Find research datasets worth reusing

opencc-by-4.0Aug 2024View details →

Correlations for aerodynamic force coefficients of non-spherical particles in compressible flows

<div> <div># Data repository for the paper</div> <div># _Correlations for aerodynamic force coefficients of non-spherical particles in compressible flows_</div> <div>Corresponding author:</div> <div>Berend.van.Wachem@multiflow.org</div> <div>This repository consists of the data and exemplary python scripts for the paper "Correlations for aerodynamic force coefficients of non-spherical particles in compressible flows" by Christian Gorges, Victor Chéron, Anjali Chopra, Fabian Denner and Berend van Wachem. The data stored in this repository have the following data format:</div> <div>- .csv files consisting the raw data of the simulations used for the coefficient plots in the results' chapter of the paper</div> <div> </div> <div>- .py files containing python scripts serving as examples on how to use and plot the raw data of the .csv files and the correlations</div> <div>The main folders of this repository are named as the non-spherical particle shapes (Oblate, Prolate, Rod-like) and a folder with the data on which the correlations are based.</div> <div>The folders named after the non-spherical particle shapes contain the raw simulation data. For instance, the Oblate folder contains the individual .csv files of all simulations of the oblate spheroid for all Reynolds numbers, Mach numbers, and angles of attack.</div> <div>The folder Correlations/ consists of the temporally averaged drag, lift and torque coefficients, which are written in .csv files and stored in the folder ResultsCoefficients/, as well as Python scripts for plotting the correlations. </div> <div>The naming style of the raw data files and the subfolders for each section is explained in the following:</div> <div>The file names of the .csv files within the particle shape folders consist of the Reynolds number, followed by the Mach number and the angle of attack. For example "log_Re100M2_0_alpha_90.csv" consists of the data for a Reynolds number of 100, a Mach number of 2.0 and an angle of attack of 90 degrees. The content in the .csv files is given as: "%f,%f,%f,%f\n" which corresponds to "Physical time, drag coefficient, lift coefficient, torque coefficient". The first row in each file gives the headers of each column.</div> <div>The .csv files in the folder Correlations/ResultsCoefficients/ are split per coefficient, shape, and particle Reynolds numbers, which can be identified by the name of the .csv file. For instance, the results obtained for the lift coefficient of</div> <div>the prolate spheroid particle for at a particle Reynolds numbers 100 for all orientation angles and Mach numbers are given in the file:</div> <div>"Prolate_100_CL.csv". In these files, the results are ordered per orientation angle (rows) and Mach</div> <div>number (column). </div> <div>The python scripts have been tested with Python 3.11.5.</div> <div>PlotCoefficients.py is an example python script to read the .csv files and plot the aerodynamic force coefficients as it is done in the results section of the paper.</div> <div>The python scripts in the directory Correlations/ are split in three main functions in two files:</div> <div>- Getter.py (read the .csv files storing the coefficients - separate functions</div> <div> for the drag, lift and torque coefficients)</div> <div>- ManuscriptCorrelation.py with all the correlations derived in this work for an</div> <div> effective implementation in any solver, and a plotting function to have visual</div> <div> representation of the correlations.</div> <div>- generalmain.py (calls Getter and Plotter)</div> <div>The Getter is called from the generalmain.py file. (run python3 generalmain.py) so that all coefficients can be gathered in a 3D array.</div> <div>First dimension : Reynolds number</div> <div>Second dimension : Orientation angle</div> <div>Third dimension : Mach number</div> <div>The user just needs to give the absolute path to the folder ResultsCoefficients/.</div> <div>This project has received funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), grant number 447633787.</div> </div>

opencc-by-4.0Oct 2023View details →

Simulated datasets for detector and particle flow reconstruction: CLIC detector, machine learning format

