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SUPPLEMENTARY NUMERICAL DATA: Pressure and shear flow singularities: fluid splitting and printing nip hydrodynamics
No full textThis data set contains supplementary numerical data for the following paper:
Pressure and shear flow singularities: fluid splitting and printing nip
hydrodynamics.
Matthias Elia Rieckmann, Pauline Brumm, Hans Martin Sauer, Edgar Doersam,
Florian Kummer.
Physics of Fluids, 2023
Content of this data set:
\- Data tables: 4 data tables in .csv and easy readable .html format
containing numerical results.
\- Plot files: 497 .plt files containing raw vertex data from the numerical
simulations. Plot files and data tables can be cross referenced.
The source code used to run the simulations included in this dataset is open-
source and available under:
[https://github.com/FDYdarmstadt/BoSSS
](https://github.com/FDYdarmstadt/BoSSS)
See 'README.txt' (last item in the dataset) for more details
Overview of the main effects present in a real gas turbine combustor
No full textAn overview of the thermo-physical effects present in a real Rich-Quench-Lean aero-engine gas turbine combustor. The geometry of the combustion chamber prescribes the main features of the fluid flow such as flow separation, flow attachment, vortex shedding or the formation of recirculation zones. The fuel is injected into the combustion chamber in it's liquid form, breakes up and evaporates. The resulting combustion process leads to the formation of different emissions such as CO, NOx or soot. Additionally, thermoacoustic oscillations might accur and affect the systems stability. For a numerical simulation of this setup, all of these effect have to be respected in the modeling approach.
The dataset is only available for members of STFS but is also obtainable from the authors upon reasonable
request
On the rate of convergence of image classifiers based on convolutional neural networks: Implementations of the estimates and links to image data sets
No full textThis repository contains the Python code required to reproduce the simulation part of the paper "On the rate of convergence of image classifiers based on convolutional neural networks" from Kohler, Krzyżak, and Walter (2022) referenced below. The Python version used is Python 3.9.7. This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under project number 449102119. The Cifar-10 image dataset consisting of the real images from which the classes "cars" and "ships" were used can be downloaded from the link given below. In the Techincal Report "Learning Multiple Layers of Features from Tiny Images" from Alex Krizhevsky (2009) (for a link see below) this dataset of real images is described in more detail
Darmstadt stair ambulation dataset including level walking, stair ascent, stair descent and gait transitions at three stair heights
No full textThe Darmstadt stair ambulation dataset was collected to improve the control and assistance of wearable lower limb robotics. It contains the kinematics, kinetics and electromyographic data (EMG) for transitions between level walking and stair ascent, and between stair ascent and level walking. Further, it contains the data for transitions in between level walking and stair descent, and between stair descent and level walking.
Twelve physically unimpaired male subjects with a mean age of 25.4 yrs and a mean weight of 74.6 kg participated in the experiments.
A motion capture system was used to capture the body kinematics. Seven force plates were used in two setups to collect the ground reaction forces of eleven strides for the stair ascent and of eleven strides for the stair descent transitions. The center of pressure, the center of mass, joint angles and angular velocities were determined. Further, inverse dynamics were used to determine the lower limb joint moments and the lower limb joint power. Sixteen EMG sensors were used to collect the muscle activity of twelve muscles. As each EMG sensor also contains an inertial measurement unit (IMU), including 3D-Gyroscope, 3D-Accelerometer and 3D-Magnetometer, they were also used to capture the lower limb kinematics in parallel to the motion capture system. Stair ambulation was performed at three stair slopes. The data is provided at different processing levels including raw to fully processed data. The attached documentation will provide details about the data acquisition, the data processing and the provided data structure and format.Version 1.
Listing 2
No full textWorking example for listing 2 in the article titled "Creating application-specific metadata profiles while improving interoperability and consistency of research data in engineering", consisting of: one turtle file containing the shapes graph (metadata profiles), one turtle file containing the data graph (valid as well as invalid example data), the python file that runs the validation and returns the report, one text file containing the content of the report
Master's Thesis - Investigation of Sports-Specific Problems with Tables using Algorithmic Analyses and Rating Methods
No full textThe data set contains the source code and the measured values of the master's thesis. This deals with the analysis of sport-specific problems, especially the championship problem. There are two projects on this topic. The first implements a set of network algorithms and tests runtime and graph-specific operations. The second project deals with a heuristic for the NP-complete problem. New concepts and ideas are implemented and tested with regard to runtime and iterations. The focus is on reducing the search space through the appropriate application of termination criteria. In addition, the search depths are critically examined and new parameters are introduced. Another sorting tool is used to prepare the original data. The fourth application implements some rating methods and compares the resulting ranking with the real table
Listing 4
No full textWorking example for listing 4 in the article titled "Creating application-specific metadata profiles while improving interoperability and consistency of research data in engineering", consisting of: one turtle file containing the shapes graph (metadata profiles), one turtle file containing the data graph (valid as well as invalid example data), the python file that runs the validation and returns the report, one text file containing the content of the report
Versuchsdaten und Simulationsmodelle für additiv gefertigte Meso-Strukturen
No full text1. Abaqus-Input-Files verwendeter Simulationsmodelle für die Simulation homogener und gradierter additiv gefertigter Meso-Strukturen unter Zug/Druck und Schub
2. Versuchsrohdaten einachsiger Druckversuche homogener additiv gefertigter Meso-Strukturen mit BCC-Topologie
Benennung: Kantenlänge_relativeDichte_Probennummer26.06.202
Neuron Yield Spectra simulated with Monte Carlo code
No full textMonte Carlo Data created by PHITS 3.28A.
Simulations were used to investigate large parameterspaces relevant for subspallation neutron production.
The data was used in a PhD Thesis and a corresponding article.