403 research outputs found
Dataset for Surface correlations of hydrodynamic drag for transitionally rough engineering surfaces
No full textDetails of the content of the dataset can be found in the readme.txt file.
Dataset supporting:
Thakkar, Manan, Busse, Angela and Sandham, Neil (2016) Surface correlations of hydrodynamic drag for transitionally rough engineering surfaces. Journal of Turbulence.</span
Data from DNS of Fourier-Filtered Rough Surfaces
No full textData created and presented by F. Alves Portela, A. Busse N. D. Sandham, "Numerical Study of Fourier-Filtered Rough Surfaces". Physical Review Fluids. 2021 (doi: 10.1103/PhysRevFluids.6.084606) as well as height maps of the five surfaces considered.</span
Dataset for 'DNS of turbulent channel flow over a surrogate for Nikuradse-type roughness'
No full textData related to the publication:
Thakkar, M., Busse, A. & Sandham, N.D. (2017) DNS of turbulent channel flow over a surrogate for Nikuradse-type roughness.
Table1.csv contains data from Table 1 in the paper. Additionally, it contains two more columns, showing the values of ks+ (equivalent sand-grain roughness height in wall-units) and Nikuradse's A parameter, for all cases considered.</span
Parametric forcing approach to rough-wall turbulent channel flow
No full textThe effects of rough surfaces on turbulent channel flow are modelled by an extra force term in the Navier–Stokes equations. This force term contains two parameters, related to the density and the height of the roughness elements, and a shape function, which regulates the influence of the force term with respect to the distance from the channel wall. This permits a more flexible specification of a rough surface than a single parameter such as the equivalent sand grain roughness. The effects of the roughness force term on turbulent channel flow have been investigated for a large number of parameter combinations and several shape functions by direct numerical simulations. It is possible to cover the full spectrum of rough flows ranging from hydraulically smooth through transitionally rough to fully rough cases. By using different parameter combinations and shape functions, it is possible to match the effects of different types of rough surfaces. Mean flow and standard turbulence statistics have been used to compare the results to recent experimental and numerical studies and a good qualitative agreement has been found. Outer scaling is preserved for the streamwise velocity for both the mean profile as well as its mean square fluctuations in all but extremely rough cases. The structure of the turbulent flow shows a trend towards more isotropic turbulent states within the roughness layer. In extremely rough cases, spanwise structures emerge near the wall and the turbulent state resembles a mixing layer. A direct comparison with the study of Ashrafian, Andersson & Manhart (Intl J. Heat Fluid Flow, vol. 25, 2004, pp. 373–383) shows a good quantitative agreement of the mean flow and Reynolds stresses everywhere except in the immediate vicinity of the rough wall. The proposed roughness force term may be of benefit as a wall model for direct and large-eddy numerical simulations in cases where the exact details of the flow over a rough wall can be neglecte
Statistics of passive tracers in three-dimensional magnetohydrodynamic turbulence
No full textMagnetohydrodynamic (MHD) turbulence is studied from the Lagrangian viewpoint by following fluid particle tracers in high resolution direct numerical simulations. Results regarding turbulent diffusion and dispersion as well as Lagrangian structure functions are presented. Whereas turbulent single-particle diffusion exhibits essentially the same behavior in Navier-Stokes and MHD turbulence, two-particle relative dispersion in the MHD case differs significantly from the Navier-Stokes behavior. This observation is linked to the local anisotropy of MHD turbulence which is clearly reflected by quantities measured in a Lagrangian frame of reference. In the MHD case the Lagrangian structure functions display a lower level of intermittency as compared to the Navier-Stokes case contrasting Eulerian results. This is not only true for short time increments [ H. Homann, R. Grauer, A. Busse, and W.-C. Müller, J. Plasma Phys. 73, 821 (2007) ] but also holds for increments up to the order of the integral time scale. The apparent discrepancy can be explained by the difference in the characteristic shapes of fluid particle trajectories in the vicinity of most singular dissipative structure
Change in drag, apparent slip and optimum air layer thickness for laminar flow over an idealised superhydrophobic surface
No full textAnalytic results are derived for the apparent slip length, the change in drag and the optimum air layer thickness of laminar channel and pipe flow over an idealised superhydrophobic surface, i.e. a gas layer of constant thickness retained on a wall. For a simple Couette flow the gas layer always has a drag reducing effect, and the apparent slip length is positive, assuming that there is a favourable viscosity contrast between liquid and gas. In pressure-driven pipe and channel flow blockage limits the drag reduction caused by the lubricating effects of the gas layer; thus an optimum gas layer thickness can be derived. The values for the change in drag and the apparent slip length are strongly affected by the assumptions made for the flow in the gas phase. The standard assumptions of a constant shear rate in the gas layer or an equal pressure gradient in the gas layer and liquid layer give considerably higher values for the drag reduction and the apparent slip length than an alternative assumption of a vanishing mass flow rate in the gas layer. Similarly, a minimum viscosity contrast of four must be exceeded to achieve drag reduction under the zero mass flow rate assumption whereas the drag can be reduced for a viscosity contrast greater than unity under the conventional assumptions. Thus, traditional formulae from lubrication theory lead to an overestimation of the optimum slip length and drag reduction when applied to superhydrophobic surfaces, where the gas is trapped
Ruther, Angela (Death, 1906-12-02)
No full textAddress: West Fork Rd.Age at death: 1 yr.Pg 146/1906/070/F W S/City/Dr. E. J. Kehoe/Busse & Borgmann/St. Joseph oldOriginal record filed in drawer labeled 'Runk-Ryan'
Dataset for 'Investigation of turbulent flow over irregular rough surfaces using direct numerical simulations'
No full textThis dataset is associated with the PhD thesis:
Thakkar, M. (2017) Investigation of turbulent flow over irregular rough surfaces using direct numerical simulations (PhD Thesis), University of Southampton, Southampton, UK
Dataset description: each of the 17 folders comprises of five .csv files containing figure data from chapter 6 in the thesis. Files in each folder have the same naming convention with only the first few characters changing depending on the sample name. For example, folder s1 contains the following files.
