Helmholtz Institute Freiberg for Resource Technology
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Data publication: Solubility, aqueous speciation and sorption properties of Be(II) in sedimentary rock formations
No full textThe data results from the measurement of the irradiated target material using gamma spectrometry
DistributionModelsPHT: Julia package for distribution models for statistics of random cells of Poisson hyperplane tessellations
No full textDistributionModelsPHT is a Julia package that provides an implementation of distribution models for statistics of random polytopal cells that occur in connection with a splitting/fracturing of the plane or space via Poisson line tessellations or Poisson plane tessellations
Multiphase Cases Repository by HZDR for OpenFOAM Foundation Software
No full textThis repository contains simulation setups for the Multiphase Code Repository by HZDR for OpenFOAM Foundation Software. The simulation setups are separated into mono- and polydisperse bubbly flows utilising the Baseline model by HZDR set, setups for a morphology-adaptive multifield two-fluid model (disperse and resolved interfaces) and miscellaneous cases.Acknowledgement: OpenFOAM(R) is a registered trade mark of OpenCFD Limited, producer and distributor of the OpenFOAM(R) software via www.openfoam.com. The Multiphase Cases Repository by HZDR for OpenFOAM Foundation Software is not compatible with the software released by OpenCFD Limited, but is based on the software released by the OpenFOAM Foundation via www.openfoam.or
Hydrodynamics in a bubble column – Part 2: Three-phase flow
No full textMultiphase computational fluid dynamics (CFD) simulation is a useful tool to study the hydrodynamics in a bubble column, if appropriate closure models are known. Systematic assessment of different models is an ongoing venture that benefits from improved validation data. The present study accumulates a database on three-phase flow experiments in a bubble column. This is achieved by using a combination of Particle Image Velocimetry and Shadowgraphy to measure the liquid velocity, solid velocity, solid concentration and gas dispersion properties simultaneously. This methodology is applied for different needle diameters, gas flow rates and particle concentrations.
A detailed description of the experimental setup can be found in XXX.
The experimental data (Table 1) described in this repository is structured into different folders and files as follows:
Level 1: Folders classified by measurement configuration: TW_Jg_X_Di_YYY_C_ZZZ as outlined in Table 1
TW = Identifier
Jg_X = Superficial gas velocity in mm/s
Di_YYY = Inner diameter of the needle in µm
C_ZZZ = Particle concentration * 100 in %
Level 2: Folders classified by measurement height: Z_XXX
Z_XXX = Measurement height in mm
Level 3: csv files classified by their analysis parameter:
Gas_Eg_ub_over_x.csv: Each csv file consists of five columns, namely the x-coordinate (in m), the gas holdup, the uncertainty of the gas holdup, the averaged bubble rising velocity (in m/s) and the corresponding uncertainty (in m/s).
Liquid_v_z_over_x.csv: Each csv file consists of three columns, namely the x-coordinate (in m), the averaged liquid velocity (in m/s) and the corresponding uncertainty (in m/s).
Solid_alpha_over_z.csv: Each csv file consists of three columns, namely the z-coordinate (in m), the averaged solid fraction and the corresponding uncertainty .
Solid_v_z_over_x.csv: Each csv file consists of three columns, namely the x-coordinate (in m), the averaged solid velocity (in m/s) and the corresponding uncertainty (in m/s).
Table 1: Overview of the measurement cases in this repository.
| ID | Needle diameter [µm] | Superficial gas velocity [mm/s] | Particle concentration [vol%] |
|-----|----------------------|---------------------------------|-------------------------------|
| L1 | 200 | 2 | 0.05 |
| L2 | 600 | 2 | 0.05 |
| L3 | 200 | 2 | 0.1 |
| L4 | 600 | 2 | 0.1 |
| L5 | 200 | 2 | 0.15 |
| L6 | 600 | 2 | 0.15 |
| L7 | 200 | 4 | 0.05 |
| L8 | 600 | 4 | 0.05 |
| L9 | 200 | 4 | 0.1 |
| L10 | 600 | 4 | 0.1 |
| L11 | 200 | 4 | 0.15 |
| L12 | 600 | 4 | 0.15 |
| L13 | 200 | 6 | 0.05 |
| L14 | 600 | 6 | 0.05 |
| L15 | 200 | 6 | 0.1 |
| L16 | 600 | 6 | 0.1 |
| L17 | 200 | 6 | 0.15 |
| L18 | 600 | 6 | 0.15 |This project has received funding from the European Union's Horizon 2020 Marie Skłodowska-Curie Actions (MSCA), Innovative Training Networks (ITN), H2020-MSCA-ITN-2020 under grant agreement No. 955805, and the European Institute of Innovation and Technology (EIT). This body of the European Union receives support from the European Union's Horizon 2020 research and innovation programme
Multiphase Code Repository by HZDR for OpenFOAM Foundation Software
No full textThe Multiphase Code Repository by HZDR for OpenFOAM Foundation Software is a software publication released by Helmholtz-Zentrum Dresden-Rossendorf according to the FAIR principles (Findability, Accessibility, Interoperability, and Reuseability). It contains experimental research work for the open-source software released by The OpenFOAM Foundation. The developments are dedicated to the numerical simulation of multiphase flows, in particular to the multi-field two-fluid model (Euler-Euler method).Acknowledgement: OpenFOAM(R) is a registered trade mark of OpenCFD Limited, producer and distributor of the OpenFOAM(R) software via www.openfoam.com. The Multiphase Code Repository by HZDR for OpenFOAM Foundation Software is not compatible with the software released by OpenCFD Limited, but is based on the software released by the OpenFOAM Foundation via www.openfoam.orgHighlights of the Multiphase Code Repository by HZDRHZDR Baseline Model: addonMultiphaseEuler solver with full support of the HZDR baseline model set for polydisperse bubbly flows, including configuration files and tutorials for simplified setup of Baseline cases (Hänsch et al., 2021).Morphology-adaptive Multifield Two-fluid Model (MultiMorph): cipsaMultiphaseEuler solver featuring a morphology-adaptive modelling approach (dispersed and resolved interfaces, Meller et al., 2021) with an interface to the multiphaseEuler framework to utilise all available interfacial models, and configuration files and tutorials for easy setup of cases with the MultiMorph Model.more ..
