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17 results for “CFD simulation”
CFD simulation and measurements of effect of wind on non-catching rain gauge
<p>Simulation_dataset file shows the results of a CFD simulation of the measurements of a Thies laser precipitation monitor under different conditions of wind.</p> <p>Wind_tunnel_dataset shows the results of the model validation using an actual wind tunnel.</p>
CFD airfoil simulations
<p>Arfoil CFD simulation dataset. Each example has the flow at the back of the airfoil. The ground truth is the drag and lift forces on the airfoil. The aim of this dataset is to compare feature extraction techniques from flow fields for machine learning.</p>
Simulation data and scripts for CFD-DEM simulation of saturated bi-disperse granular flows
<ul> <li>Data set 1 - contains the raw data required to replicate and validate all plots in the main article. </li> <li>Sample case - a .zip file which includes codes which are needed to simulate a CFD-DEM case of a steady granular flow in water with cyclic boundaries in the stream wise direction. Also enclosed is a ReadMe.txt file detailing the implementation instructions for both Esys particle and OpenFOAM codes. Download links for Esys particle and OpenFOAM are also included</li> <li>Geo file generator - a .zip file which includes Esys particle codes that can be used to generate a .geo file specifying the initial position of the particles used in the test simulations. A ReadMe.txt file is enclosed with more detailed implementation instructions.</li> </ul>
Figure 4. CFD simulation results for Models A in Drag of suction cup tags on swimming animals: Modeling and measurement
Figure 4. CFD simulation results for Models A (panels A and C) and B (panels B and D) in steady 5.6 m/s flow. The blue, yellow, and green regions are areas of reduced flow speed that generate forces on the tags. The upper panels show the flow speed over a horizontal cross-section at the tag midline. The lower panels show flow speed over a vertical cross-section at the centerline of the tag. The improved flow around Model B is evident in the smaller magnitude of blue coloration in the wake behind the tag.
Simulation cases of a lab-scale wet-operated stirred media mill using coupled CFD-DEM
<p>Simulation cases described in the article, "Coupled CFD-DEM simulation of pin-type wet stirred media mills using immersed boundary approach and hydrodynamic lubrication force", DOI: <a href="https://doi.org/10.1016/j.powtec.2024.120060" rel="nofollow">https://doi.org/10.1016/j.powtec.2024.120060</a></p> <p><strong>Pre-requisites:</strong> LIGGGHTS, OpenFOAM-6, cfdemCoupling, and their corresponding dependencies, Python (>3.6)</p> <p>*The versions of simulation softwares used in the simulation cases are taken from Institute for Particle Technology's (iPAT) GitLab repository: <a href="https://git.rz.tu-bs.de/partikeltechnik/" rel="nofollow">https://git.rz.tu-bs.de/partikeltechnik/</a></p> <p>To run the simulations in this repository, one should first install the pre-requisites i.e., LIGGGHTS, OpenFOAM-6 and cfdemCoupling. The repositiries can be found at Institute for Particle Technology's GitLab (<a href="https://git.rz.tu-bs.de/partikeltechnik/" rel="nofollow">https://git.rz.tu-bs.de/partikeltechnik/</a>) if not, they shall be requested.</p> <p>Running the simulations in the repositories includes, generation of the cases in "Base_Cases_Init", using the "generateCases.py" file (Python3), then run the "variables_Modify.py" file. Running of the "jobfile_Modify.py" and "jrun.py", sequentially, will submit the simulations to a HPC cluster. After the successful run of these simulations, the cases in the folders "Base_Cases_Stable" and "Base_Cases_Stable_Lubrication" can be launched in the same manner as described above, i.e., sequentially running "generateCases.py", "variables_Modify.py", "jobfile_Modify.py" and "jrun.py" (one needs to check if the corresponding restart files are existing in the Base_Cases_Stable*/Base_Case_Stable/Restart folder, which are generated from the "Base_Cases_Init" runs). Following this, the cases in "Base_Cases_Run_800_um", "Base_Cases_Run_1100_um", and "Base_Cases_Run_Lubrication" can be run using the same method as described above (one needs to check if the corresponding restart files are existing in the Base_Cases_Run*/Base_Case_Run/Restart folder, which are generated from the "Base_Cases_Stable" runs). After successfully running of the simulations the python file "generateAndRunPostFiles.py", in each of the corresponding "Base_Cases_Run_800_um", "Base_Cases_Run_1100_um", and "Base_Cases_Run_Lubrication" folders should be run.