1,721,037 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
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
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
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
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
Direct numerical simulation of turbulent flow over a rough surface based on a surface scan
No full textTypical engineering rough surfaces show only limited resemblance to the artificially constructed rough surfaces that have been the basis of most previous fundamental research on turbulent flow over rough walls. In this article flow past an irregular rough surface is investigated, based on a scan of a rough graphite surface that serves as a typical example for an irregular rough surface found in engineering applications. The scanned map of surface height versus lateral coordinates is filtered in Fourier space to remove features on very small scales and to create a smoothly varying periodic representation of the surface. The surface is used as a no-slip boundary in direct numerical simulations of turbulent channel flow. For the resolution of the irregular boundary an iterative embedded boundary method is employed. The effects of the surface filtering on the turbulent flow are investigated by studying a series of surfaces with decreasing level of filtering. Mean flow, Reynolds stress and dispersive stress profiles show good agreement once a sufficiently large number of Fourier modes are retained. However, significant differences are observed if only the largest surface features are resolved. Strongly filtered surfaces give rise to a higher mean-flow velocity and to a higher variation of the streamwise velocity in the roughness layer compared with weakly filtered surfaces. In contrast, for the weakly filtered surfaces the mean flow is reversed over most of the lower part of the roughness sublayer and higher levels of dispersive shear stress are found
Recent developments in the theory of magnetohydrodynamic turbulence
No full textRecent results based on high?resolution direct numerical simulations of incompressible magnetohydrodynamic (MHD) turbulence are summarized. With regard to the nonlinear dynamics of turbulent energy a yet unexplained scaling behavior is found in systems permeated by a strong mean magnetic field which contradicts the phenomenological Goldreich?Sridhar picture. For macroscopically isotropic and anisotropic MHD turbulence EDQNM closure analysis leads to a simple relation between the spectra of total (EkK?+?EkM) and residual (?EkK???EkM?) energy. The relation is based on a clear physical picture and is well confirmed by numerical simulations. In addition, the Lagrangian approach is presented as a straightforward diagnostic for the investigation of turbulent diffusion and pair?dispersion. Some results from a comparative study of pair?dispersion in Navier?Stokes and MHD turbulence are briefly outlined. It is shown that the presence of magnetic fluctuations significantly reduces turbulent dispersion due to the alignment of velocity fluctuations with the local mean magnetic fiel
Parametric forcing approach to rough-wall turbulent channel flow
Full text linkThe 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
Wind-evoked anemotropism affects the morphology and mechanical properties of Arabidopsis
Full text linkPlants are known to exhibit a thigmomorphogenetic response to mechanical stimuli by altering their morphology and mechanical properties. Wind is widely perceived as mechanical stress and in many experiments its influence is simulated by applying mechanical perturbations. However, it is known that wind-induced effects on plants can differ and at times occur even in the opposite direction compared to those induced by mechanical perturbations. In the present study the long-term response of Arabidopsis thaliana to a constant unidirectional wind was investigated. We found that exposure to wind resulted in a positive anemotropic response and in significant alterations to Arabidopsis morphology, mechanical properties, and anatomical tissue organisation that were associated with the plant’s acclimation strategy to a windy environment. Overall, the observed response of Arabidopsis to wind differs significantly from previously reported responses of Arabidopsis to mechanical perturbations. The presented results suggest that the Arabidopsis’ response is sensitive to the type of mechanical stimulus applied, and that it is not always straightforward to simulate one type of perturbation by another
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