Synopsis Machine-learning friendly format of tracks, clusters and target particles in electron-positron events, simulated with the CLIC detector. Ready to be used with <a href="https://zenodo.org/records/14930299">jpata/particleflow:v2.3.0</a>. Derived from the EDM4HEP ROOT files in <a href="https://zenodo.org/record/8260741">https://zenodo.org/record/8260741</a>. <ul> <li>clic_edm_ttbar_pf.zip: e+e- -> ttbar, center of mass energy at 380 GeV</li> <li>clic_edm_qq_pf.zip: e+e- -> Z* -> qqbar, center of mass energy at 380 GeV</li> <li>clic_edm_ww_fullhad_pf.zip: e+e- -> WW -> W decaying hadronically, center of mass energy at 380 GeV</li> <li>clic-tfds.ipynb: an example notebook on how to load the files</li> </ul> Contents Each .zip file contains the dataset in the <a href="https://github.com/tensorflow/datasets">tensorflow-datasets</a>, <a href="https://github.com/google/array_record">array_record</a> format. We have split the full datasets into 10 subsets, due to space considerations on zenodo, two subsets from each dataset are uploaded. Each dataset contains a train and test split of events. Dataset semantics (to be updated) Each dataset consists of events that can be iterated over using the tensorflow-datasets library and used in either tensorflow or pytorch. Each event has the following information available: <ul> <li>X: the reconstruction input features, i.e. tracks and clusters</li> <li>ytarget: the ground truth particles with the features ["PDG", "charge", "pt", "eta", "sin_phi", "cos_phi", "energy", "jet_idx"], with "jet_idx" corresponding to the gen-jet assignment of this particle</li> <li>ycand: the baseline Pandora PF particles with the features ["PDG", "charge", "pt", "eta", "sin_phi", "cos_phi", "energy", "jet_idx"], with "jet_idx" corresponding to the gen-jet assignment of this particle</li> </ul> The full semantics, including the list of features for X, are available at https://github.com/jpata/particleflow/blob/v2.3.0/mlpf/heptfds/clic_pf_edm4hep/utils_edm.py and https://github.com/jpata/particleflow/blob/v2.3.0/mlpf/data/key4hep/postprocessing.py.

opencc-by-4.0Mar 2023View details →

Dataset for "Infiltration and resuspension of dilute particle suspensions in micro cavity flow "

This dataset contains the numerical simulation data describing the migration behaviour of particles. Detailed study on the infiltration and resuspension of particle in a cavity flow is presented in a journal paper entitled "Infiltration and resuspension of dilute particle suspensions in micro cavity flow ". We provide all the essential data in a single excel file with the description below:   Dataset 1- Particle trajectory for different releasing positions. The parameters are Re=56.9, h=32, l=80. The particle densities are ρp=1.3 in plot (Fig. 7a) and ρp=2.0 in plot (Fig. 7b). (Data of Fig. 7) Dataset 2 - Particle trajectory for different periodic domain lengths. The parameters are ρp=1.3, Re=56.9, h=32 and l=80. (Data of Fig. 8) Dataset 3 - The time evolution of (Fig. 9a) the vertical position z, (Fig. 9b) the horizontal velocity ux, and (Fig. 9c) the vertical velocity uz of a single particle with the same Renolds number Re=56.9 and cavity size of h=32 and l=80, but different particle densities. (Data of Fig. 9) Dataset 4 - The trajectories of a single particle over the cavity with: (Fig. 10a) different particle densities but the same Renolds number Re=85.3 and cavity size of h=16 and l=80; (Fig. 10b) different Reynolds numbers but the same particle density ρp=1.3 and cavity size of h=32 and l=80; (Fig. 10c) different cavity sizes but the same Renolds number Re=56.9 and particle density ρp=1.3. The dashed lines represent the boundary of the cavity. (Data of Fig. 10) Dataset 5 - Typical particle trajectories in the cavity. The dashed lines represent the boundary of the trap. (Data of Fig. 12) Dataset 6 - The variation of trap efficiency with particle density for (Fig. 13a) different cavity sizes with the same Re=56.9, and (Fig. 13b) different Reynolds numbers with the same cavity size h=32, l=80, respectively. (Data of Fig. 13) Dataset 7 - The critical entering distance as a function of the scaling parameter Th. The fitting line is performed with an exponential function with the same form as Eq. (19). (Data of Fig.14) Dataset 8 - The variation of the trap efficiency with the trap number. (Data of Fig.15)

