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.
Polystyrene particles simulating bacteria flow down a micro-channel in the presence of potassium chloride solution. Depending on the ionic concentration or flow rates, portion of the particles are trapped on the glass substrate due to intrinsic surface forces. A novel quartz crystal microbalance (QCM) is built into the microfluidic device to track the real-time particle deposition by shift of the resonance frequency. The new technique is promising to quantify water filtration.
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