Public transport has been identified as high risk as the corona-virus carrying droplets generated by the infected passengers could be distributed to other passengers. Therefore, predicting the patterns of droplet spreading in public transport environment is of primary importance. This paper puts forward a novel computational and artificial intelligence (AI) framework for fast prediction of the spread of droplets produced by a sneezing passenger in a bus. The formation of droplets of salvia is numerically modelled using a volume of fluid methodology applied to the mouth and lips of an infected person during the sneezing process. This is followed by a large eddy simulation of the resultant two phase flow in the vicinity of the person while the effects of droplet evaporation and ventilation in the bus are considered. The results are subsequently fed to an AI tool that employs deep learning to predict the distribution of droplets in the entire volume of the bus. This combined framework is two orders of magnitude faster than the pure computational approach. It is shown that the droplets with diameters less than 250 micrometers are most responsible for the transmission of the virus, as they can travel the entire length of the bus.

This study numerically investigates heat convection and entropy generation in a hybrid nanofluid (Al2O3–Cu–water) flowing around a cylinder embedded in porous media. An artificial neural network is used for predictive analysis, in which numerical data are generated to train an intelligence algorithm and to optimize the prediction errors. Results show that the heat transfer of the system increases when the Reynolds number, permeability parameter, or volume fraction of nanoparticles increases. However, the functional forms of these dependencies are complex. In particular, increasing the nanoparticle concentration is found to have a nonmonotonic effect on entropy generation. The simulated and predicted data are subjected to particle swarm optimization to produce correlations for the shear stress and Nusselt number. This study demonstrates the capability of artificial intelligence algorithms in predicting the thermohydraulics and thermodynamics of thermal and solutal systems.

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This paper investigates heat and mass transport around a cylinder featuring non-isothermal homogenous and heterogeneous chemical reactions in a surrounding porous medium. The system is subject to an impinging flow, while local thermal non-equilibrium, non-linear thermal radiation within the porous region, and the temperature dependency of the reaction rates are considered. Further, non-equilibrium thermodynamics, including Soret and Dufour effects are taken into account. The governing equations are numerically solved using a finite-difference method after reducing them to a system of non-linear ordinary differential equations. Since the current problem contains a large number of parameters with complex interconnections, low-cost models such as those based on artificial intelligence are desirable for the conduction of extensive parametric studies. Therefore, the simulations are used to train an artificial neural network. Comparing various algorithms of the artificial neural network, the radial basic function network is selected. The results show that variations in radiative heat transfer as well as those in Soret and Dufour effects can significantly change the heat and mass transfer responses. Within the investigated parametric range, it is found that the diffusion mechanism is dominantly responsible for heat and mass transfer. Importantly, it is noted that the developed predictor algorithm offers a considerable saving of the computational burden.

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