opencc-by-4.0Aug 2023View details →

Simulated datasets for detector and particle flow reconstruction: CLIC detector

Data description Datasets generated using Key4HEP and the CLIC detector model suitable for particle flow reconstruction studies. The datasets contain generator particles, reconstructed tracks and calorimeter hits, reconstructed Pandora PF particles and their respective links in the <a href="https://doi.org/10.5281/zenodo.7221473">EDM4HEP</a> format. The following processes have been simulated with Pythia 8: <ul> <li>p8_ee_tt_ecm380: ee -> ttbar, center of mass energy at 380 GeV</li> <li>p8_ee_qq_ecm380: ee -> Z* -> qqbar, center of mass energy at 380 GeV</li> <li>p8_ee_ZH_Htautau: ee -> ZH -> Higgs decaying to tau leptons, center of mass energy at 380 GeV</li> <li>p8_ee_WW_fullhad: ee -> WW -> W decaying hadronically, center of mass energy at 380 GeV</li> <li>p8_ee_tt_ecm380_PU10: ee -> ttbar with on average 10 Poisson-distributed events from ee->gg overlayed, center of mass energy at 380 GeV</li> </ul> The following single particle gun samples have been generated with ddsim: <ul> <li>e+/e-: single electron with energy between 1 and 100 GeV</li> <li>mu+/mu-: single muon with energy between 1 and 100 GeV</li> <li>kaon0L: single K0L with energy between 1 and 100 GeV</li> <li>neutron: single neutron with energy between 1 and 100 GeV</li> <li>pi+/pi-: single charged pion with energy between 1 and 100 GeV</li> <li>pi0: single neutral pion with energy between 1 and 100 GeV</li> <li>gamma: single photon with energy between 1 and 100 GeV</li> </ul> The detector simulation has been done with Geant4, the reconstruction with Marlin interfaced via Key4HEP which includes PF reconstruction with Pandora, all using publicly available models and code.   Contents This record includes the following files: <ul> <li>*_10files.tar: small archives of 10 files for each data sample, suitable for testing</li> <li>dataset_full.txt: the full list of files, hosted at the Julich HPC courtesy of the Raise CoE project, ~2.5TB total</li> <li>*.cmd: the Pythia8 cards</li> <li>pythia.py: the pythia steering code for Key4HEP</li> <li>run_sim.sh: the steering script for generating, simulating and reconstructing a single file of 100 events from the p8_ee_tt_ecm380, p8_ee_qq_ecm380, p8_ee_ZH_Htautau, p8_ee_WW_fullhad datasets</li> <li>run_sim_pu.sh: the steering script for generating, simulating and reconstructing a single file of 100 events from the p8_ee_tt_ecm380_PU10 dataset</li> <li>run_sim_gun.sh: the steering script for generating the single-particle gun samples</li> <li>run_sim_gun_np.sh: the steering script for generating multi-particle gun samples (extensive datasets have not yet been generated)</li> <li>check_files.py: the main driver script that configures the full statistics and creates submission scripts for all the simulations</li> <li>PandoraSettings.zip: the settings used for Pandora PF reconstruction</li> <li>main19.cc: the Pythia8+HepMC driver code for generating the events with PU overlay</li> <li>clicRec_e4h_input.py: the steering configuration of the reconstruction modules in Key4HEP</li> <li>clic_steer.py: the steering configuration of the Geant4 simulation modules in Key4HEP</li> <li>clic-visualize.ipynb: an example notebook demonstrating how the dataset can be loaded and events visualized in Python</li> <li>visualization.mp4: an example visualization of the hits and generator particles of a single ttbar event from the dataset</li> </ul>   Dataset semantics Each file consists of event records. Each event contains structured branches of the relevant physics data. The branches relevant to particle flow reconstruction include: <ul> <li>MCParticles: the ground truth generator particles</li> <li>ECALBarrel, ECALEndcap, ECALOther, HCALBarrel, HCALEndcap, HCALOther, MUON: reconstructed hits in the various calorimeter subsystems</li> <li>SiTracks_Refitted: the reconstructed tracks</li> <li>PandoraClusters: the calorimeter hits, clustered by Pandora to calorimeter clusters</li> <li>MergedRecoParticles: the reconstructed particles from the Pandora particle flow algorithm</li> <li>CalohitMCTruthLink: the links between MC particles and reconstructed calorimeter hits</li> <li>SiTracksMCTruthLink: the links between MC particles and reconstructed tracks</li> </ul> The full details of the EDM4HEP format are available <a href="https://doi.org/10.5281/zenodo.7221473">here</a>.   Dataset characteristics The full dataset in dataset_full.txt consists of 43 tar files of up to 100GB each. The tar files contain in total 58068 files, 2.5TB in the ROOT EDM4HEP format. The subset in *_10files.tar for consists of 150 files, 26GB in the ROOT EDM4HEP format.   How can you use these data? The ROOT files can be directly loaded with the <a href="https://zenodo.org/record/8239801">uproot</a> Python library.   Disclaimer These are simulated samples suitable for conceptual machine learning R&D and software performance studies. They have not been calibrated with respect to real data, and should not be used to derive physics projections about the detectors. Neither CLIC nor CERN endorse any works, scientific or otherwise, produced using these data. All releases will have a unique DOI that you are requested to cite in any applications or publications.

