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864 results for “numerative”
H2020 ENODISE: VKI Experimental dataset of configuration C for numerical optimization
<p>The database includes acoustic and load measurements performed in the ALCOVES anechoic facility of the von Karman Institute on co-rotating coaxial propellers. This configuration corresponds to Config. C of the ENODISE project. This database has been used for numerical optimization. </p>
H2020 ENODISE: GPUP Configuration A Numerical Databases with Mitigation
<p>This dataset contains the numerical prediction for A2 configuration as defined in the H2020 ENODISE project (<a href="https://www.vki.ac.be/index.php/about-enodise">https://www.vki.ac.be/index.php/about-enodise</a>)</p> <p>The dataset contains numerical simulation results for Band-Limited Overall Sound Pressure Level (BL-OASPL) for a rotor operating at inflow velocities of 15 m/s, 27 m/s, 45 m/s, 60 m/s, and 75 m/s with the rotation rate of 6500 RPM. Three setups are investigated with the rotor located at 1000mm, 800mm, and 480mm. The BL OASPL is obtained at 90-degree observer angle in an overhead array. These simulations are based on the experimental setup by the University of Bristol on the investigation of turbulence-ingestion-rotor noise. </p>
H2020 ENODISE: GPUP Configuration B Numerical Databases with Mitigation
<p>This dataset contains the numerical prediction for A2 configuration as defined in the H2020 ENODISE project (<a href="https://www.vki.ac.be/index.php/about-enodise">https://www.vki.ac.be/index.php/about-enodise</a>)</p> <p>The dataset corresponds to the Band-Limited Overall Sound Pressure Level (BL OASPL) measurements obtained for Configuration B, focusing on the acoustic performance of propellers under different configurations. Two main setups were tested:</p> <ol> <li><strong>Propeller without Serrations</strong>: This configuration represents the baseline case where the propeller operates without any modifications to its trailing edge.</li> <li><strong>Propeller with Serrations</strong>: This configuration represents the case where the propeller operates with serrations at the trailing edge of the propeller.</li> </ol> <h1><strong>Setup </strong></h1> <ol> <li> <p><strong>Rotors</strong>: The setup involves three rotors:</p> <ul> <li><strong>Rotor_C (Central Rotor)</strong></li> <li><strong>Rotor_L (Left Rotor)</strong></li> <li><strong>Rotor_R (Right Rotor)</strong></li> </ul> <p>The Left and Right Rotors (Rotor_L and Rotor_R) are fixed at a position of <strong>x = -0.22m</strong>.</p> </li> <li> <p><strong>Axial Locations and Phase Angles</strong>: The central rotor (Rotor_C) was moved in the axial direction, and three distinct setups were investigated:</p> <ul> <li><strong>Baseline Case (Δx = 0Db)</strong>: Rotor_C located at <strong>x = -0.22m</strong>.</li> <li><strong>Forward Case (Δx = -0.2Db)</strong>: Rotor_C moved forward to <strong>x = -0.26m</strong>.</li> <li><strong>Backward Case (Δx = +0.2Db)</strong>: Rotor_C moved backward to <strong>x = -0.18m</strong>.</li> </ul> <p>For each axial location, four phase angles were tested: <strong>0°, 45°, 90°, 135°</strong>.</p> </li> </ol> <h1>Dataset :</h1> <ol> <li> <p><strong>Total Simulations</strong>:</p> <ul> <li>Each propeller configuration (serrated and non-serrated) was tested under the three axial locations and four phase angles, resulting in a total of <strong>12 simulations</strong> for each setup.</li> <li>This results in <strong>24 simulations</strong> (12 simulations with serrations + 12 simulations without serrations) across all configurations.</li> </ul> </li> <li> <p><strong>Measured Parameters</strong>:</p> <ul> <li><strong>Area-averaged Bandlimited Prms</strong>: The overall sound pressure level averaged over the area, given in both linear scale and converted to decibels (dB).</li> <li><strong>Maximum Bandlimited Prms</strong>: The maximum sound pressure level observed within the band, also provided in both linear scale and dB.</li> </ul> </li> <li><strong>Script</strong> <ul> <li>Python script is provided which converts original dataset to log scale </li> </ul> </li> </ol>
