Composite Overwrapped Pressure Vessels Research Articles

Automated manufacture and experimentation of composite overwrapped pressure vessel with embedded optical sensor

In recent years, the mobility (air/land) industry has been developing fuel-cell vehicles for mass adoption as hydrogen technology matures. Compared to conventional metallic design, the composite overwrapped pressure vessel without a metal or polymer liner results in a lightweight solution for gaseous hydrogen storage which further improves fuel efficiencies. Since the capabilities of automated fiber placement for making multi-stiffened structures are distinct from other automated manufacturing techniques, this study aimed to evaluate the feasibility of implementing AFP into the manufacture of a small scale COPV with a partially composite liner. The manufacturing related challenges are identified and discussed. During the manufacturing process, Distributed fiber optic sensor was embedded between the plies, as a structural health monitoring solution, to monitor the mechanical performance of the pressurized tank. In addition, finite element study was done to predict the internal strains for different pressure levels and the measured experimental strains at the sensor's location are found to be in close agreement with the finite element results. It is shown that such sensors can be integrated into automated fiber placement manufactured tanks for internal strain monitoring.

Experimental and numerical investigation of radial displacement of COPV using CT and FPP method

The radial displacement of hydrogen storage composite overwrapped pressure vessel (COPV) is a key factor related to the collapse failure and burst pressure. However, there is still lack of effective method to monitor the radial displacement during the hydrostatic test and service process of the vessels. This paper proposed a method to measure the radial displacement based on fringe projection profilometry (FPP). Hydrostatic test with a composite overwrapped pressure vessel verifies the proposed method. The variation of displacement changing with inner pressures observed in the experiments is in agreement with that obtained from finite element analysis. Also Computed Tomography (CT) scanning is conducted and a widely spread micro defects is found in the composite layer of the cylinder. 78.9% of the total observed defects are small (<1000 mm2). In regions where large-sized defects (>1000 mm2) are concentrated, the total area of defects reaches 16,200 mm2. According to the statistical analysis, the strong correlation is found between the defects area and radial displacement.

Modeling, fabrication, and burst testing of type-IV 3D printed plastic liner composite overwrapped pressure vessels

Modeling, fabrication, and burst testing of type-IV 3D printed plastic liner composite overwrapped pressure vessels

The optimal design of composite overwrapped pressure vessels based on geodesic dome trajectories using population-based techniques

Pressure vessels are frequently fabricated using composite materials which stand out with their high strength-to-weight ratio, and provide considerable advantages compared to conventional steel ones. In our previous study, the impacts of Polar Opening Radius (POR) on the mechanical characteristics of Composite Overwrapped Pressure Vessels COPVs were numerically examined, and it was determined that effective volume and structural weight responses improved with higher POR, but significant deterioration took place in first-ply interlaminar shear stress (ILSS) responses after 20 mm POR. Therefore, to achieve the best possible mechanical and structural behaviors, the present study aimed to discover an optimum POR that provides maximum internal volume as well as minimum weight and ILSS responses. In this regard, the optimum geodesic dome trajectory based on the 10–30 mm POR range was determined using the Bees Algorithm (BA) and the Particle Swarm Optimization (PSO). According to the findings, the optimum POR was found as 17.9643 and 17.9671 mm due to the BA and PSO algorithms, respectively. Moreover, first-ply ILSS, structural weight and effective internal volume for the optimal COPVs design were achieved as 52.444 MPa, 1070.81 g, and 2721.94 cm3 from the BA, and thus COPVs with maximum strength and minimum structural weight has been designed. In this way, thanks to the optimization studies, an approximately 18.31% improvement in ILSS responses was achieved despite the negligible deterioration in weight and volume compared to the COPVs with a 30 mm POR.

