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Symmetry
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Symmetry Studies in Heat and Mass Transfer
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Symmetry Studies in Heat and Mass Transfer

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Engineering and Materials".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1172

Special Issue Editors

Prof. Dr. Tao Wang

E-Mail Website
Guest Editor
College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China
Interests: research on the mechanism and intelligent regulation of phase change heat transfer; R&D and industrialization of efficient thermal management technology; development and optimization of new thermal cycle systems
Dr. Seolha Kim

E-Mail Website
Guest Editor
Department of Precise Mechanical Engineering, Kyungpook National University, Sangju, Republic of Korea
Interests: thermal; fluid engineering

Special Issue Information

Dear Colleagues,

This Special Issue aims to consolidate cutting-edge research in the field of heat and mass transfer, focusing on the fundamental role of symmetry in transport phenomena. We seek to bring together theoretical insights, computational advancements, and experimental findings that elucidate the symmetrical aspects of transfer processes and their practical applications in engineering systems. The scope encompasses a wide range of topics, from molecular-level interactions to macroscale industrial applications. We particularly encourage contributions that explore novel approaches to enhance heat and mass transfer efficiency, develop innovative analytical methods, or uncover patterns in complex transfer systems.

This Special Issue welcomes high-quality, original research papers that advance our understanding of symmetry in heat and mass transfer processes and their engineering implications. 

Prof. Dr. Tao Wang
Dr. Seolha Kim
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. Symmetry 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 2400 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

  • Fundamental transport phenomena.  
    • Conduction and thermal diffusion. 
    • Convection (natural, forced, and mixed). 
    • Radiation heat transfer.
  • Multiphase systems.  
    • Boiling and evaporation. 
    • Condensation.  
    • Interfacial phenomena. 
    • Multiphase flow dynamics.
  • Advanced materials and processes.  
    • Micro/nanofluidics and heat transfer. 
    • Porous media transport. 
    • Functional and smart materials for heat transfer. 
    • Thermal management in electronic systems.
  • Energy systems and applications.  
    • Heat exchangers and thermal systems design. 
    • Renewable energy technologies (solar, geothermal). 
    • Nuclear thermal hydraulics. 
    • Thermal energy storage.
  • Computational and analytical methods.  
    • Machine learning in heat and mass transfer. 
    • Optimization of symmetric transfer processes.
  • Experimental techniques and measurements.  
    • Advanced visualization methods. 
    • Thermophysical property measurements. 
    • Non-intrusive diagnostic techniques

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

Published Papers (2 papers)

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Research

28 pages, 5256 KiB  
Article
Accurate Gas–Steam Combined Cycle Efficiency Prediction Based on Neural Network Model
by Tao Wang, Changtong Ye, Hemin Hu, Bing Zhang, Jian Qi and Zhaoming Wang
Symmetry 2025, 17(3), 318; https://doi.org/10.3390/sym17030318 - 20 Feb 2025
Viewed by 313
Abstract
(1) Background: To enhance the efficiency and minimize the energy consumption of combined cycle power plants (CCPPs), it is crucial to research gas–steam combined cycle (GSCC) performance prediction under various conditions. However, current studies focus more on the subsystems of GSCC, including simpler [...] Read more.
(1) Background: To enhance the efficiency and minimize the energy consumption of combined cycle power plants (CCPPs), it is crucial to research gas–steam combined cycle (GSCC) performance prediction under various conditions. However, current studies focus more on the subsystems of GSCC, including simpler systems like gas turbines and steam turbines, lacking an overall perspective on the GSCC system as a whole. (2) Methods: this paper focuses on GSCC efficiency prediction, while employing continuous and fluctuating operational data from a CCPP. Specifically, variables from two symmetric gas turbines of the GSCC were employed as model inputs. Deep Neural Network, Simple Recurrent Neural Network, Long Short-Term Memory, and Gated Recurrent Unit (GRU) were tested. Furthermore, the GRU network was employed to evaluate the Plate Heat Exchanger (PHE) installation modification of the CCPP. (3) Results: GRU outperformed the other models, achieving a Mean Absolute Percentage Error of 0.855%. Utilizing multiple variables as model inputs provided the models better accuracy. The evaluation of the CCPP modification indicates that the PHE brought a maximum increase of 7.82 percentage points in combined cycle efficiency. (4) Conclusions: Recurrent Neural Networks, represented by GRU, are capable of predicting GSCC efficiency. Meanwhile, utilizing multiple inputs is essential to GSCC overall performance prediction. The research also proved the PHE to be effective in boosting GSCC thermal efficiency. Full article
(This article belongs to the Special Issue Symmetry Studies in Heat and Mass Transfer)
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16 pages, 4920 KiB  
Article
Molecular Dynamics Simulations of CeO2 Nano-Fuel: Thermodynamic and Kinetic Properties
by Rui Zhang, Jianbo Zhou, Yingjie Zhao, Zhen He, Wenxiong Xi and Weidong Zhao
Symmetry 2025, 17(2), 296; https://doi.org/10.3390/sym17020296 - 16 Feb 2025
Viewed by 574
Abstract
This study explores the thermodynamic and kinetic properties of CeO2 nano-fuels, with a particular focus on the influence of nanoparticle additives on the diffusion and thermal conductivity of C14-based fuel systems. Using molecular dynamics simulations and the COMPASS force field, we model [...] Read more.
This study explores the thermodynamic and kinetic properties of CeO2 nano-fuels, with a particular focus on the influence of nanoparticle additives on the diffusion and thermal conductivity of C14-based fuel systems. Using molecular dynamics simulations and the COMPASS force field, we model the interactions between C14 molecules and CeO2 nanoparticles, varying nanoparticle size and concentration. Our results reveal that the inclusion of CeO2 nanoparticles leads to significant enhancements in both thermal conductivity (increasing by 9.8–23.6%) and diffusion coefficients (increasing by approximately 140%) within the 20 °C to 100 °C temperature range. These improvements are attributed to the interactions between nanoparticles and fuel molecules, which facilitate more efficient energy and mass transport. Notably, nanoparticles with smaller sizes (0.2 nm and 0.5 nm) exhibit more pronounced effects on both the thermodynamic and kinetic properties than larger nanoparticle analogs (20 nm and 50 nm). The study also highlights the temperature-dependent nature of these properties, demonstrating that nanoparticle additives enhance the fuel’s thermal stability and diffusion behavior, particularly at elevated temperatures. This work provides valuable insights into the optimization of nano-fuel systems, with potential applications in enhancing the performance and efficiency of diesel combustion and heat transfer processes. Full article
(This article belongs to the Special Issue Symmetry Studies in Heat and Mass Transfer)
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