The distribution of turbulent kinetic energy (TKE), temperature, and velocity of humid air inside the greenhouse solar dryer (GHSD) was numerically investigated using 3D CFD ANSYS FLUENT code. The effect of solar radiation was coupled with the energy equations using the discrete ordinate model. Numerical simulations were based on two geometric models: Real model and model with reduced height, the solution was in good agreement with experimental data of temperature. The results of the real model showed that the TKE is ranged between 1.27 m2/s2 and 6 m2/s2 with an average of about 1.6 m2/s2 for the entire greenhouse dryer (GHD) volume. The greatest TKE magnitude is in the paths of the diffusers, which caused a temperature drop of about 2 K in the areas near the walls. Consequently, almost homogeneous temperature distribution was obtained in the entire volume of the GHD, although the average temperature was 315 K, and a gradient with respect to ambient temperature was of 14 K, that is, suitable for drying. Also, the average air velocity at 1 m height was 0.71 m/s, which is a value near the lowest limit (0.6 m/s) of forced convection drying. The improvement in the GHD by 36.5% volume reduction allowed an increase in the average TKE of 3.8 m2/s2 (2.4 times more than the previous one) located in the middle of the greenhouse; the average temperature reached 316.5 K with a gradient of 15.5 K, which represents an increase of 1.5 K (11%) compared to the real geometric model. The air velocity at 1 m height increased to 0.9 m/s in the improved geometric model (a growth of 35.7% compared with the previous geometry). More than 95% of the improved GHD volume has a uniform temperature, which is very suitable for a good quality drying process with higher speed.
The proposed approach requires limited interaction and reduced computation time, making it relevant for intraoperative use. Experimental results and evaluations were performed offline. The developed tool could be useful for brain tumor resection supporting neurosurgeons to improve tumor border visualization in the iUS volumes.
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