Modal and random vibration responses of composite overwrapped pressure vessels with various geodesic dome trajectories

In the current study, Composite Overwrapped Pressure Vessels (COPVs) with various geodesic dome profiles have been built and their modal and random vibration responses were numerically examined. In this regard, geodesic dome profiles were established for five different polar opening radii of 10, 15, 20, 25, and 30 mm, and thus the impacts of fibre orientations on vibration characteristics depending on the filament winding trajectories were discovered. Furthermore, non-prestressed modal and random vibration responses for COPVs, as well as prestressed vibration behaviours under 50, 100, and 150 Bar internal pressure, were investigated and thus the effects of internal pressure severity on dynamic characteristics were identified. The study found that higher polar opening radius triggered a reduction in the natural frequencies, and these reductions were linked to an increment in the filament winding angle, and a consequent deterioration in the structural stiffness of COPVs. On the other hand, raising the internal pressure severity shifts the natural frequencies to higher frequencies, which are also attributed to ascended structural stiffness. Furthermore, it was established that internal pressure severity and polar opening radius had a coupled effects on the modal and random responses, and that both parameters should be evaluated simultaneously.

On the Multidisciplinary Design of a Hybrid Rocket Launcher with a Composite Overwrapped Pressure Vessel

A multidisciplinary design optimisation (MDO) study of a hybrid rocket launcher is presented, with a focus on quantifying the impact of using composite overwrapped pressure vessels (COPVs) as the oxidiser tank. The rocket hybrid propulsion system (RHPS) consists of a combination of solid fuel (paraffin) and liquid oxidiser (NOx). The oxidiser is conventionally stored in metallic vessels. Alternative design concepts involving composite-based pressure vessels are explored that could lead to significant improvements in the overall performance of the rocket. This design choice may potentially affect parameters such as total weight, thrust curve, and maximum altitude achieved. With this eventual impact in mind, structural considerations such as wall thickness for the COPV are integrated into an in-house MDO framework to conceptually optimise a hybrid rocket launcher.

Model-experiment comparison of quasi static loading on a composite overwrap pressure vessel (Part 2)

A methodology to develop a 3D Finite Element (FE) model of a full metal-lined Composite Overwrapped Pressure Vessel (COPV) was developed and is presented in this paper. The model is intended for prediction of the metal-composite delamination and residual dent depth developed in the metal liner as a result of quasi-static indentation loading. Cohesive elements are used to model the composite and the metal-composite interfaces. Experimental and numerical comparisons of force–displacement curves, delamination area and residual dent depth are presented. Numerical results are in good agreement to the experimental data.

Investigation of Hydrogen Transport Properties through the Liner Material of 70 MPa Type IV Composite Overwrapped Pressure Vessels

In zero-emission solutions for the hydrogen supply chain, high-pressure hydrogen storage in Type IV composite overwrapped pressure vessels (COPVs) is crucial for being safe and secure for a variety of stationary and transport applications. These COPVs consist of a polymeric liner material that works as a barrier for the stored hydrogen, while the carbon fiber-based composite provides the strength to hold the high-pressure hydrogen (of nominal working pressure more than 70 MPa to achieve the competitive driving range). Several experimental studies and simulation work have been performed on the viability of various polymers as low-permeability materials under low-pressure conditions. However, because of their proprietary nature and commercialized research, the characteristics of hydrogen permeation through polymers and their behavior at high pressure are still readily available in the literature. It is a major concern and the most crucial component of Type IV COPVs that these polymeric liners hold high pressure and maintain the necessary density of hydrogen (state of charge) toward operational requirements and safety concerns related to the cause of failures due to permeation (such as blistering, buckling, cracking, and so on). This paper investigates the role of polymeric liners on hydrogen permeation behavior using finite element modeling. For this, a UMATHT subroutine is developed within the Abaqus software to implement a new hydrogen permeation transport model with extended governing equations. Through the simulations and material modeling, the role of hydrogen transport properties (permeability, diffusivity, and solubility), structural characteristics (crystallinity, free volume, thickness) of plastic liners, and the role of operational parameters (concentration, pressure) are combined to validate the model from the experimental data in the literature to estimate the effective thickness required to maintain the permeation limit of the polymeric liner of 70 MPa Type IV COPVs.

