Research Data

Point cloud file (e57) of the main campus of Hamburg University of Technology containing intensity and color information for 125 separate scans.

Videos illustrating 3-D Reciprocal Space Mapping Using Synchrotron-Based X-ray diffraction. Electronic Support Information of the article "Self-Assembly of Liquid Crystals in Nanoporous Solids for Adaptive Photonic Metamaterials" Nanoscale DOI:10.1039/C9NR07143A (2019)

The 4 Step Creation Method is a method for systematic new development. It promotes creativity in interdisciplinary teams and provides a framework for cooperation so that original innovations can emerge. At the same time, the method can be used for the continuous improvement of existing systems.

Nanoporous metals with their complex microstructure represent an ideal candidate for method developments that combine physics, data and machine learning. They allow to tune the solid fraction, ligament size and connectivity density within a large range. These microstructural parameters have a large impact on the macroscopic mechanical properties. This makes this class of materials an ideal science case for the development of strategies for dimensionality reduction, supporting the analysis and visualization of the underlying structure-property relationships. Efficient finite element beam modeling techniques are used to generate ~200 data sets for macroscopic compression and nanoindentation of open pore nanofoams. A data base is provided that uses consistent settings of structural and mechanical properties on the microscale for which the elastic-plastic macroscopic compression behavior and the hardness is predicted. Ligament geometries of two different initial solid fractions are chosen, for which the structural randomization, the connectivity density, the yield stress and the work hardening rate are randomly varied in large ranges. This data base allows deriving the microstructure-properties relationships of nanoporous metals by means of dimensionality reduction, data mining and machine learning.

This is the Source Code of the ML Algorithm that was trained with the corresponding data set to predict the optimal design of the infill structure based on a given set of mechanical requirements

This animation displays a leader always located at formation center and follows a desired trajectory (solid cyan line), and followers track it with the required formations. Because the robots are moving, a consensus on orientation is achieved. It also shows the robots' orientation during the journey, and at every formation reference change, the orientations converge.

Simulation code and supplementary material for the article "Finite Sections of Periodic Schrödinger Operators".

Anhang zum Beitrag in Kreutz, S. und Stokman, A. (Hrsg.) (2024): Transformation urbaner linearer Infrastrukturlandschaften - Wie Straßen und Gewässer zu attraktiven und klimaangepassten Stadträumen werden können. oekom Verlag. Die vorliegenden Tabellen stammen aus dem Sammelbandartikel: Sozial, ökologisch oder technisch-verkehrlich? – Straßenkonzepte als Ansatzpunkt für multifunktionale Straßenräume, der im Sammelband: Transformation urbaner linearer Infrastrukturlandschaften - Wie Straßen und Gewässer zu attraktiven und klimaangepassten Stadträumen werden können, erschienen ist. Die Tabelle in Anhang 1 gibt eine Übersicht der im Rahmen des Artikels durchgeführten Literatur-Review zu Straßenkonzepten und gibt Auskunft über die Autoren, das Publikationsjahr und den geographischen Bezug. Neben der Überblicksfunktion der verwendeten Literatur können zudem die zeitlichen Entwicklungs- und Anwendungshorizonte, als auch die geographische Verbreitung von Straßenkonzepten verdeutlicht werden. Anhang 2 gibt einen Überblick der im Rahmen des Literatur-Reviews herausgearbeiteten Maßnahmen und deren Ziele bzw. Eigenschaften. Dabei werden die jeweiligen Maßnahmen übergeordneten Zielkategorien zugeordnet, die induktiv aus den herausgearbeiteten Maßnahmen abgeleitet werden und auf eine Vielzahl an fachdisziplinären Perspektiven zurückzuführen ist.

