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Numerical Modeling and Hybrid Methods for Thermal Management, Storage, and...
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Numerical Modeling and Hybrid Methods for Thermal Management, Storage, and Optimization

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Engineering Mathematics".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 8010

Special Issue Editors

Prof. Dr. Yuan Dong

E-Mail Website
Guest Editor
1. School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
2. Department of Mechanical and Aerospace Engineering, University of Missouri, Columbia, MO 65201, USA
3. Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China
Interests: thermal management; integrated circuits; industrial robots; numerical methods
Dr. Yuchao Hua

E-Mail Website
Guest Editor
Laboratoire de Thermique et Énergie de Nantes, University of Nantes, 44300 Nantes, France
Interests: thermal management; heat transfer optimization; Monte Carlo simulations; thermal energy storage; nanoscale thermal transport; numerical simulation and modeling

Special Issue Information

Dear Colleagues,

Energy has long been at the center of the sustainable development paradigm. Increasing energy efficiency is identified as one of the main challenges for energy systems. Almost all energy systems involve heat. In this sense, thermal management, storage, and optimization play an important role in achieving highly efficient systems, which has attracted the attentions of both the academic and industrial communities. Generally, mathematical modeling is the basis for the study on the heat-related topics. 

This Special Issue will focus on recent theoretical and computational studies on Thermal Management, Storage, and Optimization. Topics include, but are not limited to:

  1. Numerical simulation in heat transfer, including heat conduction, heat convection, and thermal radiation;
  2. Modeling and analysis of heat storage process or system;
  3. Thermal management and design of electronics;
  4. Heat transfer intensification and optimization;
  5. Modeling and simulation in energy materials;
  6. Modeling and analysis of thermal transport at small scales and non-Fourier regimes;
  7. Study of thermal problems using AI/Machine Learning techniques.

Prof. Dr. Yuan Dong
Dr. Yuchao Hua
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. Mathematics 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

  • heat transfer
  • heat conduction
  • heat convection
  • thermal radiation
  • thermal management
  • heat storage
  • thermal problems
  • thermal design and optimization
  • energy harvesting
  • energy materials
  • photovoltaic models
  • mathematical modeling
  • numerical modeling
  • AI/ML modeling

Published Papers (6 papers)