Data publication: A deep-learning-based surrogate model for Monte-Carlo simulations of the linear energy transfer in primary brain tumor patients treated with proton-beam radiotherapy
No full textThis repository contains the outputs and result data of our deep-learning-based experiments for the approximation of Monte-Carlo-simulated linear energy transfer distributions, which build the foundation for the corresponding article.
The Pytorch checkpoint of our finally chosen SegResNet architecture trained on the UPTD dose distributions is located at dd_pbs/Dose-LETd/clip_let_below_0.04/segresnet/all_trainvalid_data/training/lightning_logs/version_6358843/checkpoints/last.ckpt.
Moreover, we provide an exemplary data sample from a water phantom for trying our analysis pipeline
Data publication: A deep-learning-based surrogate model for Monte-Carlo simulations of the linear energy transfer in primary brain tumor patients treated with proton-beam radiotherapy
No full textThis repository contains the outputs and result data of our deep-learning-based experiments for the approximation of Monte-Carlo-simulated linear energy transfer distributions, which build the foundation for the corresponding article.
The Pytorch checkpoint of our finally chosen SegResNet architecture trained on the UPTD dose distributions is located at dd_pbs/Dose-LETd/clip_let_below_0.04/segresnet/all_trainvalid_data/training/lightning_logs/version_6358843/checkpoints/last.ckpt.
Moreover, we provide an exemplary data sample from a water phantom for trying our analysis pipeline.
Update:
In this new version we added results of the gamma analyses and the results obtained when trained on the same data as the above model with the difference that we did not clip Monte-Carlo-simulated LET maps as requested during the review process
3D Empirical Dissolution Model (Winardhi 2024)
No full text3D empirical dissolution model aimed at examining the time-series evolution of macroscopic features together with the corresponding changes in the dissolution rate under far from equilibrium batch reactor conditions. The developed empirical model is based on the mineral geometry (surface topography and volume) derived from X-ray computed tomography (CT) measurements. The macroscopic features are identified using surface curvature which are then used to generate reactivity maps for dissolution models
Towards electronic microplates with multimodal sensing for bioassays
Get PDFScientists and clinicians across various disciplines rely on the use of microplates in laboratories and clinical settings. Traditional optical measurement techniques involving cumbersome microplate readers and advanced microscopes, offer valuable insights into biological systems. These techniques typically require trained personnel, often limiting their use to dedicated core laboratories. In addition, many bioassays require staining, increasing complexity, and sample processing times. We introduce a novel thermal-based readout method that offers a cost-effective, user-friendly, and real-time alternative to complement the traditional techniques. This new approach has the potential to broaden the accessibility and simplify the bioassay analysis. Thermal sensors can be seamlessly integrated into standardized microplate formats. The sensing principle relies on the inversely proportional relationship between resistance change and heating pulses, generated through Joule heating. The so-called modified Transient Plane Source technique is sensitive to changes in the thermal effusivity of the sample, which can be related to changes in biological properties. Additionally, by precisely regulating the current flowing through the single-element sensor between the measured pulses, we gain the capability to control temperature, providing both, incubation and sensing functions using a single thermal element. This added versatility enhances the potential applications of thermal-based readouts in various bioassays. We aim to demonstrate our proof-of-concept using a straightforward and reliable biological system tracking bacterial growth. Yet, our approach extends beyond the integration of thermal sensors. Our device The overarching vision is to create a versatile multimodal sensing interface capable of not only controlling the environment but also measuring a range of factors, including thermal bulk properties, electrical bulk properties, and specific biomarkers
Spremberg Hyperspectral Drillcore Data
No full textThis hyperspectral drillcore dataset (shed) contains 70 drill holes, totalling 383 boxes that cumulatively contain 1323 meters of scanned cores. Hyperspectral data is stored in the widely used ENVI format (.dat and associated .hdr files), which can be opened using e.g., napari-hippo (GUI) and hylite (python). The whole directory structure is compatible with hycore, for easier out-of-core processing and visualisation.
These hyperspectral data and associated visualisations can also be viewed interactively here.
The scanned cores come from the Spremberg–Graustein Kupferschiefer exploration zone, located in Lusatia, Germany. Five extensive and uninterrupted intervals were scanned, from three boreholes and their deviations drilled by Kupferschiefer Lausitz (KSL) between 2009 and 2010. An additional 65 smaller intervals were also scanned from material drilled during the period spanning 1957 to 1979, a component of the Spremberg exploration initiative executed by the former German Democratic Republic (GDR). Further information on the Spremberg–Graustein exporation zone can be found here.
Stratigraphically, the KSL cores intersect the Rotliegend (Permian) sandstones and conglomerates (S1), the overlying Kupferschiefer mudstone (T1), which transitions upwards into the Zechstein marls, carbonates and evaporites. Data from one hole also includes the base of the Buntsandstein.
The GDR drill cores, housed at the drill core repository of the Geological Survey of Brandenburg (LBGR), predominantly cover a few meters around the immediate intersection of the Kupferschiefer mudstones.
These data were acquired as part of the Horizons Europe project Vector. Kupferschiefer Lausitz and the Geological Survey of Brandenburg are thanked for providing access to core material and their support during data acquisition