</p> <p> </p> <p> </p> <p> </p> <p><strong>Description:</strong> This repository provides the simulation cases to generate and run the simulation cases of the "stirred media mill" (MiniCeR). The simulations are setup to couple the CFD and DEM via two-way coupling and the corresponding files in the "Run" folder contain the post-processing scripts to extract the "collision/stress energies" and assemble them into a "collision/stress energy distribution". The simulations are setup in three stages, namely, "Init", "Stable", and "Run". The combinations of operating settings can be easily modified and the respective cases can be generated using the python scripts in the corresponding repositories. The scripts to run the simulations on the HPC-cluster systems are also added.</p> <p><strong><em>a. Init:</em></strong> This stage is to initialize the system with the particles. Three insertion faces are used to generate and insert the required number of particles (calculated according to their size and filling degree) into the system. The "base case" folder contains the necessary DEM scripts of the case setup and the required CAD (geometry) files. The python script "generateCases.py" generates the requested simulation cases according to the specified operating settings. It uses the help of "MakeCases.sh". The "variables_Modify.py" file modifies the variables in the generated folders of the simulation cases to alter the operation setting values. The "jobfile_Modify.py", and the "jrun.py" are used to modify the cluster job files and run the submit the simulation jobs onto the cluster, respectively.</p> <p><strong><em>b. Stable:</em></strong> This is the first stage couples the CFD and DEM. The restart files generated in the "Init" stage are used to start the coupling and run for a specified time. It follows the similar system as init, i.e., to generate the cases and modify the variables, but with additional generation and modifications in the CFD folder i.e., the mesh generation, etc. The simulations are launched in the same way as described above and the corresponding restart files are extracted.</p> <p><strong><em>c. Run:</em></strong> This second stage of the coupling of CFD and DEM launches the srabilized system and extracts the collision energies and stores them in ".txt" files which are postprocessed later to assemble the stress energy distribution. The post-processing to extract the stress energy distribution is done using the "Stress_Energy_Calculation.py" and "generateAndRunPostFiles.py", which generate corresponding folders of post-processing in each of the corresponding case folders.</p>
KMC Models and CFD Simulations for Catalytic CO Oxidation over RuO2
<p>The large tar.gz file (~15GB) has the CFD Data Set for CFD Simulations for Catalytic CO Oxidation over RuO2 associated with the publication: doi: 10.1021/acscatal.8b00713 , https://pubs.acs.org/doi/abs/10.1021/acscatal.8b00713</p> <p>The smaller zip file(s) have the KMC model creation files, which are actually from this publication: https://doi.org/10.1088/1361-648X/aacb6d https://iopscience.iop.org/article/10.1088/1361-648X/aacb6d</p> <p>For the 110 KMC file, it must be compiled using the lat_int backend of kmos. The xml file generated takes less than an hour, though the exporting (compiling) may take 5 to 10 hours. With lateral interactions, the 110 xml file is >25MB. Without lateral interactions, the xml file is very small ( << 1 MB). Setting the "upToDistance" to 0 for each reaction in the python file called CO_O_RuO2_110.py will create a 110 model without lateral interactions (currently the "upToDistance" is set at 1 for each reaction, and thus includes nearest neighbor interactions). How to compile the model requires reading the documentation for the KMC software. The final syntax is " kmos export -b lat_int CO_O_RuO2_110.xml "</p> <p>For the 111 KMC file, it should be compiled with the default ( local_smart ) backend of kmos. The lateral interactions are already included and cannot be easily turned off. The resulting files are small. How to compile the model requires reading the documentation for the KMC software. The final syntax is " kmos export -b local_smart CO_O_RuO2_111.xml "</p> <p>Primitive example KMC runfiles are included, which would need to be run in the compiled KMC model's directories, though current conventions of kmos simulations use more advanced runfiles.</p> <p> </p>
Simulation data and scripts for CFD-DEM simulation of granular flows in ambient fluid
<ul> <li>Data sets - contains the raw data required to replicate and validate all plots in the main article. Contains folders in containing measurements of basic flow properties, e.g. velocities, shear rates, etc., of monodisperse and bidisperse granular flows in different flow regimes. Each folder contains a ReadMe.txt briefly explaining the content and lay out of each data set.</li> <li>Sample case - a .zip file which includes codes which are needed to simulate a CFD-DEM case of a steady granular flow in water with cyclic boundaries in the stream wise direction. Also enclosed is a ReadMe.txt file detailing the implementation instructions for both Esys particle and OpenFOAM codes. Download links for Esys particle and OpenFOAM are also included.</li> <li>Geo file generator - a .zip file which includes Esys particle codes that can be used to generate a .geo file specifying the initial position of the particles used in the test simulations. A ReadMe.txt file is enclosed with more detailed implementation instructions.</li> </ul>
