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Hydrogen pressure vessels are among the most essential components for reliable hydrogen technology. Under current regulations, a mostly conservative strategy is employed, restricting the usage time of hydrogen pressure vessels without providing information on the real remaining lifetime. During the service life, pressure vessels are inspected periodically. However, no established method that can provide continuous monitoring or information on the remaining safe service life of the vessel. In this paper, we propose a sensor network for Structural Health Monitoring (SHM) of hydrogen pressure vessels where data from all sensors are collected and centrally evaluated. Specifically, we integrate three different SHM sensing technologies namely Guided Wave ultrasonics (GW), Acoustic Emission testing (AT), and distributed Fiber Optic Sensing (FOS). This integrated approach offers significantly more information and could therefore enable a transition from costly and time-consuming periodic inspections to more efficient and modern predictive maintenance strategies, including Artificial Intelligence (AI)-based evaluation. This does not only have a positive effect on the operational costs but enhances safety through early identification of critical conditions in the overall system in real-time. We demonstrate an experimental set-up of a lifetime test where a Type IV Composite Overwrapped Pressure Vessel (COPV) is investigated under cyclic loading instrumented with AT, FOS, and GW methods. We acquired data from the sensor network until the pressure vessel failed due to material degradation. The data collected using the three different SHM sensor technologies is planned to be evaluated individually, using data fusion, and AI. In the future, we aim to integrate the measurement setup into a hydrogen refueling station with the data stream implemented into a digital signal processing chain and a digital twin.
The transition toward a hydrogen-based economy requires a widespread transport and distribution network, and repurposed natural gas pipelines are a viable option. An assessment of the hydrogen-induced degradation of pipeline steels is needed to inject H2 gas into the existing infrastructure safely. The conservative and standardized method consists of in-situ tensile tests in an autoclave filled with high-pressure hydrogen gas. A proposed alternative method involves
using a hollow specimen as containment volume and applying the gas pressure in the inner cavity.
This technique has lower costs and shorter test preparation time but is not standardized yet. This study aims to evaluate and compare the tensile properties of API 5L X65 pipeline steel in two states: vintage and modern. The influence of the surface roughness is investigated through parallel tests with drilled and reamed specimens. Hydrogen tests are compared with reference tests in an inert environment. A significant hydrogen-induced decrease in tensile properties is observed, and no significant difference between vintage and modern X65 can be drawn. The reduction in tensile properties is more significant in specimens with higher inner surface roughness. The evaluation of surface conditions appears crucial when assessing the HE susceptibility of hydrogen transport and storage equipment.
Structural health monitoring of hydrogen pressure vessels using distributed fiber optic sensing
(2024)
We report on distributed fiber optic sensing-based monitoring of hydrogen composite overwrapped pressure vessels (COPV) to simultaneously increase the operational lifespan and mitigate maintenance costs. Our approach represents, to the best of our knowledge, the first application of distributed fiber optic sensing for COPV Type IV monitoring, where the sensing fibers are attached to the surface, rather than integrated into the composite material. Specifically, we attach an optical fiber of 50 m to the pressure vessel's surface, covering both the cylindrical and dome sections. We note that our fiber optic sensing technique relies on swept wavelength interferometry providing strain information along the entire length of the optical fiber with high spatial resolution even at the millimeter scale. When the vessel is pressurized, the sensing optical fiber shows a linear strain response to pressure at every position along the fiber. After thousands of load cycles, the vessel finally fails with the optical fiber detecting and precisely localizing the damage in the vessel’s blind dome area. Furthermore, we discuss the potential of state-of-the-art signal processing methods and machine learning for advancing predictive maintenance. This could reduce the number of regular inspections, mitigate premature maintenance costs, and simultaneously increase the vessel’s remaining safe service life. We believe that the structural health monitoring of hydrogen pressure vessels with fiber optic sensors can enhance trust in hydrogen technology contributing to the energy transition in the future.