Numerical data and higher resolution images for Abdulkareem et al (2024), Thermal stabilisation of lysozyme through ensilication, Molecules
<p>Numerical data and higher resolution images for Abdulkareem et al (2024), Thermal stabilisation of lysozyme through ensilication, Molecules</p> <p><em><strong>Please cite our publication in any use of this data</strong></em></p>
Topography Response to Horizontal Slab Tearing and Oblique Continental Collision: 3D Thermomechanical Numerical Modelling
<p>This repository submission provides the 3D thermo-mechanical numerical modelling code I3ELVIS developed by the workgroup of Prof. Taras Gerya (ETH Zürich). The code is based on finite-difference and marker-in-cell methods (Gerya & Yuen, 2003, 2007; Gerya, 2019). The code solves the momentum, continuity, and energy equations on the fixed Eulerian grid and transports the physical properties by Lagrangian markers using the velocity field. The code also accounts for the major phase transitions in the Earth’s mantle and internal heat sources arising from adiabatic, radiogenic, and frictional heating. Partial melting and melt extraction processes are neglected for the sake of simplicity. A more detailed description of the code, including the governing equations and the adopted rheological model can be found in various published literature (Andrić-Tomašević et al., 2023; Boonma et al., 2023; Maiti et al., 2024). Interested users are recommended to contact Prof. Taras Gerya (taras.gerya@erdw.ethz.ch). </p> <p>The repository also provides input and output files to run and reproduce model results and manuscript figures of Maiti et al., 2024 (JGR Solid Earth). Paraview States for processing .vtr result files and visualizing the model output data. Matlab script to visualise the topography from .grd files. </p>
Refractory materials used in the numerical model of an aluminothermic reduction vessel
<p>This constitutes underlying data for a publication titled "Numerical Model of Aluminothermic Reduction Vessel Preheating and Charge Heating with Graphite Heating Rods" by S. Semenov, P. Namy, A. Kale, S. Tsebe. </p>
Refractory materials used in the numerical model of TBRC
<p>This constitutes underlying data for a publication titled "Numerical Model of a Top-Blown Rotary Converter Preheating and Charge Heating with an Oxy-Fuel Burner" by S. Semenov, P. Namy, A. Kale, S. Tsebe.</p>
Movies of the simulations performed in Baïsset et al. paper entitled « Weakening induced by phase nucleation in metamorphic rocks: insights from numerical models »
<p>Here you can find the movies of the evolution of the accumulated plastic strain of the simulations performed in the study. You will also find the table that summarizes the conditions of the different simulations (Table 1), as well as the colorbar (legend.png) corresponding to all the movies.</p>
Dataset for the numerical simulation in the article "Catalytically biased self-assembly by hybridization of reversibility and irreversibility in a reaction network"
<p>This dataset includes the essential source code and the corresponding numerical data for the self-assembly of a M6L4 square-based pyramid (SP) complex. </p> <p>The associated study is described in </p> <p><strong>"Catalytically biased self-assembly by hybridization of reversibility and irreversibility in a reaction network"</strong>, by T. Abe, S. Takahashi, H. Sato, and S. Hiraoka.</p>
Numerical data achieved to plot the graphs
Open the record for dataset details and reuse information.