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Research

17 pages, 4383 KiB  
Article
Unsteady Heat Transfer of Pulsating Gas Flows in a Gas-Dynamic System When Filling and Emptying a Cylinder (as Applied to Reciprocating Machines)
by Leonid Plotnikov
Mathematics 2023, 11(15), 3285; https://0-doi-org.brum.beds.ac.uk/10.3390/math11153285 - 26 Jul 2023
Viewed by 695
Abstract
The thermal and mechanical perfection of the processes in the gas exchange system during the filling and emptying of the cylinder makes it possible to increase the productivity and efficiency of reciprocating machines for various purposes. The study was designed to obtain experimental [...] Read more.
The thermal and mechanical perfection of the processes in the gas exchange system during the filling and emptying of the cylinder makes it possible to increase the productivity and efficiency of reciprocating machines for various purposes. The study was designed to obtain experimental data on the local heat transfer of pulsating flows in the intake and outlet pipelines of a piston engine model, their analysis, and mathematical description. The scientific novelty of the study is as follows: (1) the patterns of change in the local heat transfer coefficients of pulsating gas flows in the inlet and outlet pipelines for the piston engine model were obtained for the first time; (2) a mathematical description of the experimental data on local and average heat transfer in the inlet and outlet pipelines is proposed. The physical features of the change in the rate of heat transfer in the intake and exhaust systems for a full engine cycle are discussed. A spectral analysis of the harmonic functions of the change in the local heat-transfer coefficient in gas exchange systems is performed. A set of mathematical dependencies of changes in the local and average heat-transfer coefficients of flows in the inlet and outlet pipelines on operation factors are presented. These data can be used to assess the quality of filling and cleaning the cylinder, determining thermal stresses in the details of gas exchange systems, developing devices for using exhaust gas energy, creating engine control systems, and so on. Moreover, the results obtained can be used to adjust (and test) mathematical models, as well as refine engineering methods for calculating gas exchange processes in reciprocating machines for various purposes. Full article
(This article belongs to the Special Issue Numerical Modeling and Hybrid Methods for Thermal Management, Storage, and Optimization)
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30 pages, 7582 KiB  
Article
An Improved Fick’s Law Algorithm Based on Dynamic Lens-Imaging Learning Strategy for Planning a Hybrid Wind/Battery Energy System in Distribution Network
by Mohana Alanazi, Abdulaziz Alanazi, Ahmad Almadhor and Hafiz Tayyab Rauf
Mathematics 2023, 11(5), 1270; https://0-doi-org.brum.beds.ac.uk/10.3390/math11051270 - 06 Mar 2023
Cited by 3 | Viewed by 1199
Abstract
In this paper, optimal and multi-objective planning of a hybrid energy system (HES) with wind turbine and battery storage (WT/Battery) has been proposed to drop power loss, smooth voltage profile, enhance customers reliability, as well as minimize the net present cost of the [...] Read more.
In this paper, optimal and multi-objective planning of a hybrid energy system (HES) with wind turbine and battery storage (WT/Battery) has been proposed to drop power loss, smooth voltage profile, enhance customers reliability, as well as minimize the net present cost of the hybrid system plus the battery degradation cost (BDC). Decision variables include the installation site of the hybrid system and size of the wind farm and battery storage. These variables are found with the help of a novel metaheuristic approach called improved Fick’s law algorithm (IFLA). To enhance the exploration performance and avoid the early incomplete convergence of the conventional Fick’s law (FLA) algorithm, a dynamic lens-imaging learning strategy (DLILS) based on opposition learning has been adopted. The planning problem has been implemented in two approaches without and considering BDC to analyze its impact on the reserve power level and the amount and quality of power loss, voltage profile, and reliability. A 33-bus distribution system has also been employed to validate the capability and efficiency of the suggested method. Simulation results have shown that the multi-objective planning of the hybrid WT/Battery energy system improves voltage and reliability and decreases power loss by managing the reserve power based on charging and discharging battery units and creating electrical planning with optimal power injection into the network. The results of simulations and evaluation of statistic analysis indicate the superiority of the IFLA in achieving the optimal solution with faster convergence than conventional FLA, particle swarm optimization (PSO), manta ray foraging optimizer (MRFO), and bat algorithm (BA). It has been observed that the proposed methodology based on IFLA in different approaches has obtained lower power loss and more desirable voltage profile and reliability than its counterparts. Simulation reports demonstrate that by considering BDC, the values of losses and voltage deviations are increased by 2.82% and 1.34%, respectively, and the reliability of network customers is weakened by 5.59% in comparison with a case in which this cost is neglected. Therefore, taking into account this parameter in the objective function can lead to the correct and real calculation of the improvement rate of each of the objectives, especially the improvement of the reliability level, as well as making the correct decisions of network planners based on these findings. Full article
