Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
7
datasets available to search
ShareScore release 0.7.1
Dataset results
7 results for “flow equation”
Movies: herringbone flows and flapping waves, 2d inclined inviscid Saint-Venant equations
<p>Supplementary movies for <em><strong>Multidimensional stability and transverse bifurcation of hydraulic shocks and roll waves in open channel flow</strong>.</em></p> <p>See <a href="https://github.com/zyang-pde/Multi-d_inviscid_Saint-Venant_eqs">https://github.com/zyang-pde/Multi-d_inviscid_Saint-Venant_eqs</a> for source codes used to generate these movies.</p> <p><strong>dam_break_F_equals_2_point_25.mp4</strong></p> <p>Dam-break initial data with left fluid height 1, right fluid height 0.7, and Froude number 2.25. The discontinuous hydraulic shock is 2d convectively unstable. Herringbone flows are seen at the end.</p> <p><strong>flat_F_equals_2_point_25.mp4</strong></p> <p>Flat initial data with co-moving speed set to be that of discontinuous hydraulic shock with left limiting fluid height 1, right limiting fluid height 0.7, and Froude number 2.25. Herringbone flows, parabola, and roll waves are seen at the end.</p> <p><strong>dam_break_comparison.mp4</strong></p> <p>Comparison of simulations with dam break initial data and with Froude numbers 2.13,2.14,2.15.</p> <p><strong>flat_comparison.mp4</strong></p> <p>Comparison of flat simulations with flat initial data and with Froude numbers 2.13,2.14,2.15.</p> <p> </p> <p>Movies below are simulated with perturbed roll waves initial data. The Froude number is set to be 6 and the minimum fluid height is set to be 0.28 and the channel width and the y-boundary condition are varied.</p> <p><strong>roll_width_point15.mp4</strong></p> <p>With <strong>wall</strong> boundary condition, the width of the channel is set to be <strong>0.15</strong>. No flapping front is seen at the end. Channel roll waves are stable.</p> <p><strong>roll_width_point16.mp4</strong></p> <p>With <strong>wall</strong> boundary condition, the width of the channel is set to be <strong>0.16</strong>. No flapping front is seen at the end. Channel roll waves are stable.</p> <p><strong>roll_width_point17.mp4</strong></p> <p>With <strong>wall</strong> boundary condition, the width of the channel is set to be <strong>0.17</strong>. No flapping front is seen at the end. Channel roll waves are stable.</p> <p><strong>roll_width_point18.mp4</strong></p> <p>With <strong>wall</strong> boundary condition, the width of the channel is set to be <strong>0.18</strong>. Wave fronts start to flap after a while. The flapping waves are persistent and do not become chaotic.</p> <p><strong>roll_width_point18_refined.mp4</strong></p> <p>With <strong>wall</strong> boundary condition, the width of the channel is set to be <strong>0.18</strong>. Wave fronts start to flap after a while. The flapping waves are persistent and do not become chaotic. Finer mesh grid is used compared with roll_width_point18.py. Raw data files are used to generate figures in the paper.</p> <p><strong>roll_width_point18_periodic.mp4</strong></p> <p>With <strong>periodic</strong> y-boundary condition, the width of the channel is set to be <strong>0.18</strong>. No flapping front is seen at the end. Channel roll waves are stable.</p> <p><strong>roll_width_point36_periodic.mp4</strong></p> <p>With <strong>periodic</strong> y-boundary condition, the width of the channel is set to be <strong>0.36</strong>. Wave fronts start to flap after a while. The flapping waves are persistent and do not become chaotic.</p> <p><strong>roll_width_point19.mp4</strong></p> <p>With <strong>wall</strong> boundary condition, the width of the channel is set to be <strong>0.19</strong>. Wave fronts start to flap after a while. The flapping waves are persistent and do not become chaotic.</p> <p><strong>roll_width_point2.mp4</strong></p> <p>With <strong>wall</strong> boundary condition, the width of the channel is set to be <strong>0.2</strong>. Wave fronts start to flap after a while. The flapping waves are also unstable, transitioning to chaotic flow at the end.</p> <p><strong>roll_width_1.mp4</strong></p> <p>With <strong>wall</strong> boundary condition, the width of the channel is set to be <strong>1</strong>. Wave fronts start to flap after a while. The flapping waves are also unstable, transitioning to chaotic flow at the end.</p> <p> </p>
Solutions of the mass continuity equation in hollow fibers for fully developed flow with some notes on the Lévêque correlation
<p><strong>Solutions of the mass continuity equation in hollow fibers for fully developed flow with some notes on the </strong><strong>Lévêque</strong><strong> correlation</strong></p> <p>The file includes:</p> <p>Appendix A. Code in SageMath and Maple</p> <p>Appendix B. Semi-analytical method of lines for solving the linear-BC case</p> <p>The complete scientific article that can be found at https://zenodo.org/record/5675723#.YhNn8JPMIeY</p>
Data underpinning "Unraveling long-time quantum dynamics using flow equations"
<p>The study of many-body quantum dynamics in strongly-correlated systems is extremely challenging. To date few numerical methods exist which are capable of simulating the non-equilibrium dynamics of two-dimensional quantum systems, in part reflecting complexity theoretic obstructions. In this work, we present a new technique able to overcome this obstacle, by combining continuous unitary flow techniques with the newly developed method of scrambling transforms. We overcome the prejudice that approximately diagonalizing the Hamiltonian cannot lead to reliable predictions for relatively long times. To the contrary, we show that the method works well in both localized and delocalized phases, and makes reliable predictions for a number of quantities including infinite-temperature autocorrelation functions. We complement our findings with rigorous incremental bounds on the truncation error. This approach shows that in practice, the exploration of intermediate-scale time evolution may be more feasible than is commonly assumed, challenging near-term quantum simulators.</p>
A Data-facilitated Numerical Method for Richards Equation to Model Water Flow Dynamics in Soil Dataset
<p>This dataset contains the reference solutions used for training the two neural networks in 1-, 2- and 3-D cases for the article:"A Data-facilitated Numerical Method for Richards Equation to Model Water Flow Dynamics in Soil" by Zeyuan Song and Zheyu Jiang, submitted to the journal Water Resources Research. </p> <p>This dataset which describes the relationship between the pressure head and number of particles used to train two MLPs in D-GRW based solvers consists of three files, i.e., 1-, 2- and 3-D case study. There are two parts, original reference solutions and reference solutions, corresponding to the original solutions generated by coarse mesh solvers and solutions after data augmentation process, respectively.The dataset is generated by GRW based solvers and simulation results (e.g., Celia's finite difference method). Original reference solutions admit GRW proportionality assumption. We initialize the number of particles by multiplying the initial condition and 1E10. </p>
Fast Modelling of Vegetated Flow and Sediment Transport over Mobile Beds Using Shallow Water Equations with Anisotropic Porosity
<p>The data are the numerical solutions and errors in different cases by the present model.</p> <p> </p>
Data from: A lattice Boltzmann model for the open channel flows described by the Saint-Venant equations
Open the record for dataset details and reuse information.
Development of a Data Assimilation Method Using Vibration Equation for Large-Eddy Simulations of Turbulent Boundary Layer Flows
<p>The dataset shown in the following manuscript is made.</p> <p>Development of a Data Assimilation Method Using Vibration Equation for Large-Eddy Simulations of Turbulent Boundary Layer Flows</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.