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Replication Data for: Large-Area Hydrogenated Amorphous Silicon Schottky-Photosensor Arrays for Display Integration
No full textThis dataset includes all raw data and evaluation scripts for the data contained in the paper "Large-Area Hydrogenated Amorphous Silicon Schottky-Photosensor Arrays for Display Integration" submitted to JSID. The repository data structure aligns with the figures contained in the paper. Each folder contains raw data and a python script for generating the included figure
Universal Timber Slab: Simulated Multidisciplinary Performance Data for UTS Solid Timber Slab in Triangular Slab Bays at 1m Resolution
No full textOverview
This dataset includes preliminary results for Computational Design, Structural Design, Acoustics + Building Physics, and Life Cycle Analysis in relation to the default benchmark defined in the
Slab Building Blocks dataset, "1. Triangular Slabs every 1.0m – Default".
The slabs included were computed through various disciplinary layers, capturing performance data across multiple domains. The results are intended to serve as a baseline for further research and comparative analysis.
Data format
This data format uses the BHoM framework.
It is possible to load the dataset with the core version of BHoM, even though a UTS BHoM Toolkit was used to organize the data. This ensures compatibility with the computational design method and future developments.
All data is stored in JSON files that contain BHoM objects.
Using SI units unless spefified differently.
Data Structure
Following the structure of BHoM and JSON files, the data is organized in a nested structure. For each design geometry, a "Performance" section contains multiple "PerformanceSolutions" per bay. Thus, each disciplinary layer can autonomously generate different solutions, while a versioning system enables them to refer to and align with outputs from other layers, maintaining links between interconnected results.
Below you can find the description of the relevant data points that are the core results to measure and reproduce the dataset layer by layer.
Layers 1-3 - Computational Design, design and Geometry
Defines the geometry and organization of the slabs.
Input Datapoint:
Evert specimen defined in the default set at Slab Building Blocks dataset conforming Layer 0.
Output Datapoints:
UTS.Slab: Slab general data container
UTS.Slab.Baylayout.Bays.Bay: Bay geometry data container
Performance Solutions
Each bay of the slab is analyzed individually and different disciplines calculate different performance solutions based on the case and the previous information.
Layer 4 - Slab Rerquirements
Assumptions for slab dimensioning, such as floor occupancy, vibration requirements and additional loads.
Datapoint: PeformanceSolution.Requirements
Input Datapoints: None
Location: UTS.Slab.Baylayout.Bays.Bay.Performance.PerformanceSolutions
Datapoint container: PeformanceSolution.Requirements
Output Datapoints:
OccupationalUse
AdditionalDeadload
ServiceClass
FireRequirement
MaxDeflectionFactor
VibrationRequirement
Layer 5 - Structural Design
Engineering assessment and validation of slab performance under various load cases.
Input Datapoints:
Bays - from Layers 0 to 3
Requirements - from Layer 4
Location: UTS.Slab.Baylayout.Bays.Bay.Performance.PerformanceSolutions
Datapoint container: PeformanceSolution.Structure
Output Datapoints:
CalculatedSlabBuildUp
EigenFrequencyRequirement
MaxEstimatedDeflection
MaxUtlization
Layer 6 - Acoustics + Building Physics
Simulation of acoustics performance and building physics parameters under defined load cases.
Input Datapoints:
Bays - from Layers 0 to 3
EigenFrequencyRequirement - from Layer 5
Location: UTS.Slab.Baylayout.Bays.Bay.Performance.PerformanceSolutions
Datapoint: PeformanceSolution.BuildingPhysics
Output Datapoints:
NormalizedSoundPressure_L_n ([dB])
SoundReductionR ([dB])
HeatAmount ([kWh/m²])
EffectiveHeatStorageCapacity_C_eff ([kg/m²])
EstimatedOverheatingHours ([Kh])
Layer 7 - Fabrication
The data for the fabrication is removed due to IP protection, yet the calculated results at the Bill of quantities (BOQ) are taken into account for other Peformance Layers.
Input Datapoints:
Bays - from Layers 0 to 3
CalculatedSlabBuildUp - from Layer 5
Location: UTS.Slab.Baylayout.Bays.Bay.Performance.PerformanceSolutions
Datapoint: PeformanceSolution.Fabrication
Output Datapoints:
BOQ
Layer 8 - Life Cycle Assessment
Evaluation of different scenarios for the environmental performance results based on the bill of quantities (BOQ).
Input Datapoints:
BOQ - from Layer 7
Location: UTS.Slab.Baylayout.Bays.Bay.Performance.PerformanceSolutions
Datapoint: PeformanceSolution.LCA
Output Datapoints:
GPW (Global Warming Potential) ([kg CO2 eq./m²] for modules A1 - C4 according to EN 15804)
PEF (Product Enviromental Footprint [-/m²])
File naming
Name example:
Slab_2_3_5_LT1_CR2_GC5
Name parameters:
Slab_A_B_C_LT#_CR#_GC#
Each parameter is indexed with an integer to keep the names simple.
-Triangle Edge Length (A_B_C) = refers to the triangle edges selection parameter in our sample domain. The range for this data set was 4 to 12 meters..
-Column Head Radii (CR): CR2 = 0.65m
-Lamella Thickness (LT): LT1 = 0.03m (with max 200x Bend Radius)
-Group Count Cap (GC): 5 max. The amount of lamella groups (packages) was capped at 5.<br
Replication Data for: Super-resolution of turbulent velocity fields in two-way coupled particle-laden flows
No full textThe repository contains files required to reproduce the results. The three compressed filed are (i) torch_code, (ii) datasets, and (iii) experiments.