i) s1_Fig6p1.csv - consisting of data from Figure 6.1 (mean streamwise velocity profiles for the 17 rough surface samples).
ii) s1_Fig6p2.csv - consisting of data from Figure 6.2 (mean streamwise velocity defect profiles for the 17 rough surface samples).
iii) s1_Figs6p5-6p8-6p11-6p14.csv - consisting of data from Figures 6.5, 6.8, 6.11 and 6.14 (Reynolds streamwise, spanwise, wall-normal and shear stress profiles for the 17 rough surface samples).
iv) s1_Fig6p17.csv - consisting of data from Figure 6.17 (TKE profiles for the 17 rough surface samples).
v) s1_Figs6p19-6p23-6p26-6p30.csv - consisting of data from Figures 6.19, 6.23, 6.26 and 6.30 (dispersive streamwise, spanwise, wall-normal and shear stress profiles for the 17 rough surface samples).
Folder s2 contains files s2_Fig6p1.csv, s2_Fig6p2.csv, s2_Figs6p5-6p8-6p11-6p14.csv, s2_Fig6p17.csv, s2_Figs6p19-6p23-6p26-6p30.csv, and so on for the remaining folders.
For files containing data from more than one figure (for example, s1_Figs6p5-6p8-6p11-6p14.csv), the x-axis values of each plot are the same and are stored in the first column (titled 'z/delta') of the data matrix. Only the y-axis values differ depending on the relevant statistic and are stored in subsequent columns with appropriate titles.
Some data may contain NaN or Inf values in their first and last few rows. These occur due to small errors during spatial averaging arising from the differences between fluid and solid regions of the computational domain. These values do not have a significant impact on the results and may be ignored.</span
SBM flow statistics over a grit-blasted surface
No full textData created and presented by F. Alves Portela and N. D. Sandham, "A DNS/URANS approach for simulating rough-wall turbulent flows". International Journal of Heat and Fluid Flow. 2020 (doi: 10.​1016/​j.​ijheatfluidflow.​2020.​108627) as well as height maps of the two surfaces considered.</span
Surface correlations of hydrodynamic drag for transitionally rough engineering surfaces
No full textRough surfaces are usually characterised by a single equivalent sand-grain roughness height scale that typically needs to be determined from laboratory experiments. Recently this method has been complemented by a direct numerical simulation approach, whereby representative surfaces can be scanned and the roughness effects computed over a range of Reynolds number. This development raises the prospect over the coming years of having enough data for different types of rough surfaces to be able to relate surface characteristics to roughness effects, such as the roughness function that quantifies the downward displacement of the logarithmic law of the wall. In the present contribution, we use simulation data for 17 irregular surfaces at the same friction Reynolds number, for which they are in the transitionally rough regime. All surfaces are scaled to the same physical roughness height. Mean streamwise velocity profiles show a wide range of roughness function values, while the velocity defect profiles show a good collapse. Profile peaks of the turbulent kinetic energy also vary depending on the surface. We then consider which surface properties are important and how new properties can be incorporated into an empirical model, the accuracy of which can then be tested. Optimised models with several roughness parameters are systematically developed for the roughness function and profile peak turbulent kinetic energy. In determining the roughness function, besides the known parameters of solidity (or frontal area ratio) and skewness, it is shown that the streamwise correlation length and the root-mean-square roughness height are also significant. The peak turbulent kinetic energy is determined by the skewness and root-mean-square roughness height, along with the mean forward-facing surface angle and spanwise effective slope. The results suggest feasibility of relating rough-wall flow properties throughout the range from hydrodynamically smooth to fully-rough to surface parameters
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