(This article belongs to the Special Issue Numerical Modeling and Hybrid Methods for Thermal Management, Storage, and Optimization)
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26 pages, 8663 KiB  
Article
Computational Modeling of Latent Heat Thermal Energy Storage in a Shell-Tube Unit: Using Neural Networks and Anisotropic Metal Foam
by Jana Shafi, Mehdi Ghalambaz, Mehdi Fteiti, Muneer Ismael and Mohammad Ghalambaz
Mathematics 2022, 10(24), 4774; https://0-doi-org.brum.beds.ac.uk/10.3390/math10244774 - 15 Dec 2022
Cited by 8 | Viewed by 1664
Abstract
Latent heat storage in a shell-tube is a promising method to store excessive solar heat for later use. The shell-tube unit is filled with a phase change material PCM combined with a high porosity anisotropic copper metal foam (FM) of high thermal conductivity. [...] Read more.
Latent heat storage in a shell-tube is a promising method to store excessive solar heat for later use. The shell-tube unit is filled with a phase change material PCM combined with a high porosity anisotropic copper metal foam (FM) of high thermal conductivity. The PCM-MF composite was modeled as an anisotropic porous medium. Then, a two-heat equation mathematical model, a local thermal non-equilibrium approach LTNE, was adopted to consider the effects of the difference between the thermal conductivities of the PCM and the copper foam. The Darcy–Brinkman–Forchheimer formulation was employed to model the natural convection circulations in the molten PCM region. The thermal conductivity and the permeability of the porous medium were a function of an anisotropic angle. The finite element method was employed to integrate the governing equations. A neural network model was successfully applied to learn the transient physical behavior of the storage unit. The neural network was trained using 4998 sample data. Then, the trained neural network was utilized to map the relationship between control parameters and melting behavior to optimize the storage design. The impact of the anisotropic angle and the inlet pressure of heat transfer fluid (HTF) was addressed on the thermal energy storage of the storage unit. Moreover, an artificial neural network was successfully utilized to learn the transient behavior of the thermal storage unit for various combinations of control parameters and map the storage behavior. The results showed that the anisotropy angle significantly affects the energy storage time. The melting volume fraction MVF was maximum for a zero anisotropic angle where the local thermal conductivity was maximum perpendicular to the heated tube. An optimum storage rate could be obtained for an anisotropic angle smaller than 45°. Compared to a uniform MF, utilizing an optimum anisotropic angle could reduce the melting time by about 7% without impacting the unit’s thermal energy storage capacity or adding weight. Full article
(This article belongs to the Special Issue Numerical Modeling and Hybrid Methods for Thermal Management, Storage, and Optimization)
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32 pages, 5065 KiB  
Article
Photovoltaic Models’ Parameter Extraction Using New Artificial Parameterless Optimization Algorithm
by Mohana Alanazi, Abdulaziz Alanazi, Ahmad Almadhor and Hafiz Tayyab Rauf
Mathematics 2022, 10(23), 4617; https://0-doi-org.brum.beds.ac.uk/10.3390/math10234617 - 06 Dec 2022
Cited by 6 | Viewed by 1388
Abstract
Identifying parameters in photovoltaic (PV) cell and module models is one of the primary challenges of the simulation and design of photovoltaic systems. Metaheuristic algorithms can find near-optimal solutions within a reasonable time for such challenging real-world optimization problems. Control parameters must be [...] Read more.
Identifying parameters in photovoltaic (PV) cell and module models is one of the primary challenges of the simulation and design of photovoltaic systems. Metaheuristic algorithms can find near-optimal solutions within a reasonable time for such challenging real-world optimization problems. Control parameters must be adjusted with many existing algorithms, making them difficult to use. In real-world problems, many of these algorithms must be combined or hybridized, which results in more complex and time-consuming algorithms. This paper presents a new artificial parameter-less optimization algorithm (APLO) for parameter estimation of PV models. New mutation operators are designed in the proposed algorithm. APLO’s exploitation phase is enhanced by each individual searching for the best solution in this updating operator. Moreover, the current best, the old best, and the individual’s current position are utilized in the differential term of the mutation operator to assist the exploration phase and control the convergence speed. The algorithm uses a random step length based on a normal distribution to ensure population diversity. We present the results of a comparative study using APLO and well-known existing parameter-less meta-heuristic algorithms such as grey wolf optimization, the salp swarm algorithm, JAYA, teaching-learning based optimization, colliding body optimization, as well as three major parameter-based algorithms such as differential evolution, genetic algorithm, and particle swarm optimization to estimate the parameters of PV the modules. The results revealed that the proposed algorithm could provide excellent exploration–exploitation balance and consistency during the iterations. Furthermore, the APLO algorithm shows high reliability and accuracy in identifying the parameters of PV cell models. Full article