opencc-by-4.0Oct 2020View details →

overview of intermittent flow of hydrogel particles

The repository of videos show the flowing process of hydrogel particles (diameter 6.5mm) in a silo with different size of orifice, 10mm,11mm,12mm,15mm,18mm. To compare intermittent flow behavior of HGS with hard spheres (ASB), the videos for ASB(diameter 6mm) are also in the dataset with different size of orifice, 34mm and 35mm. The spatial resolution of videos in files is 1920 × 1080 (0.327mm per pixel) correpondingly the real size of the silo showed in the video is 40cm width and 50cm height in the snapshot. The frame rate in each video is 60fps. The recording is for the study of intermittent flow and transient congestion of soft spheres passing through narrow orifices (https://doi.org/10.5281/zenodo.4066466)

opencc-by-4.0Apr 2024View details →

Drag, lift, and torque correlations for axi-symmetric rod-like non-spherical particles in linear wall-bounded shear flow

Data linked to the manuscript:  Drag, lift, and torque coefficients of fixed axi-symmetric rod-like particles in linear wall-bounded shear flow Authors: Victor Cheron, Berend van Wachem Corresponding author: Berend.van.Wachem@gmail.com Files Temporally averaged drag, lift and torque coefficients are written in .txt files stored in the folder ResultsCoefficients. Python scripts used to plot the correlations are stored in the folder PythonScript. Two simulation results are provided in the folder SimulationResults. Results and Coefficients The .txt files are split per coefficient, aspect ratio and shear rate, which can be identified by the name of the .txt file. The results obtained for the torque coefficient of the particle of aspect ratio 2.5 for a uniform flow configuration are given in the file: Uniform-Torque-Angles-Size2-5.txt The results obtained for the lift coefficient of the particle of aspect  ratio 10 for a shear rate 0.2 configuration are given in the file: Shear02-Lift-Angles-Size10.txt In the files, the results are ordered per orientation angle and particle Reynolds number.  PythonScripts The python scripts are split among three files: - Getter.py: this script reads the .txt files storing the coefficients. - ManuscriptCorrelations.py : this script returns the functions to read plot the correlations for the drag, lift and torque coefficients. - generalmain.py : calls the functions The scripts Getter.py and ManuscriptCorrelations.py are called from the script generalmain.py file.  This will return a 1D column vector ordering the variables used to derive the correlations: - Coefficients - Reynolds number - Orientation Angle - Dimensionless distance to the wall - Aspect ratio Simulation Results A simulation result is provided: - Aspect ratio 5, particle Reynolds number 100, orientation angles 30 and 150,   dimensionless distance 1. The data of all fields (pressure, velocity, source terms from the particles) are stored in .h-files. A .xmf reader is provided to read the simulation results in Paraview. Data for one converged simulation time are provided due to storage limits.   Acknowledgments This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Project-ID 448292913 and by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Project-ID 422037413 - TRR 287.

