Submission Deadline: 25 November 2024 (closed) View: 183 Submit to Special Issue
Numerical simulations are widely applied to different energy systems whose mathematical models are complicated to apply to analytical solutions. The main benefits of numerical modeling of complex systems are increased understanding of the behavior, finding of unfamiliar conditions, troubleshooting, flexibility, and validation of designs. Different approaches are usable for numerical modeling and simulations of energy systems, such as Finite Difference Method (FDM) and Finite Element Method (FEM). Conventional approaches for numerical simulations have high computational costs and are time-consuming in cases of applying them to complex systems with large domains. In this regard, attention has been directed to more updated techniques, such as meshless and mesh reduction methods. Using intelligent techniques like Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy Inference System (ANFIS) have gained attention in recent decades for numerical modeling and simulation of various systems due to their significant advantages, namely fast performance, accuracy of outputs and relatively low computational cost.
Different scholars have employed intelligent methods for simulations of a variety of energy systems like turbomachines, heat exchangers, and thermal power plants. In addition to energy systems consuming fossil fuels, the use of intelligent techniques has been developed for technologies based on renewable energy sources, such as solar, wind, and geothermal. For instance, these techniques are applicable to the modeling and simulation of solar collectors, wind turbines, solar photovoltaic modules, etc. Both original and review articles with high quality are welcome to be submitted to this special issue for consideration and possible publication. The most attractive-bot not limited to- subjects for the present issue are as follows:
· Intelligent methods for simulation of renewable energy systems.
· Application of machine learning approaches for the modeling, simulation and prediction of data, i.g. weather data, affecting the performance of energy systems.
· Comparison of intelligent methods and conventional numerical approaches in the simulation of energy systems.
· Optimization of intelligent methods parameters for performance improvement in terms of time and accuracy.
· Development of new intelligent methods for modeling of energy systems.
· Comparison of different intelligent techniques in numerical simulation of energy systems in terms of accuracy, computational cost, and time consumption.
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