural networks (1)
- BOFDA (1)
- BOTDA (1)
- COPV (1)
- Catalyst Layer (1)
- Composite Overwrapped Pressure Vessels (1)
Organisationseinheit der BAM
- 8 Zerstörungsfreie Prüfung (11) (entfernen)
In recent years, low-temperature polymer electrolyte fuel cells have become an increasingly important pillar in a zero-carbon strategy for curbing climate change, with their potential to power multiscale stationary and mobile applications. The performance improvement is a particular focus of research and engineering roadmaps, with water management being one of the major areas of interest for development. Appropriate characterisation tools for mapping the evolution, motion and removal of water are of high importance to tackle shortcomings.
This article demonstrates the development of a 4D high-speed neutron imaging technique, which enables a quantitative analysis of the local water evolution. 4D visualisation allows the time-resolved studies of droplet formation in the flow fields and water quantification in various cell parts. Performance parameters for water management are identified that offer a method of cell classification, which will, in turn, support computer modelling and the engineering of next-generation flow field designs.
The digitalization of quality control processes and the underlying data infrastructures for safety relevant components, such as hydrogen pressure vessels, plays a significant role in the transition towards Industry 4.0. In the current safety regulations for hydrogen pressure vessels, there is no established concept for structural health monitoring. The development of a reliable structural health monitoring methodology for monitoring the structural integrity of pressure vessels enables a fast-forward transition from personnel- and costintensive recurring inspections, a.k.a. periodic maintenance, to predictive maintenance. In the work presented; we investigated the application of ultrasonic guided wave propagation to monitor and assess the condition of Type IV composite overwrapped pressure vessel (COPV). A sensor network of fifteen piezo-electric wafers is placed on the carbon fibre reinforced composite cylinder. Five different artificial damage configurations are created by gluing two different weight blocks on three different locations. The database containing measured guided wave data sets is enriched by two different boundary conditions. We utilized an open-source software, openBIS labnotebook, to store and analyse experimental datasets. The guided wave ultrasonic signals were investigated and analysed by using commonly used ultrasonic features (e.g., amplitude, frequency, time of flight) as well as non-traditional time-series features (kurtosis, skewness, variance). The features were used to calculate damage index and the detection performance for the results has been evaluated. The results suggest that both traditional and non-traditional features assume significant importance in artificial damage detection. The future works will additionally involve the impacts of operational conditions, such as periodic pressure variations temperature loadings as well as material degradations.
The mechanical compression of metal foam flow-field based polymer electrolyte fuel cells (PEFCs) is critical in determining the interfacial contact resistance with gas diffusion layers (GDLs), reactant flow and water management.
The distinct scale between the pore structure of metal foams and the entire flow-field warrant a multilength scale characterization that combines ex-situ tests of compressed metal foam samples and in-operando analysis of operating PEFCs using X-ray computed tomography (CT) and neutron radiography. An optimal ‘medium’ compression was found to deliver a peak power density of 853 mW/cm². The X-ray CT data indicates that the compression process significantly decreases the mean pore size and narrows the pore size distribution of metal foams. Simulation results suggest compressing metal foam increases the pressure drop and gas velocity, improving the convective liquid water removal. This is in agreement with the neutron imaging results that demonstrates an increase in the mass of accumulated liquid water with minimum compression compared to the medium and maximum compression cases. The results show that a balance between Ohmic resistance, water removal capacity and parasitic power is imperative for the optimal performance of metal foam based PEFCs.
A systematic, nature-inspired chemical engineering approach is employed to solve the issue of flooding in electrochemical devices. The mechanism of passive water transport utilized by lizards living in arid environments is leveraged to design flow-fields with a microchannel structure on their surface, through which capillary pressure rapidly removes the water generated in the electrochemical device. This water management strategy is implemented in proton exchange membrane fuel cells (PEMFCs) with a lunginspired flow-field, which ensures uniform distribution of reactants across the catalyst layer. Jointly, this nature-inspired approach results in flood-free, stable operation at 100% RH and a B60% increase in current (B1.9 A cm-2) and peak power density (B650 mW cm−2) compared to current PEMFCs with a flood-prone, serpentine flow-field (B0.8 A cm-2 and 280 mW cm-2, respectively). This significant advance allows for PEMFC operation at fully humidified conditions.
The application of composite overwrapped pressure vessels (COPV) to store hydrogen and other compressed gases, especially when operating at high pressures, imposed the need for an innovative and reliable approach to ensure the safe operation of the system. Continuous structural health monitoring (SHM) based on ultrasonic guided waves (GWs) is a promising approach due to the ability of the wave to propagate for long distances and go around complex structures, moreover the high sensitivity to various failure modes such as delamination, matrix cracking and debonding. In this study, we scrutinize the behavior of (GWs) within the COPV using a network of PZT elements that are used for excitation and sensing. A laser doppler vibrometer (LDV) was also used to scan the surface of the vessel in various directions. L(0,1) and L(0,2) were observed in the captured signal. The L(0, 2) appeared to be a dominant mode in the COPV and was capable of propagating along the entire length and maintaining a good signal-to-noise ratio. The L(0,2) mode maintained the same phase velocity when it is captured at various excitation angles (0, 45, and 90 degrees). The reduced effect of the orthotropy of the materials on the propagating waves is an important result as it will reduce the complexity in data processing when performing damage identification.
