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Thermo
Special Issues
Numerical Simulations for Thermal Engineering and Thermodynamic Systems
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Numerical Simulations for Thermal Engineering and Thermodynamic Systems

A special issue of Thermo (ISSN 2673-7264).

Deadline for manuscript submissions: closed (15 September 2024) | Viewed by 6545

Special Issue Editors

Prof. Dr. Emmanouil Rogdakis

E-Mail Website
Guest Editor
Laboratory of Applied Thermodynamics, School of Mechanical Engineering, National Technical University of Athens, 9 Heroon Polytechneiou, 15780 Athens, Greece
Interests: thermodynamics; stirling engines; heat transfer; thermal engines; cryogenics; energy engineering; engine modelling and simulations; renewables
Special Issues, Collections and Topics in MDPI journals
Dr. George-Rafael Domenikos

E-Mail Website
Co-Guest Editor
Applied Thermodynamics Laboratory, School of Mechanical Engineering, National Technical University of Athens, 15780 Athens, Greece
Interests: thermodynamics; cryogenics; engine modelling and simulations; statistical thermodynamics; social physics; low-temperature physics; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Guest Editors of the MDPI Thermo Special Issue “Numerical Simulations for Thermal Engineering and Thermodynamic Systems” are inviting authors to submit work for publication. This publication intends to include a wide range of heat transfer and thermodynamics papers covering both theoretical models as well as applications. Models and simulations of heat transfer are complicated procedures dealing with a lot of different phenomena. Those that this Special Issue is aiming towards can be under the scope of CFD modelling or pure thermodynamical analysis, while applications of informational statistical descriptions of such systems are also of interest.

Overall, the topics of interest for this Special Issue include, but are not limited to:

  • Numerical models of heat exchangers;
  • Modelling and simulations of 1D and 3D thermal engines and refrigeration systems;
  • Numerical solution methods for the engine and thermal designs;
  • Numerical validation/verification of CFD and heat transfer models against simulations or experimental data;
  • Descriptions of heat transfer models at extreme temperature ranges and phases of matter (plasma physics, Bose–Einstein Condensates, superfluids etc.);
  • Mathematical models for novel solutions of heat transfer or thermofluids models;
  • Combination of numerical and AI models for the description of systems (radiation, porous media, heat exchanges etc.);
  • Applications of renewable technologies relating to power production from applications, such as solar cells, under the scope of their thermal behaviour;
  • Derivations of heat transfer properties of materials;
  • Applications of thermodynamic simulations to informational systems.

In this issue, authors can also submit review papers which consider the modelling and simulations of heat transfer or closely related subjects. In terms of review papers, authors are encouraged to contact the editors to verify that the proposed review is of interest to the Issue.

You may choose our Joint Special Issue in Entropy.

Prof. Dr. Emmanouil Rogdakis
Dr. George-Rafael Domenikos
Guest Editors

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. Thermo is an international peer-reviewed open access quarterly 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 1000 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

  • thermodynamics
  • heat transfer
  • numerical modelling
  • simulations
  • optimization
  • renewables
  • energy transfer