(This article belongs to the Special Issue Numerical Modeling and Hybrid Methods for Thermal Management, Storage, and Optimization)
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19 pages, 10544 KiB  
Article
Numerical Modelling and Experimental Validation of Novel Para Winglet Tape for Heat Transfer Enhancement
by Thejaraju Rajashekaraiah, Girisha Kanuvanahalli Bettaiah, Parvathy Rajendran, Mohamed Abbas, Sher Afghan Khan and C. Ahamed Saleel
Mathematics 2022, 10(16), 2893; https://0-doi-org.brum.beds.ac.uk/10.3390/math10162893 - 12 Aug 2022
Viewed by 1124
Abstract
Heat exchangers are predominantly used in the industries of production, manufacturing, power, oil and gas, petroleum, and cooling solutions. The competence of the heat exchanger is optimized through active and passive augmented techniques. The current study revolves around the performance evaluation of Novel [...] Read more.
Heat exchangers are predominantly used in the industries of production, manufacturing, power, oil and gas, petroleum, and cooling solutions. The competence of the heat exchanger is optimized through active and passive augmented techniques. The current study revolves around the performance evaluation of Novel Para winglet tape for flow and friction characteristics. Turbulence flow properties from Re of 30,000-to-6000 were explored for three different inclinations and pitches, respectively. Experimental and numerical solutions are derived to showcase the flow behavior over Para winglet tape inserts in the double pipe heat exchanger. Appreciable results were obtained in enhancing the Nusselt number (Nup) for a better heat transfer enforcement through the DEX. All case studies also increased when compared to the smooth pipe. Experimentally, the maximum Nu and Nusselt number ratio was observed to be 398.23 and 5.05 times over the plain tube. Similarly, the maximum friction factor and its ratio were observed to be near 0.33 and 8.89 times over the plain tube. Finally, the maximum POI of 2.68 to 2.37 was achieved with 20° inclinations. The experimental and numerical outcomes of Para winglet tape with the higher inclination and shorter pitch were found to be best out of the others. Full article
(This article belongs to the Special Issue Numerical Modeling and Hybrid Methods for Thermal Management, Storage, and Optimization)
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19 pages, 2884 KiB  
Article
Thermal-Economic Optimization of Plate–Fin Heat Exchanger Using Improved Gaussian Quantum-Behaved Particle Swarm Algorithm
by Joo Hyun Moon, Kyun Ho Lee, Haedong Kim and Dong In Han
Mathematics 2022, 10(14), 2527; https://0-doi-org.brum.beds.ac.uk/10.3390/math10142527 - 20 Jul 2022
Cited by 6 | Viewed by 1274
Abstract
Heat exchangers are usually designed using a sophisticated process of trial-and-error to find proper values of unknown parameters which satisfy given requirements. Recently, the design of heat exchangers using evolutionary optimization algorithms has received attention. The major aim of the present study is [...] Read more.
Heat exchangers are usually designed using a sophisticated process of trial-and-error to find proper values of unknown parameters which satisfy given requirements. Recently, the design of heat exchangers using evolutionary optimization algorithms has received attention. The major aim of the present study is to propose an improved Gaussian quantum-behaved particle swarm optimization (GQPSO) algorithm for enhanced optimization performance and its verification through application to a multivariable thermal-economic optimization problem of a crossflow plate–fin heat exchanger (PFHE). Three single objective functions: the number of entropy generation units (NEGUs), total annual cost (TAC), and heat exchanger surface area (A), were minimized separately by evaluating optimal values of seven unknown variables using four different PSO-based methods. By comparing the obtained best fitness values, the improved GQPSO approach could search quickly for better global optimal solutions by preventing particles from falling to the local minimum due to its modified local attractor scheme based on the Gaussian distributed random numbers. For example, the proposed GQPSO could predict further improved best fitness values of 40% for NEGUs, 17% for TAC, and 4.5% for A, respectively. Consequently, the present study suggests that the improved GQPSO approach with the modified local attractor scheme can be efficient in rapidly finding more suitable solutions for optimizing the thermal-economic problem of the crossflow PFHE. Full article
(This article belongs to the Special Issue Numerical Modeling and Hybrid Methods for Thermal Management, Storage, and Optimization)
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