Revisiting rough-wall turbulent boundary layers over sand-grain roughness

Gul, Melika; Ganapathisubramani, Bharathram

doi:10.1017/jfm.2020.1004

Cited by 10 publications

(20 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the other R → S cases (i.e. when the downstream surface is also rough), however, it seems that the differences in the Q 2 and Q 4 events are not significant, similar to our previous findings over the same sandpapers without any surface change (Gul & Ganapathisubramani 2021). Thus, the differences in the contribution of the Q 2 , Q 4 events for these R → S cases could be due to the smooth wall where the absence of rough elements mitigate (enhance) the Q 2 (Q 4 ) events.…”

Section: Sweep and Ejection Eventssupporting

confidence: 90%

“…Surfaces with a step change in roughness (along the streamwise direction) were created with combinations of P, P and P grit sandpapers as well as a smooth wall (S). The nominal roughness information and corresponding sandgrain roughness for the three different rough surfaces used are listed in Gul & Ganapathisubramani (2021). Surface transitions P24 S, P36 S, P60 S, P24 P60, P24 P36 and P36 P60 form the R S cases (where the upstream surface is rough and the downstream is either a smooth wall or a smoother surface compared to the upstream part), whereas P60 P24, P60 P36 and P36 P24 surface transitions constitute S R cases (where the downstream surface is rougher: both surfaces are rough).…”

Section: Experimental Set-up and Methodologymentioning

confidence: 99%

“…Note that throughout this paper, unless otherwise stated, we use the friction velocity of the upstream surface denoted as for the inner normalisation. The friction velocity of each upstream roughness/flow condition was approximated based on the floating element drag measurements of Gul & Ganapathisubramani (2021), who conducted statistically steady turbulent boundary layer experiments over the same sandpapers (homogeneous rough surfaces without any type of surface transition) in the range of the Reynolds numbers and roughness Reynolds numbers . Here, in their experiments, is the streamwise-averaged boundary layer thickness, and is the equivalent sandgrain roughness height which is mm, mm and mm for the P24, P36 and P60 grit sandpapers, respectively (as determined by Gul & Ganapathisubramani 2021).…”

Section: Experimental Set-up and Methodologymentioning

confidence: 99%

“…The friction velocity of each upstream roughness/flow condition was approximated based on the floating element drag measurements of Gul & Ganapathisubramani (2021), who conducted statistically steady turbulent boundary layer experiments over the same sandpapers (homogeneous rough surfaces without any type of surface transition) in the range of the Reynolds numbers and roughness Reynolds numbers . Here, in their experiments, is the streamwise-averaged boundary layer thickness, and is the equivalent sandgrain roughness height which is mm, mm and mm for the P24, P36 and P60 grit sandpapers, respectively (as determined by Gul & Ganapathisubramani 2021). In the present study, represents the local boundary layer thickness at each streamwise location, whereas corresponds to the average boundary layer thickness of the upstream homogeneous surface.…”

Section: Experimental Set-up and Methodologymentioning

confidence: 99%

See 3 more Smart Citations

Experimental observations on turbulent boundary layers subjected to a step change in surface roughness