opencc-by-4.0Sep 2024View details →

Conceptual illustration of particle emission in azimuth and relationship to elliptic flow and collectivity

This set of images represents, in a schematic and simplified manner, the various scenarios for particle emission in high-energy interactions (heavy ions but also others). Individual images can be used alone or also in an animation explanatory sequence. The various versions are: <ol> <li>The file `1-initialConditionIncoherentColored` shows how a number of particle-emitting sources will ultimately populate azimuth uniformly in case they emit incoherently. </li> <li>The file `2-initialConditionCoherentColored` shows how particle-emitting sources might generate a polar pattern in their particle emission provided any kind of overlap (e.g. strong interaction) leads to coherent emission. </li> <li>The file `3-initialConditionVelocityField` illustrates the concept of a common velocity field at the edges of the system. This is to be used, for instance, in a transition slide in which a speaker introduces some various limiting options of which entities actually have a common velocity. </li> <li>The file `4-initialConditionMassOrdering` illustrates the possibility that hadrons are emitted in a common velocity field, leading to mass-ordered elliptic flow such as the one observed at low transverse momentum in heavy-ion collisions. This serves to directly illustrate the patterns that are numerically created by e.g. the blast-wave model or the Cooper-Frye formalism.</li> <li>The file `5-initialConditionCoalescenceNCQ` illustrates the possibility that quarks are in a common velocity field just prior to hadronization via coalescence, leading to baryon/meson grouping of anisotropic flow. </li> <li>The file `6-combined-incoherent-vs-ncq` illustrates the incoherent scenario vs the coalescence scenario without the polar graph for simplicity.</li> <li>The file `7-combined-incoherent-vs-massordering-vs-ncq` illustrates the incoherent scenario, the mass ordering and the coalescence scenarios without the polar graph for simplicity.</li> </ol>

opencc-by-4.0Jun 2023View details →

Dataset accompanying the publication "Transport and retention of micro-Polystyrene in coarse riverbed sediments: Effects of flow velocity, particle and sediment sizes"

The dataset in this repository is accompanying the publication "Transport and retention of micro-Polystyrene in coarse riverbed sediments: Effects of flow velocity, particle and sediment sizes" (in Microplastics and Nanoplastics, 2023, submitted 09.06.2023) The repository contains the raw image files of all sample filters which were scanned using the fluorescence imaging system ChemiDoc and used to analyse the infiltration behaviour of microplastic polystyrene in the manuscript. In addition, we provide the resulting data from the particle identification and geometric analysis which were derived from the raw data using ImageJ in tabular excel format. The data is structured in folders following the naming of the columns from the manuscript.

opencc-by-4.0Aug 2023View details →

Data for figures in "Next-generation ice nucleating particle sampling on aircraft: Characterization of the High-volume flow aERosol particle filter sAmpler (HERA)"

