Articles & Issues
- Conflict of Interest
- In relation to this article, we declare that there is no conflict of interest.
- Publication history
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Received September 2, 2024
Revised January 31, 2025
Accepted April 5, 2025
Available online July 25, 2025
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This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits
unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Application of EOS Based on Machine Learning Method on CFD Study of Rapid Hydrogen Refueling Process
https://doi.org/10.1007/s11814-025-00460-x
Abstract
Hydrogen is attracting attention as an eco-friendly energy source that can replace fossil fuels. In particular, hydrogen fuel cell
electric vehicles (FCEVs) have been developed to reduce carbon dioxide emissions in the transportation sector. Currently,
commercially available FCEVs store hydrogen as highly compressed gas form to increase volumetric energy density. To
provide a refueling time similar to that of internal combustion engine vehicles (ICEVs), hydrogen refueling stations (HRSs)
are installed to supply gaseous hydrogen into FECVs up to 35 MPa or 70 MPa in a relatively short time. The refueling process
of fi lling compressed gas within a confi ned volume of the on-board storage tank is inevitably accompanied by the temperature
increase. However, the refueling process should be carried out under a limited temperature considering the thermal and
mechanical safety of the storage tank. Since the hydrogen storage tank installed in the commercial FCEV is equipped with
a single temperature sensor, only the average temperature can be measured and monitored during the refueling process.
Therefore, modeling the refueling process is useful for understanding the gas fi lling phenomenon and fi nding the optimal
refueling strategy. In particular, the CFD study method that considers the motion of the fl uid inside the tank enables prediction
of local temperature changes inside the storage tank, which cannot be measured in the commercial vehicle refueling
process. The CFD research is conducted by combining expressions representing the fl uid properties and a model describing
the fl ow characteristics. Therefore, an appropriate combination of equations should be examined before developing a CFD
model and simulating the refueling process. In this study, the hydrogen refueling process is simulated using three equations
of state (EOSs) and fi ve turbulent models. The results are compared and quantitatively analyzed using experimental data to
propose an appropriate EOS with an accurate turbulence model. Experiments of hydrogen fi lling into Type III tank of 74 L
up to 35 MPa within 1 min have been chosen to make the assumption of axial symmetry for CFD model valid. Comparing
the three EOSs (SRK, PR, and ML), it is found that the reduction of simulation time can be attained with good accuracy
when using ML EOS which has been developed to describe the volumetric property of hydrogen. Among the fi ve turbulence
models (yPlus, k – ε , realizable k – ε , low-Reynolds k – ε , and k – ω ) generally used in many CFD studies, the realizable k – ε
model shows satisfactory results on the reproduction of mean and local thermal behaviors inside of on-board storage tanks.

