Browsing by Author "Karbasi, Masoud"

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    On the assessment of specific heat capacity of nanofluids for solar energy applications: Application of Gaussian process regression (GPR) approach
    (Journal of Energy Storage, 2020-10-29) Jamei, Mehdi; Ahmadianfar, Iman; Olumegbon, Ismail A.; Karbasi, Masoud; Asadi, Amin
    To characterize the performance of nanofluids for heat transfer applications in solar systems, an accurate estimation of their specific heat capacity (SHC) is of paramount importance. To this end, having such properties of nanofluids via computational approaches has gained attention as an effective method to eliminate the timeconsuming process of experimental investigations. This study focuses on modeling the SHC of different carbon-based and metal oxide-based nanoparticles dispersed in various base fluids. Herein, we propose a novel data-driven dynamic model based on the Gaussian process regression (GPR) technique in comparison with the random forest (RF) approach and generalized regression neural network (GRNN) to predict the SHC of nanofluids. The developed models employ the solid volume fraction (ϕ), temperature (T), mean diameter of nanoparticle (Dp), and SHC of base fluid (CPBase) as the input parameters. The data has been collected from 10 reliable references. The results showed that the GPR model (R=0.99974, RMSE=0.01506 J/K.g) shows superior performance than the results of the RF (R=0.99761, RMSE=0.04598 J/K.g) and GRNN (R=0.99563, RMSE=0.06085 J/K.g). The results proved that the developed model would accurately estimate the SHC of the studied nanofluids. In addition, the sensitivity analysis of the dependence of input variables on the SHC of nanofluids revealed that the mean diameter of nanoparticles and the SHC of base fluid are the major critical factors in the determination of SHC of nanofluids.
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    On the specific heat capacity estimation of metal oxide-based nanofluid for energy perspective – A comprehensive assessment of data analysis techniques
    (International Communications in Heat and Mass Transfer, 2021) Jamei, Mehdi; Ahmadianfara, Iman; Olumegbon, Ismail A.; Asadi, Amin; Karbasi, Masoud; Said, Zafar; Sharifpur, Mohsen; Meyer, Josua P.
    The main aim of the present study is to investigate the capabilities of four robust machine learning method - the Kernel Extreme Learning Machine (KELM), Adaptive Regression Spline (MARS), M5 Model Tree (M5Tree), and Gene Expression Programming (GEP) model in predicting specific heat capacity (SHC) of metal oxide-based nanofluids implemented in solar energy application. Sets of 1180 data of different metal oxide-based nanofluids containing Al2O3, ZnO, TiO2, SiO2, MgO, and CuO dispersed in various base fluids were collected from reliable literature to provide the predictive model of SHC of nanofluids. The volume fraction, temperature, SHC of the base fluid, and mean diameter of nanoparticles were used as an input variable to predict nanofluids' SHC as the output variable. The artificial intelligence (AI) models were validated using several statistical performance criteria, graphical devices, and conventional models. The results obtained from all datasets demonstrated that the KELM model significantly outperformed the MARS, M5Tree, and GEP model in predicting the SHC of nanofluid. Moreover, the sensitivity analysis showed that the mean diameter of the nanoparticle and SHC of the base fluid have the most considerable impact on estimating the SHC of metal oxide-based nanofluids.
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    On the Thermal Conductivity Assessment of Oil-Based Hybrid Nanofluids using Extended Kalman Filter integrated with feed-forward neural network
    (International Journal of Heat and Mass Transfer, 2021) Jamei, Mehdi; Olumegbon, Ismail A.; Karbasi, Masoud; Ahmadianfar, Iman; Asadi, Amin; Mosharaf-Dehkordif, Mehdi
    Regarding their ability to enhance conventional thermal oils' thermophysical properties, oil-based hybrid nanofluids have recently been widely investigated by researchers, especially on lubrication and cooling application in the automotive industry. Thermal conductivity is one of the most crucial thermophysical properties of oil-based hybrid nanofluids, which has been studied in a minimal case of studies on the specific types of them. In this research, for the first time, a comprehensive data-intelligence analysis performed on 400 gathered data points of various types of oil-based hybrid nanofluids using a novel hybrid machine learning approach; the Extended Kalman Filter-Neural network (EKF-ANN). The genetic programming (GP) and response surface methodology (RSM) approaches were examined to appraise the main paradigm. In this research, the best subset regression analysis, as a novel feature selection scheme, was provided for finding the best input parameter among all existing predictive variables (the volume fraction, temperature, thermal conductivity of the base fluid, mean diameter, and bulk density of nanoparticles). The provided models were examined using several statistical metrics, graphical tools and trends, and sensitivity analysis. The results assessment indicated that the EKF-ANN in terms of (R=0.9738, RMSE=0.0071 W/m.K, and KGE=0.9630) validation phase outperformed the RSM (R=0.9671, RMSE=0.0079 W/m.K, and KGE=0.9593) and GP (R=0.9465, RMSE=0.010 W/m.K, and KGE=0.9273), for accurate estimation of the thermal conductivity of oil-based hybrid nanofluids.