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49 results for “numerical analysis”
Diffusion models with time-dependent parameters: "An analysis of computational effort and accuracy of different numerical methods"
<p>Software repository for the reproduction of the test cases from</p> <p><strong>Thomas Richter, Rolf Ulrich, Markus Janczyk:</strong> <em>Diffusion models with time-dependent parameters: "An analysis of computational effort and accuracy of different numerical methods"</em></p> <p>This software is used in particular for the reproducibility of the results.</p> <p>However, the algorithms can also be used directly for own purposes. If you have any questions about possibly necessary adaptations, please contact thomas.richter@ovgu.de.</p> <p>Parts of this repository</p> <p>General setup</p> <p><strong>Python</strong> collects all Python script. Here, <strong>Python/PythonTools</strong> are several internal functions, e.g. the realizations of KFE and random walks. <strong>Python/results</strong> and <strong>Python/pics</strong> are the directories where the results (figures and text-files) are put.</p> <p><strong>C++</strong> collects the C++ scripts.</p> <p>Case I</p> <p>Reproduces Case I of the paper (time-independent)</p> <ul> <li>Python/TestCase1.py</li> </ul> <p>runs the test-case with random walks, integral equation method and with KFE. It produces output in <strong>Python/pics</strong> and <strong>Python/results</strong>. These results will be used in <strong>C++/testcase1.cc</strong> (as reference solution) and by <strong>Python/TestCase1-Plot.py</strong></p> <ul> <li>C++/testcase1.cc</li> </ul> <p>runs the stochastic Euler simulation. Script is started by <strong>C++/run-testcase1.sh</strong>. It reads in the reference solution generated by <strong>Python/TestCase1.py</strong> for computing errors.</p> <ul> <li>Python/TestCase1-Plot.py</li> </ul> <p>produces Fig. 6 of the paper. It requires the outputs of <strong>Python/TestCase1.py</strong> and <strong>C++/testcase1.cc</strong></p> <p>Case II</p> <p>Reproduces Case II of the paper (time-dependent thresholds and drift)</p> <ul> <li>Python/TestCase2.py</li> </ul> <p>runs the test-case with random walks, integral equation method and with KFE. It produces output in <strong>Python/pics</strong> and <strong>Python/results</strong>. These results will be used in <strong>C++/testcase2.cc</strong> (as reference solution) and by <strong>Python/TestCase2-Plot.py</strong></p> <ul> <li>C++/testcase2.cc</li> </ul> <p>runs the stochastic Euler simulation. Script is started by <strong>C++/run-testcase2.sh</strong>. It reads in the reference solution generated by <strong>Python/TestCase2.py</strong> for computing errors.</p> <ul> <li>Python/TestCase2-Plot.py</li> </ul> <p>produces Fig. 7 of the paper. It requires the outputs of <strong>Python/TestCase2.py</strong> and <strong>C++/testcase2.cc</strong></p> <ul> <li>Python/TestCase2-AdjustRandomWalks.py</li> </ul> <p>runs simulations to reproduce Fig. 11 of the paper and implements the modification of the random walk strategy to limit oscillations.</p> <p>Case III</p> <p>Reproduces Case III of the paper (dependency of the accuracy on the derivative of the drift)</p> <ul> <li>Python/TestCase3.py</li> </ul> <p>runs the test-case with random walks, integral equation method and with KFE for a fixed discretization but with different values of the drift tau. It produces first part of Fig. 8.</p> <ul> <li>C++/testcase3.cc</li> </ul> <p>runs the stochastic Euler simulation. Script is started by <strong>C++/run-testcase3.sh</strong>. It reads in the reference solution generated by <strong>Python/TestCase3.py</strong> for computing errors.</p> <ul> <li>Python/TestCase3-Plot.py</li> </ul> <p>produces second part of Fig. 8. Depends on the output of <strong>Python/TestCase3.py</strong></p> <p>Case IV</p> <p>Reproduces Case IV of the paper (accuracy and efficiency for Dirac initial data)</p> <ul> <li>Python/TestCase4.py</li> </ul> <p>runs the test-case with random walks, integral equation and with KFE for a refined discretizations.