Atmospheric ice nucleating particle (INP) concentration data from the free troposphere are sparse, but urgently needed to understand vertical transport processes of INPs and their influence on cloud formation and properties. Here, we introduce the new High-volume flow aERosol particle filter sAmpler (HERA) which was specially developed for installation on research aircraft and subsequent offline INP analysis. HERA is a modular system constisting of a sampling unit and a powerful pump unit and has several features which were integrated specifically for INP sampling. Firstly, the pump unit enables sampling at flow rates exceeding 100 L min−1, which is well above typical flow rates of aircraft INP sampling systems described in the literature (~10 L min−1). Consequently, required sampling times to capture rare, high-temperature INPs (≥-15 °C) are reduced in comparison to other systems and potential source regions of INPs can be confined more precisely. Secondly, the sampling unit is designed as a seven-way valve, enabling switching between six filter holders and a bypass with one filter being sampled at a time. In contrast to other aircraft INP sampling systems, the valve position is controlled remotely via software so that manual filter changes in-flight are eliminated and the potential for sample contamination is decreased. This design is compatible with a high degree of automation, i.e., triggering filter changes depending on parameters like flight altitude, geographical location, temperature, or time. In addition to the design and principle of operation of HERA, this paper presents laboratory characterization experiments with size-selected test substances, i.e., SNOMAX® and Arizona Test Dust. The particles were sampled on filters with HERA, varying either particle diameter (300 nm to 800 nm) or flow rate (10 L min−1 to 100 L min−1) between experiments. The subsequent offline INP analysis showed good agreement with literature data and comparable sampling efficiencies for all investigated particle sizes and flow rates. Furthermore, the deposition efficiency of atmospheric INPs in HERA was compared to a straightforward filter sampler and good agreement was found. Finally, results from the first campaign of HERA on the High Altitude and LOng range research aircraft (HALO) demonstrate the functionality of the new system in the context of aircraft application. The given csv files contain the data for reproducing the figures in the publication. The data structure of the csv files is explained in the README file.

opencc-by-4.0Oct 2023View details →

Simulated datasets for detector and particle flow reconstruction: CLIC detector, hit-based data, machine learning format

Derived from https://zenodo.org/record/8260741, prepared in a machine-learning friendly TFDS format, ready to be used with https://zenodo.org/record/8397954. <ul> <li>clic_edm_ttbar_hits_pf10k.tar: ee -> ttbar, center of mass energy at 380 GeV, 10k events</li> <li>clic_edm_qq_hits_pf10k.tar: ee -> Z* -> qqbar, center of mass energy at 380 GeV, 10k events</li> </ul> Contents Each .tar file contains the dataset in the <a href="https://github.com/tensorflow/datasets">tensorflow-datasets</a> (minimum version v4.9.1), <a href="https://github.com/google/array_record">array_record</a> format. Dataset semantics Each dataset consists of events that can be iterated over using the tensorflow-datasets library in either tensorflow or pytorch. Each event has the following information available: <ul> <li>X: the reconstruction input features, i.e. tracks and calorimeter hits</li> <li>ygen: the ground truth particles with the features ["PDG", "charge", "pt", "eta", "sin_phi", "cos_phi", "energy", "jet_idx"], with "jet_idx" corresponding to the gen-jet assignment of this particle</li> <li>ycand: the baseline Pandora PF particles with the features ["PDG", "charge", "pt", "eta", "sin_phi", "cos_phi", "energy", "jet_idx"], with "jet_idx" corresponding to the gen-jet assignment of this particle</li> </ul> The full semantics, including the list of features for X, are available at https://github.com/jpata/particleflow/blob/v1.6/mlpf/heptfds/clic_pf_edm4hep_hits/utils_edm.py.

opencc-by-4.0Mar 2022View details →

Particle tracks of 3D flow field during the initiation of a fluvial particle (case 6S)

The dataset belongs to experimental work described in the paper "Role of low-order proper orthogonal decomposition modes and large-scale coherent structures on sediment particle entrainment" published in the Journal of Hydraulics Research. The paper has been published as open access: https://doi.org/10.1080/00221686.2020.1869604 The dataset consists of particle tracks of the three-dimensional flow field during the entrainment of a fluvial particle of test case 6S.

opencc-by-4.0Apr 2022View details →

Dataset - Generalization of deep recurrent optical flow estimation for particle-image velocimetry data

