On the Thermal Conductivity Assessment of Oil-Based Hybrid Nanofluids using Extended Kalman Filter integrated with feed-forward neural network
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Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
International Journal of Heat and Mass Transfer
Abstract
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.
Description
Staff Publication
Keywords
Nanofluids, thermal conductivity, oil-based hybrid nanofluids, Kalman filter, response surface methodology