</p> <ul> <li>Python/TestCase4-Plot.py</li> </ul> <p>produces Fig. 9. Depends on the output of <strong>Python/TestCase4.py</strong></p> <ul> <li>Python/TestCase4-showsolution.py</li> </ul> <p>Solves with the KFE and plots the solution as surface plot over time and space variable. This skript is used to create Fig. 10 of the paper. Problem parameters and discretization can be adjusted at the top of the script. To test the different stabilization strategies, one can either adjust the value of theta, or one activates Rannacher time-marching by commenting in the marked lines in the skript PythonTools/kfe.py, here in kfe_ale(..)</p> <p>Data Fitting</p> <p>Python scripts to fit the KFE model to the Data published by Rolf Ulrich et al. in</p> <p><strong>R. Ulrich, H. Schröter, H. Leuthold, T. Birngruber</strong> <em>Automatic and controlled stimulus processing in conflict tasks: Superimposed diffusion processes and delta functions.</em>Cognitive Psychology, 78 , 148–174</p> <ul> <li>Python/DataFitting-Simon.py</li> </ul> <p>runs the parameter fitting for the Simon task and produces data for Fig. 9 and Table 1.</p> <ul> <li>Python/Eriksen-Fletcher.py</li> </ul> <p>runs the parameter fitting for the Eriksen Fletcher task and produces data for Fig. 9 and Table 2.</p> <p>Installation & running the examples</p> <p>Python</p> <p>The python skripts can just be started. Just note that they depend on each other, i.e.: <strong>Python/TestCase1.py</strong> produces a reference solution that is required by <strong>C++/testcase1.cc</strong> and the results of both are needed in <strong>Python/TestCase1-Plot.py</strong></p> <p>The scripts only depend on standard packages like numpy or scipy and all Python environments should work. One suggestion is to use Spyder as part of Anaconda.</p> <p>C++</p> <p>The C++-programs are not intended for performing the simulations in a stand-alone application. Instead, the SDE is simulated for a given number of trials <strong>N_tr</strong> and a given time step <strong>dt</strong> and this simulation is repeated <strong>64</strong> times in order to estimate the average error. It should however be simple to use the scripts as basis for an efficient parallel simulation tool that uses multithreading.</p> <p>Configuration</p> <p>The C++ test cases must be compiled. The test cases are set up to use <strong>cmake</strong>. We suggest the following (in a Linux-environment or on a Mac using homebrew or MacPorts):</p> <ol> <li>Create a directory for compilation, e.g. <strong>C++/bin</strong> now called the <strong>bin-dir</strong></li> <li>In the <strong>bin-dir</strong> calls cmake by <strong>cmake ..</strong> (adjust the path, if the <strong>bin-dir</strong> is not a subdirectory of the <strong>C++-dir</strong>.</li> <li>Several options can be adjusted. In <strong>C++/bin</strong> call <strong>ccmake .</strong> to make all necessary changes.</li> </ol> <p>If you change the location of the <strong>bin-dir</strong> you will have to modify the run-scripts <strong>run-testcase[123].sh</strong>.</p> <p>Compilation</p> <p>Initially and whenever you change the code, the programs must be re-compiled</p> <ol> <li>In <strong>C++/bin</strong> just call <strong>make</strong></li> </ol> <p>Running the examples</p> <p>The programs are started in <strong>C++</strong>. For each of the test-case there is a skript to start the program.</p> <ol> <li>In <strong>C++</strong> call <strong>sh ./run-testcase1.sh</strong> (or <strong>sh ./run-testcase2.sh</strong>, etc.)</li> </ol> <p>Each script will start the programs several times. For <strong>Case I</strong>, <strong>Case II</strong> and <strong>Case IV</strong> the simulation is started on a sequence of finer and finer discretizations, for <strong>Case III</strong> the value of <em>tau</em> will be changed.</p> <p>The scripts store the output in <strong>C++/results</strong>. Old outputs will be overwritten! Further, the scripts read information about the reference solution from <strong>Python/resuts</strong>.</p> <p>The C++ programs use multithreading the OpenMP. If you do not specify the number of threads to be used, all available threads are taken including all hyperthreads. This is usually not efficient it is therefore advisable to set the number of threads by hand, e.g. by calling</p> <p><strong>export OMP_NUM_THREADS=8</strong></p> <p>before calling the run-scripts.</p> <p>License Information</p> <p>Initially the software has been written Thomas Richter, Otto-von-Guericke University Magdeburg, Germany in 2022, 2023 (thomas.richter@ovgu.de)</p> <p>You are free to use the scripts under the <em>Creative Commons Attribution 4.0 License</em>.</p>
