Bulletin of the American Physical Society
76th Annual Meeting of the Division of Fluid Dynamics
Sunday–Tuesday, November 19–21, 2023; Washington, DC
Session J16: CFD: Uncertainty Quantification, RANS Modeling
4:35 PM–6:32 PM,
Sunday, November 19, 2023
Room: 145A
Chair: Robert Kunz, Penn State
Abstract: J16.00006 : Upward Normal Drag Prediction in Distributed Element Roughness Modeling
5:40 PM–5:53 PM
Presenter:
Robert F Kunz
(Penn State)
Authors:
Robert F Kunz
(Penn State)
VISHAL WADHAI
(Pennsylvania State University)
Shyam Nair
(Penn State Department of Mechanical Engineering)
Xiang Yang
(Pennsylvania State University)
ing approach, that provides better generality than equivalent sand grain height (ks) based Reynolds-
Averaged Navier-Stokes (RANS) modeling, while retaining the cost-effectiveness of RANS. DERM
models are based on the Double Averaged Navier-Stokes (DANS) equations. To ensure the accu-
racy of the DERM model, it is crucial to consistently model the interfacial drag term, spatial
dispersion term, and Reynolds Stress term, which emerge in the DANS equation after volumet-
ric and time averaging of the Navier-Stokes equations. Traditionally, the drag term in DANS has
been modeled using a convective drag law, which only accounts for pressure/form drag. However,
this approach has limitations when capturing drag in flows over high-packing density arrays. The
streamwise drag force from the roughness element can be partitioned into pressure force on the
windward and leeward faces of the roughness element (τxx), the viscous drag on the side faces of
the roughness element (τxy), and the viscous drag on the top faces of the roughness element (τxz ).
The first two components can be combined into flow sheltering drag (fP ) and the remaining com-
ponent, here termed upward normal drag (fT ), can be modeled separately. In this work, we develop
an upward normal drag model by considering DNS data of flow over cube arrays, sinusoidal rough
surfaces, and quasi-random additively manufactured rough surfaces. We calibrate this viscous drag
based on several geometric roughness morphology statistics. Significant improvements in predic-
tive capability are demonstrated for all three classes of roughness compared to other methods that
have appeared.
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