Data from: Study on the optimization of the deposition rate of planetary GaN-MOCVD films based on CFD simulation and the corresponding surface model
Metal-organic chemical vapour deposition (MOCVD) is a key technique for fabricating GaN thin film structures for light-emitting and semiconductor laser diodes. Film uniformity is an important index to measure equipment performance and chip processes. This paper introduces a method to improve the quality of thin films by optimizing the rotation speed of different substrates of a model consisting of a planetary with seven 6-inch wafers for the planetary GaN-MOCVD. A numerical solution to the transient state at low pressure is obtained using computational fluid dynamics. To evaluate the role of the different zone speeds on the growth uniformity, single factor analysis is introduced. The results show that the growth rate and uniformity are strongly related to the rotational speed. Next, a response surface model was constructed by using the variables and the corresponding simulation results. The optimized combination of the matching of different speeds is also proposed as a useful reference for applications in industry, obtained by a response surface model and genetic algorithm with a balance between the growth rate and the growth uniformity. This method can save time, and the optimization can obtain the most uniform and highest thin film quality.
Non-intrusive semi-analytical uncertainty quantification using Bayesian quadrature with application to CFD simulations
<p>The data contained in the uploaded '.zip' file is for some of the plots in the paper ‘Duan Y*, Eaton MD, Bluck MJ, 2021, Non-intrusive semi-analytical uncertainty quantification using Bayesian quadrature with application to CFD simulations, International Journal of Heat and Fluid Flow.’ (accepted)</p>
CFD Simulation Dataset for Airborne Pathogen Transmission in Turbulent Channels
<p>This dataset contains results from computational fluid dynamics (CFD) simulations conducted using <strong>Ansys</strong> to model airborne pathogen transmission in turbulent airflow channels. The simulations focus on the dispersion of pathogen-laden droplets and aerosols, mimicking scenarios such as coughing-induced flows. The data includes the reception statistics of airborne particles, which are used to derive the infection probability. The dataset supports the studygiven below and can be used for statistical characterization and further analysis of airborne pathogen transmission between two people.</p> <p>F. Gulec, F. Dressler and A. W. Eckford, "A Computational Approach for the Characterization of Airborne Pathogen Transmission in Turbulent Molecular Communication Channels," in IEEE Transactions on Molecular, Biological, and Multi-Scale Communications, vol. 9, no. 2, pp. 124-134, June 2023, doi: 10.1109/TMBMC.2023.3273193.</p> <p> </p>
Data from: Study on the optimization of the deposition rate of planetary GaN-MOCVD films based on CFD simulation and the corresponding surface model
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Supplementary material of 3D transient CFD simulation of an in-vessel Loss-Of-Coolant Accident in the EU DEMO fusion reactor
<p>Supplementary material associated to publication "3D transient CFD simulation of an in-vessel Loss-Of-Coolant Accident in the EU DEMO fusion reactor": videos showing time evolution of distribution of</p> <ul> <li>Mach number</li> <li>Temperature</li> <li>Speed</li> </ul> <p>close to the break during an in-vessel LOCA in a helium-cooled EU DEMO.</p>
CFD models for the PROTEUS simulator
<p>This archive file contains the CFD simulation files and corresponding models to use with the PROTEUS simulator. You can download the model of your choice, unzip it, and place it in the "geometry_data" folder of the PROTEUS simulator.</p> <p>You may choose between the standard version, which includes the original .vtu file, or the lighter "_no_vtu" version that only includes the files needed to run the simulator.</p>
The development and validation of an Inhomogeneous Wind Scheme for Urban Street, Part B: perpendicular CFD simulations
<p>The CFD simulation results of perpendicular scenerios.</p>
The development and validation of an Inhomogeneous Wind Scheme for Urban Street, part C: parallel CFD simulations
<p>The CFD simulation results of parallel scenerios.</p>
Modeling the nutritional value of thermally processed food products based on CFD simulations - Results of the Miniatura 6 NCN grant
