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International Renewable Energy Congress

作者： Ridha Djebali Mokhtar Ferhi Dep. of Computer Sciences ISLAIB UR22ES12: Modeling Optimization and Augmented Engineering University of Jendouba Béja Tunisia

ISBN: (纸本)9781665488983

This paper aims to perform heat transfer and entropy generation in a micro medium filled with nanoliquid in the slip flow regime. The case of microchannel is investigated using a mesoscopic numerical analysis. The nanofluid flow is driven along the microchannel by a co- flow at the inlet. Near the top cold wall the flow is induced by a constant velocity (U in ) and hot temperature (T H ) but near the bottom heated wall the nanoliquid is driven by a constant velocity (U in /4) and a cold temperature (T C ). The slip velocity and the temperature jump conditions are imposed to the walls. Lattice Boltzmann method was used to solve the obtained governing equation system by means of the SRT-BGK model. Attention was focused on the influence of the emerging input parameters such as Knudsen number (Kn), Reynolds number (Re), nanoparticles diameter (Dp) and volume fraction (Vf) on the heat transfer enhancement and entropy generation minimization throughout this paper. Correlations of heat transfer enhancement and volumetric entropy have been inscribed based on response surface methodology.

关键词： Nanoparticles Renewable energy sources Numerical analysis Minimization Entropy Mathematical models Response surface methodology

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Appraising heat transfer enhancement by nanofluid and entropy generation minimization in a micro tall cavity: : Magnetic field and sinusoidal heating effects

Appraising heat transfer enhancement by nanofluid and entrop...

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International Renewable Energy Congress

作者： Ridha Djebali Mokhtar Ferhi Dep. of Computer Sciences ISLAIB UR22ES12: Modeling Optimization and Augmented Engineering University of Jendouba Béja Tunisia

ISBN: (纸本)9781665488983

Improving heat transfer is a challenging area of research. In recent years, new ideas have continuously emerged on this subject. Microchannels and microcavities have proven better heat transfer efficiency [1-3]. The present study aims to analyze the magnetohydrodynamics (MHD) heat transfer enhancement and entropy generation in a micro open tall cavity filled with nanoliquid under the effects of uniform magnetic field and bottom sinusoidal heating in the slip flow regime. A mesoscopic numerical analysis based on the SRT-BGK lattice Boltzmann numerical method (LBM) is used to resolve the governing equations with boundary conditions special treatment. The slip velocity and the temperature jump conditions are used to incorporate the micro aspect. The flow pattern, heat transfer characteristics and the irreversibility pattern are studied dependently on various dimensionless independent monitoring parameters in a set of ranges, namely: Rayleigh number (Ra) (10 2 -10 4 ), Knudsen number (Kn) (0-0.1) and Hartmann number (Ha) (0-80), nanosuspensions volume fraction Vf (0-0.04%) as well as the magnitude A (0-1) and wave length parameter f of the sinusoidal heating function T(x) =A sin(fx/L), 1≤f=2πL/λ≤9 and λ is the signal wavelength.

关键词： Renewable energy sources Magnetohydrodynamics Temperature Numerical analysis Minimization Entropy Microcavities

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Micro Scale modeling of Heat Transfer and Entropy Generation in a Partially Porous Channel Using Lattice Boltzmann Method and Design of Experiments

Micro Scale Modeling of Heat Transfer and Entropy Generation...

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International Renewable Energy Congress

作者： Mokhtar Ferhi Dorra Khilifi Ridha Djebali Dep. of Computer Sciences UR22ES12: Modeling Optimization and Augmented Engineering ISLAIB University of Jendouba Béja Tunisia Higher institute of human sciences University of Jendouba Jendouba Tunisia

Micro porous structures such as micro porous channel and micro porous cavities are characterized by very high energy efficiency. In this context, a numerical investigation based on the Lattice Boltzmann method is carried out in a micro porous channel filled with Cu/Water nanofluid in order to determinate the maximum of heat transfer by minimizing the irreversibility inside the micro porous geometry using design of experiments. In order to scrutinize the performance of this micro heat-exchanger from the continuous to the slip flow regime a coolant nanoliquid is diffused through the micro porous medium. The impact of independent several input design factors such as Kn, Re, Da and 8 is presented. The combination between the input design factors leads to obtain an optimized model in which the heat transfer is the maximum and the entropy generation is minimum. The results are presented in terms of response surface of heat transfer and total entropy generation. Correlations of the optimized model are then determinate.

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Fluid-structure interaction over a backward-facing step equipped with an elastic baffle

Fluid-structure interaction over a backward-facing step equi...

