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Dynamic Control and Machine Learning for Thermal Management, Energy Utilization, and Environment

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

Deadline for manuscript submissions: closed (15 February 2022) | Viewed by 11486
Submit your paper and select the Journal “Energies” and the Special Issue “Dynamic Control and Machine Learning for Thermal Management, Energy Utilization, and Environment” via: https://susy.mdpi.com/user/manuscripts/upload?journal=energies. Please contact the guest editor or the journal editor ([email protected]) for any queries.

Special Issue Editors

Dr. Asif Afzal

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Guest Editor
Department of Mechanical Engineering, P. A. College of Engineering (Affiliated to Visvesvaraya Technological University, Belagavi), Mangaluru 574153, India
Interests: nano-materials; machine learning; optimization; design of experiments; thermal sciences; numerical analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Manzoore Elahi M. Soudagar

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of Malaya, Kuala Lumpur, Malaysia
Interests: renewable energies; nanotechnology; IC engines
Special Issues, Collections and Topics in MDPI journals
Dr. Islam Md Rizwanul Fattah

grade E-Mail Website
Guest Editor
School of Civil and Environmental Engineering, Faculty of Engineering and I.T., University of Technology Sydney, Ultimo, NSW 2007, Australia
Interests: renewable energy; emissions; nanoparticles; combustion; alternative fuel; machine learning; sustainable materials
Special Issues, Collections and Topics in MDPI journals
Dr. Nazia Hossain

E-Mail Website
Guest Editor
School of Engineering, Royal Melbourne Institute of Technology (RMIT) University, Melbourne, VIC 3000, Australia
Interests: biochar; biofuel; biomass utilization; functionalized nanomaterial; machine learning; techno-economic analysis; life cycle assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Temperature is controlled using technology in a thermal management system. Thermodynamics, heat transfer, and fluid flow are the foundations of this technology. The gap between the system's specifications and its requirements is the issue that thermal management systems must address. Filling this void necessitates a variety of strategies, ranging from heating and cooling to heat removal, temperature cycling, and temperature uniformity. Thermal management is essential in the automotive, aerospace, foundry, and manufacturing industries for protecting and insulating devices.

On the other hand, the world is rapidly becoming a global village as the world's population demands more energy on a daily basis, despite the fact that the Earth's shape cannot change. The demand for energy and related services to support human social and economic development, welfare, and health is increasing. All societies demand energy services to meet basic human needs such as health, lighting, cooking, space comfort, mobility, and communication, as well as to serve as generative processes.

Optimized and effective thermal management systems and energy utilization are not continuously growing research areas in academia and industry. This Special Issue bonds these two aspects as energy and thermal management are strongly inter-related. The role of different types of controls, especially dynamics control, in enhancing the thermal processes and fluid flow situations in high-speed vehicles, automobiles, aerospace vehicles, etc. are focused on. Numerical simulation methods and experimental analysis for various types of thermal management systems are covered. The focus of this Special Issue will also extend to research on energy utilization and the environment. The use of different modeling methods using machine learning techniques is quite new and tremendously growing in these fields and, hence, also falls within the scope of this Special Issue.

Dr. Asif Afzal
Dr. Manzoore Elahi M. Soudagar
Dr. Islam Md Rizwanul Fattah
Dr. Nazia Hossain
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

  • thermal analysis
  • heat transfer
  • fluid flow
  • battery modules
  • phase change materials
  • bio-fuels
  • alternative sources
  • emission control
  • performance
  • characterization
  • numerical simulation
  • optimization
  • machine learning

Published Papers (5 papers)

