Applicability of Methods for Inflow Turbulence Generation Developed in a CFD Field to the Thermally Driven Convective Boundary Layer Simulations

Abstract

Abstract This study focuses on the application of two standard inflow turbulence generation methods for growing convective boundary layer (CBL) simulations: the recycle–rescale (R-R) and the digital filter–based (DF) methods, which are used in computational fluid dynamics. The primary objective of this study is to expand the applicability of the R-R method to simulations of thermally driven CBLs. This method is called the extended R-R method. However, in previous studies, the DF method has been extended to generate potential temperature perturbations. This study investigated whether the extended DF method can be applied to simulations of growing thermally driven CBLs. In this study, idealized simulations of growing thermally driven CBLs using the extended R-R and DF methods were performed. The results showed that both extended methods could capture the characteristics of thermally driven CBLs. The extended R-R method reproduced turbulence in thermally driven CBLs better than the extended DF method in the spectrum and histogram of vertical wind speed. However, the height of the thermally driven CBL was underestimated in about 100 m compared with the extended DF method. Sensitivity experiments were conducted on the parameters used in the extended DF and R-R methods. The results showed that underestimation of the length scale in the extended DF method causes a shortage of large-scale turbulence components. The other point suggested by the results of the sensitivity experiments is that the length of the driver region in the extended R-R method should be sufficient to reproduce the spanwise movement of the roll vortices. Significance Statement Inflow turbulence generation methods for large-eddy simulation (LES) models are crucial for the better downscaling of meteorological mesoscale models (RANS models) to microscale models (LES models). Various CFD methods have been developed, but few have been applied to simulations of thermally driven convective boundary layers (CBLs). To address this problem, we focused on a method that recycles turbulence [the recycle–rescale (R-R) method] and another method that synthetically generates turbulence [the digital filter–based (DF) method]. This study extends the R-R method to manage turbulence in thermally driven CBLs. In addition, this study investigated the applicability of the DF method to thermally driven CBL simulations. Both extended methods are effective for downscaling experiments and capture the characteristics of thermally driven CBLs.