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International Renewable Energy Congress

作者： Memni Asma Mokhtar Ferhi Ridha Djebali Karim Khemiri Montassar Najari Dep. of Computer Sciences ISLAIB UR22ES 12: Modeling Optimization and Augmented Engineering University of Jendouba Beja Tunisia Higher National Engineering School of Tunis University of Tunis Tunis Tunisia

ISBN: (数字)9798331516772

ISBN: (纸本)9798331516789

This study focus on a particular Fluid-structure interaction (FSI) issue: the interaction of a flow over a backward-facing step with an elastic baffle located in the recirculation area behind the step. The complex flow separation and recirculation characteristics of the backward facing step flow make it an excellent case study for investigating FSI. The inclusion of an elastic baffle adds more complexity, as the flow field affects the baffle's deformation and vice versa in a dynamic way. Comprehending this interaction relationship is essential for enhancing designs when flexible parts are exposed to fluctuating fluid currents. This research examines the interaction between a turbulent flow over a backward-facing step and an elastic baffle placed in the recirculation zone, known as fluid-structure interaction (FSI). The numerical simulation involves using a connected fluid-structure solver, utilizing ANSYS Fluent for fluid dynamics and ANSYS Mechanical for structural mechanics. The effect of baffle size, position and baffle deformation on the hydrodynamic and thermal behavior is conducted for various Reynolds numbers. Findings indicate a notable change in the flow and thermal fields and caused by the flexibility of the baffle. The results offer important information about the possibility of using elastic baffles for passive flow control in various applications like micro fluidic devices and heat exchangers.

关键词： Heating systems Renewable energy sources Fluidic microsystems Deformation Fluid dynamics Numerical simulation Hydrodynamics Finite element analysis Microfluidics Heat transfer

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Deep Learning Analysis and Numerical Simulation of Exergy and Nanofluid Heat Transfer Efficiency in a Two-Compartment Heat Exchanger with Internal Heat Generation and Baffles 15

Deep Learning Analysis and Numerical Simulation of Exergy an...

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15th International Renewable Energy Congress, IREC 2025

作者： Djebali, Ridha Ferhi, M. Mechighel, F. Khemiri, K. Bjaoui, M. Ouerhani, M. Najari, M. Amri, C. Ennetta, R. UR22ES12: Modeling Optimization and Augmented Engineering Islaib University of Jendouba Beja9000 Tunisia Université Badji Mokhtar Annaba Lab. LR3MI Dép. Génie Mécanique Fac. Technologie B.P.12 Annaba Algeria Laplace Université de Toulouse Cnrs Inpt Ups Toulouse France Icam Carquefou44470 France College of Science United Arab Emirates University Department of Physics Al Ain15551 United Arab Emirates Enig Gabes6029 Tunisia

ISBN: (纸本)9798331516772

This paper investigates the heat transfer enhancement in a two-compartment heat exchanger using nanofluids, employing numerical simulations and deep learning. The study systematically examines the influence of key parameters: Rayleigh number (Ra=106-109), conductivity ratio (kr=1-15), nanoparticle volume fraction (φ=0-3%), nanofluid temperature (Temp=293-323K), and scaled heat exchanger wall thickness (0.02-0.05). The first compartment features internal heat generation, while the second incorporates baffles and nanofluids to optimize mixing and heat transfer. Computational Fluid Dynamics (CFD) is used to analyze Nusselt number, isotherms, streamlines, velocity vector magnitude, exergy loss, entropy generation, and the Bejan number. Deep learning models are developed to predict and optimize heat transfer performance based on these five input parameters. Results demonstrate that increasing the Rayleigh number and conductivity ratio significantly enhances heat transfer, while nanoparticles higher volume fractions improve performance, albeit with potential viscosity increases. Exergy analysis reveals opportunities for design optimization to minimize entropy generation. The integrated approach of CFD and deep learning provides a powerful tool for optimizing the design and operation of nanofluid-based heat exchangers for improved thermal management in various applications. © 2025 IEEE.

关键词： Rayleigh number

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Integration of Artificial Neural Networks and Process Simulation for Optimizing Direct Current Atmospheric Plasma Spraying of Enhanced Zirconia Coatings 15

Integration of Artificial Neural Networks and Process Simula...

