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Artificial Intelligence and Computational Issues in Engineering Applications

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: closed (17 October 2021) | Viewed by 22870

Special Issue Editors


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Guest Editor
Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, Armii Krajowej 13/15, 42-200 Czestochowa, Poland
Interests: modeling; adsorption chillers; CFB boilers; oxy-fuel combustion; CLC; CaL; biomass; machine learning; artificial neural networks; fuzzy logic; genetic algorithms
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, Armii Krajowej 13/15, 42-200, Czestochowa, Poland
Interests: artificial intelligence; computational fluid dynamics; modeling; adsorption chillers; renewable energy sources; heat and mass transfer; energy storage
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor

E-Mail Website
Guest Editor
Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, 13/15 Armii Krajowej Av., 42-200 Czestochowa, Poland
Interests: finite element analysis; design engineering; cad; modeling and simulation; finite element modeling; optimization; stress analysis; engineering, applied and computational mathematics; engineering drawing; mechanical processes

Special Issue Information

Dear Colleagues,

As entropy generation accomplished by exergy destruction is a characteristic of complex systems, their irreversibilities should be reduced to increase their performance. Optimization via modeling and simulations allows an increase in a system’s efficiency.

This Special Issue aims to bring together research related to the modeling of complex systems in a wide variety of engineering applications. Original research articles, as well as review articles, with a particular focus on (but not limited to) optimization by artificial intelligence (AI) algorithms and computational fluid dynamics (CFD), are welcome.

Dr. Jaroslaw Krzywanski
Dr. Karolina Grabowska
Dr. Marcin Sosnowski
Dr. Dorian Skrobek
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. Entropy 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 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

  • computing
  • simulation
  • machine learning
  • artificial intelligence
  • computational fluid dynamics
  • optimization
  • energy systems
  • renewable energy
  • energy policy
  • complex systems

Published Papers (8 papers)

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Editorial

Jump to: Research, Review

4 pages, 171 KiB  
Editorial
Artificial Intelligence and Computational Issues in Engineering Applications
by Karolina Grabowska, Jaroslaw Krzywanski, Marcin Sosnowski and Dorian Skrobek
Entropy 2023, 25(1), 5; https://doi.org/10.3390/e25010005 - 21 Dec 2022
Viewed by 973
Abstract
High-performance supercomputers and emerging computing clusters created in research and development centres are rapidly increasing available computing power, which scientists are eager to use to implement increasingly advanced computing methods [...] Full article

