Topic Editors

Faculty of Mechanical Engineering, Shahrood University of Technology, Shahrood 3619995161, Iran
Department of Mathematics, Faculty of Arts and Sciences, Kırklareli University, Kırklareli, Turkey

New Results on Mathematical Methods–Models and Their Applications to Energy Systems

Abstract submission deadline
closed (31 January 2024)
Manuscript submission deadline
31 March 2024
Viewed by
4585

Topic Information

Dear Colleagues,

Energy systems have different areas of interdisciplinary research, bringing together mathematicians, physicists, and engineers. In energy systems, precise mathematical models and algorithms are needed for a good match between reality and theory. The aim of this Special Issue is to consider and publish original research articles covering the new results and developments in mathematical methods (such as optimization, control systems, model theory, analysis, computational techniques, fuzzy logic, representation theory, linear problems, ordinary and partial differential equations, etc.) applied to energy systems. Using these models and methods, energy systems are analyzed–designed to be reliable, efficient, flexible, and sustainable for real-life industry and science. Furthermore, the use of optimization algorithms in finding the optimal performance of energy systems is an interesting topic of discussion for researchers in the field of energy systems. This Special Issue will focus on various problems in these fields for mathematicians, engineers, physicians, and so on through comprehensive considerations. The topics of this Special Issue on applied sciences include but are not limited to the following:

  • Linear and nonlinear dynamics
  • Algorithms
  • Markov decision process and Monte Carlo theory
  • Optimization and control theory
  • Game theory
  • Econometric analysis
  • Statistics
  • Functional analysis and its applications
  • Mathematical biology
  • Mathematical physics
  • Engineering applications of mathematics
  • Industrial applications of mathematical models 
  • Energy systems modeling
  • Fractional calculus and its applications
  • Numerical analysis
  • Simulation
  • Neural network
  • Artificial intelligence in energy systems
  • Mathematical biology
  • Stability analysis of systems

Dr. Mohammad Hossein Ahmadi
Dr. Ozen Ozer
Topic Editors

Keywords

  • algorithms
  • applied and industrial mathematics
  • mathematical methods
  • optimization
  • applications in sciences
  • engineering and technology
  • energy systems

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Entropy
entropy
2.7 4.7 1999 20.8 Days CHF 2600 Submit
Mathematics
mathematics
2.4 3.5 2013 16.9 Days CHF 2600 Submit
Processes
processes
3.5 4.7 2013 13.7 Days CHF 2400 Submit
Sustainability
sustainability
3.9 5.8 2009 18.8 Days CHF 2400 Submit

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Published Papers (3 papers)

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22 pages, 9073 KiB  
Article
Ultra-Short-Term Load Forecasting for Customer-Level Integrated Energy Systems Based on Composite VTDS Models
Processes 2023, 11(8), 2461; https://doi.org/10.3390/pr11082461 - 16 Aug 2023
Viewed by 750
Abstract
A method is proposed to address the challenging issue of load prediction in user-level integrated energy systems (IESs) using a composite VTDS model. Firstly, an IES multi-dimensional load time series is decomposed into multiple intrinsic mode functions (IMFs) using variational mode decomposition (VMD). [...] Read more.
A method is proposed to address the challenging issue of load prediction in user-level integrated energy systems (IESs) using a composite VTDS model. Firstly, an IES multi-dimensional load time series is decomposed into multiple intrinsic mode functions (IMFs) using variational mode decomposition (VMD). Then, each IMF, along with other influential features, is subjected to data dimensionality reduction and clustering denoising using t-distributed stochastic neighbor embedding (t-SNE) and fast density-based spatial clustering of applications with noise (FDBSCAN) to perform major feature selection. Subsequently, the reduced and denoised data are reconstructed, and a time-aware long short-term memory (T-LSTM) artificial neural network is employed to fill in missing data by incorporating time interval information. Finally, the selected multi-factor load time series is used as input into a support vector regression (SVR) model optimized using the quantum particle swarm optimization (QPSO) algorithm for load prediction. Using measured load data from a specific user-level IES at the Tempe campus of Arizona State University, USA, as a case study, a comparative analysis between the VTDS method and other approaches is conducted. The results demonstrate that the method proposed in this study achieved higher accuracy in short-term forecasting of the IES’s multiple loads. Full article
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14 pages, 4462 KiB  
Article
Investigation of Mass-Transfer Performance for Biodiesel Reaction in Microchannel Reactor using Volume-of-Fluid with Species-Transport Model
Sustainability 2023, 15(7), 6148; https://doi.org/10.3390/su15076148 - 03 Apr 2023
Viewed by 1675
Abstract
A microchannel reactor improves the overall mass and heat transfer as compared with a conventional reactor. This is attributed to the creation of a high area-to-volume ratio and enhanced mixing due to the presence of the vortices inside the slug. In this paper, [...] Read more.
A microchannel reactor improves the overall mass and heat transfer as compared with a conventional reactor. This is attributed to the creation of a high area-to-volume ratio and enhanced mixing due to the presence of the vortices inside the slug. In this paper, the mass-transfer performance was studied using a cross-junction microchannel. Subsequently, the computational fluid dynamic (CFD) method was used to observe the oil concentration contour inside a slug using volume-of-fluid (VOF) with the species-transport model. Based on the simulation results, the oil concentration was accumulated in both the slug’s rear and front regions. Hence, the creation of four vortices resulted in the creation of dead zones at the low-oil-concentration region. Furthermore, it has been observed that an optimum flow rate in a microchannel reactor is required to achieve a high mass transfer. A higher oil concentration was measured during the slug formation at a low flow regime due to the long residence time. In contrast, a high mass transfer has been reported during the slug-moving stage due to the higher vortices velocity, resulting in enhanced mixing and mass transfer. Hence, slug forming and the moving stage substantially influenced mass transfer at low and high flow rates, respectively. Full article
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21 pages, 4031 KiB  
Article
Time-Transient Optimization of Electricity and Fresh Water Cogeneration Cycle Using Gas Fuel and Solar Energy
Mathematics 2023, 11(3), 571; https://doi.org/10.3390/math11030571 - 21 Jan 2023
Cited by 1 | Viewed by 1041
Abstract
In this study, a cogeneration cycle in a time-transient state is investigated and optimized. A quasi-equilibrium state is assumed because of the small time increments. Air temperature and solar power are calculated hourly. The cycle is considered in terms of energy, exergy, and [...] Read more.
In this study, a cogeneration cycle in a time-transient state is investigated and optimized. A quasi-equilibrium state is assumed because of the small time increments. Air temperature and solar power are calculated hourly. The cycle is considered in terms of energy, exergy, and economic and environmental analyses. Increasing the net present value (the difference between the present value of the cash inflows and outflows over a period of time) and reducing exergy destruction are selected as two optimization objective functions. The net present value is calculated for the period of 20 years of operation according to the operation parameters. The optimization variables are selected in such a way that one important variable is selected from each system. To optimize the cycle, the particle swarm optimization method is used. The number of particles used in this method is calculated using the trial-and-error method. This cycle is optimized using 13 particles and 42 iterations. After optimization, the energy efficiency increased by 0.5%, the exergy efficiency increased by 0.25%, and the exergy destruction decreased by 1% compared to the cycle with existing parameters. Full article
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