Modeling thermal conductivity of hydrogen-based binary gaseous mixtures using generalized regression neural network-Reference-Cited by-同舟云学术

Modeling thermal conductivity of hydrogen-based binary gaseous mixtures using generalized regression neural network

Published:2024-03 Issue: Volume:59 Page:242-250
ISSN:0360-3199
Container-title:International Journal of Hydrogen Energy
language:en
Short-container-title:International Journal of Hydrogen Energy

Author:

Naghizadeh Arefeh,Amiri-Ramsheh Behnam^ORCID,Atashrouz Saeid,Abuswer Meftah Ali^ORCID,Abedi Ali,Mohaddespour Ahmad,Hemmati-Sarapardeh Abdolhossein^ORCID

Publisher

Elsevier BV

Reference48 articles.

1. A review on potential use of hydrogen in aviation applications;Dincer;Int J Sustain Aviat,2016

2. Excellent hydrolysis performances of Mg3RE hydrides;Ouyang;Int J Hydrogen Energy,2013

3. Fueling the future: a comprehensive review of hydrogen energy systems and their challenges;Le;Int J Hydrogen Energy,2024

4. Discovery of a new Al species in hydrogen reactions of NaAlH4;Ivancic;J Phys Chem Lett,2010

5. Natural gas as a bridge to hydrogen transportation fuel: insights from the literature;Ogden;Energy Pol,2018

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A comparative study of machine learning frameworks for predicting CO2 conversion into light olefins;Fuel;2025-01

2. Research on quantitative analysis method of shale oil reservoir sensitivity based on mineral analysis;Geoenergy Science and Engineering;2024-08

3. Modeling thermo-physical properties of hydrogen utilizing machine learning schemes: Viscosity, density, diffusivity, and thermal conductivity;International Journal of Hydrogen Energy;2024-06