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Computational Thermodynamics and Its Applications

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Thermodynamics".

Deadline for manuscript submissions: 30 April 2024 | Viewed by 420

Special Issue Editor


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Guest Editor
Senior Consultant of GTT Technologies, Kaiserstraße 103, 52134 Herzogenrath, Germany
Interests: computational thermodynamics; CALPHAD; Gibbs energy modelling; phase diagrams; thermochemical assessments; teaching thermodynamics; process modelling

Special Issue Information

Dear Colleagues,

Computational thermodynamics plays a crucial role in integrating phase diagrams and the thermochemistry of multi-component multi-phase systems through computational techniques. This field is continually evolving and progressing towards the integration of kinetic simulations with thermodynamic calculations, thus enabling time to become a parameter in the calculations. As we move forward, databases encompassing thermodynamic, mobility, and physical properties of multi-component and multi-phase materials serve as fundamental resources for materials design. By combining these computational techniques with their associated databases, researchers can simulate phase transformations and accurately predict microstructure evolution in real materials in the near future. Furthermore, the integration of micro- and macro-scale simulations facilitates the development of a multi-scale computational framework, which aids in identifying quantitative relationships between chemistry, process parameters, microstructures, and materials properties, thereby accelerating materials development and deployment.

The Special Issue on "Computational Thermodynamics" invites contributions on various aspects of computational thermodynamics and kinetics, including:

  • Advanced applications of classical approaches, such as complex equilibrium calculations and/or multi-component phase diagrams.
  • Exploring methodologies beyond complex equilibria, such as the utilization of the method of local equilibria interconnected with material streams and the incorporation of empirical methods to account for kinetic inhibitions.
  • Modelling materials properties based on Gibbs-energy models for phase internal or multi-phase compositions, encompassing viscosities, densities, and surface tensions of melts (including metallic, salt, and oxide systems, among others).
  • Establishing links between classical thermodynamic calculations and kinetic data, involving transport phenomena and reaction kinetics, for instance, the dissolution or precipitation of inclusions in steels and phase transformations in solid materials.
  • Advancements in the generation of Gibbs energy data, including novel approaches to Calphad assessments and the development of ab-initio based Gibbs-energy datasets for elements, complex stoichiometric compounds, and solid solutions.

Prof. Dr. Klaus Hack
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computational thermodynamics
  • Gibbs-energy models
  • complex equilibrium calculations
  • multi-component phase diagrams
  • advanced process modelling

Published Papers (1 paper)

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Research

20 pages, 8265 KiB  
Article
Simulation of Natural Convection with Sinusoidal Temperature Distribution of Heat Source at the Bottom of an Enclosed Square Cavity
by Min Zeng, Zhiqiang Wang, Ying Xu and Qiang Ma
Entropy 2024, 26(4), 347; https://doi.org/10.3390/e26040347 - 19 Apr 2024
Viewed by 223
Abstract
The lattice Boltzmann method is employed in the current study to simulate the heat transfer characteristics of sinusoidal-temperature-distributed heat sources at the bottom of a square cavity under various conditions, including different amplitudes, phase angles, initial positions, and angular velocities. Additionally, a machine [...] Read more.
The lattice Boltzmann method is employed in the current study to simulate the heat transfer characteristics of sinusoidal-temperature-distributed heat sources at the bottom of a square cavity under various conditions, including different amplitudes, phase angles, initial positions, and angular velocities. Additionally, a machine learning-based model is developed to accurately predict the Nusselt number in such a sinusoidal temperature distribution of heat source at the bottom of a square cavity. The results indicate that (1) in the phase angle range from 0 to π, Nu basically shows a decreasing trend with an increase in phase angle. The decline in Nu at an accelerated rate is consistently observed when the phase angle reaches 4π/16. The corresponding Nu decreases as the amplitude increases at the same phase angle. (2) The initial position of the sinusoidal-temperature-distributed heat source Lc significantly impacts the convective heat transfer in the cavity. Moreover, the decline in Nu was further exacerbated when Lc reached 7/16. (3) The optimal overall heat transfer effect was achieved when the angular velocity of the non-uniform heat source reached π. As the angular velocity increases, the local Nu in the square cavity exhibits a gradual and oscillatory decline. Notably, it is observed that Nu at odd multiples of π surpasses that at even multiples of π. Furthermore, the current work integrates LBM with machine learning, enabling the development of a precise and efficient prediction model for simulating Nu under specific operational conditions. This research provides valuable insights into the application of machine learning in the field of heat transfer. Full article
(This article belongs to the Special Issue Computational Thermodynamics and Its Applications)
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