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29 results for “turbulent boundary layer”
Data and scripts for reproducing "Optimisation and Analysis of Streamwise-Varying Wall-Normal Blowing in a Turbulent Boundary Layer"
<p>This is the accompanying data and Python scripts to reproduce the figures in "Optimisation and Analysis of Streamwise-Varying Wall-Normal Blowing in a Turbulent Boundary Layer", submitted to Flow, Turbulence and Combustion.</p>
Dataset of "Genetically-inspired convective heat transfer enhancement in a turbulent boundary layer"
<p>Dataset of the article "Genetically-inspired convective heat transfer enhancement in a turbulent boundary layer" (<a href="https://doi.org/10.1016/j.applthermaleng.2023.120621">https://doi.org/10.1016/j.applthermaleng.2023.120621</a>). The dataset contains:</p> <p>- the velocity fields, measured with Particle Image Velocimetry, for the case of the boundary layer without actuation, with actuation with a steady jet, and for the best individual obtained after the optimization of the pulsed jet parameters.</p> <p>- the parameters of the individuals generated in the optimization process.</p>
Data Sets: Estimating scalar turbulent fluxes with slow-response sensors in the stable atmospheric boundary layer
<p>Date of data analysis: Statistical analyses conducted throughout the 2023 year </p><p>Information about funding sources that supported the collection of the data:</p><p>The research was supported by the Cooperative Institute for Modeling the Earth System at Princeton University under Award NA18OAR4320123 from the National Oceanic and Atmospheric Administration, and by the US National Science Foundation under award number AGS 2128345. Also, it was supported by the National Defense Science and Engineering Graduate Fellowship from the U.S. Department of Defense and Army Research Office. Similarly, the National Science Foundation provided support to complete the PHOXMELT field studies (Grant PLR- 1417914) to collect the data. Also, the study was supported by the U.S. National Science Foundation (NSF-AGS-2028633) and the Department of Energy (DE-SC0022072).</p><p>The statements, findings, conclusions, and recommendations are those of the authors and do not necessarily reflect the views of the National Oceanic and Atmospheric Administration.</p><p>This dataset contains the observational data for the two field experiments (Barrow and Wendell) in .nc file format.</p>
Turbulent Mechanisms for the Deep Convective Boundary Layer in the Taklimakan Desert
<p>The deep convective boundary layer (CBL) in the Taklimakan Desert plays an important role in the climate system in East Asia. Based on the observation experiment and large-eddy simulation, turbulent mechanisms for its formation were revealed in this study. This explained why the daily maximum CBL depth was independent of surface heating. In the late-morning, there was a weak temperature inversion and a near-neutral residual layer (RL) above the CBL. With the development of the CBL, stronger convection could penetrate the RL and even overshoot the top of the RL. The distinctive boundary-layer process entrained free-tropospheric air to warm the RL and then promoted the entrainment of the warmed air in the RL into the CBL. This extra energy supply effectively contributed to the growth of the CBL. With further positive feedback between the CBL and RL depths, a deeper CBL would form in consecutive fair-weather conditions.</p>
Supersonic/Hypersonic Zero-Pressure-Gradient Turbulent Boundary Layers
<p>Datasets M2.zip and M5.zip of long ZPG supersonic/hypersonic turbulent boundary layers related to the work<br>"Ceci, A., Palumbo, A., Larsson, J. et al. Numerical tripping of high-speed turbulent boundary layers. Theor. Comput. Fluid Dyn. 36, 865–886 (2022). https://doi.org/10.1007/s00162-022-00623-0"</p> <p>Files cf_fav*dat (Favre averaged velocities) contain columns with streamwise dstribution of the following quantities:<br> Supersonic cases, x/delta0,cf,retau,retheta,redelta2,delta99/delta0,p'_w^2/tauw, turbuent tau_xy peak, turbulent tau_xx peak<br> Hypersonic cases, x/delta0,cf,retau,retheta,redelta2,delta99/delta0,p'_w^2/tauw, turbuent tau_xy peak, ch<br><br>Files meanfields_*.prof (Favre averaged velocities) contain 2D streamwise and wall-normal dstribution of the following mean fields:<br> All cases, x/delta0,y/delta0,retau,y/delta99,y+,u+,rho/rhow,rho/rho0,T/Tw,T/T0,p/pw,p/p0,u''u''+,v''v''+,w''w''+,-u''v''+,ufav/u0</p>
Turbulence statistics in smooth wall oscillatory boundary layer flow
<p>Experimental and numerical datasets belonging to Van der A, D.A., Scandura, P., O’Donoghue, T. (2018). Turbulence statistics in smooth wall oscillatory boundary layer flow, Journal of Fluid Mechanics, 849, 192-230. </p> <p> </p>
Turbulent Mechanisms for the Deep Convective Boundary Layer in the Taklimakan Desert
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Data from: Turbulence organization and mean profile shapes in the stably stratified boundary layer: Zones of uniform momentum and air temperature
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Variations of Subgrid-scale Turbulent Fluxes in the Convective Boundary Layer at Gray Zone Resolutions
<p>This dataset contains the data used in the submitted manuscript of Liu and Zhou 2022 JAS. Please refer to the manuscript for the detailed description of the dataset.</p>
Varying partitioning of surface turbulent fluxes regulates temperature-humidity dissimilarity in the convective atmospheric boundary layer
