Omar Reffas
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Omar Reffas has a total of 11 co-authors. Here are the top co-authors by number of shared publications:
- Haithem Boumediri
Mechanical Department, Institute of Applied Sciences and Techniques, University of Constantine 1, Constantine 25017, Algeria
2 shared publications - Sabbah Ataya
Department of Mechanical Engineering, College of Engineering, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
2 shared publications - Yacine Karmi
Electromechanical Department, Institute of Science and Applied Technics, University of Constantine 1, Constantine, Algeria
2 shared publications - Abdessalam Otmani
Department of Process and Energy Engineering, National Higher School of Technology and Engineering, Annaba 23005, Algeria
1 shared publication - Aissa Laouissi
Department of Mechanical Engineering, Faculty of Science and Technology, University of Mohamed El Bachir El Ibrahimi, Bordj Bou Arreridj 34000, Algeria
1 shared publication - Issam Bousba
Faculty of Sciences and Technology, University of Jijel, Jijel 18000, Algeria
1 shared publication
Latest Publications
- Open Access
Numerical Investigation of a New Segmented Annular Ring Tube for Heat Transfer Enhancement in Solar Water Heating Systems
This study presents a numerical investigation of a parabolic trough absorber tube equipped with a novel Angularly Segmented Ring Turbulator (ASRT), designed to enhance heat transfer through periodic flow disturbance and improved wall–fluid interaction. The proposed ASRT geometry consists of segmented annular rings arranged along the tube length, characterized by two key parameters: the number of angular segments per ring (Nr = 4, 6, 8) and the angular spacing of each segment (α = 20° and 40°). Three dimensional simulations were performed using the finite volume method under turbulent flow conditions, with Reynolds numbers ranging from 3300 to 11,000. A non-uniform solar heat flux, obtained via Monte Carlo Ray Tracing (MCRT), was applied as a boundary condition at the outer wall to replicate realistic solar concentration. The results reveal that the ASRT significantly improves convective heat transfer, with the Nusselt number ratio reaching up to 3.7 for α = 20° and Nr = 8. This enhancement is accompanied by a moderate rise in the friction factor ratio , reaching approximately 7.5 at Re = 3300, indicating efficient turbulence promotion with acceptable hydraulic penalties. The Performance Evaluation Criterion (PEC) ranges from 1.7 to 1.9, confirming the superiority of ASRT over the smooth tube.Open AccessNumerical Investigation of a New Segmented Annular Ring Tube for Heat Transfer Enhancement in Solar Water Heating Systems
This study presents a numerical investigation of a parabolic trough absorber tube equipped with a novel Angularly Segmented Ring Turbulator (ASRT), designed to enhance heat transfer through periodic flow disturbance and improved wall–fluid interaction. The proposed ASRT geometry consists of segmented annular rings arranged along the tube length, characterized by two key parameters: the number of angular segments per ring (Nr = 4, 6, 8) and the angular spacing of each segment (α = 20° and 40°). Three dimensional simulations were performed using the finite volume method under turbulent flow conditions, with Reynolds numbers ranging from 3300 to 11,000. A non-uniform solar heat flux, obtained via Monte Carlo Ray Tracing (MCRT), was applied as a boundary condition at the outer wall to replicate realistic solar concentration. The results reveal that the ASRT significantly improves convective heat transfer, with the Nusselt number ratio reaching up to 3.7 for α = 20° and Nr = 8. This enhancement is accompanied by a moderate rise in the friction factor ratio , reaching approximately 7.5 at Re = 3300, indicating efficient turbulence promotion with acceptable hydraulic penalties. The Performance Evaluation Criterion (PEC) ranges from 1.7 to 1.9, confirming the superiority of ASRT over the smooth tube. - Open Access
Integration of Hybrid Machine Learning and Multi-Objective Optimization for Enhanced Turning Parameters of EN-GJL-250 Cast Iron
This study aims to optimize the turning parameters for EN-GJL-250 grey cast iron using hybrid machine learning techniques integrated with multi-objective optimization algorithms. The experimental design focused on evaluating the impact of cutting tool type, testing three tools: uncoated and coated silicon nitride (Si3N4) ceramic inserts and coated cubic boron nitride (CBN). Key cutting parameters such as depth of cut (ap), feed rate (f), and cutting speed (Vc) were varied to examine their effects on surface roughness (Ra), cutting force (Fr), and power consumption (Pc). The results showed that the coated Si3N4 tool achieved the best surface finish, with minimal cutting force and power consumption, while the uncoated Si3N4 and CBN tools performed slightly worse. Advanced optimization models including improved grey wolf optimizer–deep neural networks (DNN-IGWOs), genetic algorithm–deep neural networks (DNN-GAs), and deep neural network–extended Kalman filters (DNN-EKF) were compared with traditional methods like Support Vector Machines (SVMs), Decision Trees (DTs), and Levenberg–Marquardt (LM). The DNN-EKF model demonstrated exceptional predictive accuracy with an R2 value of 0.99. The desirability function (DF) method identified the optimal machining parameters for the coated Si3N4 tool: ap = 0.25 mm, f = 0.08 mm/rev, and Vc = 437.76 m/min. At these settings, Fr ranged between 46.424 and 47.405 N, Ra remained around 0.520 µm, and Pc varied between 386.518 W and 392.412 W. The multi-objective grey wolf optimization (MOGWO) further refined these parameters to minimize Fr, Ra, and Pc. This study demonstrates the potential of integrating machine learning and optimization techniques to significantly enhance manufacturing efficiency.
Open AccessIntegration of Hybrid Machine Learning and Multi-Objective Optimization for Enhanced Turning Parameters of EN-GJL-250 Cast Iron
This study aims to optimize the turning parameters for EN-GJL-250 grey cast iron using hybrid machine learning techniques integrated with multi-objective optimization algorithms. The experimental design focused on evaluating the impact of cutting tool type, testing three tools: uncoated and coated silicon nitride (Si3N4) ceramic inserts and coated cubic boron nitride (CBN). Key cutting parameters such as depth of cut (ap), feed rate (f), and cutting speed (Vc) were varied to examine their effects on surface roughness (Ra), cutting force (Fr), and power consumption (Pc). The results showed that the coated Si3N4 tool achieved the best surface finish, with minimal cutting force and power consumption, while the uncoated Si3N4 and CBN tools performed slightly worse. Advanced optimization models including improved grey wolf optimizer–deep neural networks (DNN-IGWOs), genetic algorithm–deep neural networks (DNN-GAs), and deep neural network–extended Kalman filters (DNN-EKF) were compared with traditional methods like Support Vector Machines (SVMs), Decision Trees (DTs), and Levenberg–Marquardt (LM). The DNN-EKF model demonstrated exceptional predictive accuracy with an R2 value of 0.99. The desirability function (DF) method identified the optimal machining parameters for the coated Si3N4 tool: ap = 0.25 mm, f = 0.08 mm/rev, and Vc = 437.76 m/min. At these settings, Fr ranged between 46.424 and 47.405 N, Ra remained around 0.520 µm, and Pc varied between 386.518 W and 392.412 W. The multi-objective grey wolf optimization (MOGWO) further refined these parameters to minimize Fr, Ra, and Pc. This study demonstrates the potential of integrating machine learning and optimization techniques to significantly enhance manufacturing efficiency.