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15th International Renewable Energy Congress, IREC 2025

作者： Djebali, Ridha Ferhi, M. Khemiri, K. Mechighel, F. Pateyron, B. Ouerhani, M. Najari, M. Amri, C. Ennetta, R. UR22ES12: Modeling Optimization and Augmented Engineering Islaib University of Jendouba Beja9000 Tunisia Lab. LR3MI Dép Génie Mécanique Fac. Technologie Université Badji Mokhtar Annaba B.P.12 Annaba Algeria Ircer Umr Cnrs 7315 University of Limoges 12 rue Atlantis Limoges87068 France Icam Carquefou44470 France College of Science United Arab Emirates University Department of Physics Al Ain15551 United Arab Emirates Enig University of Gabes Gabes6029 Tunisia

ISBN: (纸本)9798331516772

This study investigates the application of Direct Current Atmospheric Plasma Spraying (DC-APS) for depositing zirconium dioxide (ZrO2) coatings to enhance component performance and durability in advanced applications. ZrO2's excellent thermal and mechanical properties make it ideal for industries like aerospace and renewable energy. We focus on optimizing critical process parameters, such as plasma jet temperature and particle injection velocity, to improve coating characteristics. Using the Jets&Poudres (JP) simulation code, we analyze particle dynamics and heat transfer, revealing how process variations influence coating morphology and adhesion. Additionally, we employ artificial neural networks (ANNs) to model the deposition process, achieving high accuracy in predicting performance metrics. Our findings confirm that DC-APS is an effective method for producing high-quality ZrO2 coatings and underscore the importance of process optimization and AI integration for enhancing thermal spray applications. © 2025 IEEE.

关键词： Plasma spraying

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Three-Phase PV Pumping System With Advanced Control for Enhanced Efficiency and Robustness: modeling, Experimental Validation, and optimization

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INTERNATIONAL JOurNAL OF CIRCUIT THEORY AND APPLICATIONS 2025年

作者： Bjaoui, Marwen Larroque, Benoit Djebali, Ridha Khemissi, Lotfi Ruscassie, Robert Benadli, Ridha Luthon, Franck Sellami, Anis Univ Pau & Pays Adour SIAME EA4581 UPPA E2S Anglet France Univ Jendouba ISLAIB Modeling Optimizat & Augmented Engn UR22ES12 Beja Tunisia Res & Technol Ctr Energy CRTEn Lab Nanomat & Syst Energy LaNSER Borj Cedria Tunisia Univ Pau & Pays Adour E2S UPPA Lab Appl Mech & Elect Engn Sci IPRA Federat Pau France Univ Nantes IREENA Lab St Nazaire France Natl Higher Engn Sch Tunis Res Unit LISIER Tunis Tunisia

This paper presents a detailed study of a three-phase photovoltaic pumping system (PVPS), comprising a 1.5-kW three-phase induction motor for water pumping, a three-phase voltage source inverter (VSI), and a DC-DC boost converter designed to maximize power extraction from a 1.88-kWp photovoltaic generator (PVG). The system is characterized by its cost-effectiveness, high efficiency, and robust performance. To reduce overall system costs, the inductor current of the boost converter and the rotational speed of the induction motor are estimated rather than measured directly. A nonlinear neural network observer (NNO) is employed to estimate the inductor current, whereas a sliding mode observer (SMO) is utilized to estimate the motor speed. To enhance the system's resilience against internal and external disturbances, a hybrid incremental conductance super-twisting sliding mode controller (InC-STSMC) is implemented for maximum power point tracking (MPPT) from the PVG, and a flux-oriented sliding mode vector control (FO-SMC) is adopted for precise regulation of the motor speed. The effectiveness of the proposed control strategy is evaluated through model-in-the-loop (MIL) simulations conducted in the MATLAB-Simulink environment, demonstrating significant improvements in dynamic performance, particularly in terms of stability and robustness, compared to conventional proportional-integral (PI) control methods. The practicality and suitability of the proposed InC-STSMC combined with the NNO scheme are further validated through a processor-in-the-loop (PIL) test using the STM32F769I board, highlighting its potential for real-world applications.

关键词： maximum power point tracking observers photovoltaics sliding mode control water pumping

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Optimized Energy Management Strategy for Non-Autonomous Microgrids Using Crow Search optimization

Optimized Energy Management Strategy for Non-Autonomous Micr...

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International Renewable Energy Congress

作者： Marwen Bjaoui K. Khemiri L. Khemissi R. Djebali A. Sellami LAPLACE université de Toulouse CNRS INPT UPS Toulouse France UR22ES12: Modeling Optimization and Augmented Engineering ISLAIB University of Jendouba Beja Tunisia Laboratory of Nanomaterials and Systems for Energy (LaNSER) Research and Technology Centre of Energy (CRTEn) Borj Cedria Tunisia Research unit: LISIER National Higher Engineering School of Tunis Tunis Tunisia

