Browsing by Author "Jamei, Mehdi"

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    Intelligent prediction of rock mass deformation modulus through three optimized cascaded forward neural network models
    (Springer, 2022) Hasanipanah, Mahdi; Jamei, Mehdi; Mohammed, Ahmed Salih; Amar, Menad Nait; Ouaer, Hocine; Khedher, Khaled Mohamed
    Rock mass deformation modulus (Em) is a key parameter that is needed to be determined when designing surface or underground rock engineering constructions. It is not easy to determine the deformability level of jointed rock mass at the laboratory; thus, researchers have suggested different in-situ test methods. Today, they are the best methods; though, they have their own problems: they are too costly and time-consuming. Addressing such difficulties, the present study offers three advanced and efficient machine-learning methods for the prediction of Em. The proposed models were based on three optimized cascaded forward neural network (CFNN) using the Levenberg–Marquardt algorithm (LMA), Bayesian regularization (BR), and scaled conjugate gradient (SCG). The performance of the proposed models was evaluated through statistical criteria including coefficient of determination (R2) and root mean square error (RMSE). The computational results showed that the developed CFNN-LMA model produced better results than other CFNN-SCG and CFNN-BR models in predicting the Em. In this regard, the R2 and RMSE values obtained from CFNN-LMA, CFNN-SCG, and CFNN-BR models were equal to (0.984 and 1.927), (0.945 and 2.717), and (0.904 and 3.635), respectively. In addition, a sensitivity analysis was performed through the relevancy factor and according to its results, the uniaxial compressive strength (UCS) was the most impacting parameters on Em
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    Turbulent forced convective flow in a conical diffuser : hybrid and single nanofluids
    (Elsevier, 2023) Iachachene, Farida; Haddad, Zoubida; Arıcı, Müslüm; Jamei, Mehdi; Mataoui, Amina
    Turbulent forced convective flow of hybrid and single nanofluids in a conical diffuser is investigated numerically. Simulations are conducted for various Reynolds (Re=10000-70000) and different concentrations (ϕ=0-1.5 vol%) at equal ratio of TiO2:SiO2. The impact of using theoretical and experimental correlations for dynamic viscosity and thermal conductivity on turbulent forced convection of TiO2 showed that the mean Nusselt (Nu) number is considerably reduced with the use of the experimental model. However, when the theoretical model is used, Nu varies insignificantly. Addition of TiO2 nanoparticles decreases the heat transfer inside the diffuser, whereas addition of TiO2–SiO2 nanoparticles either enhances or decreases the heat transfer rate. Compared to the pure fluid, hybrid nanofluids show a maximum enhancement of 5% and a maximum decrease of 9.7% at ϕ= 0.5 vol% and ϕ= 0.5 vol% at Re=10000, respectively. However, TiO2 nanofluids show a maximum decrease of 19% at ϕ=1.5 vol% and Re= 30000. As Re increases, the deviations between TiO2 and SiO2-TiO2 nanofluids diminish. Moreover, the Gene Expression Programming model can accurately evaluate Nu versus Re number and nanoparticle concentration