Quantifying Uncertainties in Direct-Numerical-Simulation Statistics Due to Wall-Normal Numerics and Grids
NSF Award No. 2231037, NSFC Award No. 91752202.
Physical Review Fluids
This paper takes the perspective of a user of direct-numerical-simulation (DNS) data and quantifies the uncertainties in DNS statistics for plane channel flows. We focus on high-order statistics, such as skewness, kurtosis, and viscous dissipation, and quantify the uncertainties due to wall-normal numerics and grids while minimizing the sampling error and the discretization error in the wall-parallel directions. Two grid distributions and four discretization methods are considered, which are representative of the existing DNSs. Our results show that the available DNS data contain at least a 7% uncertainty in the computed mean viscous dissipation in the buffer layer. Moreover, since turbulence becomes more intermittent at higher Reynolds numbers, the flow will be less well-resolved at the higher Reynolds number if the same grid resolution in terms of the viscous units is employed. Specifically, our estimate shows that a grid that resolves 90% of the dissipation events at Reτ =544 resolves about 87% of the dissipation events at Reτ= 10,000. .
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Chen, P. E. S., Zhu, X., Shi, Y., & Yang, X. I. A. (2023). Quantifying uncertainties in direct-numerical-simulation statistics due to wall-normal numerics and grids. Physical Review Fluids, 8(7). https://doi.org/10.1103/physrevfluids.8.074602