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Prof. Dr. Klaus Hack

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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

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Research

16 pages, 4306 KiB

Open AccessFeature PaperArticle

Extended Regression Analysis for Debye–Einstein Models Describing Low Temperature Heat Capacity Data of Solids

by Ernst Gamsjäger and Manfred Wiessner

Entropy 2024, 26(6), 452; https://doi.org/10.3390/e26060452 - 26 May 2024

Viewed by 987

Abstract

Heat capacity data of many crystalline solids can be described in a physically sound manner by Debye–Einstein integrals in the temperature range from

0 K

300 K

. The parameters of the Debye–Einstein approach are either obtained by a Markov chain Monte [...] Read more.

Heat capacity data of many crystalline solids can be described in a physically sound manner by Debye–Einstein integrals in the temperature range from

0 K

300 K

. The parameters of the Debye–Einstein approach are either obtained by a Markov chain Monte Carlo (MCMC) global optimization method or by a Levenberg–Marquardt (LM) local optimization routine. In the case of the MCMC approach the model parameters and the coefficients of a function describing the residuals of the measurement points are simultaneously optimized. Thereby, the Bayesian credible interval for the heat capacity function is obtained. Although both regression tools (LM and MCMC) are completely different approaches, not only the values of the Debye–Einstein parameters, but also their standard errors appear to be similar. The calculated model parameters and their associated standard errors are then used to derive the enthalpy, entropy and Gibbs energy as functions of temperature. By direct insertion of the MCMC parameters of all

4 \cdot 10^{5}

computer runs the distributions of the integral quantities enthalpy, entropy and Gibbs energy are determined. Full article

(This article belongs to the Special Issue Computational Thermodynamics and Its Applications)

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20 pages, 8265 KiB

Open AccessArticle

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

Cited by 1 | Viewed by 1080

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 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 L_c significantly impacts the convective heat transfer in the cavity. Moreover, the decline in Nu was further exacerbated when L_c 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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