This dataset highlights a specialized application of automation and optimization of infill structures using the Synera platform. By developing a Synera code to automatically apply infill structures to any model, the design process becomes significantly more efficient, allowing engineers to expedite the transition from concept to production. The practical relevance of this technology is demonstrated in its application to injection molds for bipolar plates within the Metal Injection Molding (MIM) process. Bipolar plates are critical components in fuel cells that require precisely manufactured parts to ensure structural integrity and optimal material usage. Furthermore, the use of Material Extrusion of Metals (MEX/M) processes emphasizes the interdisciplinary integration of additive and traditional manufacturing technologies. Insights from automated infill generation can thus be directly applied to the physical production of prototypes or end-use parts, showcasing the potential of Synera in design automation and the synergy between digital design and conventional manufacturing methods.

The data uploaded is the source code for the automation system of a H2O2-stat mode (electrochemical generation of H2O2). The code was writen in and for NI LabVIEW software (2021 SP1 Lab VIEW Professional Development System National Instruments, Version 21.0.1f1, 32-bit Windows) . This code enables a controllable electrochemical generation of H2O2 and able to maintain a constant H2O2 concetration inside the medium. Important to note, this code is specifically written to be used in LabVIEW 2021 SP1 software and combined with DULCOTEST PEROX H3 E H2O2 sensor (ProMinent, Heidelberg, Germany), a DULCOMETER dialog DACb H2O2 sensor module (ProMinent, Heidelberg, Germany) and Keithley 2231a-30–3 DC (Tektronix, Oregon, USA) power supply. 1.) 2231A-001 USB driver.exe : is an add-ons/ tool kit that allows Keithley 2231a-30–3 DC power supply to connect and communicate with the interface of NI LabVIEW. 2.) H2O2.vi: is a source code that allows the value measured by the on-line H2O2 sensor (DULCOTEST PEROX H3 E H2O2 sensor and DULCOMETER dialog DACb H2O2 sensor module) to be registered and saved by the NI LabVIEW. 3.) Giovanni H2O2.vi: is a source code that controls the output (current or potential) of Keithley 2231a-30–3 DC power supply to the elctrodes based on the set value and the measured value of H2O2 concentration. In order to use them, put all aforementiones files in a single folder so that the file "Giovanni H2O2.vi" could still recognize and extracts data from the file "H2O2.vi". Important to note that these codes were written specifically for the aforementioned hardwares.

Laser powder bed fusion (PBF-LB/M) of metals belongs to the advanced additive manufacturing processes on the brink of industrialization. Successful manufacturing often requires the utilization of support structures to support overhangs, dissipate heat, and prevent distortion due to residual stresses. Since the support structures result in increased costs, research, as well as industry, aim at optimizing the application of those or the support structures themselves. New approaches are validated with individual use cases, though, preventing an objective comparison of optimization strategies. This paper contributes to the advance of support structure optimization by providing a benchmark strategy including part geometries, which enables to evaluate technical as well as economical aspects of support structures or support strategies. The benchmark process is demonstrated with the help of the currently most used block and pin support structures.

The available data include the raw data and calculations on chemical analyses of various urban and rural green wastes. The wastes were selected in the context of a biorefinery concept and characterised within the FLEXIBI project. The data can be used to evaluate potentials for different recovery pathways. One pathway included in the evaluation is anaerobic digestion. The description of the methods, calculations and results is included in the PDF file.

Experimental data corresponding to the manuscript "Chemical Interface Damping by Electrochemical Gold Oxidation". The dataset contains ellipsometric measurements and fits together with electrochemical potential and current curves that indicate the chemical itnerface damping effect originating from electrochemical oxidation of gold samples.

The CATS is a standardized instrument in form of a concept inventory to measure student conceptual understanding of statics. It was developed by Paul Steif and is the focus of investigation as well as the applied measurement tool in the dissertation "Assessing the Effectiveness of Research-Based Active Learning Materials for Introductory Engineering Mechanics" by Julie Direnga. To prevent possible misuse of the instrument, the developer has granted online publication only under the condition of controlled access. Therefore, the file is password protected. Please contact Julie Direnga (jdirenga@posteo.de) or Christian Kautz (kautz@tuhh.de) for access.