Research

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14 pages, 647 KiB  
Article
A Soar-Based Space Exploration Algorithm for Mobile Robots
by Fei Luo, Qin Zhou, Joel Fuentes, Weichao Ding and Chunhua Gu
Entropy 2022, 24(3), 426; https://doi.org/10.3390/e24030426 - 19 Mar 2022
Cited by 8 | Viewed by 2723
Abstract
Space exploration is a hot topic in the application field of mobile robots. Proposed solutions have included the frontier exploration algorithm, heuristic algorithms, and deep reinforcement learning. However, these methods cannot solve space exploration in time in a dynamic environment. This paper models [...] Read more.
Space exploration is a hot topic in the application field of mobile robots. Proposed solutions have included the frontier exploration algorithm, heuristic algorithms, and deep reinforcement learning. However, these methods cannot solve space exploration in time in a dynamic environment. This paper models the space exploration problem of mobile robots based on the decision-making process of the cognitive architecture of Soar, and three space exploration heuristic algorithms (HAs) are further proposed based on the model to improve the exploration speed of the robot. Experiments are carried out based on the Easter environment, and the results show that HAs have improved the exploration speed of the Easter robot at least 2.04 times of the original algorithm in Easter, verifying the effectiveness of the proposed robot space exploration strategy and the corresponding HAs. Full article
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14 pages, 663 KiB  
Article
An Edge Server Placement Method Based on Reinforcement Learning
by Fei Luo, Shuai Zheng, Weichao Ding, Joel Fuentes and Yong Li
Entropy 2022, 24(3), 317; https://doi.org/10.3390/e24030317 - 23 Feb 2022
Cited by 13 | Viewed by 2420
Abstract
In mobile edge computing systems, the edge server placement problem is mainly tackled as a multi-objective optimization problem and solved with mixed integer programming, heuristic or meta-heuristic algorithms, etc. These methods, however, have profound defect implications such as poor scalability, local optimal solutions, [...] Read more.
In mobile edge computing systems, the edge server placement problem is mainly tackled as a multi-objective optimization problem and solved with mixed integer programming, heuristic or meta-heuristic algorithms, etc. These methods, however, have profound defect implications such as poor scalability, local optimal solutions, and parameter tuning difficulties. To overcome these defects, we propose a novel edge server placement algorithm based on deep q-network and reinforcement learning, dubbed DQN-ESPA, which can achieve optimal placements without relying on previous placement experience. In DQN-ESPA, the edge server placement problem is modeled as a Markov decision process, which is formalized with the state space, action space and reward function, and it is subsequently solved using a reinforcement learning algorithm. Experimental results using real datasets from Shanghai Telecom show that DQN-ESPA outperforms state-of-the-art algorithms such as simulated annealing placement algorithm (SAPA), Top-K placement algorithm (TKPA), K-Means placement algorithm (KMPA), and random placement algorithm (RPA). In particular, with a comprehensive consideration of access delay and workload balance, DQN-ESPA achieves up to 13.40% and 15.54% better placement performance for 100 and 300 edge servers respectively. Full article
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9 pages, 2605 KiB  
Communication
Thermodynamics Irreversibilities Analysis of Oxy-Fuel Diffusion Flames: The Effect of Oxygen Concentration
by Huibo Yan, Guangtong Tang, Chaoyang Wang, Lujiang Li, Yuanke Zhou, Zhongnong Zhang and Chun Lou
Entropy 2022, 24(2), 205; https://doi.org/10.3390/e24020205 - 28 Jan 2022
Cited by 5 | Viewed by 1695
Abstract
In studies on the combustion process, thermodynamic analysis can be used to evaluate the irreversibility of the combustion process and improve energy utilization efficiency. In this paper, the combustion process of a laminar oxy-fuel diffusion flame was simulated, and the entropy generation due [...] Read more.
In studies on the combustion process, thermodynamic analysis can be used to evaluate the irreversibility of the combustion process and improve energy utilization efficiency. In this paper, the combustion process of a laminar oxy-fuel diffusion flame was simulated, and the entropy generation due to the irreversibilities of the radiation process, the heat conduction and heat convection process, the mass diffusion process, and the chemical reaction process was calculated. The effect of the oxygen concentration in the oxidizer on the entropy generation was analyzed. The results indicated that, as the oxygen concentration in the oxidizer increases, the radiative entropy generation first increases and then decreases, and the convective and conductive entropy generation, the mass diffusion entropy generation, the chemical entropy generation, and the total entropy generation gradually increase. Full article
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21 pages, 3529 KiB  
Article
Study of Nonlinear Models of Oscillatory Systems by Applying an Intelligent Computational Technique
by Naveed Ahmad Khan, Fahad Sameer Alshammari, Carlos Andrés Tavera Romero and Muhammad Sulaiman
Entropy 2021, 23(12), 1685; https://doi.org/10.3390/e23121685 - 15 Dec 2021
Cited by 6 | Viewed by 2605
Abstract
In this paper, we have analyzed the mathematical model of various nonlinear oscillators arising in different fields of engineering. Further, approximate solutions for different variations in oscillators are studied by using feedforward neural networks (NNs) based on the backpropagated Levenberg–Marquardt algorithm (BLMA). A [...] Read more.
In this paper, we have analyzed the mathematical model of various nonlinear oscillators arising in different fields of engineering. Further, approximate solutions for different variations in oscillators are studied by using feedforward neural networks (NNs) based on the backpropagated Levenberg–Marquardt algorithm (BLMA). A data set for different problem scenarios for the supervised learning of BLMA has been generated by the Runge–Kutta method of order 4 (RK-4) with the “NDSolve” package in Mathematica. The worth of the approximate solution by NN-BLMA is attained by employing the processing of testing, training, and validation of the reference data set. For each model, convergence analysis, error histograms, regression analysis, and curve fitting are considered to study the robustness and accuracy of the design scheme. Full article
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39 pages, 1897 KiB  
Article
A Hybrid Metaheuristic Based on Neurocomputing for Analysis of Unipolar Electrohydrodynamic Pump Flow
by Muhammad Fawad Khan, Muhammad Sulaiman, Carlos Andrés Tavera Romero and Ali Alkhathlan
Entropy 2021, 23(11), 1513; https://doi.org/10.3390/e23111513 - 14 Nov 2021
Cited by 8 | Viewed by 1813
Abstract
A unipolar electrohydrodynamic (UP-EHD) pump flow is studied with known electric potential at the emitter and zero electric potential at the collector. The model is designed for electric potential, charge density, and electric field. The dimensionless parameters, namely the electrical source number [...] Read more.
A unipolar electrohydrodynamic (UP-EHD) pump flow is studied with known electric potential at the emitter and zero electric potential at the collector. The model is designed for electric potential, charge density, and electric field. The dimensionless parameters, namely the electrical source number (Es), the electrical Reynolds number (ReE), and electrical slip number (Esl), are considered with wide ranges of variation to analyze the UP-EHD pump flow. To interpret the pump flow of the UP-EHD model, a hybrid metaheuristic solver is designed, consisting of the recently developed technique sine–cosine algorithm (SCA) and sequential quadratic programming (SQP) under the influence of an artificial neural network. The method is abbreviated as ANN-SCA-SQP. The superiority of the technique is shown by comparing the solution with reference solutions. For a large data set, the technique is executed for one hundred independent experiments. The performance is evaluated through performance operators and convergence plots. Full article
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16 pages, 5720 KiB  
Article
A Crop Image Segmentation and Extraction Algorithm Based on Mask RCNN
by Shijie Wang, Guiling Sun, Bowen Zheng and Yawen Du
Entropy 2021, 23(9), 1160; https://doi.org/10.3390/e23091160 - 3 Sep 2021
Cited by 36 | Viewed by 5262
Abstract
The wide variety of crops in the image of agricultural products and the confusion with the surrounding environment information makes it difficult for traditional methods to extract crops accurately and efficiently. In this paper, an automatic extraction algorithm is proposed for crop images [...] Read more.
The wide variety of crops in the image of agricultural products and the confusion with the surrounding environment information makes it difficult for traditional methods to extract crops accurately and efficiently. In this paper, an automatic extraction algorithm is proposed for crop images based on Mask RCNN. First, the Fruits 360 Dataset label is set with Labelme. Then, the Fruits 360 Dataset is preprocessed. Next, the data are divided into a training set and a test set. Additionally, an improved Mask RCNN network model structure is established using the PyTorch 1.8.1 deep learning framework, and path aggregation and features are added to the network design enhanced functions, optimized region extraction network, and feature pyramid network. The spatial information of the feature map is saved by the bilinear interpolation method in ROIAlign. Finally, the edge accuracy of the segmentation mask is further improved by adding a micro-fully connected layer to the mask branch of the ROI output, employing the Sobel operator to predict the target edge, and adding the edge loss to the loss function. Compared with FCN and Mask RCNN and other image extraction algorithms, the experimental results demonstrate that the improved Mask RCNN algorithm proposed in this paper is better in the precision, Recall, Average precision, Mean Average Precision, and F1 scores of crop image extraction results. Full article
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Review

