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Energies
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Numerical Modeling and Machine Learning Techniques
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Numerical Modeling and Machine Learning Techniques

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A: Sustainable Energy".

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 9173

Special Issue Editors

Dr. Rubén Lostado Lorza

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of La Rioja, 26004 Logroño, La Rioja, Spain
Interests: finite element analysis; water and wastewater treatment technologies; bioremediation; environmental modelling; biomechanics and softcomputing
Special Issues, Collections and Topics in MDPI journals
Dr. Marina Corral Bobadilla

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of La Rioja, 26004 Logroño, La Rioja, Spain
Interests: biodiesel; waste cooking oil; water waste treatment; bioremediation; biosorption; adsorption; heavy metals remove; biomass
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The modeling and optimization of processes or products is today one of the most outstanding points for the advancement of today’s society. Numerical modeling and machine learning techniques are undoubtedly among the most powerful methods and techniques for modeling and optimizing processes and products, reducing their cost of design and subsequent manufacturing.

The main aim of this Special Issue on “Numerical Modeling and Machine Learning Techniques” is to present new knowledge and trends using numerical modeling or machine learning techniques for modeling and optimizing processes or products. Numerical modeling techniques of interest in this Special Issue include but are not limited to finite element analysis, the finite volume method, the finite difference method, the boundary element method, discrete element methods, multibody simulation, and computational fluid dynamics. Classification, regression, and optimization algorithms could be considered in developing machine learning or Artificial Intelligence techniques.

Prof. Dr. Rubén Lostado Lorza
Prof. Dr. Marina Corral Bobadilla
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. Energies is an international peer-reviewed open access semimonthly 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

  • Energy and power consumed during modeling and/or optimization of processes and products
  • Temperature field and strain energy
  • Current, voltage, Joule effect, and electric power
  • Heat transfer by radiation, convection, and conduction
  • Magnetic flux and eddy currents

Published Papers (3 papers)

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Research

23 pages, 3866 KiB  
Article
Numerical Simulation of Heat Transport Problems in Porous Media Coupled with Water Flow Using the Network Method
by José Antonio Jiménez-Valera, Gonzalo García-Ros and Iván Alhama
Energies 2021, 14(18), 5755; https://doi.org/10.3390/en14185755 - 13 Sep 2021
Cited by 1 | Viewed by 1189
Abstract
In the present work, a network model for the numerical resolution of the heat transport problem in porous media coupled with a water flow is presented. Starting from the governing equations, both for 1D and 2D geometries, an equivalent electrical circuit is obtained [...] Read more.
In the present work, a network model for the numerical resolution of the heat transport problem in porous media coupled with a water flow is presented. Starting from the governing equations, both for 1D and 2D geometries, an equivalent electrical circuit is obtained after their spatial discretization, so that each term or addend of the differential equation is represented by an electrical device: voltage source, capacitor, resistor or voltage-controlled current source. To make this possible, it is necessary to establish an analogy between the real physical variables of the problem and the electrical ones, that is: temperature of the medium and voltage at the nodes of the network model. The resolution of the electrical circuit, by means of the different circuit resolution codes available today, provides, in a fast, simple and precise way, the exact solution of the temperature field in the medium, which is usually represented by abaci with temperature-depth profiles. At the end of the article, a series of applications allow, on the one hand, to verify the precision of the numerical tool by comparison with existing analytical solutions and, on the other, to show the power of calculation and representation of solutions of the network models presented, both for problems in 1D domains, typical of scenarios with vertical flows, and for 2D scenarios with regional flow. Full article
(This article belongs to the Special Issue Numerical Modeling and Machine Learning Techniques)
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22 pages, 4203 KiB  
Article
Deep Reinforcement Learning Based Optimal Route and Charging Station Selection
by Ki-Beom Lee, Mohamed A. Ahmed, Dong-Ki Kang and Young-Chon Kim
Energies 2020, 13(23), 6255; https://doi.org/10.3390/en13236255 - 27 Nov 2020
Cited by 33 | Viewed by 4235
Abstract
This paper proposes an optimal route and charging station selection (RCS) algorithm based on model-free deep reinforcement learning (DRL) to overcome the uncertainty issues of the traffic conditions and dynamic arrival charging requests. The proposed DRL based RCS algorithm aims to minimize the [...] Read more.
This paper proposes an optimal route and charging station selection (RCS) algorithm based on model-free deep reinforcement learning (DRL) to overcome the uncertainty issues of the traffic conditions and dynamic arrival charging requests. The proposed DRL based RCS algorithm aims to minimize the total travel time of electric vehicles (EV) charging requests from origin to destination using the selection of the optimal route and charging station considering dynamically changing traffic conditions and unknown future requests. In this paper, we formulate this RCS problem as a Markov decision process model with unknown transition probability. A Deep Q network has been adopted with function approximation to find the optimal electric vehicle charging station (EVCS) selection policy. To obtain the feature states for each EVCS, we define the traffic preprocess module, charging preprocess module and feature extract module. The proposed DRL based RCS algorithm is compared with conventional strategies such as minimum distance, minimum travel time, and minimum waiting time. The performance is evaluated in terms of travel time, waiting time, charging time, driving time, and distance under the various distributions and number of EV charging requests. Full article
(This article belongs to the Special Issue Numerical Modeling and Machine Learning Techniques)
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11 pages, 2514 KiB  
Article
Numerical Simulation and Experimental Investigation of Temperature and Residual Stress Distributions in a Circular Patch Welded Structure
by Mato Perić, Sandro Nižetić, Zdenko Tonković, Ivica Garašić, Ivan Horvat and Ivanka Boras
Energies 2020, 13(20), 5423; https://doi.org/10.3390/en13205423 - 17 Oct 2020
Cited by 8 | Viewed by 2284
Abstract
In this study, we performed a numerical simulation and experimental measurements on a steel circular patch welded structure to investigate the temperature and residual stress field distributions caused by the application of buried-arc welding technology. The temperature histories during the welding and subsequent [...] Read more.
In this study, we performed a numerical simulation and experimental measurements on a steel circular patch welded structure to investigate the temperature and residual stress field distributions caused by the application of buried-arc welding technology. The temperature histories during the welding and subsequent cooling process were recorded for two locations, with the thermocouples mounted inside the plate close to the weld bead. On the upper surface of the welded model, the temperature-time changes during the cooling process were monitored using an infrared camera. The numerically calculated temperature values correlated well with the experimentally measured ones, while the maximum deviation of the measured and calculated temperatures was within 9%. Based on the numerical result analysis regarding circumferential and radial stresses after the completion of the welding process, it is concluded that both stresses are primarily tensile within the circular disk. Outside the disk, the circumferential stresses turn from tensile to compressive, while on the other hand the radial stresses disappear towards the ends of the plate. Full article
(This article belongs to the Special Issue Numerical Modeling and Machine Learning Techniques)
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