Design of Type-IV Composite Pressure Vessel Based on Comparative Analysis of Numerical Methods for Modeling Type-III Vessels

Compressed gas storage of hydrogen has emerged as the preferred choice for fuel cell vehicle manufacturers, as well as for various applications, like road transport and aviation. However, designers face increasing challenges in designing safe and efficient composite overwrapped pressure vessels (COPVs) for hydrogen storage. One challenge lies in the development of precise software programs that consider a multitude of factors associated with the filament winding process. These factors include layer thickness, stacking sequence, and the development of particularly robust models for the dome region. Another challenge is the formulation of predictive behavior and failure models to ensure that COPVs have optimal structural integrity. The present study offers an exploration of numerical methods used in modeling COPVs, aiming to enhance our understanding of their performance characteristics. The methods examined include finite element analysis in Abaqus, involving conventional shell element, continuum shell element, three-dimensional solid element, and homogenization techniques for multilayered composite pressure vessels. Through rigorous comparisons with type-III pressure vessels from the literature, the research highlights the most suitable choice for simulating COPVs and their practicality. Finally, we propose a new design for type-IV hydrogen composite pressure vessels using one explored method, paving the way for future developments in this critical field.

Development of machine learning based classifier for the pressure test result prediction of type IV composite overwrapped pressure vessels

The stringent safety regulations of type IV composite overwrapped pressure vessels (COPVs) for commercial vehicles mandate a certification process involving pressurization up to 1050 bar, with the critical requirement of withstanding burst pressures of 1570 bar. Analyzing proof test data is crucial to enhance and ensure tank safety regarding burst pressure. In this study, we developed various machine learning classifiers for structure health monitoring and damage prediction of COPVs. The classifiers were trained using a substantial amount of acoustic emission data collected during burst and pressure cycling tests. The test results were employed as label inputs during the training process. Statistical features were extracted per time unit and trained using Naive Bayes, Logistic Regression, Decision Tree, XGBoost, and TabNet models. Upon training the data collected from the burst pressure test, TabNet, Decision Tree, and XGBoost achieved classification accuracies above 0.94. Notably, TabNet demonstrated also the best performance for the pressure cycling test with an accuracy of 0.98. Furthermore, TabNet provided visualizations of feature sensitivity in relation to classification results. This study marks the first development of a machine learning classifier for predicting the damage state of COPV tanks in commercial applications pertaining to required safety tests.

Advanced methods for the detection of measurement outliers in fibre optic strain measurements for non-destructive monitoring of the structural health of composite over-wrapped pressure vessels of type-IV

To analyse the applicability of fibre optic measurements as a measurement method for structural health monitoring, fibre-optic sensors are placed on the vessel surface. The influence of the surface profile of the vessel on the measurement result is analysed by correlating the 3D scanned surface and measurement results. One outcome is that the measurement is substantially influenced by the surface topography. The surface height can be directly correlated to the strain magnitude, which is also verified by analytical calculations. Outliers and invalid values can be traced back to surface-related influences on the sensor fibre. For the outlier detection, a novel method is presented, which is based on quantitative evaluation criteria and inductive reasoning and enables transient cycle- by-cycle outlier detection adapted to the load case. Based on the results an estimate for the expanded measurement uncertainty is given for of the measurement chain consisting of vessel, adhesive and sensor fibre.

Primenenie iskusstvennogo intellekta dlya analiza protsessa izgotovleniya leynera

The paper demonstrates a technique that combines the use of artificial intelligence and the finite element method to analyze the production process of the liner – the seamless part of composite overwrapped pressure vessels obtained by tube forming. Instead of expensive physical experiments, computer simulation was used, which made it possible to quickly obtain "big data", including the main technological parameters of tube forming process, some metal flow features and the tendency for defects to appear. The processing of the data array was carried out in MATLAB using neural networks and a genetic algorithm for the multi-objective optimization problem. As a result, the influence of technological parameters on the formation of the liner geometry was determined, and the optimal variant of their manufacture was proposed, which is of practical interest to companies producing metal-composite cylinders.

