ESTIMATING UNCERTAINTY OF LOW- AND HIGH-ORDER TURBULENCE STATISTICS IN WALL TURBULENCE

Saleh Rezaeiravesh; Donnatella Xavier; Ricardo Vinuesa; Jie Yao; Fazle Hussain; Philipp Schlatter

ESTIMATING UNCERTAINTY OF LOW- AND HIGH-ORDER TURBULENCE STATISTICS IN WALL TURBULENCE

Saleh Rezaeiravesh, Donnatella Xavier, Ricardo Vinuesa, Jie Yao, Fazle Hussain, Philipp Schlatter

Mechanical Engineering

Research output: Contribution to conference › Paper › peer-review

4 Scopus citations

Abstract

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.

Original language	English
State	Published - 2022
Event	12th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2022 - Osaka, Virtual, Japan Duration: Jul 19 2022 → Jul 22 2022

Conference

Conference	12th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2022
Country/Territory	Japan
City	Osaka, Virtual
Period	07/19/22 → 07/22/22

Cite this

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title = "ESTIMATING UNCERTAINTY OF LOW- AND HIGH-ORDER TURBULENCE STATISTICS IN WALL TURBULENCE",

abstract = "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.",

author = "Saleh Rezaeiravesh and Donnatella Xavier and Ricardo Vinuesa and Jie Yao and Fazle Hussain and Philipp Schlatter",

note = "Publisher Copyright: {\textcopyright} 2022 12th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2022. All rights reserved.; 12th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2022 ; Conference date: 19-07-2022 Through 22-07-2022",

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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

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 -

ESTIMATING UNCERTAINTY OF LOW- AND HIGH-ORDER TURBULENCE STATISTICS IN WALL TURBULENCE

Abstract

Conference

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