Accurate prediction of dynamic viscosity of polyalpha-olefin boron nitride nanofluids using machine learning.

This study focuses on predicting the dynamic viscosity of nanofluids, specifically Polyalpha-Olefin-hexagonal boron nitride (PAO-hBN) using machine learning models. The primary goal of this research is to assess and contrast the effectiveness of three distinct machine learning models: Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS). The main objective is the identification of a model that demonstrates the highest level of accuracy in predicting a nanofluid's viscosity namely, PAO-hBN nanofluids.

Dataset for transient 3D simulations of turbulent premixed flames of Gas-to-Liquid (GTL) fuel.

A fan-stirred combustion vessel is used to study the premixed turbulent combustion of diesel, Gas to Liquids (GTL) and 50/50 diesel-GTL and to generate these datasets. A numerical simulation approach is implemented for modelling the premixed combustion of the three fuels under different thermodynamics and turbulence initial conditions, using Zimont Turbulent Flame Speed Closure (Zimont TFC) model. Different parameters are obtained from these simulation runs such as turbulent eddy viscosity (µ), turbulent kinetic energy (k), Damkohler number (Da), Reynolds number (Re) and turbulent flame speed (S).

Dataset for measured viscosity of Polyalpha-Olefin- boron nitride nanofluids.

Datasets of measured viscosity of Polyalpha-Olefin- boron nitride (PAO/hBN) nanofluids are reported. An AR-G2 rheometer (from TA Instruments) experimental setup is used for measuring the rheological property of PAO/hBN nanofluids, which is a combined motor and transducer (CMT) instrument. The test fluid sample size is approximately 1.