Publications Scientifiques

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    Optimised heat exchange in a magnetised nanofluid-filled cavity using hybrid deep neural network and metaheuristic algorithms
    (Taylor and Francis Ltd., 2025) Benderradji, Razik; Laouissi, Aissa; Karmi, Yacine; Abderazek, Hammoudi; Chetbani, Yazid; Belaadi, Ahmed; Mukalazi, Herbert; Ghernaout, Djamel; Chamkha, Ali
    This study presents a comprehensive numerical investigation into steady-state mixed convection heat transfer within a square ventilated cavity containing a centrally positioned isothermal cold cylinder. The objective is to assess the combined effects of nanofluids and magnetic fields on thermal performance. The working fluids considered include pure water and water-based nanofluids enhanced with copper (Cu) and aluminium oxide (Al2O3) nanoparticles. Simulations were conducted across a range of Richardson numbers (0.1 < Ri < 100), Hartmann numbers (0 < Ha < 100), and nanoparticle volume fractions (0% < φ < 8%), using the finite volume method and the SIMPLER algorithm. Distinct from prior studies, this work bridges two gaps: (i) quantifying how high magnetic fields (Ha > 50) diminish nanoparticle-enhanced heat transfer and (ii) integrating artificial intelligence not only for prediction but also optimisation. Specifically, three machine learning models Decision Tree (DT), K-Nearest Neighbors (KNN), and a Deep Neural Network optimised via Genetic Algorithm (DNN-GA) were trained on 160 high-fidelity simulation datasets to estimate the average Nusselt number. Results demonstrated the DNN-GA’s superior accuracy (R² = 0.999, RMSE = 0.021) over DT (R² = 0.978) and KNN (R² = 0.921). Furthermore, five metaheuristic algorithms Queuing Search Algorithm (QSA), Barnacles Mating Optimiser (BMO), Search and Rescue (SAR), Gradient-Based Optimiser (GBO), and Manta Ray Foraging Optimisation (MRFO) were applied to maximise heat transfer. Optimisation identified Cu nanoparticles at Ri = 109.7, Ha = 9.0, and φ = 6% as optimal (Nu = 34.95), validated experimentally with 0.89% error. The findings confirm that increasing Ri and Ha enhances heat transfer efficiency (by 12–18%), while nanoparticle contribution declines (to 3–5%) at higher Ha. This work offers a dual contribution: advancing understanding of MHD nanofluid interactions in ventilated cavities and demonstrating a robust AI-driven framework for thermal system design. Highlights: Analysis of mixed convection in a ventilated cavity using Cu-water and Al2O3-water nanofluids under varying Richardson and Hartmann numbers. Examination of magnetic field impacts on heat transfer and nanofluid flow. Comparative study of Al2O3 and Cu nanoparticles on heat transfer enhancement. Provides valuable insights into the combined effects of nanoparticles, magnetic fields, and convection parameters. Machine learning models are very useful for predicting the Nusselt number. Metaheuristics algorithms are highly effective in optimising heat transfer processes
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    Comparative analysis on heat transfer, between a steady and oscillating jet in a cavity
    (Inderscience Publishers, 2024) Iachachene, Farida; Mataoui, Amina
    This paper numerically investigates the cooling of a heated rectangular cavity by a cold slot jet. The study aims to examine the effect of the jet location inside the cavity (Lf and Lh) and Reynolds number on heat transfer, using URANS turbulence modelling. Different flow behaviours, including oscillatory and steady flows, are generated depending on the jet location inside the cavity. The study identifies and discusses the optimal jet locations for achieving optimal cavity cooling. The results indicate that the lateral placement of the jet has a negligible effect on heat transfer across all cavity walls. Additionally, oscillatory flow consistently expands the heat exchange zone along all three walls, resulting in a wider effective exchange area compared to steady flow conditions. The study proposes optimised jet positions within the cavity for specific wall cooling requirements. By considering the optimal combination of jet height and impinging distance, the cooling performance can be optimised.
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    Numerical investigation and optimization of melting performance for thermal energy storage system partially filled with metal foam layer: New design configurations
    (Elsevier, 2023) Haddad, Zoubida; Iachachene, Farida; Sheremet, Mikhail A.; ;Abu-Nada, Eiyad
    Low thermal performance of storage systems represents a barrier to their industrial/engineering application and commercialization. Among all the proposed methods, combination of phase change material with metal foams appears more promising due to the high thermal conductivity of metal foams. However, the insertion of metal foams reduces the PCM volume; hence, a lower amount of stored energy. The present numerical study thoroughly addresses this issue with a focus on the optimization of melting performance for thermal energy storage system partially filled with metal foam layer. A finite volume method based on the enthalpy–porosity technique has been adopted for the numerical simulations. The metal foam location, porosity, and nanoparticle volume fraction were optimized to explore their effects on the melting performance. The results showed that inserting the foam layer diagonally from the top left to the right bottom leads to the lowest melting time. Compared to pure PCM, the melting time increases by 77.7%, while the stored energy decreases by 6.7%. The optimum porosity was found to be 0.88 as it gives approximately the same amount of stored energy as that of pure PCM with a deviation of 4%. Adding nanoparticles to pure PCM increases the melting rate by approximately 8%, while it decreases the stored energy by almost 3%. It is concluded that hybrid systems, i.e., metal foam at an optimum porosity and nanoparticles is more efficient than using each technique separately
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    Heat exchanges intensification through a flat plat solar collector by using nanofluids as working fluid
    (2018) Maouassi, Ammar; Baghidja, Abdelhadi; Douad, Salima; Zeraibi, Noureddine
    This paper illustrates how practical application of nanofluids as working fluid to enhance solar flat plate collector efficiency. A numerical investigation of laminar convective heat transfer flow throw a solar collector is conducted, by using CuO-water nanofluids. The effectiveness of these nanofluids is compared to conventional working fluid (water), wherein Reynolds number and nanoparticle volume concentration in the ranges of 25–900 and 0–10 % respectively. The effects of Reynolds number and nanoparticles concentration on the skin-friction and heat transfer coefficients are presented and discussed later in this paper. Results show that the heat transfer increases with increasing both nanoparticles concentration and Reynolds number, where nanofluid CuO-water gives best improvement in terms of heat transfer