Processing and structural health monitoring of a composite overwrapped pressure vessel for hydrogen storage

A process and Structural Health Monitoring system was implemented on a Composite Overwrapped Pressure Vessel (COPV) for hydrogen storage at 350 bar to be used in a fuel-cell system of an Unmanned Aerial Vehicle. This work reports the embedment strategy of optical fibre Bragg grating (FBG) sensors to monitor the full life cycle of the vessel, consisting of an aluminium liner and a wound carbon fibre reinforced polymer composite overwrap. A FBG sensing array, bonded on the aluminium liner circumferential section, was covered with a localised unidirectional prepreg composite tape, enabling composite winding and curing monitoring. The sensing array strategy allowed to detect and locate Barely Visible Impact Damage resulting from drop-weight impact tests, based on the ratio of the residual strain amplitude between FBG sensor pairs. Errors as small as 17 mm and up to 56 mm were determined between the predicted and ‘real’ impact locations. To simulate the real-life operational pressure charging and discharging cycles, the COPV was subjected to cycling testing at different pressure ranges. The FBG sensors were able to monitor a total of 20 980 pressure cycles, revealing a linear response to the applied pressure, and remained operational after COPV failure. Furthermore, the FBG sensing array was able to detect the residual plastic strain caused in the aluminium liner by the autofrettage process that the COPV was subjected to prior to pressure cycling, at 600 bar for 2 min, to improve its fatigue performance. This manuscript also reports the COPV structural design by Finite Element Modelling (FEM), its manufacturing process and burst pressure testing for the FEM analysis validation. A small difference of 0.7% was found between the simulated and experimental determined burst pressure of 1061 ± 26 bar.

Quasi-static compression tests of overwrapped composite pressure vessels under low velocity impact

Pressure vessels are being utilised in different applications that are indispensable including automobile, aerospace, underwater vehicles, oil and gas, chemical engineering among other applications. However, there is lack of knowledge on the influence of induced damage and the resulting performance of such vessels under quasi-static loading and axial compression. Specifically, the vessels studied in this study is made up of a high-density polyethylene liner and glass fibre overwraps. Therefore, this research investigated the load bearing capacities and the energy absorbed of the indented vessels in axial and hoop directions to determine the resistance of the vessels after such damaged using experiment, and damage characterisation microscopy, non-destructive testing and analysis.Quasi-static transverse and axial compression testing was performed on composite cylinders made of polyethylene liner and glass fibre overwraps. Both quasi-static and axial compression tests were performed with Universal testing Machines at crosshead speed of 500 mm/min 2.5 mm/min respectively. Microstructural damage characterisation was conducted using microscopy and Dolphicam2 ultrasonic non-destructive testing equipment.This study provides a new understanding on the performance of composite pressure vessel under damaged and undamaged conditions has established the reliability and residual strength capabilities of composites under the scenarios investigated. The results shows that the damage profile and the effect on compressive strength of the composite damaged and non-damaged cylinders was found to be relatively similar. However, when the cylinders are subjected to quasi-static compression, the polyethylene absorbs enough elastic strain energy to recover from the applied compressive load and recover without being plastically deformed. Additionally, the results demonstrate that the quasi-static compression have little or no influence on the axial strength of the cylinders. The damage characterisation on the cylinders revealed fibre break and delamination and local bucking and brooming failure at the bottom of the cylinders. This study has direct impact in composite overwrapped pressure vessels (COPVs) safety design tolerances, manufactururing strategy and operational failure conditions.

Numerical and experimental investigations of low-velocity impact on composite overwrapped pressure vessel with different stacking sequences