ISBN: (数字)9798331516772

ISBN: (纸本)9798331516789

The need for renewable energy sources is growing because of climate change and rising fossil fuel prices. However, the variability of these sources necessitates an additional energy management unit to ensure smooth operation and a reliable power supply, prompting the development of an effective energy management strategy (EMS). This study introduces an innovative EMS for a grid-connected DC microgrid (MG) that utilizes the crow search optimization (CSO) algorithm. The hybrid energy system consists of a battery energy storage system (BesS), wind turbine (WT), and photovoltaic generator (GPV), with a focus on maximizing the use of renewables to enhance economic efficiency. A proportional-integral (PI) controller regulates source-side converters to optimize energy captured from the wind and PV sources while managing battery current to align with a reference generated by the CSO. The controller also oversees the grid-side inverter. In addition to minimizing various costs associated with energy consumption, battery degradation, and hourly grid energy usage, the CSO algorithm seeks to maintain the battery's state of charge (SOC) between 20% and 80%, with a target of 50%. It computes the overall energy costs by considering the battery's SOC, real-time renewable energy generation, and actual energy demand to determine instantly the optimal battery power. The performance of the CSO is assessed in the MATLAB/Simulink environment and compared to a rules-based EMS. The suggested CSO was highly promising for possible hybrid renewable energy management systems applications based on the obtained performances.

关键词： Renewable energy sources Energy consumption Costs Heuristic algorithms Microgrids Batteries State of charge Energy management MATLAB optimization

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Artificial Neural Networks for Optimizing Alumina Al_(2)O_(3)Particle and Droplet Behavior in 12kK Ar-H_(2)Atmospheric Plasma Spraying

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Frontiers in Heat and Mass Transfer 2025年第2期23卷 441-461页

作者： Ridha Djebali Bernard Pateyron Mokhtar Ferhi Mohamed Ouerhani Karim Khemiri Montassar Najari M.Ammar Abbassi Chohdi Amri Ridha Ennetta Zied Driss UR22ES12:Modeling Optimization and Augmented EngineeringISLAIBUniversity of JendoubaBeja9000Tunisia IRCER UMR CNRS 7315University of Limoges12 rue AtlantisLimoges87068France ICAM School of Engineering Nantes Campus35 Av.Champ de ManoeuvresCarquefou44470France Research Lab Technology Energy and Innovative MaterialsFaculty of SciencesUniversity of GafsaGafsa2112Tunisia Department of Physics College of ScienceUnited Arab Emirates UniversityAl Ain15551United Arab Emirates Lab.Mechanical Modeling Energy and Materials(M2EM)ENIGUniversity of GabesGabes6029Tunisia Laboratory of Electro-Mechanic Systems ENISUniversity of SfaxSfax3038Tunisia

This paper investigates the application of Direct Current Atmospheric Plasma Spraying(DC-APS)as a versatile thermal spray technique for the application of coatings with tailored properties to various *** process uses a high-speed,high-temperature plasma jet to melt and propel the feedstock powder particles,making it particularly useful for improving the performance and durability of components in renewable energy systems such as solar cells,wind turbines,and fuel *** integration of nanostructured alumina(Al_(2)O_(3))thin films into multilayer coatings is considered a promising advancement that improves mechanical strength,thermal stability,and environmental *** study highlights the importance of understanding injection parameters and their impact on coating properties and uses simulation tools such as the Jets&Poudres(JP)code for in-depth ***,the paper discusses the implementation of Artificial Neural Networks(ANN)to optimize the coating process by predicting flight characteristics and improving operating *** results show that ANN models are effective in achieving highly accurate prediction values,highlighting the potential of AI in improving thermal spray technology.

关键词： ANN modeling and simulation powder injection particle dynamics and heat transfer impact characteristics analysis

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Integration of Artificial Neural Networks and Process Simulation for Optimizing Direct Current Atmospheric Plasma Spraying of Enhanced Zirconia Coatings

Integration of Artificial Neural Networks and Process Simula...

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International Renewable Energy Congress

作者： Ridha Djebali M. Ferhi K. Khemiri F. Mechighel B. Pateyron M. Ouerhani M. Najari C. Amri R. Ennetta UR22ES12: Modeling Optimization and Augmented Engineering ISLAIB University of Jendouba Beja Tunisia Lab. LR3MI Dép Génie Mécanique Fac. Technologie Université Badji Mokhtar Annaba Annaba Algérie IRCER UMR CNRS 7315 University of Limoges Limoges France Icam Carquefou France Department of Physics College of Science United Arab Emirates University Al Ain United Arab Emirates Lab. Mechanical Modeling Energy and Materials (M2EM) ENIG University of Gabes Gabes Tunisia

ISBN: (数字)9798331516772

ISBN: (纸本)9798331516789

关键词： Zirconium Temperature Atmospheric modeling Artificial neural networks Thermal spraying Aerodynamics Plasmas Coatings Vehicle dynamics optimization

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