TY - GEN
T1 - Modelling cryogenic hydrogen jet dispersion
T2 - Australasian Fluid Mechanics Conference (AFMC) (24th : 2024)
AU - Mohammadpour, Javad
AU - Li, Xuefang
AU - Salehi, Fatemeh
N1 - Copyright the Author(s). Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.
PY - 2024/12
Y1 - 2024/12
N2 - Safe and efficient methods for storing and transporting liquid hydrogen are crucial for decarbonisation. This study develops a precise method for analysing complex cryogenic hydrogen releases, integrating large eddy simulation (LES), proper orthogonal decomposition (POD), and a novel hybrid machine learning model. Focusing on LES predictions of Sandia's experiment on cryogenic hydrogen jet release at stagnation pressure (5 bar) and temperature (50 K), POD extracts dominant modes, demonstrating that the first 10 modes effectively predict hydrogen dispersion. The hybrid model combines POD with Long Short-Term Memory neural networks and Simple Exponential Smoothing techniques to forecast temporal coefficients, showing acceptable agreement with LES and POD predictions. This research provides significant insights into effective approaches in cryogenic hydrogen applications, significantly reducing the time and costs in numerical and experimental investigations.
AB - Safe and efficient methods for storing and transporting liquid hydrogen are crucial for decarbonisation. This study develops a precise method for analysing complex cryogenic hydrogen releases, integrating large eddy simulation (LES), proper orthogonal decomposition (POD), and a novel hybrid machine learning model. Focusing on LES predictions of Sandia's experiment on cryogenic hydrogen jet release at stagnation pressure (5 bar) and temperature (50 K), POD extracts dominant modes, demonstrating that the first 10 modes effectively predict hydrogen dispersion. The hybrid model combines POD with Long Short-Term Memory neural networks and Simple Exponential Smoothing techniques to forecast temporal coefficients, showing acceptable agreement with LES and POD predictions. This research provides significant insights into effective approaches in cryogenic hydrogen applications, significantly reducing the time and costs in numerical and experimental investigations.
KW - Liquid Cryogenic Hydrogen
KW - Proper Orthogonal Decomposition
KW - Long Short-Term Memory
KW - Simple Exponential Smoothing
KW - Large Eddy Simulation
U2 - 10.5281/zenodo.14213531
DO - 10.5281/zenodo.14213531
M3 - Conference proceeding contribution
SP - 1
EP - 8
BT - Proceedings of the 24th Australasian Fluid Mechanics Conference
PB - Australasian Fluid Mechanics Society
CY - Perth
Y2 - 1 December 2024 through 5 December 2024
ER -