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Numerical three dimensional studies of the combined natural convection and radiation heat loss from downward facing open cavity receiver of different shapes is carried out in this paper. The investigation is undertaken in two categories: same inner heat transfer area and aperture area (case I) and same aspect ratio and aperture area (case II). These studies are carried out for five isothermal wall temperatures (523 to 923 K in steps of 100K). The effect of inclination is studied for seven inclinations from 0° (cavity aperture facing sideways) to 90° (cavity aperture facing down), in steps of 15°. The cavity shapes used are: cylindrical, conical (frustum of a cone), cone-cylindrical (combination of frustum of cone and cylindrical shape), dome-cylindrical (combination of hemispherical and cylindrical shape), hetro-conical, reverse-conical (frustum of a cone in the reverse orientation) and spherical. For both cases, conical cavity yields the lowest convective loss among the cavities investigated whereas spherical cavity results in the highest convective loss. Convective heat loss from cavities of different shapes and sizes are characterized by using different internal zone areas of the cavity (A cw , A cz , A cb and A w ) . A cb is found to be better parameter for characterization of the convective heat loss. Nusselt number correlation is developed using convective zone area (A cb ). It correlates 91% of data within ±11% deviation, 99% of data within ±16% deviation. Radiative losses (Q rad ) have been determined numerically from cavities of both cases. The ratio of Q rad /A ap is found to be more or less constant (variation within 5%) for all types of cavities and for 0 ≤ ε ≤ 1. Thus radiative loss is dependent on aperture area and effective emissivity of cavity rather than the shape of the cavity. Further, it also matches well with the analytical formula based on effective emissivity.

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wing Chau (2019) Applicability of connectionist methods to predict dynamic viscosity of silver/water nanofluid by using ANN-MLP,

The experimental analysis of base pressure in a high-speed compressible flow is carried out. The flow is made to expand abruptly from the nozzle into an enlarged duct at fifteen sonic and supersonic Mach numbers. The analysis is made for variation in the nozzle pressure ratio (NPR), length to diameter ratio, and area ratio. The effect of active micro-jets on the base and wall pressure is assessed. The data visualization of the huge experimental data generated is performed using heat maps. For the first time, six back-propagation neural network models (BPMs) are developed based on input and output possibilities to predict the pressure in high-speed flows. The experimental analysis revealed that depending upon the type of expansion, the base pressure changes. A jet of air blown at the base using micro-jets is found to be effective in increasing the base pressure during the under-expansion regime, while the wall pressure remains unaffected. The data visualization provided an insight into the highest impact on the base pressure by the NPR. The six BPMs with two hidden layers having four neurons per layer are found to be most suitable for the regression analysis. BPM 5 and BPM 6 accurately predict the highly non-linear data of the base and wall pressure.

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