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15 results for “numerical measurements”
Numerical simulations and experimental measurements of the ULB semi-industrial furnace for the development of a Digital Twin
<p>This dataset contains the numerical and experimental data used to build the Digital Twin in Aversano et al. (https://doi.org/10.1016/j.proci.2020.06.045) and the adaptive Digital Twin in Procacci et al. (https://doi.org/10.1016/j.proci.2022.07.029).</p> <p>The directory "Numerical_data" includes 45 text files containing the data coming from the CFD simulations of the ULB furnace. <br> In each file, for each computational cell the features reported are: <br> - the cell's position in x, y, z coordinates and in meters.<br> - the cell's temperature in K. <br> - the cell's species mass fraction of NO (mf-pollut-pollutant-0), CO, OH, H2, H2O, CO2, O2, CH4.<br> The details of the setup of the numerical simulations are reported in Aversano et al.</p> <p>The numerical simulations have been computed for different values of the equivalence ratio (phi), blend of H2-CH4 (H2) and <br> inlet diameter (D).<br> The simulations for different inlet diameter where computed using different meshes, with slightly different numbers of cells.<br> In the file 'cases_parameters.csv', the value of the parameters is reported for of each simulation. There is a <br> discrepancy between the naming of the simulations in Aversano et al. and the one used in naming the files, so both are reported.</p> <p>The experimental measurements used to validate the numerical simulations can be found in the directory "Experimental_data". Each<br> file contains the value of the measured temperature along with the position in x and z in meters (y being 0). The temperature is<br> in K. The experimental uncertainty is estimated at 10 K.</p> <p>The file 'grid.vtu' contains the computational grid used to solve the CFD simulations. It can be opened using VTK-based software <br> such as Paraview or Pyvista.</p> <p>Changelog:</p> <p>- In version V1, some simulations were corrupted during data export.<br> - Added the grid file in V3</p>
Research data and code for Numerical investigation of the influence of the source and detector position for optical measurement of lung volume and oxygen content in preterm infants
<p># Research data repository</p> <p>## Introduction</p> <p>This repository contains the research data, scripts and codes to process the simulation and generate the figures in research article: <br> <em>"Numerical investigation of the influence of the source and detector position for optical measurement of lung volume and oxygen content in preterm infants"</em></p> <p>This work uses the discrete 3D mesh of the thorax of a newborn that is available at: http://doi.org/10.5281/zenodo.4916863</p> <p>This article has been submitted and publied in Journal of Biophotonics:<br> - DOI: 10.1002/jbio.202200041<br> - Link: <a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/jbio.202200041">https://onlinelibrary.wiley.com/doi/abs/10.1002/jbio.202200041</a></p> <p>## Article status</p> <p> [X] Submitted <br> [X] Under review <br> [X] Corrections <br> [X] Published<br> <br> ## Content</p> <p>- Folder "data": This folder needs to be unzipped and contains the raw data from the simulation, as well as some processed data needed to generate the figures. This folder contains all the data necessary to generate the figures. However some intermediate data files (the Photon Hitting Density values interpolated on the elements of the mesh) are not given here because the files are too big. These can be created using the convertPHD2element script.<br> - Folder "function": This folder needs to be unzipped and contains some functions that are used by the scripts to process data or generate the figures<br> - Matlab .m files: The .m files are scripts that are used to generate the figures (generateFigX.m) or to process the data computeYYY.m. The description of each script and function is given in the comment section at the beginning of each file.</p> <p>## Licence<br> This data is published under the creative common CC-BY licence. You are free to use this data as long as you cite this dataset and the article (when DOI available)</p> <p>## Digital Object Identifier<br> DOI: 10.5281/zenodo.5996855</p> <p>## Authors<br> Simulation: Andrea Pacheco<br> Article writing: Andrea Pacheco<br> Data processing and figure generation: Andrea Pacheco and Baptiste Jayet<br> Conceptualisation, investigation, review and editing: Emilie Krite Svanberg, Hamid Dehghani and Eugene Dempsey<br> Project supervision: Stefan Andersson-Engels</p> <p>## Funding<br> The research leading to these results was funded by Science Fundation Ireland project no. SFI/15/RP/2828</p>
Data for: Direct numerical simulation surface layer and IR-based measurements