Supporting dataset for "Numerical analysis of Josephson junction arrays for multi-order quantum voltage steps"
<p>The TXT files in the <strong>IV_SIN_Beta_0.00-Njj_1.zip</strong> zipped folder include the simulated IV characteristics in normalized units for an overdamped Josephson junction, as described by the RSJ model. This simulation is performed in Python and considers various bias currents and junction parameters.</p> <p>The junction is biased by both a dc current and an ac (rf) current at a normalized frequency <em><strong>Ω_rf</strong></em> and rf-current amplitude<strong> <em>i_rf</em></strong>.</p> <p><em>Ω_rf</em> ranges from 0.2 to 2 in steps of 0.05.<br><em>i_rf</em> ranges from 0 to 4 in steps of 0.1.<br>The dc current varies from 0 to 6 in steps of 0.015.</p> <p>In addition to normalized current and voltage, fundamental to determine the width and position of rf-induced Shapiro steps, other relevant parameters (supercurrent and total dissipated power) are also calculated.</p> <p>The filename is organized as follows (example):</p> <p><strong>IV_SIN__Beta_0.00-Omega_rf_2.000-I_rf_4.000-N_jj_1.txt</strong></p> <ul> <li><strong>IV</strong>: Indicates that the IV characteristic can be extracted from this data (first two columns)</li> <li><strong>SIN</strong>: Specifies that the rf current bias is sinusoidal.</li> <li><strong>Beta_0.00</strong>: Indicates that the Stewart-McCumber parameter (<em>β</em>) of the junction is 0, meaning that the junction is overdamped.</li> <li><strong>Omega_rf_2.000</strong>: Indicates that the normalized rf frequency is 2.</li> <li><strong>I_rf_4.000</strong>: Indicates that the normalized rf current is 4.</li> <li><strong>N_jj_1</strong>: Indicates that a single Josephson junction is simulated.</li> </ul> <p>Each data file consists of four columns and 400 rows (excluding the header). The columns respectively represent Current, Voltage, Supercurrent, and Power, all expressed in normalized units.</p>
Theoretical Resolution of the Birch and Swinnerton-Dyer Conjecture: A Comprehensive Approach: Stability Approach and Numerical Investigation of Elliptic Curves
<p>Abstract</p> <p> </p> <p>This paper introduces a comprehensive theoretical analysis of the Birch-Swinnerton-Dyer Conjecture, a longstanding and profound challenge in modern number theory. Our approach is built upon a novel method of stable numerical integration and Fourier transformation, specifically tailored to analyze elliptic curves under extreme mathematical and computational conditions. The method developed aims to ensure the stability of numerical computations, even when dealing with highly complex parameters and challenging computational environments. By maintaining computational stability, this approach allows for precise analysis of elliptic curves, revealing deeper insights into their properties without compromising the accuracy of the results.</p> <p> </p> <p>The significance of this method lies in its ability to provide reliable convergence results, which are crucial for understanding the deeper mathematical structure of elliptic curves. These structures are intimately tied to the Birch-Swinnerton-Dyer Conjecture, making this work a potentially important contribution to the ongoing efforts to address this conjecture.</p> <p> </p> <p>f(x) = e^{-tx^2} \quad \sqrt{\infty} \int_{0}^{\infty} t dt \quad (\text{Stable numerical integration})</p> <p> </p> <p>F{g(t)} = \int_{-\infty}^{\infty} g(t)e^{-i\omega t} dt \quad (\text{Stability of Fourier transformation}) ]</p> <p> </p> <p>y^2 = x^3 + ax + b \quad (\text{Elliptic curve and its numerical stability})</p> <p> </p> <p>The results presented in this theoretical study provide a significant step forward in the analysis of elliptic curves and their relation to the Birch-Swinnerton-Dyer Conjecture. While the analysis offers substantial progress, the findings remain theoretical and are subject to further review and validation by the broader mathematical community.</p> <p> </p> <p>In this paper, we will showcase the methods and frameworks used to achieve stable numerical results under extreme conditions, alongside a discussion of the potential implications for future research. These methods open new possibilities for tackling the Birch-Swinnerton-Dyer Conjecture and offer a foundation for further exploration into the deeper properties of elliptic curves. While detailed calculations and specific results are beyond the scope of this abstract, we present an overview of the approach taken and the rigorous mathematical tools employed. These include advanced techniques in numerical integration and Fourier analysis, both of which have been crucial in ensuring the accuracy and stability of the elliptic curve analysis.</p> <p> </p> <p>This theoretical work marks a significant contribution to the ongoing dialogue surrounding the Birch-Swinnerton-Dyer Conjecture. However, the results are preliminary, and further evaluation and peer review will be necessary to determine their full impact on the field. The proposed method offers a solid foundation for future investigations and potential advancements in the theory of elliptic curves.</p> <p> </p> <p> </p> <p>---</p> <p> </p> <p> </p>
