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Heat Diffusion: Dynamical Modelling, Control

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "J: Thermal Management".

Deadline for manuscript submissions: closed (24 September 2021) | Viewed by 4860

Special Issue Editor


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Guest Editor
Department of Electrical Engineering, Institute of Engineering-Polytechnic of Porto (ISEP/IPP), Rua Dr. António Bernardino de Almeida, 431, 4249-015 Porto, Portugal
Interests: photovoltaic systems; fractional order control systems; fuzzy control systems; evolutionary algorithms
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Special Issue Information

Dear Colleagues,

Many industries have heat diffusion systems incorporated in their processes. Due to the delay in reaching the desired temperature and the dificulty of maintaining this value, several control algorithms have been developed in the last decades to produce better systems responses. In the previous century, the most common algorithms used in the control of systems were PID controllers, with their tuning parameters based in the Ziegler–Nichols or Cohen–Coon methods. More recently, several other methodologies have been implemented using genetic algorithms, particle swarm optimization, fuzzy control systems, and other algorithms, which were found to be helpful tools for determining the parameters of the controllers, consequently improving this kind of system. These algorithms introduce concepts based on nature, evolution, or human knowledge to better tune the PID parameters. This Special Issue seeks to gather contributions to the control of heat diffusion systems using these suggestions or other methodologies, such as the incorporation of fractional order controllers.

Prof. Dr. Isabel Jesus
Guest Editor

Manuscript Submission Information

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Keywords

  • Heat diffusion systems
  • Fractional order control systems
  • Genetic algorithms
  • Fuzzy control systems
  • PSO algorithm

Published Papers (2 papers)

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Research

17 pages, 2250 KiB  
Article
Management of the Torch Structure with the New Methodological Approaches to Regulation Based on Neural Network Algorithms
by Konstantin Osintsev, Sergei Aliukov and Yuri Prikhodko
Energies 2021, 14(7), 1909; https://0-doi-org.brum.beds.ac.uk/10.3390/en14071909 - 30 Mar 2021
Cited by 1 | Viewed by 1259
Abstract
A method for evaluating the thermophysical characteristics of the torch is developed. Mathematically the temperature at the end of the zone of active combustion based on continuous distribution functions of particles of solid fuels, in particular coal dust. The particles have different average [...] Read more.
A method for evaluating the thermophysical characteristics of the torch is developed. Mathematically the temperature at the end of the zone of active combustion based on continuous distribution functions of particles of solid fuels, in particular coal dust. The particles have different average sizes, which are usually grouped and expressed as a fraction of the total mass of the fuel. The authors suggest taking into account the sequential nature of the entry into the chemical reactions of combustion of particles of different masses. In addition, for the application of the developed methodology, it is necessary to divide the furnace volume into zones and sections. In particular, the initial section of the torch, the zone of intense burning and the zone of afterburning. In this case, taking into account all the thermophysical characteristics of the torch, it is possible to make a thermal balance of the zone of intense burning. Then determines the rate of expiration of the fuel-air mixture, the time of combustion of particles of different masses and the temperature at the end of the zone of intensive combustion. The temperature of the torch, the speed of flame propagation, and the degree of particle burnout must be controlled. The authors propose an algorithm for controlling the thermophysical properties of the torch based on neural network algorithms. The system collects data for a certain time, transmits the information to the server. The data is processed and a forecast is made using neural network algorithms regarding the combustion modes. This allows to increase the reliability and efficiency of the combustion process. The authors present experimental data and compare them with the data of the analytical calculation. In addition, data for certain modes are given, taking into account the system’s operation based on neural network algorithms. Full article
(This article belongs to the Special Issue Heat Diffusion: Dynamical Modelling, Control)
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25 pages, 4216 KiB  
Article
Multi-Layer Transient Heat Conduction Involving Perfectly-Conducting Solids
by Giampaolo D’Alessandro and Filippo de Monte
Energies 2020, 13(24), 6484; https://0-doi-org.brum.beds.ac.uk/10.3390/en13246484 - 08 Dec 2020
Cited by 3 | Viewed by 3066
Abstract
Boundary conditions of high kinds (fourth and sixth kind) as defined by Carslaw and Jaeger are used in this work to model the thermal behavior of perfect conductors when involved in multi-layer transient heat conduction problems. In detail, two- and three-layer configurations are [...] Read more.
Boundary conditions of high kinds (fourth and sixth kind) as defined by Carslaw and Jaeger are used in this work to model the thermal behavior of perfect conductors when involved in multi-layer transient heat conduction problems. In detail, two- and three-layer configurations are analyzed. In the former, a thin layer modeled as a lumped body is subject to a surface heat flux on the front side while it is in perfect (fourth kind) or in imperfect (sixth kind) thermal contact with a semi-infinite or finite body on the back side. When dealing with a semi-infinite body in imperfect contact, the temperature solution is derived by means of the Laplace transform method. Green’s function approach is also used but for solving the companion case of a finite body in perfect contact with the thin film. In the latter, a thin layer with internal heat generation is located between two semi-infinite or finite bodies in perfect/imperfect contact. For the sake of thermal symmetry, such a three-layer structure reduces to a two-layer configuration. Results are given in both tabular and graphical forms and show the effect of heat capacity and thermal resistance on the temperature distribution of conductive layers. Full article
(This article belongs to the Special Issue Heat Diffusion: Dynamical Modelling, Control)
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