<p>This is the primary dataset used in a manuscript in the process of being submitted to JTECH. The bulk of the dataset is a direct numerical simulation (DNS) of an open channel flow with a shear-free surface. The DNS includes scalar, velocity, and divergence fields over 181 realizations. From the scalar field, another vector field was generated by a method called feature image velocimetry that successively cross-correlates the scalar fields to derive the underlying velocity field. The two velocity fields are then compared by their spectra, one from the DNS and the other derived from the scalar field.</p>
Numerical dataset for quantum trajectory simulations of a dissipative qubit under continuous measurement and feedback
<p>The data consist of numerical results obtained from quantum trajectory simulations of a dissipative qubit under continuous measurement and feedback.</p> <p>These data are used in the preprint titled <em>"Heat current and fluctuations between a dissipative qubit and a monitor under continuous measurement and feedback."</em></p> <p>We performed numerical simulations of the stochastic master equation using the supercomputer at ISSP.</p> <p>The contents are as follows:</p> <ul> <li>Numerical results for the correlation functions (<code>F*.dat</code>).</li> <li>Numerical results for the Fano factor and the power spectrum (<code>fano_factor*.dat</code>).</li> <li>Python code to calculate the Fano factor and the power spectrum from the correlation functions (<code>fano_factor_v6.py</code>).</li> <li>Figures illustrating the Fano factor and the power spectrum (<code>fig*.eps</code>).</li> </ul>
Experimental Data for "Seismic wave attenuation and dispersion due to partial fluid saturation: Direct measurements and numerical simulations based on X-Ray CT"
<p>Experimental Data from a Berea sandstone sample. Includes X-ray CT scans and mechanical response of sample.</p> <p>Abstract</p> <p>Quantitatively assessing seismic attenuation caused by fluid pressure diffusion (FPD) in partially saturated rocks is challenging because of its sensitivity to the spatial fluid distribution. To address this challenge we performed depressurisation experiments to induce the exsolution of carbon dioxide from water in a Berea sandstone sample. In a first set of experiments we used medical X-ray computed tomography (CT) to characterise the fluid distribution. At an equilibrium pressure of ~1 MPa and applying a fluid pressure decline rate of ~0.6 MPa per minute, we allowed a change in saturation of less than 1 %. The gas was heterogeneously distributed along the length of the sample, with most of the gas exsolving near the sample outlet. In a second set of experiments, at the same pressure and temperature, following a very similar exsolution protocol, we measured the frequency dependent attenuation and modulus dispersion between 0.1 and 1000 Hz using the forced oscillation method. We observed significant attenuation and dispersion in the extensional and bulk deformation modes, however not in the shear mode. Lastly, we use the fluid distribution derived from the X-ray CT as an input for numerical simulations of FPD to compute the attenuation and modulus dispersion. The numerical solutions are in close agreement with the attenuation and modulus dispersion measured in the laboratory. Our methodology allows for accurately relating attenuation and dispersion to the fluid distribution, which can be applied to improving the seismic monitoring of the subsurface.</p>
ESM of determination of transdermal rate of metallic microneedle array through impedance measurements-based numerical check screening algorithm
<p>Microneedle systems have been widely used in health monitoring, painless drug delivery, and medical cosmetology. Although many studies on microneedle materials, structures, and applications have been conducted, the applications of microneedles often suffered from issues of inconsistent penetration rates due to the complication of skin-microneedle interface. In this study, we demonstrated a methodology of determination of transdermal rate of metallic microneedle array through impedance measurements-based numerical check screening algorithm. Metallic sheet microneedle array sensors with different sizes were fabricated to evaluate different transdermal rates. In vitro sensing of hydrogen peroxide confirmed the effect of transdermal rate on the sensing outcomes. A FEM simulation model of a microneedle array revealed the monotonous relation between the transdermal state and test current. Accordingly, two methods were first derived to calculate the transdermal rate from the test current. First, an exact logic method provided the number of unpenetrated tips per sheet, but it required more rigorous testing results. Second, a fuzzy logic method provided an approximate transdermal rate on adjacent areas, being more applicable and robust to errors. Real-time transdermal rate estimation may be essential for improving the performance of microneedle systems, and this study provides various fundaments toward that goal.</p>
Repeatability and Concordance of VA Measured With ETDRS Numeric , Landolt C, and Letter Charts in Thai Individuals
ClinicalTrials.gov study NCT02810665. IPD Sharing: NO. Countries: 1. Publications: 4.
