In this research, a very popular alternative computational technique, the lattice Boltzmann method (LBM), has been used to simulate the indoor airflow and heat transfer in a model hospital ward. Different Reynolds numbers have been used to study the airflow pattern. Boundary conditions for velocity and temperature have also been discussed in detail. Several tests have been conducted for code validation. LBM is demonstrated through simulation in forced convection inside hospital ward with six beds for two different situations: ward without partition and ward with partition. Changes in average rate of heat transfer in terms of average Nusselt numbers have also been recorded for those situations. Average Nusselt numbers were found to differ for different cases. In terms of airflow, it has been found that, for various Reynolds numbers, airflow changes its pattern and leads to few recirculations for relatively higher Reynolds number but remains steady for low Reynolds number. It was observed that partition narrowed the channel for airflow and once the air overcame this barrier, it gets free space and recirculation appears more. For higher Reynolds number, the average rate of heat transfer increases and patients near the recirculation zone release maximum heat and will feel more comfortable.

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This study aims to consider lattice Boltzmann method (LBM)–magnetohydrodynamics (MHD) data to develop equations to predict the average rate of heat transfer quantitatively. The present approach considers a 2D rectangular cavity with adiabatic side walls, and the bottom wall is heated while the top wall is kept cold. Rayleigh–Bénard (RB) convection was considered a heat-transfer phenomenon within the cavity. The Hartmann (Ha) number, by varying the inclination angle (θ), was considered in developing the equations by considering the input parameters, namely, the Rayleigh (Ra) numbers, Darcy (Da) numbers, and porosity (ϵ) of the cavity in different segments. Each segment considers a data-driven approach to calibrate the Levenberg–Marquardt (LM) algorithm, which is highly linked with the artificial neural network (ANN) machine learning method. Separate validations have been conducted in corresponding sections to showcase the accuracy of the equations. Overall, coefficients of determination (R2) were found to be within 0.85 to 0.99. The significant findings of this study present mathematical equations to predict the average Nusselt number (Nu¯). The equations can be used to quantitatively predict the heat transfer without directly simulating LBM. In other words, the equations can be considered validations methods for any LBM-MHD model, which considers RB convection within the range of the parameters in each equation.

In the present investigation the airflow and heat transfer for mixed convection have been simulated for a model general ward of hospital with six beds and partitions using the Lattice Boltzmann Method (LBM). Three different Reynolds numbers 100, 250, and 350 have been considered. Bounce-back condition has been applied at the wall. Results have been represented in three different case studies and the changes have been discussed in terms of streamlines and isotherms. Code validation has also been included before going through the simulation process and it shows good agreement with previously published papers when the comparison is made on average Nusselt number. Results show that the pattern of indoor airflow is varied in each and every case study due to the effect of mixed convection flow and placement of partition. In addition, the changes in average rate of heat transfer indicate that patients closer to inlet get the most air and feel better and if any patient does not need much air, he or she should be kept near the outlet to avoid temperature related complications.

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