Fake it till you make it: Synthetic turbulence to achieve swift converged turbulence statistics in a pressure-driven channel flow

Abstract

In this study, we introduced a simple yet innovative application: the isotropic synthetic turbulence field generator (iSTFG), based on the synthetic turbulent inflow generator. The iSTFG leverages the homogeneity in the streamwise direction for channel flows and triggers turbulence to achieve statistically stationary flow conditions faster than standard community-used strategies. We compare this new method with two other well-established methods: linear and log-law profiles superposed with random noise and descending counter-rotating vortices. We find that iSTFG provides a computationally cheap and effective way to reduce simulation spin-up costs/time/emissions to achieve statistically stationary flow conditions when a precursor turbulent initial condition is unavailable. At a one-time cost between 1-10 Central Processing Unit (CPU) hour(s) to generate the synthetic turbulent initial condition based on the target friction Reynolds numbers (1 CPU hour - $R{e}_{\tau }=500$, 7 CPU hours - $R{e}_{\tau }=2000$), the flow achieves statistically stationary turbulent flow (SSTF) state within three eddy turnovers for all the parameters of interest in wall-bounded pressure-driven channel flow simulations when compared to other alternatives that can take more than ten eddy turnovers resulting in substantial savings in the computational cost. We also demonstrate that the transition and convergence to an SSTF state using conventional methods are sensitive to the computational domain size, while iSTFG is agnostic to the domain size. Furthermore, we explored the sensitivity of the iSTFG method to the non-dimensional integral length scale parameter and mismatch in reference and target input data to find iSTFG robust in such scenarios.