THE ROLE OF ARTIFICIAL INTELLIGENCE TRAINED WITH PRE-MEASURED NUMERICAL DATA IN PREDICTION OF DIFFICULT INTUBATION
ClinicalTrials.gov study NCT06961240. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
ESM of determination of transdermal rate of metallic microneedle array through impedance measurements-based numerical check screening algorithm
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Data from: Predicting evolutionary potential: a numerical test of evolvability measures
Despite sophisticated mathematical models, the theory of microevolution is mostly treated as a qualitative rather than a quantitative tool. Numerical measures of selection, constraints, and evolutionary potential are often too loosely connected to theory to provide operational predictions of the response to selection. In this paper, we study the ability of a set of operational measures of evolvability and constraint to predict short‐term selection responses generated by individual‐based simulations. We focus on the effects of selective constraints under which the response in one trait is impeded by stabilizing selection on other traits. The conditional evolvability is a measure of evolutionary potential explicitly developed for this situation. We show that the conditional evolvability successfully predicts rates of evolution in an equilibrium situation, and further that these equilibria are reached with characteristic times that are inversely proportional to the fitness load generated by the constraining characters. Overall, we find that evolvabilities and conditional evolvabilities bracket responses to selection, and that they together can be used to quantify evolutionary potential on time scales where the G‐matrix remains relatively constant.
Mechanical data of rotary shear experiments, temperature measurements, and temperature numerical models for the manuscript: "Mechanical energy dissipation during seismic dynamic weakening in calcite-bearing faults"
<p>All data included in this data repository is ancillary to the manuscript "Energy dissipation during dynamic weakening in calcite-bearing fault rocks", submitted to Journal of Geophysical Research: Solid Earth. </p><p>The data consists in time series of high velocity friction experiments run with SHIVA (INGV, Rome), time series acquired from a two-color pyrometer (UC3M), the synchronization of the two, and numerical models. The data format is .mat, proprietary to Matlab, but they can be easily accessed with Python (see <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html">link</a>). Each .mat contains vector of the measured variables when opened from Matlab, or dictionaries when opened using the scipy.loadmat() function. </p><p>SHIVA and PYRO red data (calibrated data), fin data (synchronized data), and shivaRED vect data (numerical model data) are included in this data repository in separate folders. Numerical models are grouped in subfolder by type of model (the relation fin data to model is 1:n). We included the scripts to convert SHIVA raw data into SHIVA red data (<a href="https://github.com/aretu/shivaUNIX">link to shivaUNIX</a>), SHIVA and PYRO red data into fin data (/scripts/syncing2021.m), to obtain numerical models from fin data (<a href="https://github.com/aretu/shivaRED">link to shivaRED</a>), and to plot data (/scripts/making plots.ipynb).</p>
Data for "Improving Four-electrode Impedance Measurements by Numerical Simulations"
<p>Replication data for "Improving Four-electrode Impedance Measurements by Numerical Simulations"</p>
Data from: Predicting evolutionary potential: a numerical test of evolvability measures
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MEasurement by a Numerical Approach of BREAst Volume
ClinicalTrials.gov study NCT03715738. IPD Sharing: NO. Countries: 1. Publications: 0.
The measurements and three operational numerical forecasts of surface wind speed over Pearl River Estuary during 2018–2021.
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