701 to 710 of 2,511 Results
Jul 17, 2025 - 2024_DFG_ZRA_Gleichlauf_verspannt
Steinle, Lukas, 2025, "Replication Data for: Compensation of the Transmission Errors in Electrically Preloaded Rack-and-Pinion Drives", https://doi.org/10.18419/DARUS-5160, DaRUS, V1, UNF:6:kkKMvMslLh6brhrMzSsgRw== [fileUNF]
This dataset contains all experimental data that is shown within the paper "Compensation of the Transmission Errors in Electrically Preloaded Rack-and-Pinion Drives". Large machine tools with long feed axes commonly utilize electrically preloaded rack-and-pinion drives, where two redundant drives on one axis are preloaded against each other by a de... |
Jul 17, 2025 - EXC IntCDC Research Project 3 'Co-Design of Multi-Storey Timber Building Systems for Building Stock Extension'
Müller, Theresa; Wagner, Hans Jakob; Menges, Achim; Leistner, Philip, 2025, "Replication Data for: Concept and Development of a Novel Timber Spring for Impact Sound Reduction in Timber Floor Slabs", https://doi.org/10.18419/DARUS-5110, DaRUS, V1
This repository contains the experimental data and documentation from the acoustic investigation presented in the related publication. The dataset includes small- and large-scale experimental measurements used to determine the structrual velocity characteristics and to calculate the sound reduction index (R) values and the normalized impact sound p... |
Jul 17, 2025 - Bioprocess
Seeger, Jan; Müller, Susanne; Gómez-Álvarez, Helena; Rashid, Goran M.M.; Bugg, Timothy; Díaz, Eduardo; Takors, Ralf, 2025, "Data for: Scaling-up the Bioconversion of Lignin to 2,4-Pyridinedicarboxylic Acid With Engineered Pseudomonas putida for Bio-Based Plastics Production", https://doi.org/10.18419/DARUS-4525, DaRUS, V1
Raw experimental data for the research article: "Scaling-up the Bioconversion of Lignin to 2,4-Pyridinedicarboxylic Acid With Engineered Pseudomonas putida for Bio-Based Plastics Production" The methodology and materials used to generate this data are described in the associated research article. The here presented data follow the structure of that... |
Jul 16, 2025 - A02: Advanced modelling concepts for coupling free flow with porous-media flow
Veyskarami, Maziar, 2025, "DuMuX code for: "Self-pumping transpiration cooling: A joint experimental and numerical study"", https://doi.org/10.18419/DARUS-5071, DaRUS, V1
This dataset contains the source code used for the work "Härter, J., Veyskarami, M., Schneider, M. et al. Self-Pumping Transpiration Cooling: A Joint Experimental and Numerical Study. Transp Porous Med 152, 56 (2025). (https://doi.org/10.1007/s11242-025-02198-w)" |
Jul 15, 2025 - Usability and Sustainability of Simulation Software
Neubauer, Felix, 2025, "Replication Data for: AI-assisted JSON Schema Creation and Mapping", https://doi.org/10.18419/DARUS-5157, DaRUS, V1, UNF:6:cu/nriFjeIsGtYAoB7K7fw== [fileUNF]
The steps and sources of the application example from the paper + the JSONata instructions for the LLM. Recommended: download all files and open the README.md with a markdown editor/viewer. The README.md document shows the steps performed in the application example and also corresponding screenshots and input and output documents. The screenshots a... |
Jul 9, 2025 - 2022_DFG_IKEPa
Reichenbach, Thomas; Clar, Johannes; Pott, Andreas; Verl, Alexander, 2025, "Replication Data for: Experimental Investigation on Adaptive Preload Control for Cable-Driven Parallel Robots", https://doi.org/10.18419/DARUS-4614, DaRUS, V1, UNF:6:yIeeojFX6ZB47EKsVBPaaA== [fileUNF]
Measurement data and analysis scripts to investigate a novel adaptive preload control (APC) for cable-driven parallel robots. The measurement data is provided as csv files and consists of data from two devices: the robot control system and the coordinate measuring system (laser tracker). The data from the robot control system is stored in the "expe... |
Jul 4, 2025 - Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport
Pelzer, Julia, 2024, "Datasets: 100 Heat Pumps + Synthetic Permeability Fields, Simulation - Raw, 3 + 1 Data Points", https://doi.org/10.18419/DARUS-4156, DaRUS, V2
This data set serves as training and testing data for modelling the temperature field emanating from open loop groundwater heat pumps (100, randomly placed). It is simulated with Pflotran and saved in h5 format. It contains 3 + 1 data points, each consisting of one simulation run until a quasi-steady state is reached. Each data point measures 12.8... |
Jul 4, 2025 - Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport
Pelzer, Julia, 2025, "Datasets: 100 Heat Pumps + Real Permeability Fields, Simulation - Raw, 4 + 1 Data Points", https://doi.org/10.18419/DARUS-5065, DaRUS, V1
This data set serves as training and testing data for modelling the temperature field emanating from open loop groundwater heat pumps (100, randomly placed). It is simulated with Pflotran and saved in h5 format. It contains 4 + 1 data points, each consisting of one simulation run until a quasi-steady state is reached. Each data point measures 12.8... |
Jul 4, 2025 - Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport
Pelzer, Julia; Verburg, Corné, 2025, "Trained Vanilla Models on Synthetic Permeability Fields, 101 Data Points", https://doi.org/10.18419/DARUS-5081, DaRUS, V1
Models are trained with [git: DDUNet] on 101 data points (dp). Both, vanilla UNet and DDU-Net, can be applied directly end-to-end. For inference follow the guidelines of Heat Plume Prediction to prepare raw data, then apply the models as described in [git: DDUNet]. Based on raw data from https://doi.org/10.18419/darus-5064. |
Jul 4, 2025 - Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport
Pelzer, Julia, 2025, "Trained Models on Real Permeability Fields, 4+1 Data Points", https://doi.org/10.18419/DARUS-5082, DaRUS, V1
Models are trained with Heat Plume Prediction on 4 data points (dp). Steps 1 and 3 of LGCNN (Local Global Convolutional Neural Network) are separate, step 2 is a numerical solver that does not require any trained model. For inference follow the guidelines of Heat Plume Prediction and applied all 3 steps/models sequentially to your input data. Based... |