Gul

Ganapathisubramani

2022

J. Fluid Mech.

Self Cite

View full text Add to dashboard Cite

Based on experimental data acquired with particle image velocimetry, we examine turbulent boundary layers that are subjected to an abrupt change in wall roughness in the streamwise direction. Three different sandpapers (P24, P36 and P60) together with a smooth wall are used to form a number of different surface transition cases, including both R $\rightarrow$ S (where upstream surface is rough and second surface is either smooth or smoother compared with the upstream surface) and S $\rightarrow$ R (where upstream surface is smoother compared with the downstream surface; both surfaces are rough). This enables us to investigate the effect of the surface transition strength ( $M = \ln [y_{02}/y_{01}]$ , where $y_{01}$ and $y_{02}$ are the roughness lengths of the upstream and downstream surfaces, respectively) on the growth of the internal boundary layer (IBL) and the corresponding flow structure. The results show that for each surface transition group (i.e. R $\rightarrow$ S and S $\rightarrow$ R), the thickness of the IBLs is proportional to the strength of the surface transition, and that the IBLs are thicker for the S $\rightarrow$ R cases compared with their R $\rightarrow$ S counterparts for similar $|M|$ , when normalised by the initial boundary layer thickness ( $\delta _0$ ). The results also show that the growth rates of the IBLs could be represented by a power law, consistent with the previous studies. However, despite a wide range of scatter in the literature for the power-law exponent, an average value of $0.75$ (varies between $0.71$ and $0.8$ with no clear trend) is obtained in the present study considering all the surface transition cases. The pre-factor for the power-law fit, on the other hand, is found to be related to the strength of the surface transition. In addition to the variations in the velocity defect and diagnostic plots with the growth of the IBLs, sweep and ejection events appear to differ significantly (depending on the type of the step change). Two-point spatial correlations, moreover, show that the structure is more elongated in the wall-normal and streamwise directions, as the flow accelerates over the downstream surface (i.e. R $\rightarrow$ S cases). For the reverse transition cases (i.e. S $\rightarrow$ R, where the flow decelerates over the downstream rougher surface), however, the correlation coefficients shrink in size in both directions.

show abstract

Section: Sweep and Ejection Eventssupporting

confidence: 90%

Section: Experimental Set-up and Methodologymentioning

confidence: 99%

Section: Experimental Set-up and Methodologymentioning

confidence: 99%

Section: Experimental Set-up and Methodologymentioning

confidence: 99%

See 2 more Smart Citations

Experimental observations on turbulent boundary layers subjected to a step change in surface roughness

Gul

Ganapathisubramani

2022

J. Fluid Mech.

Self Cite

View full text Add to dashboard Cite

show abstract

“…If only one-dimensional measurements like hot-wire are available, measurements at sufficiently many locations are required to determine the average. If two-dimensional (2-D) measurements are available, like particle image velocimetry (PIV), which has been used in rough-wall boundary-layer experiments (Gul & Ganapathisubramani, 2021; Medjnoun et al., 2021; Talapatra & Katz, 2012), we may be able to take averages directly in a 2-D plane. The 2-D plane may be the streamwise–wall-normal plane or the spanwise–wall-normal plane.…”

Section: Resultsmentioning

confidence: 99%

Flow in additively manufactured super-rough channels

Altland

Zhu

McClain

et al. 2022

Flow

View full text Add to dashboard Cite

Metal additive manufacturing has enabled geometrically complex internal cooling channels for turbine and heat exchanger applications, but the process gives rise to large-scale roughness whose size is comparable to the channel height (which is 500 $\mathrm {\mu }$ m). These super-rough channels pose previously unseen challenges for experimental measurements, data interpretation and roughness modelling. First, it is not clear if measurements at a particular streamwise and spanwise location still provide accurate representation of the mean (time- and plane-averaged) flow. Second, we do not know if the logarithmic layer survives. Third, it is unknown how well previously developed rough-wall models work for these large-scale roughnesses. To answer the above practical questions, we conduct direct numerical simulations of flow in additively manufactured super-rough channels. Three rough surfaces are considered, all of which are obtained from computed tomography scans of additively manufactured surfaces. The roughness’ trough to peak sizes are 0.1 $h$ , 0.3 $h$ and 0.8 $h$ , respectively, where $h$ is the intended half-channel height. Each rough surface is placed opposite a smooth wall and the other two rough surfaces, leading to six rough-wall channel configurations. Two Reynolds numbers are considered, namely $Re_\tau =180$ and $Re_\tau =395$ . We show first that measurements at one streamwise and spanwise location are insufficient due to strong mean flow inhomogeneity across the entire channel, second that the logarithmic law of the wall survives despite the mean flow inhomogeneity and third that the established roughness sheltering model remains accurate.

show abstract