The following table originated in the context of the LILAS-project (10/2020-12/2023), which has been founded by the Landesforschungsförderung Hamburg, Germany. The table provides an overview of the measures identified in a literature review of street concepts and their objectives and characteristics. The respective measures are assigned to superordinate target categories, which were derived inductively from the measures identified and can be attributed to a variety of disciplinary perspectives.

A cost tool was created from the mathematical equations and relationships, which determines the costs for the additive manufacturing processes depending on the number and dimensions of the component. Furthermore, weight reductions are specified in percent for known conventional costs that are required to achieve cost parity to the conventional manufacturing method. Therfore, this application allow an easy and fast estimation of suitable additive manufacturing technologies denpending on the selected material, dimension and quantity of a component. With the knowledge of the manufacturing restriction of the each technology this tool deliver a great estimation in finding the right application. It is also possible to adjust the costs and add different machines to this tool, which enables the comparison between technologies.

This artefact provides data and a detailed description of a wave flume experiment with and without an artifical Gaussian-shaped bathymetry. Provided a wave height measurements at four sensor points over time. Included in this data set are (1) the data itself in TXT format, (2) a video showing the waves in the flume in MP4 format and (3) a description of the experimental setup in PDF format.

Simulated and approximated values for the article "Analysis of semi-open queueing networks using lost customers approximation with an application to robotic mobile fulfilment systems".

This dataset was used to characterize devolatilized wood pellets for fluidized bed applications. The results and the details about the methodology are presented in "Jarolin, K.; Wang, S.; Dymala, T.; Song, T.; Heinrich, S.; Shen, L.; Dosta, M. (2021): Characterizing devolatilized wood pellets for fluidized bed applications. In: Biomass Conversion and Biorefinery." In this work, three types of wood pellets, called Type A (white sawdust pellet from spruce wood), Type B (brown sawdust pellet from foliage and coniferous wood), and Type C (brown sawdust pellet from mixed, unspecified wood types) were characterized after devolatilization. Three different methods for devolatilization were applied: 1. Muffle Furnace (MF) at 900°C 2. Fluidized Bed Reactor (FBR) at 900°C 3. empty fluidized bed reactor with high gas flow rate (HFR) at 900°C 4. empty fluidized bed reactor with low gas flow rate (LFR) at 900°C The raw data from the three main methods for characterization are provided in this repository: 1. compressionTest: Formatted force-displacement measurements of quasi-steady, uniaxial compression tests in the radial direction. The data was used to study breakage behavior. 2. impactTest: Mass loss of the pellets due to impact on a steel plate at different velocities. The mass loss is given after 10 consecutive impacts. In case of breakage, the masses of the fragments are given. The data was used to study resistance to impact and the continuous wear of the pellets. 3. uCT: Micro-computed tomography images from the same pellets before and after devolatilization as well as images of devolatilized pellet for reference. One of the pellets' end-faces was ground into an angle to allow the identification of the orientation. The data was used to study the change in the porosity of the pellets. Please refer to the linked publication for further details.

