Surface correlations of hydrodynamic drag for transitionally rough engineering surfaces

Rough 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

Status of ψ\psi (3686), ψ\psi (4040), ψ\psi (4160), Y (4260), ψ\psi (4415) and X (4630) charmonia like states

We examine the status of charmonia like states by looking into the behaviour of the energy level differences and regularity in the behaviour of the leptonic decay widths of the excited charmonia states. The spectroscopic states are studied using a phenomenological Martin-like confinement potential and their radial wave functions are employed to compute the di-leptonic decay widths. Their deviations from the expected behaviour provide a clue to consider them as admixtures of the nearby S and D states. The present analysis strongly favour \\backslash$psi \$ (3686) as admixture of $c \bar{c}$ (2S) and $c \bar{c}$g (4.1 GeV) hybrid, \\backslash$psi \$ (4040) and \$$\backslash$psi \$ (4160) as admixture states of charmonia (3S, 3D) states with mixing angle \$$\backslash$theta \$ = 11$^\circ$and 45$^\circ$respectively. We identify Y (4260) as a pure$c \bar{c}$(4S) state whose leptonic decay is predicted as 0.65 keV. While X(4630) is closer to the$c \bar{c}$(6S) state. The status of \$$\backslash$psi \$ (4415) is still not clear as it does not fit to be pure or admixture state

Surface correlations of hydrodynamic drag for transitionally rough engineering surfaces

Rough 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

Reynolds number dependence of the near-wall flow over irregular rough surfaces

The database contains representations of the two surfaces studied, a graphite and a gritblasted surface, and the corresponding time-averaged velocity data for Reynolds numbers Re� = 90; 120; 180; 240; 360; 540, and 720

Reynolds number dependence of the near-wall flow over irregular rough surfaces

The database contains representations of the two surfaces studied, a graphite and a gritblasted surface, and the corresponding time-averaged velocity data for Reynolds numbers Re� = 90; 120; 180; 240; 360; 540, and 720

Investigation of turbulent flow over irregular rough surfaces using direct numerical simulations

Incompressible turbulent flow in irregular rough channels is investigated using a finite-difference direct numerical simulation code which includes an iterative embedded boundary treatment to resolve the roughness. Seventeen industrially relevant rough surfaces with a wide variation in surface topography are considered. Various studies are conducted to understand the flow physics and the relationship between key flow parameters and surface topography. Studies at low values of friction Reynolds number, Reτ, for a single surface, show that the flow is laminar up to Reτ = 89 and begins to develop quasi-periodic fluctuations at Reτ = 89.5. Fluctuations in the three velocity components continue to grow until Reτ = 91, and the flow is turbulent for Reτ ≥ 92. Transition depends on the surface topography as some roughness peaks trigger fluctuations before others. For all the surfaces, mean and turbulent flow statistics are computed at Reτ = 180, for which the flow is fully turbulent but transitionally rough. All surfaces are scaled to the same physical roughness height. Nevertheless, a wide range of roughness function, ∆U+, values is obtained, indicating that it depends not only on the roughness height but also on the detailed roughness topography. Other mean and turbulence flow statistics also vary considerably depending on the surface topography. Next, based on the simulation results database at Reτ = 180, a newly formulated method, that determines which surface topographical properties are important and how new properties can be added to an empirical model, is tested. Optimised models with several roughness parameters are systematically developed for ∆U+ and profile peak turbulent kinetic energy. In determining ∆U+, besides the known parameters of solidity and skewness, it is shown that the streamwise correlation length and rms roughness height are also significant. The peak turbulent kinetic energy is determined by the skewness and rms roughness height, along with the mean forward-facing surface angle and spanwise effective slope. A Reynolds number dependence study is conducted for a single surface, wherein the roughness height in viscous units, k+, is varied from the transitionally rough to the fully-rough regime in the range 3.75 ≤ k+ ≤ 120. Excellent agreement with the experimental data of Nikuradse (Laws of flow in rough pipes, NACA Technical Memorandum 1292, 1933) is observed. The value of equivalent sand-grain roughness height, k+s,eq, thus obtained is close to the mean peak-to-valley height.<br/

Dataset for &#39;Investigation of turbulent flow over irregular rough surfaces using direct numerical simulations&#39;

This 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 &#39;z/delta&#39;) 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 Surface correlations of hydrodynamic drag for transitionally rough engineering surfaces

Details 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 &#39;DNS of turbulent channel flow over a surrogate for Nikuradse-type roughness&#39;

Data related to the publication: Thakkar, M., Busse, A. &amp; 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&#39;s A parameter, for all cases considered.</span

The current scope and stand of carbon capture storage and utilization ∼ A comprehensive review

Over here in this paper, we have tried to present detailed information to the best of our knowledge regarding carbon capture storage and its utilization. CCU as well as CCS may be imagined with corresponding parts inside a coordinated framework that could move towards zero discharges that may be required for environment adjustment in the near times. Carbon Capture Storage may be considered as the wide-range extraction of CO2 from various important resources, it is then allowed and made ready for long-term isolation from the atmosphere and, then followed by its appropriate use. Our objective for this paper was to incorporate many technologies in these fields from the research and developmental activities which were a part of many articles and papers till date to the commercial uses and the challenges which are faced in this field and future scopes in this area. The sole purpose of the paper is to present the information in this domain available via different sources in one single paper which would help researchers a lot during their future references or research. Still, there are many kinds of research going on in this field that could be enhanced and paced up by such informative papers where all the required information is stored in a single paper.For the past twenty years, the oil industry and several scientific institutions have given importance to the concept of carbon capture and storage (CCS). A feasible method for storing carbon must be economical, environmentally friendly, and sustainable over the long term. As a result, carbon capture, utilization, and storage (CCUS) has emerged from CCS. The development of CCUS technology goes beyond the narrow focus on storage as it expanded to use carbon dioxide in oil extraction, treat alkaline industrial waste, and conversion of CO2 into useful chemicals to make this greenhouse gas economically viable. Fossil fuels will continue to be a significant source of energy in the following decades despite worldwide commitments to limit CO2 emissions. By converting high-emission industries to low-emission ones, CCUS contributes to the development of the low-emission economy of the future. Therefore, to improve our understanding of the long-term implications of developing alternative options such as large-scale CO2 injection into geological formations, carbon mineralization, and conversion of CO2 to synthesis, state-of-the-art research on carbon storage and utilization is required, and further analysis to understand the risks of CCUS from a technical, legislative, and political perspective. For individuals up for the challenge, CCUS research offers a chance to tackle technological issues that result in innovations with substantial economic and political implications for the oil and petrol sector