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International Journal of Thermal Sciences
Research Hotspot & Journal Scope - models


International Journal of Thermal Sciences - DOI: 10.1016/j.ijthermalsci.2018.09.002
Classification of machine learning frameworks for data-driven thermal fluid models

Chih-Wei Chang · Nam T. Dinh ·

Physics
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International Journal of Thermal Sciences - DOI: 10.1016/J.IJTHERMALSCI.2019.02.037
Combined heat and mass transfer analyses in catalytic microreactors partially filled with porous material - The influences of nanofluid and different porous-fluid interface models

David G. P. Guthrie · Mohsen Torabi · Nader Karimi · Nader Karimi ·

Materials Science
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International Journal of Thermal Sciences - DOI: 10.1016/J.IJTHERMALSCI.2019.02.021
A review of the models using the Cattaneo and Vernotte hyperbolic heat equation and their experimental validation

Denis Maillet · Denis Maillet ·

Mathematics
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International Journal of Thermal Sciences - DOI: 10.1016/J.IJTHERMALSCI.2019.05.015
Dynamic simplified PCM models for the pipe-encapsulated PCM wall system for self-activated heat removal

Tian Yan · Jiajia Gao · Xinhua Xu · Tao Xu · Ziye Ling · Jinghua Yu ·

Materials Science
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International Journal of Thermal Sciences - DOI: 10.1016/j.ijthermalsci.2019.105999
A priori tests of subgrid-scale models in an anisothermal turbulent channel flow at low mach number

Dorian Dupuy · Adrien Toutant · Françoise Bataille ·

Physics
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Non-standard Bose-Hubbard models can exhibit rich ground state phase diagrams, even when considering the one-dimensional limit.

Staggered ground states in an optical lattice [10.1103/PhysRevA.100.043614]


We utilized hardening models to derive a constitutive model for DRX and simultaneously establish an understanding of rate controlling deformation mechanism in Inconel 718.

Understanding activation energy of dynamic recrystallization in Inconel 718 [10.1016/J.MSEA.2018.12.008]


Traditional neural networks (NNs) have been widely used in prediction intervals (PIs) construction method, with many improved models have been proposed.

A Construction Approach to Prediction Intervals Based on Bootstrap and Deep Belief Network [10.1109/ACCESS.2019.2938214]


ABSTRACT Contemporary models of academic work reflect a process of compression given that busy (time compression), jet setting (space compression) scholars constitute the norm of academic success.

Giving account of our (mobile) selves: embodied and relational notions of academic privilege in the international classroom [10.1080/13562517.2019.1621281]


Student investigations can range from validating the phrase “the most exciting two minutes in sports” to predicting the winning speed of next year’s race using parallel polynomial models.

Predicting the Kentucky Derby Winner! Sort of [10.1080/10691898.2019.1623137]


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