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33 pages, 2926 KiB  
Review
Recent Status and Prospects on Thermochemical Heat Storage Processes and Applications
by Tadagbe Roger Sylvanus Gbenou, Armand Fopah-Lele and Kejian Wang
Entropy 2021, 23(8), 953; https://doi.org/10.3390/e23080953 - 26 Jul 2021
Cited by 23 | Viewed by 4057
Abstract
Recent contributions to thermochemical heat storage (TCHS) technology have been reviewed and have revealed that there are four main branches whose mastery could significantly contribute to the field. These are the control of the processes to store or release heat, a perfect understanding [...] Read more.
Recent contributions to thermochemical heat storage (TCHS) technology have been reviewed and have revealed that there are four main branches whose mastery could significantly contribute to the field. These are the control of the processes to store or release heat, a perfect understanding and designing of the materials used for each storage process, the good sizing of the reactor, and the mastery of the whole system connected to design an efficient system. The above-mentioned fields constitute a very complex area of investigation, and most of the works focus on one of the branches to deepen their research. For this purpose, significant contributions have been and continue to be made. However, the technology is still not mature, and, up to now, no definitive, efficient, autonomous, practical, and commercial TCHS device is available. This paper highlights several issues that impede the maturity of the technology. These are the limited number of research works dedicated to the topic, the simulation results that are too illusory and impossible to implement in real prototypes, the incomplete analysis of the proposed works (simulation works without experimentation or experimentations without prior simulation study), and the endless problem of heat and mass transfer limitation. This paper provides insights and recommendations to better analyze and solve the problems that still challenge the technology. Full article
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