This is the official test datasets of "Generalization of deep recurrent optical flow estimation for particle-image velocimetry data" published in Measurement Science and Technology. Particle-Image Velocimetry (PIV) is one of the key techniques in modern experimental fluid mechanics to determine the velocity components of flow fields in a wide range of complex engineering problems. Current  PIV processing tools are mainly handcrafted models based on cross-correlations computed across interrogation windows. Although widely used, these existing tools have a number of well-known shortcomings, including limited spatial output resolution and peak-locking biases. Recently, new approaches for PIV processing leveraging a novel neural network architecture for optical flow estimation called Recurrent All-Pairs Field Transforms (RAFT) have been developed. These have matched or exceeded the performance of classical, handcrafted models. While the RAFT-PIV method is a promising approach, it is important for the broader fluids community to more completely understand its empirical behavior and performance. To this end, in this study, we thoroughly investigate the performance of RAFT-PIV under varying image and lighting conditions. IWe consider applications spanning synthetic and experimental data, with a breadth and depth going far beyond currently available empirical results. The results for the wide variation of experiments included in this dataset shed new light on the capabilities of deep learning for PIV processing. This dataset is given as binary TFRECORD format.

opencc-by-4.0Sep 2022View details →

Supplementary Videos for ``Influence of Fine Particle Content in Debris Flows on Alluvial Fan Morphology"

This dataset includes 8 videos for the 6 debris flow fan experiments: Video V1. Video record (side view) of the experiment process of a continuous (15 minute) flow with 6\% clay mixture. Video V2. Video record (side view) of the experiment process of three successive (5 minute) flow with 6\% clay mixture. Video V3. Video record (side view) of the experiment process of a continuous (15 minute) flow with 4\% clay mixture. Video V4. Video record (side view) of the experiment process of three successive (5 minute) flow with 4\% clay mixture. Video V5. Video record (side view) of the experiment process of a continuous (15 minute) flow with 8\% clay mixture. Video V6. Video record (side view) of the experiment process of three successive (5 minute) flow with 8\% clay mixture. Video V7. Video record (front view) of the experiment process of three successive (5 minute) flow with 4\% clay mixture. Video V8. Video record (front view) of the experiment process of three successive (5 minute) flow with 8\% clay mixture.

opencc-by-4.0Dec 2019View details →

Response of flow and saltating particle characteristics to bed roughness and particle spatial density

The data were used in the paper "Response of flow and saltating particle characteristics to bed roughness and particle spatial density" which was submitted to "Water Resources Research". In this paper, The numerical model combining LES method and point-particle method is applied for tracking particle trajectories. The effects of bed roughness and particle spatial density on the bedload transport are investigated by numerical simulations. The distributions of key parameters for saltation, including their changes, are assessed using the PDF curves.

opencc-by-4.0Dec 2022View details →

Optical trapping of micro-particles and bacterial cells in single channel and flow-focusing microfluidic devices

Video 1 - The video shows the flow-focusing and trapping of 1.84 μm bacteria-sized particles flowing at a sample flow rate of 0.1 μL/min. The horizontal sheath flow rate 1 μL/min and the vertical sheath flow rate is 0.5 μL/min. Trapping is achieved using a maximum laser power of 250mW.  Video 2 - The video shows the flow and fluorescence trapping of 1.84 μm bacteria-sized particles flowing at a flow rate of 0.013 μL/min. The channel surface is treated with pluronic F-127 to prevent cell adhesion.  Video 3 - The video shows the flow and trapping of 1.84 μm bacteria-sized particles flowing at a flow rate of 1 μL/min. Increased flow rate results in continuous transient trapping of the cells is achieved at a trapping power of 250mW.  The microchannel surface is not treated with pluronic F-127, therefore lot of particles stick to the channel surface.  Video 4 - The video shows the flow and trapping of 1.84 μm bacteria-sized particles flowing at a flow rate of 0.013 μL/min. Trapping is achieved at a laser power of 250mW. The channel surface is treated with pluronic F-127 to prevent cell adhesion.  Video 5 - The video shows the flow and trapping of E. coli MG1655 flowing at a flow rate of 0.013 μL/min. Trapping is achieved at a laser power of 250mW. The channel surface is treated with pluronic F-127 to prevent cell adhesion.  Video 6 - The video shows the flow and trapping of S. aureus 6538 flowing at a flow rate of 0.013 μL/min. Trapping is achieved at a laser power of 250mW. The channel surface is treated with pluronic F-127 to prevent cell adhesion.