<p>1. Description of the purpose of the scientific activity: The aim of the scientific activity was to optimize the heat treatment process of food products using Computational Fluid Dynamics (CFD) simulations and to assess the impact of this process on the nutritional value of the finished product. The following beef meat was selected for the study: strip beef, shin, entrecote, neck, leg, topside, topside, shoulder and brisket. The obtained results on the selection of heat treatment parameters, i.e. temperature, time, forced convection, allowed for the creation of a knowledge base on the basis of which a multi-factor optimization was carried out between the basic composition (the amount of: protein, fat, water, connective tissue and ash) and the content of conjugated linoleic acid (CLA) and vitamin E in beef. The aim and scope of the research has been achieved.</p> <p>2. Description of the implementation of the scientific activity: Experimental research was carried out at the Department of Food Technology and Design at the Warsaw University of Life Sciences. The raw material for the tests, i.e. beef meat from elements of different basic composition (protein, fat, connective tissue, ash, water): strip beef, rind entrecote, neck, leg, topside, topside, shoulder, brisket was purchased in three batches . The scope of the scientific activity included three stages:</p> <ul> <li>CFD simulation - computer simulation of heat treatment of beef characterized by a diverse basic composition and determination of the reduction of CLA and vitamin E content in various conditions of the thermal process - building a knowledge base,</li> <li>RSM optimization - using the CFD database of changes in the content of CLA and vitamin E, depending on the parameters of the thermal process, for optimization using the response surface method (RSM) - it allowed the selection of the best thermal treatment parameters ensuring the smallest losses of the above-mentioned bioactive ingredients,</li> <li>verification - laboratory tests were carried out using chromatographic techniques of vitamin E (HPLC - high-performance liquid chromatography) and CLA (GC - gas chromatography) content to confirm the predicted RSM results based on CFD simulation. Information on the mechanism of shaping the content of CLA and vitamin E in heat-treated beef allowed for the creation of a database that makes it possible to determine the optimal conditions for heat treatment of food products. The research was experimental, and the obtained results provided information on the mechanism of thermal decomposition depending on the parameters of the heat treatment process and the basic composition of food products. The scientific activity was used to develop a mechanism describing the relationship between the basic composition, method and parameters of heat treatment and the thermal stability of CLA and vitamin E in beef.</li> </ul> <p>3. Description of the effects of the implementation of the scientific activity:</p> <p>The study showed a significant effect of the basic composition of beef on changes in the content of vitamin E and CLA during heat treatment. The use of an innovative method of modeling and simulation of thermal processes with the use of CFD allowed for a precise analysis of changes in the content of the above-mentioned substances. components in meat samples of different basic composition. The analyzes carried out showed that the content of: protein, fat, tk. total, ash, water in individual elements of the beef carcass, i.e. strip beef, rib eye shank, neck, round, topside, topside, shoulder, brisket affects the temperature distribution in the entire volume of the raw material, which results in partial decomposition of vitamin E and CLA. Based on the developed experimental model, optimization was performed using the response surface method (RSM), thanks to which the best parameters of the heat treatment process using a convection steam oven were obtained. The optimization was carried out individually for each element differing in basic composition, and among the optimized process parameters were: processing temperature [℃], humidity inside the furnace chamber [%], process time [s] and fan speed ensuring forced convection of air and steam [RPM ]. The last, third stage of the research task was the verification of the optimization model with the results of laboratory analyses. The tests carried out using gas chromatography (GC) and high-performance liquid chromatography (HPLC) allowed to obtain the results of vitamin E and CLA content at a level consistent with those predicted on the basis of RSM models.</p>
Database For Finite Volume Features, Global Geometry Representations, and Residual Training for Deep Learning-based CFD Simulation
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