Machine Learning for Energy Consumption Prediction of Numerical Controlled Programs - NC Files
<p>The NC files housed within this DOI represent the Data the Machine Learning Models were Trained/Validated/Tested on, during the execution of the work in the thesis, " Machine Learning for Energy Consumption Prediction of Numerically Controlled Programs." Theses files were created by Samuel D. Stencel, a Gradute Research Assistant at Purdue University.</p> <p> </p>
Dataset of numerical model for enhancing stimulated Brillouin scattering in optical fibers
<p>Stimulated Brillouin scattering (SBS) is useful, among others for generating slow light, sensing and amplification. SBS was previously viewed as a penalty due to the limitation on optical power in high-powered photonic applications. However, considering the many possible applications using SBS, it is now of interest to enhance SBS in areas of Brillouin frequency shift together with Brillouin Gain. A numerical model, using a fully vectorial approach, by employing the finite element method, was developed to investigate methods for enhancing SBS in optical fiber. This paper describes the method related to the numerical model and discusses the analysis between the interactions of horizontal, shear and hybrid acoustic modes; and optical modes in optical fiber. Two case studies were used to demonstrate this. Based on this numerical model, we report the influence of core radius, clad radius and effective refractive index on the Brillouin frequency shift and gain. We observe the difference of Brillouin shift frequency between a normal silica optical fiber and that of a tapered fiber where nonlinearities are higher. Also observed, the different core radii used and their respective Brillouin shift. For future work, the COMSOL model can also be used for the following areas of research, including simulating "surface Brillouin shift" and also to provide in-sights to the Brillouin shift frequency v<sub>B</sub><span><span></span></span> of various structures of waveguides, e.g circular, and triangular, and also to examine specialty fibers, e.g. Thulium and Chalcogenide doped fibers, and their effects on Brillouin shift frequency.</p>
Numerical simulation data of a two-dimensional flow around a fixed circular cylinder
<p>Numerical results of a two-dimensional flow past a fixed circular cylinder in the vortex shedding regime. The solver is Cadyf, an in-house fluid-structure interaction code using finite element method (<a href="https://doi.org/10.1016/j.jcp.2008.11.032">doi.org/10.1016/j.jcp.2008.11.032</a>). The Reynolds number is Re = 100. The boundary conditions and numerical integration are described in <a href="https://doi.org/10.1017/jfm.2021.252">doi.org/10.1017/jfm.2021.252</a>.</p> <p>The fixed_cylinder_atRe100 raw file contains the values of the flow velocity, pressure, and node coordinates at each time step. Tables of the results can be extracted using the file processing code in text_flow.py.</p> <p>The Re100_fixe00.reactions raw file contains the values of the total force and torque applied on the cylinder at each time step. Tables of the results can be extracted using the file processing code in reactions_process.py.</p>
Implementation of an adaptive bias-aware extended Kalman filter for sea-ice data assimilation in the HARMONIE-AROME numerical weather prediction system: numerical experiments
