TY - CONF
T1 - Estimating uncertainty of low-and high-order turbulence statistics in wall turbulence
AU - Rezaeiravesh, Saleh
AU - Xavier, Donnatella
AU - Vinuesa, Ricardo
AU - Yao, Jie
AU - Hussain, Fazle
AU - Schlatter, Philipp
N1 - Funding Information:
This work has been supported by: i) EXCELLERAT project which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 823691 (SR, PS), and ii) TTU Distinguished Chair funding (JY, FH). The flow simulations were performed on the resources provided by the Swedish National Infrastructure for Computing (SNIC) at PDC (KTH) and Frontera at Texas Advanced Computing Center.
Funding Information:
This work has been supported by: i) EXCELLERAT project which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 823691 (SR, PS), and ii) TTU Distinguished Chair funding (JY, FH). The flow simulations were performed on the resources provided by the Swedish National Infrastructure for Computing (SNIC) at PDC (KTH) and Frontera at Texas Advanced Computing Center.
Publisher Copyright:
© 2022 12th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2022. All rights reserved.
PY - 2022
Y1 - 2022
N2 - A framework is introduced for accurate estimation of time-average uncertainties in various types of turbulence statistics. A thorough set of guidelines is provided to adjust the different hyperparameters for estimating uncertainty in sample mean estimators (SMEs). For high-order turbulence statistics, a novel approach is proposed which avoids any linearization and preserves all relevant temporal and spatial correlations and cross-covariances between SMEs. This approach is able to accurately estimate uncertainties in any arbitrary statistical moment. The usability of the approach is demonstrated by applying it to data from direct numerical simulation (DNS) of the turbulent flow over a periodic hill and through a straight circular pipe.
AB - A framework is introduced for accurate estimation of time-average uncertainties in various types of turbulence statistics. A thorough set of guidelines is provided to adjust the different hyperparameters for estimating uncertainty in sample mean estimators (SMEs). For high-order turbulence statistics, a novel approach is proposed which avoids any linearization and preserves all relevant temporal and spatial correlations and cross-covariances between SMEs. This approach is able to accurately estimate uncertainties in any arbitrary statistical moment. The usability of the approach is demonstrated by applying it to data from direct numerical simulation (DNS) of the turbulent flow over a periodic hill and through a straight circular pipe.
UR - http://www.scopus.com/inward/record.url?scp=85143769627&partnerID=8YFLogxK
M3 - Paper
AN - SCOPUS:85143769627
T2 - 12th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2022
Y2 - 19 July 2022 through 22 July 2022
ER -