This work describes the effects of catalyst layers (CLs) consisting of hydrophobic PTFE on the performance and water management of PEM fuel cells. Catalyst inks with various PTFE contents were coated on Nafion membranes and characterized using contact angle measurements, SEX-EDX, and mercury porosimetry. Fuel cell tests and electrochemical impedance spectroscopy (EIS) were conducted under varying operating conditions for the prepared materials. At dry conditions, CLs with 5 wt.% PTFE were advantageous for cell performance due to improved membrane hydration, whereas under humid conditions and high air flow rates CLs with 10 wt.% PTFE improved the performance in high current density region. Higher PTFE contents (⩾20 wt.%) increased the mass transport resistance due to reduced porosity of the CLs structure. Operando neutron radiography was utilized to study the effects of hydrophobicity gradients within CLs and cathode microporous layer (MPLC) on liquid water distribution. More hydrophobic CLs increased the water content in adjacent layers and improved performance, especially at dry conditions. MPLC with higher PTFE contents increased the overall liquid water within the CLs and GDLs and escalated the water transfer to the anode side. Furthermore, the role of back-diffusion transport mechanism on water distribution was identified for the investigated cells.
Distributed fiber optic sensors for structural health monitoring of composite pressure vessels
(2024)
In diesem Beitrag geben wir einen umfassenden Überblick über unsere Forschung auf dem Gebiet der verteilten faseroptischen Sensorik für die strukturelle Zustandsüberwachung von Wasserstoffdruckbehältern aus Verbundwerkstoffen. Insbesondere zeigen wir, wie die Integration von faseroptischen Sensoren in Druckbehälter aus Verbundwerkstoffen die Sicherheit erhöht und gleichzeitig die Wartungskosten senkt. Die geringe Größe von Lichtwellenleitern ermöglicht ihre Integration in Verbundwerkstoffstrukturen während des Herstellungsprozesses, wodurch eine kontinuierliche Überwachung sowie eine präzise Erkennung und Lokalisierung von Strukturschäden während des Betriebs der Druckbehälter ermöglicht wird. Wir erörtern auch das Potenzial modernster Signalverarbeitungsmethoden und des maschinellen Lernens für die Weiterentwicklung der vorausschauenden Instandhaltung. Die von uns vorgestellten Anwendungen von faseroptischen Sensoren zeigen, dass sie einen wichtigen Beitrag zur Energiewende hin zu erneuerbaren Energien leisten können.
This paper presents reported machine learning approaches in the field of Brillouin distributed fiber optic sensors (DFOSs). The increasing popularity of Brillouin DFOSs stems from their capability to continuously monitor temperature and strain along kilometer-long optical fibers, rendering them attractive for industrial applications, such as the structural health monitoring of large civil infrastructures and pipelines. In recent years, machine learning has been integrated into the Brillouin DFOS signal processing, resulting in fast and enhanced temperature, strain, and humidity measurements without increasing the system’s cost. Machine learning has also contributed to enhanced spatial resolution in Brillouin optical time domain analysis (BOTDA) systems and shorter measurement times in Brillouin optical frequency domain analysis (BOFDA) systems. This paper provides an overview of the applied machine learning methodologies in Brillouin DFOSs, as well as future perspectives in this area.
Phosphoric acid as the electrolyte in high-temperature polymer electrolyte membrane fuel cell plays an essential role in ist performance and lifetime. Maldistribution of phosphoric acid in the catalyst layer (CL) may result in performance degradation. In the present study, pore-scale simulations were carried out to investigate phosphoric acid’s multiphase flow in a cathode CL. A reconstructed CL model was built using focused ion beam-SEM images, where distributions of pore, carbon support, binder, and catalyst particles can be identified. The multi-relaxation time lattice Boltzmann method was employed to simulate phosphoric Acid invading and leaching from the membrane into the CL during the membrane electrode assembly fabrication process. The predicted redistribution of phosphoric acid indicates that phosphoric acid of low viscosity or low wettability is prone to leaching into the CL.
The effective transport properties and the active electrochemical active surface area (ECSA) were computed using a pore-scale model. They were subsequently used in a macroscopic model to evaluate the cell performance. A parametric study shows that cell performance first increases with increasing phosphoric acid content due to the increase of ECSA. However, further increasing phosphoric acid content results in performance degradation due to mass transfer limitation caused by acid flooding.
The applications of composite overwrapped pressure vessels (COPVs) in extreme conditions, such as storing hydrogen gases at very high pressure, impose new requirements related to the system's integrity and safety. The development of a structural health monitoring (SHM) system that allows for continuous monitoring of the COPVs provides rich information about the structural integrity of the component. Furthermore, the collected data can be used for different purposes such as increasing the periodic inspection intervals, providing a remaining lifetime prognosis, and also ensuring optimal operating conditions. Ultimately this information can be complementary to the development of the envisioned digital twin of the monitored COPVs. Guided waves (GWs) are preferred to be used in continuous SHM given their ability to travel in complex structures for long distances. However, obtained GW signals are complex and require advanced processing techniques. Machine learning (ML) is increasingly utilized as the main part of the processing pipeline to automatically detect anomalies in the system's integrity. Hence, in this study, we are scrutinizing the potential of using ML to provide continuous monitoring of COPVs based on ultrasonic GW data. Data is collected from a network of sensors consisting of fifteen Piezoelectric (PZT) wafers that were surface mounted on the COPV. Two ML algorithms are used in the automated evaluation procedure (i) a long short-term memory (LSTM) autoencoder for anomaly detection (defects/impact), and (ii) a convolutional neural network (CNN) model for feature extraction and classification of the artificial damage sizes and locations. Additional data augmentation steps are introduced such as modification and addition of random noise to original signals to enhance the model's robustness to uncertainties. Overall, it was shown that the ML algorithms used were able to detect and classify the simulated damage with high accuracy.