A data-driven physics-informed neural network for predicting the viscosity of nanofluids

Abstract

Nanofluids have been applied in various fields, such as solar collectors, petroleum engineering, and chemical engineering, due to their superior properties compared to traditional fluids. Among the various thermophysical properties of nanofluids, viscosity plays a critical role in thermal applications involving heat transfer and fluid flow. While several conventional machine learning (ML) techniques have been proposed to predict viscosity, these conventional models require many experimental measurements to be optimized and make accurate predictions. This study reports a novel ML method using a multi-fidelity neural network (MFNN) to accurately predict the viscosity of nanofluids by incorporating the physical laws into the model. The MFNN correlates a low-fidelity dataset derived from the prediction of the theoretical model with a high-fidelity dataset, which consists of experimental measurements. It is shown that the MFNN can recover the rheology of nanofluids and outperforms the conventional artificial neural network due to incorporating the underlying physics of nanofluids into a model.

Physics-Informed Neural Networks with Group Contribution Methods

“…In other studies, PINN was applied to predict the cetane number. 25 Chinifrooshan Esfahani 26 introduces a PINN model to predict the viscosity of the nanofluids. This research applies a positive output constraint on the layer corresponding to the group contribution values.…”

Evaluation of AGM and FEM method for thermal radiation on nanofluid flow between two tubes in nearness of magnetism field

“…The results appeared to be a 32.7% enhancement in convective heat transfer coefficient at a Reynolds number of 8676 and a 27% increment in thermal conductivity at 0.5 mass% and 30 °C. Nanoparticles are used in various fields, such as solar collectors, petrochemicals, and chemical engineering, due to their superior properties to conventional fluids [ 34 ]. Also, researchers conducted many studies regarding the effects of magnetic parameters and solar energy on fluids and nanofluids passing through flat plates.…”

Comparative Analysis of Soft Computing Models for Predicting Viscosity in Diesel Engine Lubricants: An Alternative Approach to Condition Monitoring

“…46−50 One notable example involves the utilization of a neural network that incorporates physical laws for viscosity prediction. 46 Similarly, the ability of ANN models to consider multiple input parameters�such as shear stress, shear strain, spindle torque, spindle angular velocity, and mass concentrations of solutions�to predict the dynamic viscosity of aqueous gelatin solutions has been demonstrated. 47 Another study employed an ANN model to predict the compositional viscosity of binary mixtures of ionic liquids (ILs) with diverse molar fractions and solvents across a range of temperatures.…”

“…Insights into predicting viscosity based on physical and chemical characteristics have been gleaned from various studies. − One notable example involves the utilization of a neural network that incorporates physical laws for viscosity prediction . Similarly, the ability of ANN models to consider multiple input parameterssuch as shear stress, shear strain, spindle torque, spindle angular velocity, and mass concentrations of solutionsto predict the dynamic viscosity of aqueous gelatin solutions has been demonstrated .…”