Numerical model and natural river data for the timescale analysis of meandering channel migration
<p>This is the archive of the numerical model and river centerline data used for analyzing the timescale related to meandering channel migration, which is tied to the manuscript submitted to Journal of Geophysical Research: Earth Surface: Li, Y., and Limaye, A. B., Timescale of the morphodynamic feedback between planform geometry and lateral migration of meandering rivers.</p> <p>Running this model needs a MATLAB® software environment. The model can be launched by the wrapper scripts saved under the folder "software code/example wrappers". The wrapper script called "wrapper01a_channelOnly_runModel.m" is used to generate all model simulations in this study.</p>
Models and post-processing codes for paper "Quantitative stratigraphic analysis in a source-to-sink numerical framework"
<p>This package contains all the files required to reproduce the experiments in the manuscript: <strong>Quantitative stratigraphic analysis in a source-to-sink numerical framework</strong>.</p>
NUMERICAL ANALYSIS OF FLOW STRUCTURE AND HEAT TRANSFER IN BUBBLING FLUIDIZED BEDS
<p>The videos show the movements of selected particles in different geometries of fluidized bed heat exchangers. Two geometries without auxiliary measures and one with air cushion technology are shown.</p>
Numerical analysis of working paper "Joint integrated production-maintenance policy of a deteriorating equipment considering random yield and maintenance delay"
<p>This is the numerical analysis code using in the working paper "Joint integrated production-maintenance policy of a</p> <p>deteriorating equipment considering random yield and maintenance delay"</p>
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>
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>
A Benchmark Analytical Solution for Variable Viscosity Flow in Fractured Media: Development and Comparative Analysis with Numerical Simulations
<p>This data is associated with <strong>(a) Figure 3.</strong> Comparison between the velocities obtained through analytical calculations and those estimated numerically, and <strong>(b) Figure 4.</strong> Comparison of the BTCs obtained through analytical calculations and those estimated numerically.</p>
Numerical Output for Analysis of Atlantic Water Pathways in Fram Strait
<p>This dataset contains data for the publication: Shifts of the Recirculation Pathways in central Fram Strait drive Atlantic Intermediate Water Variability on Northeast Greenland shelf (McPherson et al., JGR Oceans).</p>
Numerical code and data for the stellar structure and dynamical instability analysis of generalised uncertainty white dwarfs
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Data from: Combining micro-volume isotope analysis and numerical simulation to reproduce fish migration history
1. Tracking the movement of migratory fish is of great importance for efficient conservation, although this has been technically difficult to achieve in small fish to which artificial tags cannot be attached. 2. We show that migration history can be reproduced by combining high-resolution otolith stable oxygen isotope ratio (δ18O) analysis and numerical simulation. 3. High-precision micro-milling and micro-volume carbonate analysing systems had the remarkable capability of extracting the otolith δ18O profiles with 10–30 days resolution. Furthermore, reasonable movements were reproduced by searching the routes consistent with the otolith δ18O profile, using an individual-based model with random swimming behaviour. 4. This method will be a valuable alternative to tagging and electronic loggers for revealing migration routes in early life stages, thereby providing crucial information to understand population structures and the environmental cause of recruitment variabilities, and to validate and improve fish movement models.
