On the efficacy of riblets toward drag reduction of transitional and turbulent boundary layers

Malathi, Ananth Sivaramakrishnan; Nardini, Massimiliano; Vaid, Aditya; Vadlamani, Nagabhushana Rao; Sandberg, Richard D.

doi:10.2514/6.2022-0472

Cited by 2 publications

(6 citation statements)

References 36 publications

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“…Similar to the protrusion-height model, (4.10) consists of a priori quantities, which are solely based on a given riblet shape and size below the optimal ( + g 10.7). In figure 17, we note that U + VV for all riblet shapes agree with U + measured from the present DNS, and from DNSs and wall-resolved LES of past studies (El-Samni et al 2007;García-Mayoral & Jiménez 2011b, 2012Bannier et al 2015;Li & Liu 2019;Malathi et al 2022;Cipelli 2023), for riblet sizes up to + g ≈ 10.7. However, the experimental drag reduction of triangular and trapezoidal riblets from Bechert et al (1997), shown in figure 17(b,e, f,i), generally exhibits lower magnitudes compared with DNS/wall-resolved LES data.…”

Section: Drag Predictions For Ribletssupporting

confidence: 84%

“…Data from García-Mayoral & Jiménez (2011b, 2012 show similar trends with our present blade riblets. Li & Liu (2019) and Malathi et al (2022) carried out boundary-layer DNSs for triangular riblets ( and , respectively), the same as DNSs of Choi et al (1993). The percentage drag reduction from Li & Liu (2019) was converted to U + , whilst U + from Malathi et al (2022) was found directly from their mean profiles.…”

Section: ) Atmentioning

confidence: 99%

“…In the viscous regime, the smooth-wall turbulent structures are not disrupted by the small textures, apart from their wall-normal displacement relative to the mean flow, and thus − U + is the difference between the observed mean-flow origin and the turbulence origin (Gómez-de-Segura et al 2018b), which increases with size (Bechert et al 1997) up to a shape-independent size of + g ≈ 10.7 ± 1 (García-Mayoral & Jiménez 2011b). Grey markers are data extracted from the mean profiles of ( ) García-Mayoral & Jiménez (2011b, 2012, ( ) Bannier et al (2015) and ( ) Malathi et al (2022). Data from ( ) Li & Liu (2019)…”

Section: Ribletmentioning

confidence: 99%

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A viscous vortex model for predicting the drag reduction of riblet surfaces

Wong,

Camobreco,

García-Mayoral

et al. 2024

J. Fluid Mech.

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This paper introduces a viscous vortex model for predicting the optimal drag reduction of riblet surfaces, eliminating the need for expensive direct numerical simulations (DNSs) or experiments. The footprint of a typical quasi-streamwise vortex, in terms of the spanwise and wall-normal velocities, is extracted from smooth-wall DNS flow fields in close proximity to the surface. The extracted velocities are then averaged and used as boundary conditions in a Stokes-flow problem, wherein riblets with various cross-sectional shapes are embedded. Here, the same smooth-wall-based boundary conditions can be used for riblets, as we observe from the DNSs that the quasi-streamwise vortices remain unmodified apart from an offset. In particular, the position of these vortices remain unpinned above small riblets. The present approach is compared with the protrusion-height model of Luchini et al. (J. Fluid Mech., vol. 228, 1991, pp. 87–109), which is also based on a Stokes calculation, but represents the vortex with only a uniform spanwise velocity boundary condition. The key novelty of the present model is the introduction of a wall-normal velocity component into the boundary condition, thus inducing transpiration at the riblet crests, which becomes relevant as the riblet size increases. Consequently, the present model allows for the drag-reduction prediction of riblets up to the optimal size. The present approach does not rely on the scale separation formally required by homogenisation techniques, which are only applicable for vanishingly small riblets.

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Section: Drag Predictions For Ribletssupporting

confidence: 84%

Section: ) Atmentioning

confidence: 99%

Section: Ribletmentioning

confidence: 99%

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A viscous vortex model for predicting the drag reduction of riblet surfaces

Wong,

Camobreco,

García-Mayoral

et al. 2024

J. Fluid Mech.

View full text Add to dashboard Cite

show abstract

“…The solver is parallelized to scale on multi-GPUs and CPUs using OpenACC and MPI strategies, respectively. COMPSQUARE has been validated on several canonical test cases (like Taylor green vortex, inviscid vortex convection, turbulent channel flows/boundary layers [29,31,32]) and test cases of industrial relevance (crosswind flows over intakes, separation induced transition on airfoils, etc [33][34][35]). The explicit schemes are around 2× faster than the compact schemes albeit at a marginal reduction in the accuracy.…”

Section: Numerical Frameworkmentioning

confidence: 99%

“…Weighting functions -µ ,F 0 and µ ,F 1 , are estimated during the pre-processing step in the solver. A detailed discussion of BDIM is given in [36], and its validation in the COMPSQUARE framework (on the subsonic flow past a cylinder and channel flow over sinusoidal roughness elements) is presented in [35]. It is well known that the incompressible solvers are slower than the compressible solvers as these involve solving an elliptic pressure-Poisson equation to satisfy continuity.…”

Section: Numerical Frameworkmentioning

confidence: 99%