TY - JOUR
T1 - Generating synthetic turbulence with vector autoregression of proper orthogonal decomposition time coefficients
AU - Stanly, Ronith
AU - Du, Shiyu
AU - Xavier, Donnatella
AU - Perez, Adalberto
AU - Mukha, Timofey
AU - Markidis, Stefano
AU - Rezaeiravesh, Saleh
AU - Schlatter, Philipp
PY - 2024/12/10
Y1 - 2024/12/10
N2 - This study introduces vector autoregression (VAR) as a linear procedure that can be used for synthesizing turbulence time series over an entire plane, allowing them to be imposed as an efficient turbulent inflow condition in simulations requiring stationary and cross-correlated turbulence time series. VAR is a statistical tool for modelling and prediction of multivariate time series through capturing linear correlations between multiple time series. A Fourier-based proper orthogonal decomposition (POD) is performed on the two-dimensional (2-D) velocity slices from a precursor simulation of a turbulent boundary layer at a momentum thickness-based Reynolds number,. A subset of the most energetic structures in space are then extracted, followed by applying a VAR model to their complex time coefficients. It is observed that VAR models constructed using time coefficients of 5 and 30 most energetic POD modes per wavenumber (corresponding to and of turbulent kinetic energy, respectively) are able to make accurate predictions of the evolution of the velocity field at for infinite time. Moreover, the 2-D velocity fields from the POD-VAR when used as a turbulent inflow condition, gave a short development distance when compared with other common inflow methods. Since the VAR model can produce an infinite number of velocity planes in time, this enables reaching statistical stationarity without having to run an extremely long precursor simulation or applying ad hoc methods such as periodic time series.
AB - This study introduces vector autoregression (VAR) as a linear procedure that can be used for synthesizing turbulence time series over an entire plane, allowing them to be imposed as an efficient turbulent inflow condition in simulations requiring stationary and cross-correlated turbulence time series. VAR is a statistical tool for modelling and prediction of multivariate time series through capturing linear correlations between multiple time series. A Fourier-based proper orthogonal decomposition (POD) is performed on the two-dimensional (2-D) velocity slices from a precursor simulation of a turbulent boundary layer at a momentum thickness-based Reynolds number,. A subset of the most energetic structures in space are then extracted, followed by applying a VAR model to their complex time coefficients. It is observed that VAR models constructed using time coefficients of 5 and 30 most energetic POD modes per wavenumber (corresponding to and of turbulent kinetic energy, respectively) are able to make accurate predictions of the evolution of the velocity field at for infinite time. Moreover, the 2-D velocity fields from the POD-VAR when used as a turbulent inflow condition, gave a short development distance when compared with other common inflow methods. Since the VAR model can produce an infinite number of velocity planes in time, this enables reaching statistical stationarity without having to run an extremely long precursor simulation or applying ad hoc methods such as periodic time series.
KW - turbulent boundary layers
UR - http://www.scopus.com/inward/record.url?scp=85205947695&partnerID=8YFLogxK
U2 - 10.1017/jfm.2024.1034
DO - 10.1017/jfm.2024.1034
M3 - Article
SN - 0022-1120
VL - 1000
JO - Journal of Fluid Mechanics
JF - Journal of Fluid Mechanics
M1 - A83
ER -