<p>This data set provides the post-processed output of the numerical experiments performed to assess the possible effects of applying sea ice data assimilation within the surface analysis procedure of the HARMONIE-AROME NWP system. Results of five numerical experiments are provided:</p> <ul> <li>HA-REF – reference experiment <em>without</em> sea ice data assimilation applied, and with blending for upper-air initialization</li> <li>HA-EKF – sensitivity experiment with sea ice data assimilation applied, and with blending for upper-air initialization</li> <li>3DVAR-REF – reference experiment <em>without</em> sea ice data assimilation applied, and with 3DVAR for the upper-air analysis</li> <li>3DVAR-EKF – sensitivity experiment with sea ice data assimilation applied, and with 3DVAR for the upper-air analysis</li> <li>3DVAR-EKF-TS – sensitivity experiment with sea ice data assimilation, and with 3DVAR for the upper-air analysis using coupled surface and atmospheric data assimilation procedures</li> </ul> <p>For the HA-REF and HA-EKF experiments a subset of the gridded model output is provided; for the 3DVAR-REF, 3DVAR-EKF and 3DVAR-EKF-TS experiments a subset of the gridded model output and model data extracted at the positions of the SYNOP and TEMP stations within the model domain are provided. Additionally, in-situ observations, covering the same time period as the 3DVAR-REF, 3DVAR-EKF, 3DVAR-EKF-TS experiments, are provided.</p>
Numerical nine-shot seismo records for 1600 acoustic impedance distributions
<p>This dataset contains numerical seismo records for 1600 synthetic acoustic impedance distributions presented in [1]. Forward modeling was performed using grid-characteristic numerical method.</p> <p>There are nine seismo records for each impedance distribution. The records differ in excitation pulse positions. The pulses are located equidistantly on the upper boundary of the domain.</p> <p>Acoustic impedance distributions are in masks/ folder. For example, the file masks/1.npy contains the distribution for the sample number 1. Seismo records are in seismograms/ folder, each file name contains distribution number and pulse location. For example, the file seismograms/1_position_10.npy contains seismo record for the impedance distribution 1 obtained with the pulse center located 10 percent from the left border of the domain.</p> <p>The data is in NumPy native format. Total dataset size is 6.9G after extraction. Archive md5 checksum: dc9f61333f1eef17526831e0c6d9b493</p> <p>[1] Fangshu Yang and Jianwei Ma (2019). Deep-learning inversion: A next-generation seismic velocity model building method. DOI: 10.1190/geo2018-0249.1</p>
Numerical simulation of supercell thunderstorms (at 50 meter resolution) associated with above anvil cirrus plumes
<p>Four-dimensional data from high-resolution simulations (50 meter grid spacing) of supercell thunderstorms conducted on the Frontera supercomputer are contained in this archive. Output from two simulations is included, named "Strong" and "Weak". In the Strong simulation, data from parcel trajectories is also included. </p> <p>The four-dimensional (time, and three dimensions of space) data and parcel data is saved in Network Common Data Format (NetCDF), version 4, an open-source self-describing scientific data format commonly used in the atmospheric sciences. </p>
The interpretation of temperature and salinity variables in numerical ocean model output and the calculation of heat fluxes and heat content - ACCESS-CM2 data and code
<p>This dataset contains post-processed ACCESS-CM2 PI control CMIP6 climate model output and code used to produce Figs. 5, 6 and 9 in the published article:</p> <p>McDougall, T., J., Barker, P.M., Holmes, R.M., Pawlowicz, R., Griffies, S. and Durack, P. (2021): The interpretation of temperature and salinity variables in numerical ocean model output and the calculation of heat fluxes and heat content, <strong>Geoscientific Model Development</strong>, 14, 1–21, <a href="https://doi.org/10.5194/gmd-2020-426">https://doi.org/10.5194/gmd-2020-426</a></p> <p>The processed ACCESS-CM2 data is included as .mat files and is accompanied by Matlab processing routines (including code from the TEOS-10 Gibbs SeaWater Oceanographic Toolbox, https://www.teos-10.org/software.htm#1) to produce the figures. A more detailed description of the data are included in README.md. The code and data is also available under version control at <a href="https://github.com/rmholmes/ACCESS_CM2_SpecificHeat/tree/GMD_Published">https://github.com/rmholmes/ACCESS_CM2_SpecificHeat/tree/GMD_Published</a>.</p>
Numerical back-analysis of short-term convergence data of sections within zone A (from chainage 1905 to chainage 2723) in the Fréjus road tunnel
<p>Numerical back-analysis of short-term convergence data of sections within zone A (from chainage 1905 to chainage 2723) in the Fréjus road tunnel</p>
ScienceDex guides
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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.