<p>This dataset contains the data used in the submitted manuscript of Liu, Liu, Huang, and Xiao 2021. Please refer to the manuscript for the detailed description of the dataset.</p>
Boundary layer turbulence and abyssal recipes (BLT): Moored observations
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Data from: Insensitivity of the cloud response to surface warming under radical changes to boundary layer turbulence and cloud microphysics: results from the ultraparameterized CAM
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Biofilm flow data from: Roughness effects of diatomaceous slime fouling on turbulent boundary layer hydrodynamics
<p>This dataset contains the instantaneous velocity vector fields as well as the time averaged velocity and turbulence fields from PIV data taken over a large acrylic plate fouled with a relatively uniform diatomaceous biofilm. </p> <p>See associated article, Roughness effects of diatomaceous slime fouling on turbulent boundary layer hydrodynamics, for methods description.</p> <p>Time averaged velocity and turbulence fields data are stored in the file velocity_fields.mat, which contains the following variables: </p> <p>X: The streamwise distance of each column in the velocity field matrices [mm]</p> <p>Y: The vertical distance (from the bottom of the frame) of each row in the velocity field matrices [mm]</p> <p>U: Time averaged streamwise velocity [m s^-1]</p> <p>V: Time averaged vertical velocity [m s^-1]</p> <p>tke: Time averaged turbulent kinetic energy [m^2 s^-2]</p> <p>u': Time averaged streamwise Reynolds stress [m^2 s^-2]</p> <p>v': Time averaged vertical Reynolds stress [m^2 s^-2]</p> <p>u'v': Time averaged Reynolds shear stress [m^2 s^-2]</p> <p>The zip file biofilm_vector_fields contains the 4,000 statistically independent velocity vector fields used to compute the time averaged velocity and turbulence fields. The vector field data is in the variable labeled matr. Size calibration: 2302 pixels/ inch (906.3 pixels/ cm). </p> <p>Column 1: X (streamwise distance in [pixels]) </p> <p>Column 2: Y (wall-normal distance from bottom of frame in [pixels])</p> <p>Column 3: U (streamwise velocity vector in [pixels / 250 microseconds]) </p> <p>Column 4: V (vertical velocity vector in [pixels / 250 microseconds]) </p> <p>Column 5: CHC (number of tracked particles. A value < 1 gives the location of the biofilm, which was masked out)</p> <p>Column 6: U2 </p> <p>Column 7: V2</p> <p>Column 8: U3</p> <p>Column 9: V3</p> <p>Column 10: U4</p> <p>Column 11: V4</p>
Source data for "Submesoscales are a significant turbulence source in global ocean surface boundary layer"
<p>The files here provide the data and codes for reproducing figures from the paper titled "Submesoscales are a significant turbulence source in global ocean surface boundary layer" by Dong et al.</p> <p>It should be clarified that Fig.1 is originally generated by Python and then produced in Illustrator, and Fig.5 is completely produced by Illustrator. All other figures are produced by MATLAB.</p>
data for the submission in Marine Geology: Turbulence and Fine Sediment Dynamics in a Coastal Benthic Boundary Layer
<p>Data used for the plots and tables can be found in the submission in Marine Geology:Turbulence and Fine Sediment Dynamics in a Coastal Benthic Boundary Layer. The data are saved as matlab data file.</p>
Toward low-cloud-permitting cloud superparameterization with explicit boundary layer turbulence -- simulation data
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Model code and data for "Mitigation of the double ITCZ syndrome in BCC-CSM2-MR through improving parameterizations of boundary-layer turbulence and shallow convection" by Lu et al., submitted to Geoscientific Model Development, https://doi.org/10.5194/gmd-2020-40, in review, 2020.
<p>Description of the files:</p> <p>“BCC_CSM2_MR.code.tar” contains the codes and run scripts for the medium-resolution Beijing Climate Center Climate System Model version 2 (BCC-CSM2-MR). Detailed description of the model refers to the paper “The Beijing Climate Center Climate System Model (BCC-CSM): the main progress from CMIP5 to CMIP6” by Wu et al., Geosci. Model Dev., 12, 1573–1600, https://doi.org/10.5194/gmd-12-1573-2019, 2019.</p> <p>“BCC_CSM2_MR.inputdata.tar” contains the input data needed to run the model.</p> <p>“REF_amip.rar” contains the output data from the REF_amip experiment.</p> <p>“NEW_amip.rar” contains the output data from the NEW_amip experiment.</p> <p>“REF_cmip.rar” contains the output data from the REF_cmip experiment.</p> <p>“NEW_cmip.rar” contains the output data from the NEW_cmip experiment.</p> <p>“UWMT_amip.rar” contains the output data from the UWMT_amip experiment.</p> <p>“mHack_amip.rar” contains the output data from the mHack_amip experiment.</p>
Turbulence Models: Shock Boundary Layer Interaction at M=2.05
Exp: Shock Boundary Layer Interaction at M=2.05. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.
Turbulence Models: Shock Boundary Layer Interaction at M=2.05
Exp: Shock Boundary Layer Interaction at M=2.05. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.
Turbulent boundary layer wall pressure flight data
Wall pressure data acquired during flight tests at three flight conditions are provided: 48k feet altitude at Mach 0.86; 32k feet at Mach 0.70; and 24k feet at Mach 0.56. Details of the flight test, sensors, and sensor locations are given in a journal article that is referenced in the supporting documents datafile.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.