Modern dry pressing of ceramic powders using spray‐dried granulates cannot avoid the occurrence of defects related to persisting inter‐ and intra‐granulate interstitial voids. These constitute the parent defect size population limiting the application of polycrystalline ceramics in high‐stress conditions. The mitigation of such defects could widen the range of application in technical and biomedical engineering, reduce the safety range for design, and extend the lifetime of components. Herein, the Weibull size‐effect on strength in size‐partitioned Yttria‐stabilized zirconias (YSZ) feedstocks is used to explore the viability of changing the density distribution of granulate sizes as an effective strategy to obtain a denser particle packing that could reduce the size distribution of strength‐limiting pressing defects. In a direct assessment of critical defect size using multiscale strength testing with a dataset of ≈1300 values, the success of such an approach in increasing the strength reliability for small volume components is demonstrated, along with its ultimate failure in altering the defect size distribution in sintered YSZ ceramics across several length scales. Finally, it is shown that granule morphology (spherical or dimpled) fails to affect the defect density and size distribution in YSZ ceramics.
Compared to mechanisms like solid solution strengthening, the stacking fault energy (SFE) should be considered as a further factor that influences the material properties. The effect of SFE of alloys or individual elements on strength and resistance can vary considerably. In the high-temperature regime above 700 ◦C, there are still significant gaps in the knowledge about the effect of the SFE on the mechanical properties of single-phase alloys. The effect of SFE on creep resistance of two face-entered cubic equiatomic medium and high entropy alloys, CrCoNi and CrMnFeCoNi, respectively, is evaluated to fill parts of these gaps. Using the Bridgman solidification process, the alloys were produced as single crystals and crept under vacuum at 700 ◦C up to 1100 ◦C. This work shows a significant impact of the lower SFE of CrCoNi on the creep behavior compared to the results of previous investigations of CrMnFeCoNi. The creep resistance of the former is higher over the complete temperature range. At very high temperatures, the strengthening effect of the stacking faults is significantly present. The formation of tetragonal stacking faults and extended dislocation nodes can be identified as the reason for this effect.
Additively manufactured components are characterized by heterogeneous mechanical properties due to variations of the microstructure, flaws and residual stresses resulting from the inhomogeneous fabrication process. The large number of influencing factors poses a further challenge in understanding the correlation between material properties, process parameters and component geometry. Therefore, the qualification of components based on witness specimens produced within the same job is questionable. This work aims to present a new strategy for the characterization of PBF-LB/M components based on representative specimens. The key assumption is the feasibility of a transfer of the thermal history from a component to a specimen. It is assumed that similar material properties are determined for components and specimens produced adopting a similar thermal history. After the definition of a region of interest in the component, a combination of thermal analyses by means of finite elements and in-situ experimental determination of the thermal history through infrared thermography is used to produce test coupons with a similar thermal history. The effectiveness of the procedure is demonstrated on a pressure vessel for applications in the chemical industry.
Quantitative microstructural analysis of XCT 3D images is key for quality assurance of materials and components. In this paper we implement a Graph Convolutional Neural Network (GCNN) architecture to segment a complex Al-Si Metal Matrix composite XCT volume (3D image). We train the model on a synthetic dataset and we assess its performance on both synthetic and experimental, manually-labeled, datasets. Our simple GCNN shows a comparable performance, measured via the Dice score, to more standard machine learning methods, but uses a greatly reduced number of parameters (less than 1/10 of parameters), features low training time, and needs little hardware resources. Our GCNN thus achieves a cost-effective reliable segmentation.
