Experimental exploration of rheological behavior of polyethylene glycol-carbon dot nanofluid: Introducing a robust artificial intelligence paradigm optimized with unscented Kalman filter technique

Abstract

In the present study, the polyethylene glycol 200 (PEG200)-based nanofluid containing carbon dot (CD) nanoparticles was synthesized, and its rheological behavior at different temperatures and nanoparticle concentrations (φ) was investigated. The values considered for φ were 0%, 1% and 3% and 7% the values considered for temperature were 20, 30, 40, 50 and 60 °C. It was observed that the PEG200 has a Newtonian behavior, and the nanofluid has a non-Newtonian behavior which is amplified with increasing temperature. Also, a decreasing and increasing trend of viscosity was observed with temperature and φ. As another novelty of this research, a robust novel artificial neural network (ANN) model integrated with an unscented Kalman filter (UKF-ANN) was presented for accurate estimation of the viscosity of the PEG-CD nanofluid based on φ, temperature, and shear rate as the input features. Besides, two efficient data-driven approaches, including classical perceptron ANN (MLP) and response surface methodology (RSM) were developed to examine and evaluate the robustness of UKF-ANN model. The statistical and infographic assessment indicated that the UKF-ANN outperformed the MLP and RSM, respectively.