Revisiting Reynolds and Nusselt numbers in turbulent thermal convection

In this paper, we extend Grossmann and Lohse's (GL) model [S. Grossmann and D. Lohse, Thermal convection for large Prandtl numbers, Phys. Rev. Lett. 86, 3316 (2001)] for the predictions of Reynolds number (Re) and Nusselt number (Nu) in turbulent Rayleigh-Benard convection. Toward this objective, we use functional forms for the prefactors of the dissipation rates in the bulk and boundary layers. The functional forms arise due to inhibition of nonlinear interactions in the presence of walls and buoyancy compared to free turbulence, along with a deviation of the viscous boundary layer profile from Prandtl-Blasius theory. We perform 60 numerical runs on a three-dimensional unit box for a range of Rayleigh numbers (Ra) and Prandtl numbers (Pr) and determine the aforementioned functional forms using machine learning. The revised predictions are in better agreement with the past numerical and experimental results than those of the GL model, especially for extreme Prandtl numbers.

Automated summary

In this paper, the GL model is extended for predicting Reynolds number and Nusselt number in turbulent Rayleigh-Benard convection. Functional forms for dissipation rates prefactors are used, and machine learning is applied to determine revised predictions that show better agreement with past results in a three-dimensional unit box setting.