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exaFOAM Grand Challenge GC2 - DLR Confined Jet High Pressure Combustor
No full textThis work is part of the exaFOAM project that aims to enable the open-source CFD software OpenFOAM to exploit massively parallel HPC architectures and overcome performance scaling bottlenecks. The case was designed to push available HPC systems to their limits and showcase the performance gains achieved over the duration of the project. The considered combustor stems from an experimental setup constructed by Severin (2019) at the Institute of Combustion Technology of the German Aerospace Center. The burner is based on the Recirculation-Stabilized Jet Flame (RSJF) concept, which is better known as FLOX. Initially developed for industrial furnaces, this technology has great potential in the application to gas turbines. Compared to widespread swirl burners, RSJF combustors feature low NOx emissions, homogeneous temperature distribution, and operate with a wide range of fuels and loads. The main goal of the experimental work was to clarify how the recirculation contributes to the flow stabilization. Apart from the experiments, a number of numerical investigations with the same geometry was performed, e.g. by Ax et. al. (2020) and Gruhlke et. al. (2020). Detailed information and case setup can be found here: https://archive.softwareheritage.org/swh:1:dir:12277909317dd5c2c8a61c9b6807fd9b06b9b4cd or in the README.md file contained in the case setup file GC2_case_setup.tar.gz
D1244 sensor data (September 2023)
No full text
General information:
This dataset contains measurements from the adaptive high-rise demonstrator building D1244, built in the scope of the CRC1244. This 36m high building is equipped with 24 hydraulic actuators providing the basis for its structural adaptation. Strain gauges, pressure sensors and position encoders are mounted throughout the building and used for state estimation and monitoring.
Structure of the dataset:
Each zip-file contains measurements of one day in the hdf5 format.
The hdf5-files in each zip-file contain an array of 244 signals sampled over 10^6 time steps at approximately 200Hz.
labels.csv contains auxiliary information on all measured signals, including the sensor type and the sensor's location in the building
File contents:
Each hdf5-file contains signals of the following types, arranged as stated in labels.csv:
strain: strain (in mm/m) in columns and diagonal bracing elements, measured by strain gauges.
pressure: pressure (in bar) in the piston side or the rod side chamber of a hydraulic actuator. For actuators in the diagonal bracing, the rod side chamber is permanently connected to the tank.
posenc: displacement (in meters) of each actuator, measured by a position encoder.
optic: optically measured displacement (in meters) of emitters attached to the building's facade.
The building consists of four modules spanning three stories each, and all sensors within a module are connected to a control cabinet, from which all measurements are transmitted. The cameras of the optical measurement system are placed outside the building and transmit their data separately from the sensors within the building. Therefore, there are an additional two signals per module (or camera):
timestamp: unix timestamp (seconds since 1st January 1970) of the control cabinet
numvars: number of measured variables
Missing measurements are marked as NaN. The optical measurement system is currently undergoing maintenance, which is why the corresponding signals are all NaN.
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exaFOAM Industrial Benchmark B7 - Aeroacoustics DrivAer derivative
No full textThis case has been developed as is part of the exaFOAM project that aims to enable the open-source CFD software OpenFOAM to exploit massively parallel HPC architectures and overcome performance scaling bottlenecks. This industrial test case is of direct interest to Stakeholders GM and Audi (VW Group). It comprises the now ubiquitous DrivAer geometries which combine the front and rear portions of production vehicles. This derivative geometry is setup to be compatible with the requirements of aeroacoustics simulation in OpenFOAM. The geometry comprises of static wheels, suspension and brake assembly, detailed underbody, and simplified under-hood. Detailed information and case setup can be found in the README-B7.pdf file or the README.md file contained in the case setup file
Supporting Information: Chemical Potential Differences in Nanoscopic Teflon/Kapton Capillaries
No full textThis is the repository holding the supporting information for atomistic Molecular Dynamics Simulations of Teflon/Kapton capillaries. Here we list the simulation input scripts as well as analysis scripts. See the README file for more information
exaFOAM Microbenchmark MB19 - Viscoelastic Profile Extrusion
No full textThis work is part of the exaFOAM project, aimed at optimizing the open-source CFD software OpenFOAM for massively parallel HPC architectures and overcoming performance bottlenecks. The viscoelastic profile extrusion case was designed to evaluate the viscoelasticFluidFoam solver's numerical implementation and computational performance in a simplified profile extrusion problem. Due to the simplified geometry employed, the meshes required to obtain accurate results will be much smaller than those of the associated industrial scale case study (B4), allowing to perform initial exploratory studies. The problem is modelled considering the isothermal and incompressible 3D flow of a Giesekus viscoelastic fluid. Detailed information and case setup can be found in the README.md file contained in the case setup file
exaFOAM Industrial Benchmark B6 - Offshore Wind Park
No full textThis work is part of the exaFOAM project, aimed at optimizing the open-source CFD software OpenFOAM for massively parallel HPC architectures and overcoming performance bottlenecks. In the analysis of wind parks, the flow around the wind turbines is dominated by small-scale fluid dynamics (< 2 km), which in turn depends on mesoscale atmosphere dynamics (< 1000 km). The successful coupling of these two scales is essential to study wind resources, along with their uncertainty and addressing the interaction of clustered wind farms. These are critical factors for the sustainable development of wind energy. OpenFOAM has built-in models for the simulation of wind turbines and the atmospheric boundary layer, and this test case uses a modified version of the turbinesFoam actuator line model (ALM) external library. These models are used to simulate the flow over a wind park, with a focus on the power production in a challenging configuration, where the wind flows in the direction of the row of turbines. This test case represents the offshore wind farm Westermost Rough, located off the UK coast and consisting of 35 Siemens 6 MW turbines with 154 m rotor diameter and a hub height of 102 m above sea level. Detailed information and case setup can be found in the README.md file contained in the case setup file
COLife_03 - Gigamap and DIY
No full textThe dataset presents the more-than-human intervention, its codesigned gigamap that leads to it and DIY recipes for its reproduction. The dataset was achieved during the winter semester 2023/24. The gigamap was in feedback loops codesigned digitally in the Miro platform and analogue on the printouts. The DIY recipe is foldable to the user's pocket as well as unfoldable for a better overview
HPLC raw data related to Koeppl et al.: Structure, function and substrate preferences of archaeal S-adenosyl-L-homocysteine hydrolases
No full textIn this study, synthesis and cleavage reactions catalysed by S-adenosyl-L-homocysteine hydrolases originating from different domains of life were investigated. The reactions were studied using S-adenosyl-L-homocysteine or S-inosyl-L-homocysteine as substrate for the cleavage reaction, and L-homocysteine together with inosine or adenosine as substrates for the synthesis reaction.
The data contains the underlying HPLC chromatograms shown in Figures 3b and 3c as well as the Supplementary Figures S4-S24.
All chromatograms are stored within the .xlsx file. The retention times (in min) are given in column A and the absorption values (in mAU) are given in column B. Additionally, the csv folder contains all data sheets of the excel file as individual .csv files
Replication Code for: Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference
No full textThis code allows to replicate key experiments from our paper: Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference.
For further details, please refer to the README.md.</p
Replication Data for: Generating Minimal Training Sets for Machine Learned Potentials
No full textData and scripts for replicating results and the investigation presented in the paper. This includes the dft parameters for generating training data, all training and data selection scripts for the neural networks, scripts for running and analysing the production simulations with the trained potentials