---
res:
  bibo_abstract:
  - The application of hydrogen for energy storage and as a vehicle fuel necessitates
    efficient and effective storage technologies. In addition to traditional cryogenic
    and high-pressure tanks, an alternative approach involves utilizing porous materials
    such as activated carbons within the storage tank. The adsorption behaviour of
    hydrogen in porous structures is described using the Dubinin-Astakhov isotherm.
    To model the flow of hydrogen within the tank, we rely on the equations of mass
    conservation, the Navier-Stokes equations, and the equation of energy conservation,
    which are implemented in a computational fluid dynamics code and additional terms
    account for the amount of hydrogen involved in sorption and the corresponding
    heat release. While physical models are valuable, data-driven models often offer
    computational advantages. Based on the data from the physical adsorption model,
    a data-driven model is derived using various machine learning techniques. This
    model is then incorporated as source terms in the governing conservation equations,
    resulting in a novel hybrid formulation which is computationally more efficient.
    Consequently, a new method is presented to compute the temperature and concentration
    distribution during the charging and discharging of hydrogen tanks and identifying
    any limiting phenomena more easily.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Georg Heinrich
      foaf_name: Klepp, Georg Heinrich
      foaf_surname: Klepp
      foaf_workInfoHomepage: http://www.librecat.org/personId=49011
  bibo_doi: 10.1016/j.energy.2024.132318
  bibo_volume: 306
  dct_date: 2024^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/0360-5442
  - http://id.crossref.org/issn/1873-6785
  dct_language: eng
  dct_publisher: Elsevier BV@
  dct_subject:
  - Hydrogen storage
  - Adsorption
  - Activated carbon
  - Machine learning
  - Simulation
  - Computational fluid dynamics
  dct_title: Modelling activated carbon hydrogen storage tanks using machine learning
    models@
...