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Experimental evaluation of viscosity in a CuO-based nanofluid using machine learning for prediction.
The use of nanofluids in components of absorption cooling systems enhances heat and mass transfer processes. Limited information exists on the thermophysical properties of the nanofluid prepared with water–lithium bromide (H2O–LiBr) as the base fluid and CuO nanoparticles. Due to the limited data available, viscosity is experimentally assessed in this study, providing novel results. The nanofluid was formed using the two-step method, using first a magnetic stirrer and second a sonication bath. A high-accuracy sensor was utilized for viscosity measurements. The nanoparticle mass fraction in the nanofluid was 0.1 wt%, while the salt mass fraction in the base fluid ranged from 56.62 to 60.69 wt% and the temperature from 24 to 60 °C. A strong temperature and salt concentration dependence of viscosity was observed for the nanofluid, exhibiting a 3–9% lower viscosity than the base fluid. As an additional scientific novelty, the viscosity of both the H2O–LiBr mixture and the CuO/H2O–LiBr nanofluid was examined for variable shear rates, showing a slight dilatant behavior. To develop a method for predicting viscosity, machine learning techniques were used. The best performing model was the multi-layer perceptron, which closely reproduces the experimental data and was selected for creating a graphical user interface for viscosity prediction.
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Yera et al. (2026) studied this question.
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