opencc-by-4.0Mar 2023View details →

dataset for "GPU-enhanced DEM analysis of flow behaviour of irregularly shaped particles in a full-scale twin screw granulator"

This dataset contains the numerical simulation data describing the conveying behaviour of shaped-particle systems. Detailed study on the conveying behaviour is presented in a journal paper entitled "GPU-enhanced DEM analysis of flow behaviour of irregularly shaped particles in a full-scale twin screw granulator". We provide all the essential data in a single excel file with the description below: Dataset 1- Average particle speed (Fig. 7a) and XYZ components (Fig. 7b) for: Sphere at t = 2.1 s, Cube at t = 2.85 s, Biluna at t = 3.1 s, HexP at t = 3.45 s. (Data of Fig. 7) Dataset 2 - XYZ coordinates of a typical particle trapped in the granulator. (Data of Fig. 9) Dataset 3 - Particle retention numbers for different shaped particles. (Data of Fig. 10). Dataset 4 - Average particle speeds for different shaped particles. (Data of Fig. 11). Dataset 5 - Average axial particle speeds for different shaped particles (Data of Fig. 12). Dataset 6 - Residence time distributions for various shaped particles. (Data of Fig. 13) Dataset 7 - Normalised residence time distributions for various shaped particles. (Data of Fig. 14) Dataset 8 - Mean residence time for various shaped particles. (Data of Fig. 15) Dataset 9 - The variance of RTD for different shaped particles. (Data of Fig. 16) Dataset 10 - Cumulative exit age distribution for various shaped particles. (Data of Fig. 17) Dataset 11 - Normalised cumulative exit age distribution for various shaped particles. (Data of Fig. 18) Dataset 12 - Evolution of the cumulative power consumption for various shaped particles. (Data of Fig.19) Dataset 13 - Power consumption distribution for various shaped particles. (Data of Fig.20)

opencc-by-4.0Apr 2023View details →

PEPT Data - The effect of retrofit design modifications on the macro-turbulence of a three-phase flotation tank – Flow characterisation using positron emission particle tracking (PEPT).

Supporting Information: for the paper "The effect of retrofit design modifications on the macro-turbulence of a three-phase flotation tank – Flow characterisation using positron emission particle tracking (PEPT)." The file contains the trajectory data and graph data for azimuthal slices obtained with PEPT.

opencc-by-4.0Apr 2023View details →

Neuromorphic Particle Flow Cytometry Dataset

Flow cytometry dataset for two different particles (A and B) from four different experiments, recorded with an event-based camera.

opencc-by-4.0Jul 2023View details →

Energetic Particle Injection during Short Isolated Bubble as seen in RCM Simulation and Spacecraft Observations in the Flow Braking Region

The names of the RCM simulation output files specify the simulation time in a format of "hhmmss". The stored quantities are, "I","J","COLAT","ALOCT","MLT","BNDLOC","XMIN","YMIN","FTV","BMIN","V", "BIRK_fromV(NH)","P(RCM),nPa", "PV_gamma","Vtotx", "Vtoty","Vx_exb","Vy_exb","vel_x","vel_y","Ey","PEDLAM", "PEDPSI", "HALL", "RCM_T_p","RCM_T_e","RCM_N_e","EFLUX","EAVG","f_i_50-75","f_i_75-125","f_i_125-200","f_i_200-300","f_e_50-75","f_e_75-125","f_e_125-200","f_e_200-300","Vm","dbxdz","dbydz","dbrdz","dbzdx","dbzdy". For the first hour of substorm-growth-phase-like quasi-steady convection, we uploaded the RCM simulation output files at 1-minute intervals. Then, the RCM output files were uploaded at 20-second intervals for the next 7 minutes of bubble injection, and at 1-minute intervals for the rest of the simulation.