Prediction of composite layer thickness for Type III hydrogen pressure vessel at the dome part

Che, Jia-le; Han, Min-Gu; Chang, Seung‐Hwan

doi:10.1016/j.compstruct.2021.114177

Cited by 8 publications

(4 citation statements)

References 26 publications

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“…During the process, the layer thickness is influenced even by slight variations of the winding angle, especially in the dome region. [9] Since the given winding trajectory may differ from the actual trajectory, the analysis of the laminate thickness turns out to be significantly complex to perform and reproduce at the transition between the circumferential and dome regions. [9] Thus, with the available information obtained from the cameras, it was not possible to conduct a complete digital reconstruction of the laminate of the dome.…”

Section: Digital Reconstruction Of the Wound Laminatementioning

confidence: 99%

“…[9] Since the given winding trajectory may differ from the actual trajectory, the analysis of the laminate thickness turns out to be significantly complex to perform and reproduce at the transition between the circumferential and dome regions. [9] Thus, with the available information obtained from the cameras, it was not possible to conduct a complete digital reconstruction of the laminate of the dome. Using the visualization library Pyqtgraph (V0.11.0) the digital reconstruction of the circumferential region can be displayed in real-time.…”

Section: Digital Reconstruction Of the Wound Laminatementioning

confidence: 99%

“…[2] However, it has been shown that the exact laminate lay-up direction and thickness of the dome geometry are difficult to predict. [7,9] By in-line tracking of the actual position of the fiber band, the real resulting geometry can be determined, enhancing the design and analysis of COPV. Furthermore, a more exact fiber placement can increase the pressure vessel's performance.…”

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confidence: 99%

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Development of an image processing algorithm (IPA‐Delfin) for the digital reconstruction of composite overwrapped pressure vessels

et al. 2023

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Wet filament winding has been established as the primary manufacturing process for overwrapped pressure vessels. However, the prediction of the mechanical performance of pressure vessels is still not sufficiently accurate, mostly due to process‐induced deviations within the laminate structure. In this work, we present an image processing algorithm (IPA‐Delfin) that enables the real‐time generation of a digital reconstruction based on the actual geometry and position of the placed roving. A Type‐IV pressure vessel is analyzed under real manufacturing conditions. Our approach allows the measurement of the overlapping degree of roving within the laminate and the prediction of gaps. The results show that the fiber‐band width and the overlapping area vary significantly when the roving is placed directly on the vessel's polymer liner. A more constant and uniform fiber‐band width and overlapping area were observed when the filament winding took place on the previously placed roving. Hence, the IPA‐Delfin can be potentially used for the generation of digital twins of composite overwrapped pressure vessels.

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Section: Digital Reconstruction Of the Wound Laminatementioning

confidence: 99%

Section: Digital Reconstruction Of the Wound Laminatementioning

confidence: 99%

mentioning

confidence: 99%

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Development of an image processing algorithm (IPA‐Delfin) for the digital reconstruction of composite overwrapped pressure vessels

et al. 2023

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“…However, the method needed thickness values provided by experiments, which limited the application of this method. Che et al [ 16 ] proposed a prediction method for the thickness of the dome section. This method took the effect of fiber overhead caused by hoop winding, but this method did not consider the influence of bandwidth on the fiber thickness at polar openings.…”

Section: Introductionmentioning

confidence: 99%

Thickness-Prediction Method Involving Tow Redistribution for the Dome of Composite Hydrogen Storage Vessels

Wang

Chen

et al. 2022

Polymers

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Traditional thickness-prediction methods underestimate the actual dome thickness at polar openings, leading to the inaccurate prediction of the load-bearing capacity of composite hydrogen storage vessels. A method of thickness prediction for the dome section of composite hydrogen storage vessels was proposed, which involved fiber slippage and tow redistribution. This method considered the blocking effect of the port on sliding fiber tows and introduced the thickness correlation to predict the dome thickness at polar openings. The arc length corresponding to the parallel circle radius was calculated, and then, the actual radius values corresponding to the bandwidth were obtained by the interpolation method. The predicted thickness values were compared with the actual measured thickness. The maximum relative error of the predicted thickness was 4.19%, and the mean absolute percentage error was 2.04%. The results show that the present method had a higher prediction accuracy. Eventually, this prediction method was used to perform progressive damage analysis on vessels. By comparing with the results of the cubic spline function method, the analysis results of the present method approached the actual case. This showed that the present method improved the accuracy of the design.

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Multi-time-step and multi-parameter prediction for real-world proton exchange membrane fuel cell vehicles (PEMFCVs) toward fault prognosis and energy consumption prediction

Zhang

Zhou

et al. 2022

Applied Energy

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Prediction of composite layer thickness for Type III hydrogen pressure vessel at the dome part

Cited by 8 publications

References 26 publications

Development of an image processing algorithm (IPA‐Delfin) for the digital reconstruction of composite overwrapped pressure vessels

Development of an image processing algorithm (IPA‐Delfin) for the digital reconstruction of composite overwrapped pressure vessels

Thickness-Prediction Method Involving Tow Redistribution for the Dome of Composite Hydrogen Storage Vessels

Multi-time-step and multi-parameter prediction for real-world proton exchange membrane fuel cell vehicles (PEMFCVs) toward fault prognosis and energy consumption prediction

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