2022
DOI: 10.1016/j.est.2022.104979
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Thermocline thermal storage modeling towards its predictive optimal management

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Cited by 5 publications
(14 citation statements)
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“…In this formulation, the algebraic solution for thermocline HTTES is given as polynomial function, and allowing for any initial temperature profile to predict the storage temperature evolution. [8] suggested a metamodel based on data issued from 1D -PDE model simulations, representing a suitable trade-off between accuracy and computation time. In case of manufacturing defects or highly dimension-dependent phenomena like flow channeling, this metamodel can be constructed on data issued from experimental measures.…”
Section: State Of the Artmentioning
confidence: 99%
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“…In this formulation, the algebraic solution for thermocline HTTES is given as polynomial function, and allowing for any initial temperature profile to predict the storage temperature evolution. [8] suggested a metamodel based on data issued from 1D -PDE model simulations, representing a suitable trade-off between accuracy and computation time. In case of manufacturing defects or highly dimension-dependent phenomena like flow channeling, this metamodel can be constructed on data issued from experimental measures.…”
Section: State Of the Artmentioning
confidence: 99%
“…Here, the storage energy state and losses evolution in function of the power command are required for the controller to design and manage the system. This section will be dedicated for describing two 0D models, a 1D based on PDE model as well as a 1D metamodel as explained in [8], whereas 2D models are out of the scope of this work.…”
Section: Modelingmentioning
confidence: 99%
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