“Abstract

The paper considers the concept of efficient liquid hydrogen (LH2) refuelling at hydrogen refuelling stations (HRS), presents modelling approach and 3D transient CFD simulation results. The concept is based on the advantages of transforming hydrogen from equilibrium to a non-equilibrium sub-cooled state (sLH2) during compression at pump. The modelling approach comprises a thermodynamic model of LH2 transfer from the HRS tank to the pump exit and a two-phase CFD model from the pump exit through the HRS equipment, i.e. pipes with bends, automatic valve, breakaway, nozzle, and manifold to onboard storage tanks. Due to the absence of published experimental data, the modelling approach and simulations are verified against conceptual LH2 refuelling process available in the literature. The CFD model reproduces key LH2 refuelling parameters: flow rate, pressure, temperature dynamics, including non-uniform temperature in onboard tanks and predicts pipe cooldown from 88K to allowable temperatures corridor of 23.9–26.5 K.”

 

Molkov V, Ebne-Abbasi H, Makarov D. Liquid hydrogen refuelling at HRS: Description of sLH2 concept, modelling approach and results of numerical simulations. International Journal of Hydrogen Energy 2024;93:285–96.

The full report is accessible via: https://doi.org/10.1016/j.ijhydene.2024.10.392

For related publications please see Resources – UK National Clean Maritime Research Hub

“Abstract

Machine learning has started to be used in engine research to optimize combustion and predict fuel spray characteristics. This paper presents the development of a machine learning model using a Genetic Algorithm-Backpropagation (GA-BP) neural network to predict spray penetration. The GA-BP neural network was selected for its ability to optimize neural network weights and thresholds, thereby improving model convergence and avoiding local minima, which are common challenges in complex, non-linear problems such as spray prediction. The model was trained using experimental data from diesel injector spray tests, and its accuracy was evaluated through parametric sensitivity analysis, examining the influence of various input factors. A comparison between the machine learning model and the traditional empirical formulas of spray penetration revealed that the machine learning model achieved greater accuracy. In terms of the sensitivity to inputs, it is interesting to find that the cognition of machines is different from that of humans. When an input parameter does not have any functional relationship with other input parameters, the absence of this input parameter will lead to a significant decrease in the accuracy of the output result. The results demonstrate that the machine learning approach offers higher accuracy and better generalizability compared to traditional empirical methods. This study recommends the ways to get better results of penetration prediction with BP neural networks, which is efficient in training and utilizing Artificial Neural Networks (ANNs).”

 

Zhang, Y. et al. (2024) ‘Parameter sensitivity analysis for diesel spray penetration prediction based on Ga-BP Neural Network’, Energy and AI, 18, p. 100443.

The full report is accessible via: https://doi.org/10.1016/j.egyai.2024.100443

For related publications please see Resources – UK National Clean Maritime Research Hub

“Abstract

Hydrogen refuelling is imperative for the emerging market of hydrogen vehicles. The pressure control valve (PCV) at the hydrogen refuelling station (HRS) plays a major role in ensuring that hydrogen delivery to the vehicle follows the prescribed refuelling protocols. A three-dimensional CFD model with a detailed resolution of PCV motion affecting heat and mass transfer is developed. The PCV motion controlling the mass flow rate is simulated using dynamic mesh. The CFD model captures refuelling from high-pressure tanks through entire HRS equipment to onboard tanks, capturing pressure and temperature changes upstream and downstream of the PCV. The Joule-Thomson effect resulting in a hydrogen temperature increase at PCV is captured using the NIST real gas database. The model is validated against Test No.1 of NREL on refuelling through the entire equipment of HRS. The CFD model can be used to design HRS equipment parameters, including PCV, and develop efficient refuelling protocols.”

 

Ebne-Abbasi, H., Makarov, D.  and Molkov, V. (2024) ‘Modelling of refuelling through the entire equipment of HRS: use of dynamic mesh to simulate heat and mass transfer during throttling at PCV’, Hydrogen Safety, 1(1), pp. 12–32.

The full report is accessible via: https://doi.org/10.58895/hysafe.4

“Abstract

An accurate determination of minimum ignition energy (MIE) is essential for assessing electrostatic hazards and characterising potential for occurrence of combustion in flammable mixtures. This is of utmost importance for hydrogen-air mixtures characterised by a MIE equal to 0.017 mJ, whereas conventional flammable gases are characterised by MIE typically higher than 0.1 mJ. The study aims at developing and validating a CFD three-dimensional model capable to simulate complex unsteady physical and chemical phenomena underlying capacitive discharge spark. The model accounts for the experimental apparatus details, including the effect of electrodes’ gap and associated heat losses. The numerical approach accurately reproduced the experimental measurements of MIE for mixtures of hydrogen with air at initial temperature ranging from ambient (T = 288 K) to cryogenic (T = 123 K). Hydrogen concentration in air was included in the range 10–55% for tests at T = 288 K, and 20–60% for tests at T = 173 K and 123 K respectively. Simulations assess the impact of experimental characteristics and design, such as the electrodes’ dimension, and numerical features on process dynamics, growth of the flame kernel and MIE predictions.”

 

Cirrone, D. et al. (2024) ‘Numerical Study of the spark ignition of hydrogen-air mixtures at ambient and cryogenic temperature’, International Journal of Hydrogen Energy, 79, pp. 353–363. doi:10.1016/j.ijhydene.2024.06.362.

The full report is accessible via: https://doi.org/10.1016/j.ijhydene.2024.06.362