a. mishra

A computational method is employed to solve heat transfer and entropy generation within a circular pipe. The thermal boundary condition assumes a constant wall temperature, while viscosity is taken to be dependent on temperature. A power-law type shear-thinning fluid is utilized in the analysis, with sinusoidal vibration applied horizontally perpendicular to the flow direction. Temperature distributions across the pipe are illustrated. Additionally, the entropy generation rate over the entire fluid volume under vibration was examined, comparing the results between steady flow and vibrational flow for both types of fluids. It was found that radial mixing is more pronounced in non-Newtonian fluids as vibration increases the strain rate, which is higher for low Reynolds numbers.  The research provides a quantitative analysis of heat transfer and entropy generation for both Newtonian and shear-thinning fluids at different Reynolds numbers. It was observed that the effectiveness of superimposed vibrational flow is limited, especially for low Reynolds numbers and flow behavior index characteristic of shear-thinning fluids.

An Indirect Symmetrical Phase Shift Transformer (ISPST) represents both electrically connected and magnetically coupled circuits, which makes it unique compared to a power transformer. Effective differentiation between transformer inrush current and internal fault current is necessary to avoid incorrect differential relay tripping. This research proposes a system that uses a Chebyshev Neural Network (ChNN) as a core classifier to distinguish such internal faults. For simulations, we used PSCAD/EMTDC software. Internal faults and inrush have been simulated in various ways using various ISPST parameters. A large, simulated dataset is used, and performance is recorded against different sized ISPSTs. We observed an overall accuracy greater than 99%. The ChNN classifier generated exceptionally favorable results even in case of noisy signal, CT saturation, and different ISPST parameters.