The biogenesis of iron–sulfur (Fe/S) proteins entails the synthesis and trafficking of Fe/S clusters, followed by their insertion into target apoproteins. In eukaryotes, the multiple steps of biogenesis are accomplished by complex protein machineries in both mitochondria and cytosol. The underlying biochemical pathways have been elucidated over the past decades, yet the mechanisms of cytosolic [2Fe-2S] protein assembly have remained ill-defined. Similarly, the precise site of glutathione (GSH) requirement in cytosolic and nuclear Fe/S protein biogenesis is unclear, as is the molecular role of the GSH-dependent cytosolic monothiol glutaredoxins (cGrxs). Here, we investigated these questions in human and yeast cells by various in vivo approaches. [2Fe-2S] cluster assembly of cytosolic target apoproteins required the mitochondrial ISC machinery, the mitochondrial transporter Atm1/ABCB7 and GSH, yet occurred independently of both the CIA system and cGrxs. This mechanism was strikingly different from the ISC-, Atm1/ABCB7-, GSH-, and CIA-dependent assembly of cytosolic–nuclear [4Fe-4S] proteins. One notable exception to this cytosolic [2Fe-2S] protein maturation pathway defined here was yeast Apd1 which used the CIA system via binding to the CIA targeting complex through its C-terminal tryptophan. cGrxs, although attributed as [2Fe-2S] cluster chaperones or trafficking proteins, were not essential in vivo for delivering [2Fe-2S] clusters to either CIA components or target apoproteins. Finally, the most critical GSH requirement was assigned to Atm1-dependent export, i.e. a step before GSH-dependent cGrxs function. Our findings extend the general model of eukaryotic Fe/S protein biogenesis by adding the molecular requirements for cytosolic [2Fe-2S] protein maturation.
The presence of endocrine-disrupting compounds (EDCs) in water poses a significant threat to human and animal health, as recognized by regulatory agencies throughout the world. The Yeast Estrogen Screen (YES) assay is an excellent method to evaluate the presence of these compounds in water due to its simplicity and capacity to assess the bioaccessible forms/fractions of these compounds. In the presence of a compound with estrogenic activity, Saccharomyces cerevisiae cells, containing a lacZ reporter gene encoding the enzyme β-galactosidase, are induced, the enzyme is synthesised, and released to the extracellular medium. In this work, a YES-based approach encompassing the use of a lacZ reporter gene modified strain of S. cerevisiae, microcarriers as solid support, and a fluorescent substrate, fluorescein di-β-D-galactopyranoside, is proposed, allowing for the assessment of EDCs’ presence after only 2 h of incubation. The proposed method provided an EC50 of 0.17 ± 0.03 nM and an LLOQ of 0.03 nM, expressed as 17β-estradiol. The assessment of different EDCs provided EC50 values between 0.16 and 1.2 × 103 nM. After application to wastewaters, similar results were obtained for EDCs screening, much faster, compared to the conventional 45 h spectrophotometric procedure using a commercial kit, showing potential for onsite high-throughput screening of environmental contamination.
Peptide pools consist of short amino acid sequences and have proven to be versatile tools in various research areas in immunology and clinical applications. They are commercially available in many different compositions and variants. However, unlike other reagents that consist of only one or a few compounds, peptide pools are highly complex products which makes their quality control a major challenge. Quantitative peptide analysis usually requires sophisticated methods, in most cases isotope-labeled standards and reference materials. Usually, this would be prohibitively laborious and expensive. Therefore, an approach is needed to provide a practical and feasible method for quality control of peptide pools. With insufficient quality control, the use of such products could lead to incorrect experimental results, worsening the well-known reproducibility crisis in the biomedical sciences. Here we propose the use of ultra-high performance liquid chromatography (UHPLC) with two detectors, a standard UV detector at 214 nm for quantitative analysis and a high-resolution mass spectrometer (HRMS) for identity confirmation. To be cost-efficient and fast, quantification and identification are performed in one chromatographic run. An optimized protocol is shown, and different peak integration methods are compared and discussed. This work was performed using a peptide pool known as CEF advanced, which consists of 32 peptides derived from cytomegalovirus (CMV), Epstein–Barr virus (EBV) and influenza virus, ranging from 8 to 12 amino acids in length.