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Research

19 pages, 4676 KiB  
Article
Modelling and Computational Experiment to Obtain Optimized Neural Network for Battery Thermal Management Data
by Asif Afzal, Javed Khan Bhutto, Abdulrahman Alrobaian, Abdul Razak Kaladgi and Sher Afghan Khan
Energies 2021, 14(21), 7370; https://0-doi-org.brum.beds.ac.uk/10.3390/en14217370 - 05 Nov 2021
Cited by 30 | Viewed by 2179
Abstract
The focus of this work is to computationally obtain an optimized neural network (NN) model to predict battery average Nusselt number (Nuavg) data using four activations functions. The battery Nuavg is highly nonlinear as reported in the literature, which [...] Read more.
The focus of this work is to computationally obtain an optimized neural network (NN) model to predict battery average Nusselt number (Nuavg) data using four activations functions. The battery Nuavg is highly nonlinear as reported in the literature, which depends mainly on flow velocity, coolant type, heat generation, thermal conductivity, battery length to width ratio, and space between the parallel battery packs. Nuavg is modeled at first using only one hidden layer in the network (NN1). The neurons in NN1 are experimented from 1 to 10 with activation functions: Sigmoidal, Gaussian, Tanh, and Linear functions to get the optimized NN1. Similarly, deep NN (NND) was also analyzed with neurons and activations functions to find an optimized number of hidden layers to predict the Nuavg. RSME (root mean square error) and R-Squared (R2) is accessed to conclude the optimized NN model. From this computational experiment, it is found that NN1 and NND both accurately predict the battery data. Six neurons in the hidden layer for NN1 give the best predictions. Sigmoidal and Gaussian functions have provided the best results for the NN1 model. In NND, the optimized model is obtained at different hidden layers and neurons for each activation function. The Sigmoidal and Gaussian functions outperformed the Tanh and Linear functions in an NN1 model. The linear function, on the other hand, was unable to forecast the battery data adequately. The Gaussian and Linear functions outperformed the other two NN-operated functions in the NND model. Overall, the deep NN (NND) model predicted better than the single-layered NN (NN1) model for each activation function. Full article
(This article belongs to the Special Issue Dynamic Control and Machine Learning for Thermal Management, Energy Utilization, and Environment)
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22 pages, 26733 KiB  
Article
Power Plant Energy Predictions Based on Thermal Factors Using Ridge and Support Vector Regressor Algorithms
by Asif Afzal, Saad Alshahrani, Abdulrahman Alrobaian, Abdulrajak Buradi and Sher Afghan Khan
Energies 2021, 14(21), 7254; https://0-doi-org.brum.beds.ac.uk/10.3390/en14217254 - 03 Nov 2021
Cited by 33 | Viewed by 2196
Abstract
This work aims to model the combined cycle power plant (CCPP) using different algorithms. The algorithms used are Ridge, Linear regressor (LR), and upport vector regressor (SVR). The CCPP energy output data collected as a factor of thermal input variables, mainly exhaust vacuum, [...] Read more.
This work aims to model the combined cycle power plant (CCPP) using different algorithms. The algorithms used are Ridge, Linear regressor (LR), and upport vector regressor (SVR). The CCPP energy output data collected as a factor of thermal input variables, mainly exhaust vacuum, ambient temperature, relative humidity, and ambient pressure. Initially, the Ridge algorithm-based modeling is performed in detail, and then SVR-based LR, named as SVR (LR), SVR-based radial basis function—SVR (RBF), and SVR-based polynomial regression—SVR (Poly.) algorithms, are applied. Mean absolute error (MAE), R-squared (R2), median absolute error (MeAE), mean absolute percentage error (MAPE), and mean Poisson deviance (MPD) are assessed after their training and testing of each algorithm. From the modeling of energy output data, it is seen that SVR (RBF) is the most suitable in providing very close predictions compared to other algorithms. SVR (RBF) training R2 obtained is 0.98 while all others were 0.9–0.92. The testing predictions made by SVR (RBF), Ridge, and RidgeCV are nearly the same, i.e., R2 is 0.92. It is concluded that these algorithms are suitable for predicting sensitive output energy data of a CCPP depending on thermal input variables. Full article
(This article belongs to the Special Issue Dynamic Control and Machine Learning for Thermal Management, Energy Utilization, and Environment)
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16 pages, 4479 KiB  
Article
Performance of Common Rail Direct Injection (CRDi) Engine Using Ceiba Pentandra Biodiesel and Hydrogen Fuel Combination
by T. M. Yunus Khan, Manzoore Elahi M. Soudagar, S. V. Khandal, Syed Javed, Imran Mokashi, Maughal Ahmed Ali Baig, Khadiga Ahmed Ismail and Ashraf Elfasakhany
Energies 2021, 14(21), 7142; https://0-doi-org.brum.beds.ac.uk/10.3390/en14217142 - 01 Nov 2021
Cited by 5 | Viewed by 2332
Abstract
An existing diesel engine was fitted with a common rail direct injection (CRDi) facility to inject fuel at higher pressure in CRDi mode. In the current work, rotating blades were incorporated in the piston cavity to enhance turbulence. Pilot fuels used are diesel [...] Read more.
