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

Research

Title	Experimental exploration of rheological behavior of polyethylene glycol-carbon dot nanofluid: Introducing a robust artificial intelligence paradigm optimized with unscented Kalman filter technique
Type	Article
Keywords	Carbon dot nanofluid Polyethylene glycol Viscosity Unscented Kalman filter Artificial neural network Response surface methodology
Researchers	Amin Shahsavar Goldanloo، Mohamad Amin Mirzaei، Aidin Shaham، Mehdi Jamei، Masoud Karbasi، Fatemeh Seifikar، Saeid Azizian

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 () was investigated. The values considered for 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 . 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 , 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.