Data of paper "Grid ,Hydrodynamic boundary and Uncertainty analysis of 2D-SWEs in the context of digital twins: Taking numerical simulation of river networksas an example"
<p>论文数据 “数字孪生背景下2D-SWEs的网格、水动力边界和不确定性分析:以河流网络数值模拟为例”</p>
Experimental and numerical thermal analysis of a lithium-ion battery module based on a novel liquid cooling plate embedded with phase change material
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Data set for paper: "Numerical analysis of plastic deformation evolution in polycrystalline copper during cyclic loading with different frequencies"
<p>The dataset contains information necessary for performing the numerical analysis presented in the related paper. The input data are suited for the finite element code Z-set (http://www.zset-software.com/). The resulting data from the numerical calculations and source data for paper figures can be used for further analysis. These data are provided in ASCII format in text files and can be processed by any relevant software.</p>
Deliverable 1.1.1.1 BEL-Float project | Dataset containing the results of numerical simulations (motions, forces) of the operational performance analysis - Part 9: Operational and damaged scenarios in regular waves
<p>This dataset contains the results of OpenFAST simulations performed on the DeepCwind OC4 semi-submersible combined with the 5MW NREL turbine for various wind and wave conditions. The basis of the OpenFAST input files are taken from <a href="https://github.com/OpenFAST/r-test/tree/main/glue-codes/openfast/5MW_OC4Semi_WSt_WavesWN">OpenFAST r-test GitHub repository (5MW_OC4Semi_WSt_WavesWN)</a> and adapted to simulate various wind and wave conditions. The turbulent wind field as the input to the InflowWind module is generated using <a href="https://www.nrel.gov/wind/nwtc/turbsim.html">TurbSim</a>. The simulations are performed on a modified version of OpenFAST v3.5.3 to which adaptation to the code is made to extract additional Morison drag output up to 16 cylindrical members. This adapted code is <a href="https://github.com/abkpribadi/openfast/tree/Morison_additional_output">uploaded on GitHub as a branch from a forked OpenFAST repository</a>. In total there are 1152 simulation results consists of 768 irregular waves and 384 regular waves cases. The complete dataset is divided into 9 sub-datasets to which this is part number 9. A report describing this dataset is available on the BEL-Float project website: https://www.owi-lab.be/bel-float.</p>
Numerical responses of omnivorous terrestrial arthropods to plant alternative resources suppress prey populations: a meta-analysis
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Data from: Combining micro-volume isotope analysis and numerical simulation to reproduce fish migration history
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Data for "Martian Infrasound: Numerical Modeling and Analysis of InSight's Data"
<p>This dataset complements the paper "Martian Infrasound: Numerical Modeling and Analysis of InSight's Data", submitted to the Journal of Geophysical Research - Planets.</p>
Data from: Molecular mapping and candidate gene analysis for numerous spines on the fruit of cucumber
Number of spines on the fruit is an important quality trait in cucumber. The inheritance and identification of molecular markers for fruit spine density gene can provide a basis for breeding and lay the foundation for gene cloning. Cucumber inbred lines NCG-122 with numerous spines and NCG-121 with few spines were used for genetic analysis and gene mapping in this study. Genetic analysis showed that the numerous spines trait in NCG-122 was qualitative, and a single recessive nuclear gene (ns) controlled this trait. The few spines trait was dominant over the numerous spines trait. In the preliminary genetic mapping of the ns gene, 8 SSR markers were found to be linked to ns, which mapped to chromosome 2 (Chr.2) of cucumber. The closest flanking markers SSR22338 and SSR11596 were linked to the ns gene, with genetic distances of 10.2 and 1.7cM, respectively. One-hundred and thirty pairs of new SSR primers and 28 pairs of Indel primers were developed based on sequence information in the preliminary mapping region of ns. Fifteen SSR markers and 2 Indel markers were identified to be linked to the ns gene after analysis on the F2 mapping population using the new molecular markers. The 2 closest flanking markers, SSRns-127 and SSR04219, were 0.7 and 2.4 cM from ns, respectively. The physical distance between SSRns-127 and SSR04219 was 266.1kb, containing 27 predicted genes. Csa2G285390 was speculated as the probable candidate gene for numerous spines. The accuracy of the closest linked marker to the ns gene, SSRns-127, for MAS breeding was 95.0%.
Numerical simulation results for paper: Numerical Analysis of the Effect of Heterogeneity on CO2 Dissolution Enhanced by Gravity Driven Convection
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OpenNeuro
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