An existing diesel engine was fitted with a common rail direct injection (CRDi) facility to inject fuel at higher pressure in CRDi mode. In the current work, rotating blades were incorporated in the piston cavity to enhance turbulence. Pilot fuels used are diesel and biodiesel of Ceiba pentandra oil (BCPO) with hydrogen supply during the suction stroke. Performance evaluation and emission tests for CRDi mode were carried out under different loading conditions. In the first part of the work, maximum possible hydrogen substitution without knocking was reported at an injection timing of 15° before top dead center (bTDC). In the second part of the work, fuel injection pressure (IP) was varied with maximum hydrogen fuel substitution. Then, in the third part of the work, exhaust gas recirculation (EGR), was varied to study the nitrogen oxides (NOx) generated. At 900 bar, HC emissions in the CRDi engine were reduced by 18.5% and CO emissions were reduced by 17% relative to the CI mode. NOx emissions from the CRDi engine were decreased by 28% relative to the CI engine mode. At 20%, EGR lowered the BTE by 14.2% and reduced hydrocarbons, nitrogen oxide and carbon monoxide by 6.3%, 30.5% and 9%, respectively, compared to the CI mode of operation. Full article
(This article belongs to the Special Issue Dynamic Control and Machine Learning for Thermal Management, Energy Utilization, and Environment)
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17 pages, 5608 KiB  
Article
A Study on Performance and Emission Characteristics of Diesel Engine Using Ricinus Communis (Castor Oil) Ethyl Esters
by Munimathan Arunkumar, Vinayagam Mohanavel, Asif Afzal, Thanikodi Sathish, Manickam Ravichandran, Sher Afghan Khan, Nur Azam Abdullah, Muhammad Hanafi Bin Azami and Mohammad Asif
Energies 2021, 14(14), 4320; https://0-doi-org.brum.beds.ac.uk/10.3390/en14144320 - 17 Jul 2021
Cited by 26 | Viewed by 2041
Abstract
Countries globally are focusing on alternative fuels to reduce the environmental pollution. An example is biodiesel fuel, which is leading the way to other technologies. In this research, the methyl esters of castor oil were prepared using a two-step transesterification process. The respective [...] Read more.
Countries globally are focusing on alternative fuels to reduce the environmental pollution. An example is biodiesel fuel, which is leading the way to other technologies. In this research, the methyl esters of castor oil were prepared using a two-step transesterification process. The respective properties of the castor oil (Ricinus Communis) biodiesel were estimated using ASTM standards. The effect of performance and emission on diesel engines was noted for four various engine loads (25, 50, 75, and 100%), with two different blends (B5 and B20) and at two different engine speeds (1500 and 2000 rpm). The study determined that B5 and B20 samples at 1500 rpm engine speed obtained the same power, but diesel fuel generated greater control. The power increased at 2000 rpm for B5 samples, but B20 samples, as well as diesel, were almost the same values. In the 40–80% range, load and load values were entirely parallel for each load observed from the engine performance of the brake power in all samples. Full article
(This article belongs to the Special Issue Dynamic Control and Machine Learning for Thermal Management, Energy Utilization, and Environment)
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17 pages, 3734 KiB  
Article
Influence of Graphene Nano Particles and Antioxidants with Waste Cooking Oil Biodiesel and Diesel Blends on Engine Performance and Emissions
by Sandeep Krishnakumar, T. M. Yunus Khan, C. R. Rajashekhar, Manzoore Elahi M. Soudagar, Asif Afzal and Ashraf Elfasakhany
Energies 2021, 14(14), 4306; https://0-doi-org.brum.beds.ac.uk/10.3390/en14144306 - 16 Jul 2021
Cited by 18 | Viewed by 1730
Abstract
The main reason for the limited usage of biodiesel is it tends to oxidize when exposed to air. It is anticipated that the addition of an antioxidant along with graphene nano particle improves combustion of diesel-biodiesel blend. In the present research biodiesel made [...] Read more.
The main reason for the limited usage of biodiesel is it tends to oxidize when exposed to air. It is anticipated that the addition of an antioxidant along with graphene nano particle improves combustion of diesel-biodiesel blend. In the present research biodiesel made from the transesterification of waste cooking oil is used. Three synthetic antioxidants butylated hydroxytoluene (BHT), 2(3)-t-butyl-4-hydroxyanisole (BHA) and tert butylhydroquinone (TBHQ) along with 30 ppm of graphene nano particle were added at a volume fraction of 1000 ppm to diesel–biodiesel blends (B20). The performance and emission tests were performed at constant engine speed of 1500 rpm. Because of the inclusion of graphene nano particles, surface area to the volume ratio of the fuel is augmented enhancing the mixing ability and chemical responsiveness of the fuel during burning causing superior performance, combustion and emission aspects of compression ignition engine. The results revealed that there was a slight increase in brake power and brake thermal efficiency of about 0.29%, 0.585%, 0.58% and 6.22%, 3.11%, 3.31% for B20GrBHT1000, B20GrBHA1000 and B20GrTBHQ1000, respectively, compared to B20. Additionally, BSFC, HC and NOx emissions were reduced to considerable levels for the reformed fuel. Full article
(This article belongs to the Special Issue Dynamic Control and Machine Learning for Thermal Management, Energy Utilization, and Environment)
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