Composite overwrapped pressure vessels are designed to contain fluids that operate under high pressures. In addition to internal pressure, the design needs to account for out-of-plane loading in the form of low-velocity impact. In this study, a numerical analysis of four models of composite overwrapped pressure vessels with different stacking sequences is performed. A three-dimensional explicit finite element model using multi-layer stacked shell elements was used in the impact zone region to obtain reasonable computation time. Utilizing the bilinear traction-separation law, cohesive zone elements were used to simulate the delamination failure and as bonding between eight layers of CFRP covering the aluminum liner. The simulation results were then validated using drop-weight impact tests, which revealed that the response of contact force to impact time was comparable for both types of analysis. Prediction of failure was carried out by assessing the quantity of energy absorbed by the CFRP layers and was confirmed by shell element data that failed during simulations. In addition, the Hashin damage model confirmed that the matrix tensile failure mode was the predominant failure mode for all discussed impact scenarios. Model-A with Al + [90]8 stacking sequence was found to have the highest impact resistance based on the prediction of the composite’s failure area and the energy absorbed by the CFRP layers. Furthermore, it was found that COPVs with combinations of helical and hoop sequences tend to have larger areas of delamination due to high interlaminar shear stress between the CFRP layers.

Numerical Modeling of Kevlar/Jute Fiber and Hybrid Composite Pressure Vessels

In this study, nine composite overwrapped pressure vessel (COPV) models (type IV) have been developed to analyze their burst pressure performance using the ultimate tensile strength (UTS) from experiment and the von Mises stress profile from finite element ABAQUS simulation. The four-layer jute-reinforced PV shows almost five times lower burst pressure than Kevlar-based PV, indicating the inherent vulnerability of using pure jute. However, for the hybrid jute/Kevlar alternate combination, JKJK shows strength improvement over a jute PV by almost two times. Other jute hybrids with glass and aluminum foil show very comparable performance when compared to jute PV. Both yarn and braided-type jute fiber exhibit similar operating conditions, indicating that the preparation type of jute fiber is invariant to pressure performance. However, COPVs woven with 90° cross-oriented jute fibers are found to have the most degraded strength, highlighting that fiber orientation remains an important optimization factor in designing PVs with hybrid composite wraps.

Anti-interference damage localization in composite overwrapped pressure vessels using machine learning and ultrasonic guided waves

This paper describes a structural health monitoring (SHM) technique for composite overwrapped pressure vessels (COPVs), which utilizes machine learning (ML) algorithms to achieve anti-interference damage localization based on ultrasonic guided waves. Firstly, this study developed a multi-channel guided wave signal automatic acquisition system and established a comprehensive dataset comprising multi-region damage/health signals from composite pressure vessels. Subsequently, based on the interaction between guided wave features and damage, a feature extraction-ML based anti-interference damage localization scheme was proposed. This scheme achieved robust damage localization through steps such as data cleaning, feature extraction, feature selection, and ML-based damage localization. Furthermore, this study introduced the guided wave-temporal convolutional network (GW-TCN) model based on deep learning method. The GW-TCN model integrates the temporal correlation and the multi-channel physical properties of guided waves, while leveraging the advantages of deep learning's adaptive feature extraction. It effectively mitigates the vibration and sensor failure interferences that mimic real operational conditions, enabling precise damage localization. Finally, through validation on multiple data samples, it was demonstrated that the model exhibits good generalization ability. The research findings will contribute to the advancement of composite pressure vessel SHM technology, paving the way for improved safety and reliability in industrial applications.

Progressive Failure Analysis of Composite Overwrapped Pressure Vessel

Stress and progressive failure analysis (First Ply Failure to Last Ply Failure) of a composite overwrapped pressure vessel (COPV) with an aluminum liner using the finite element method (FEM) in general and commercial finite element analysis (FEA) software ANSYS in particular is presented in this paper. Ply failure prediction is based on failure criteria implemented in ANSYS software. The first-ply failure (FPF) does not necessarily imply the total failure of a laminate. After FPF, the laminate stiffness is reduced consistent with identified ply failure mode. The strength of the composite laminate is evaluated again to see if it could carry additional load. This ply-by-ply analysis progresses, until the ultimate strength is reached. In the present study the methodology is validated using benchmarks and applied to a filament wound fibre reinforced plastic composite pressure vessel with an aluminum liner. Significant results are presented and critically assessed. Directions for further work in this field are identified.

A Machine Learning Based-Guided Wave Approach for Damage Detection and Assessment in Composite Overwrapped Pressure Vessels

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.

Guided Waves Propagation in Composite Overwrapped Pressure Vessel

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.