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

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20 pages, 5620 KiB  
Article
Packed Bed Thermal Energy Storage System: Parametric Study
by Ayah Marwan Rabi’, Jovana Radulovic and James M. Buick
Thermo 2024, 4(3), 295-314; https://doi.org/10.3390/thermo4030016 - 10 Jul 2024
Cited by 1 | Viewed by 1723
Abstract
The use of thermal energy storage (TES) contributes to the ongoing process of integrating various types of energy resources in order to achieve cleaner, more flexible, and more sustainable energy use. Numerical modelling of hot storage packed bed storage systems has been conducted [...] Read more.
The use of thermal energy storage (TES) contributes to the ongoing process of integrating various types of energy resources in order to achieve cleaner, more flexible, and more sustainable energy use. Numerical modelling of hot storage packed bed storage systems has been conducted in this paper in order to investigate the optimum design of the hot storage system. In this paper, the effect of varying design parameters, including the diameter of the packed bed, the storage material, the void fraction, and the aspect ratio of the packed bed, on storage performance was investigated. COMSOL Multiphysics 5.6 software has been used to design, simulate, and validate an axisymmetric model, which was then applied to evaluate the performance of the storage system based on the total energy stored, the heat transfer efficiency, and the capacity factor. In this paper, a novel-packed bed was proposed based on the parametric analysis. This involved a 0.2 void fraction, 4 mm porous media particle diameter, and Magnesia as the optimum storage material with air as the heat transfer fluid. Full article
(This article belongs to the Special Issue Numerical Simulations for Thermal Engineering and Thermodynamic Systems)
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17 pages, 3662 KiB  
Article
Enhancing Bi2Te2.70Se0.30 Thermoelectric Module Performance through COMSOL Simulations
by Md. Kamrul Hasan, Mehmet Ali Üstüner, Hayati Mamur and Mohammad Ruhul Amin Bhuiyan
Thermo 2024, 4(2), 185-201; https://doi.org/10.3390/thermo4020011 - 6 May 2024
Cited by 1 | Viewed by 1473
Abstract
This research employs the COMSOL Multiphysics software (COMSOL 6.2) to conduct rigorous simulations and assess the performance of a thermoelectric module (TEM) meticulously crafted with alumina (Al2O3), copper (Cu), and Bi2Te2.70Se0.30 thermoelectric (TE) materials. [...] Read more.
This research employs the COMSOL Multiphysics software (COMSOL 6.2) to conduct rigorous simulations and assess the performance of a thermoelectric module (TEM) meticulously crafted with alumina (Al2O3), copper (Cu), and Bi2Te2.70Se0.30 thermoelectric (TE) materials. The specific focus is on evaluating diverse aspects of the Bi2Te2.70Se0.30 thermoelectric generator (TEG). The TEM design incorporates Bi2Te2.70Se0.30 for TE legs of the p- and n-type positioned among the Cu layers, Cu as the electrical conductor, and Al2O3 serving as an electrical insulator between the top and bottom layers. A thorough investigation is conducted into critical parameters within the TEM, which include arc length, electric potential, normalized current density, temperature gradient, total heat source, and total net energy rate. The geometric configuration of the square-shaped Bi2Te2.70Se0.30 TEM, measuring 1 mm × 1 mm × 2.5 mm with a 0.25 mm Al2O3 thickness and a 0.125 mm Cu thickness, is scrutinized. This study delves into the transport phenomena of TE devices, exploring the impacts of the Seebeck coefficient (S), thermal conductivity (k), and electrical conductivity (σ) on the temperature differential across the leg geometry. Modeling studies underscore the substantial influence of S = ±2.41 × 10−3 V/K, revealing improved thermal conductivity and decreased electrical conductivity at lower temperatures. The findings highlight the Bi2Te2.70Se0.30 TEM’s high potential for TEG applications, offering valuable insights into design and performance considerations crucial for advancing TE technology. Full article
(This article belongs to the Special Issue Numerical Simulations for Thermal Engineering and Thermodynamic Systems)
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15 pages, 5581 KiB  
Article
Analysis of Supersonic Flows inside a Steam Ejector with Liquid–Vapor Phase Change Using CFD Simulations
by Hugues Charton, Christian Perret and Hai Trieu Phan
Thermo 2024, 4(1), 1-15; https://doi.org/10.3390/thermo4010001 - 12 Jan 2024
Cited by 1 | Viewed by 1305
Abstract
In this work, different CFD models to compute flows inside a steam ejector were investigated. The results were compared to the analytical models as well as the experimental results from the literature. All the simulations gave realistic results from the hydrodynamic perspective with [...] Read more.
In this work, different CFD models to compute flows inside a steam ejector were investigated. The results were compared to the analytical models as well as the experimental results from the literature. All the simulations gave realistic results from the hydrodynamic perspective with a relative error of the entrainment ratio between 25% and 40% compared to reference experimental data. However, an analysis of the temperature profiles showed that only realistic results from the thermodynamic perspective were given by multiphase calculations. The first multiphase model tested was the so-called Wet-Steam model from ANSYS Fluent. This model gave inconsistent results for the steam ejector CFD simulation due to the physical boundaries of this model. The second model tested was the Eulerian mixture model, which gave the most realistic results in terms of the physical conditions of the liquid and vapor phases inside the ejector. It also showed that the phase change could have a significant impact on the value of the critical output pressure as a way to improve the performance of the ejector. Full article
(This article belongs to the Special Issue Numerical Simulations for Thermal Engineering and Thermodynamic Systems)
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Other

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27 pages, 5900 KiB  
Technical Note
Artificial Intelligence Applied to Microwave Heating Systems: Prediction of Temperature Profile through Convolutional Neural Networks
by Victor Rosario Núñez, Alfonso Hernández, Iván Rodríguez, Ignacio Fernández-Pacheco Ruiz and Luis Acevedo
Thermo 2024, 4(3), 346-372; https://doi.org/10.3390/thermo4030018 - 3 Aug 2024
Viewed by 1230
Abstract
Microwave heating, which is caused by the interaction of electromagnetic radiation and materials, has become an important component in industrial operations across numerous industries. Despite their importance, conventional numerical simulations of microwave heating are computationally intensive. Concurrently, advances in artificial intelligence (AI), particularly [...] Read more.
Microwave heating, which is caused by the interaction of electromagnetic radiation and materials, has become an important component in industrial operations across numerous industries. Despite their importance, conventional numerical simulations of microwave heating are computationally intensive. Concurrently, advances in artificial intelligence (AI), particularly machine learning algorithms, have transformed data processing by increasing accuracy while decreasing computational time. This study tackles the difficulty of efficient and accurate modelling in microwave heating by combining convolutional neural networks (CNNs) with traditional simulation techniques. The major goal of this research is to use CNNs to forecast temperature profiles in a variety of industrial materials, including susceptors, semi-transparent, and microwave-transparent materials, under varying power settings and heating periods. This unique strategy greatly reduces prediction times, with up to 60-fold speed increases over standard methods. Our research is based on examining the electromagnetic and thermal responses of these materials under microwave heating. This study’s findings emphasise the need for extensive datasets and show the transformational potential of CNNs in optimising material processing. It uses artificial intelligence to pave the way for more effective and exact simulations, supporting breakthroughs in industrial microwave heating applications. Full article
(This article belongs to the Special Issue Numerical Simulations for Thermal Engineering and Thermodynamic Systems)
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