This is the readme file for all data used within the publication "Exploring key ionic interactions for magnesium degradation in simulated body fluid - a data-driven approach" Created: 25th January 2020 by Dr. Berit Zeller-Plumhoff Contact: berit.zeller-plumhoff@hzg.de The publication is based on a number of experimental and computational data sets. These are: - microCT imaging data (raw and processed) before and after degradation - Fiji/ImageJ scripts for automated processing and read-out of the imaging data - SEM+EDX imaging data (raw and processed) - Matlab scripts for processing of the EDX data - Hydra/Medusa output files for precipitation prediction - Jupyter Notebooks for the final analysis and plotting of the processed data Due to the large amount of imaging data (~70GB per microCT dataset, raw+processed), we are publishing only the processed data of two exemplary data sets - one prior to degradation and one after degradation of the sample. All other data is stored and will be provided upon request. Where a differentiation of datasets into 'C' and 'D' is denoted, this corresponds to the ph-adjusted and non-adjusted cases, respectively, as described in the publication. Please find below a short description of all datasets that are made available. For better handling the data was divided into nine .zip files. The folder "microCT_sol1_sample1_degraded" contains the exemplary microCT data for the first sample degraded in the pH-adjusted solution 1, following data processing after reconstruction. All image files are in .tif format, with a 1.6 micrometer isotropic voxel size. The folder "cropped" contains the manually cropped filtered images of the 0.7 mm ROI (from the sample centre) after "script filtering.ijm". Using the trainable WEKA segmentation with the "classifier.model" file images were segmented and saved in "seg_weka" by utilising the script "Segmenting_with_weka.ijm". Some files had to be manually corrected - these were saved in "seg_weka_edited". Finally, the images were aligned along their longitudinal axis using the script "BW_Conversion_Alignment.ijm" and saved in the folder "aligned". Folders "microCT_sol1_sample1_degraded_raw" and "microCT_sol1_sample1_degraded_reco" are the raw projection data and tomogaphic reconstruction of this dataset. The folder structure for the initial scan of sample 1 (sample_C1_initial) is simimilar; however, it contains only a general segmented folder "segmented_all" with the filtered, segmented and aligned images and their respective outline (subfolder "outline") for the calculation of the initial sample surface area. The image processing was performed using the "filtering.ijm" script. Again, folders "sample_C1_initial_raw" and "sample_C1_initial_reco" are the raw projection data and tomogaphic reconstruction of this dataset. The Fiji scripts are saved in the folder "Fiji_Scripts", divided into those used for before and after degradation scans. Note that all file paths require changing for the scripts to run.The scripts "Data_Gathering_Initialscan_BZP_C_D.ijm" and was used to gather outline data from the entire sample, outline data from the ROI, and volume data from the ROI. The file "edx_analysis.m" is the Matlab file that was used to analyse all EDX data for further processing. The variable f was saved as "EDX_results.mat". The paths are given relative to the .m file and need to be adjusted if this is moved. The file accesses the "EDX_data" folder. This folder contains the "SEMEDX" folder in which the elemental wt% maps from EDX measurements for each sample are stored as .txt files. The folder "Segmented" then contains the respective segmented residual magnesium images and the segmented degradation layer images that the Matlab file requires for its computation. The folder "Data_Mg_Ion contains" all processed data that is then read and further processed by the respective Jupyter notebooks. The subfolders "Initial_data" and "Final_data" contain the histograms as calculated by Fiji/ImageJ for the volume (V) and area (A) assessment of the initial and degraded wires. The data structure is: # Final V data: /Final_data/histo_1.1C # Initial V data: /Initial_data/initial_histo_1.1C # Initial A data (whole sample, not 0.7mm ROI): /Initial_data/initial_outline_1.1C # Initial A data (0.7mm ROI): /Initial_data/initial_outline_ROI_1.1C The subfolder "Hydra_Medusa" contains the Mg and Ca precipitates as computed by Hydra Medusa for each solution in .csv format. The files "EperimentalData.csv" and "ph_per_sample.csv" contain the pH and temperature measurements for each sample/solution in averaged or full format. The file "EDX_results.mat" contains the mean elemental wt% for each element and its distribution along the degradation layer as computed in Matlab. The file "ion_concentrations.txt" contains the theoretical initial concentration for all ions. The precipitation-dependent corrections are given in "corr_conc.pkl" (which is calculated in the file "precipitation_graphs_publication.ipynb"). "statistics_lincorr_publication.ipynb" - this Jupyter notebook contains the results for the Student's t-test and the linear correlation of parameters "precipitation_graphs_publication.ipynb" - this Jupyter notebook creates the precipitation graphs used in the publication "graphs_publication.ipynb" - this Jupyter notebook creates all other graphs "tree_regression_publication.ipynb" - this Jupyter notebook contains the tree regression analysis using different input parameter sets and different regression models All figures generated by the Jupyter notebooks are saved in the "figures" subfolder. Please note that for all Jupyter notebooks to work you may need to install missing modules.