Resolvent analysis of turbulent friction drag reduction by manipulation of mean velocity profile

Riko Uekusa, Aika Kawagoe, Yusuke Nabae, Koji Fukagata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In the present study, we numerically manipulate the mean velocity profile of a turbulent channel flow and assess the friction drag reduction performance by using resolvent analysis. Building on the implication obtained from Kühnen et al. (Nat. Phys., Vol. 14, 2017, pp. 386-390) that modifying mean velocity profile flat leads to significant drag reduction, we first introduce two functions for turbulent mean velocity, which can express flattened profiles: one is derived based on the turbulent viscosity model proposed by Reynolds & Tiederman (J. Fluid Mech., Vol. 658, 2010, pp. 336-382), and the other is based on the mean velocity profile of laminar flow. These functions are used as the mean velocity profile for the resolvent analysis, and the flatness of the resulting profiles is characterized by two different measures. As a result, we confirm that, friction drag reduction is achieved if the turbulent mean velocity profile is’flattened’. However, we also find that the flatness of the mean velocity profile in the center of the channel alone is not enough to evaluate the drag reduction performance.

Original languageEnglish
Title of host publicationFluid Mechanics
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791859025
DOIs
Publication statusPublished - 2019 Jan 1
EventASME-JSME-KSME 2019 8th Joint Fluids Engineering Conference, AJKFluids 2019 - San Francisco, United States
Duration: 2019 Jul 282019 Aug 1

Publication series

NameASME-JSME-KSME 2019 8th Joint Fluids Engineering Conference, AJKFluids 2019
Volume1

Conference

ConferenceASME-JSME-KSME 2019 8th Joint Fluids Engineering Conference, AJKFluids 2019
Country/TerritoryUnited States
CitySan Francisco
Period19/7/2819/8/1

ASJC Scopus subject areas

  • Fluid Flow and Transfer Processes

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