Detailed files description
torch_code
The main Pytorch source code used for training/testing is provided in torch_code.tar.gz file.
datasets
The training/validation/testing datasets have been provided in lmdb format which is ready to use in the code. The datasets in datasets.tar.gz contain:
Main train/validation/test dataset:
Training dataset:
data_train_OF-decaying2_f0_1_11_12_2_21_22_3_31_32_FHIT_particle_128_Re52-2D_320000_lmdb.lmdb
Validation dataset:
data_valid_outOfSample_OF-decaying2_f0_1_11_12_2_21_22_3_31_32_FHIT_particle_128_Re52-2D_8000_lmdb.lmdb
Test dataset:
data_test_outOfSample_OF-decaying2_f0_1_11_12_2_21_22_3_31_32_FHIT_particle_128_Re52-2D_16000_lmdb.lmdb
Note that the samples from 20 DNS cases are collected in order (each case 16000 samples for training and 800 samples for testing) which can be recognized using the provided metadata file in each folder.
Particle-free training and test datasets (used in Fig 6 of the paper):
Particle-free training dataset:
data_train_OF-f0_FHIT_particle_128_Re52_prolonged-2D_102400_lmdb.lmdb
Particle-free test dataset:
data_test_outOfSample_OF-f0_FHIT_particle_128_Re52_prolonged-2D_800_lmdb.lmdb
Out of sample test datasets:
Test Case4 in the paper:
data_test_outOfSample_OF-f41_FHIT_particle_128_Re52_test-2D_800_lmdb.lmdb
Test Case5 in the paper:
data_test_outOfSample_OF-f51_FHIT_particle_128_Re52_test-2D_800_lmdb.lmdb
experiments
The trained models are provided in experiments.tar.gz file. Each experiment contains the log file of the training, the last training state (for restart) and the model wights used in the publication.
Conditional model:
conditionalSRGAN trained model using particle-free dataset (used in Figs 6 and 7 of the paper):
00110-01G_PFT-NoPrt_ArchT_condSRGANModel_L64SP4x_Gcond_WaveDisc_f256g128b16_I64_BS16x2_Pix1-Grada-Adva_LrG45D5_fixedLR_DS-f0-102k_cPad_20241218
conditionalSRGAN trained model using the main dataset (used in Figs 9-13 and Figs 15-16 of the paper):
01004-00H_PFT-Prt_ArchTest_condSRGANModel_L64SP4x_Gcond_WaveDisc_f256g128b16_I64_BS32x4_Pix1-Grada-Adva_LrG45D5_fixedLR_DS-fxD-320k_cPad_20241219
Traditional model:
unconditional SRGAN model trained model using the main dataset (used in Fig 14 of the paper):
01005-00H_PFT-Prt_DiscTest_condSRGANModel_L64SP4x_Gcond_TradDisc_f256g128b16_I64_BS32x4_Pix1-Grada-Adva_LrG45D5_fixedLR_DS-fxD-320k_cPad_20241224
How to
Build the environment
To build the environment required for the training and inference you need Anaconda. Go to the torch_code folder and
conda env create -f environment.yml
Then create ipython kernel for post processing,
conda activate torch_22_2025_Shamooni_POF
python -m ipykernel install --user --name ipyk_torch_22_2025_Shamooni_POF --display-name "ipython kernel for post processing of POF2025"
Perform training
It is suggested to create softlinks to the dataset directly in the torch_code folder:
cd torch_code
ln -s <path to the dataset folder> datasets
Then activate the conda environment
conda activate torch_22_2025_Shamooni_POF
An example script to run on single node with 2 GPUs:
torchrun --standalone --nnodes=1 --nproc_per_node=2 train.py -opt options/train/condSRGAN/00110-01G_PFT-NoPrt_ArchT.yml --launcher pytorch
Make sure that the paths to datasets "dataroot_gt" and "meta_info_file" for both training and validation data in option files are set correctly. </p
Supplemental Material for "Potentially Visible Set Generation with the Disocclusion Buffer"
No full textSupplemental material for our paper "Potentially Visible Set Generation with the Disocclusion Buffer":
A video showcasing our architecture and example visualizations
The source archive to replicate the results reported in our paper
</ul
Replication Data and Scripts for: Quantum vs. classical: A comprehensive benchmark study for predicting time series with variational quantum machine learning
No full textThe data and scripts provided here are for reproducing the results of "Quantum vs. classical: A comprehensive benchmark study for predicting time series with variational quantum machine learning" Mach. Learn.: Sci. Technol. 7 010501 (2026) (doi: 10.1088/2632-2153/ae365f). See the README.md file for details and instructions
Replication Data for: mmcDNSFoam v2406
No full textValidation Data for mmcDNSFoam
This data set contains the DNS and LES cases for the double shear layer setup used to validate the mmcDNSFoam solver of the mmcFoam v2406 version.
Requirements
To run the simulations, you need to download and compile the mmcFoam software based on OpenFOAM v2406. This software is open-source upon registration. If you wish to use mmcFoam please contact:
Prof. Andreas Kronenburg: [email protected]
Prof. Matthew Cleary: [email protected]
Please note that you have to merge in the OpenFOAM patch of commit 52b530fb before compiling mmcFoam-v2406. See also, issue #3277.
Additional Libraries
This data set also includes following libraries:
ofReader: Python tools to post-process and read OpenFOAM files
sigmaTurbulenceModel: Implementation of the sigma turbulence model for OpenFOAM v2312 and OpenFOAM v2412, which is compatible with GCC 11.3 compiler.
</ul
Replication Data for: Higher BTEX Aromatic Yield from Ethanol over Desilicated H,Zn-[Al]ZSM-5 Catalysts
No full textOriginal data (Catalytic measurements, Characterization) of the journal article mentioned under related publications from the Dyballa group can be found here
Parallel Lattice Boltzmann code for simulating single phase flows in porous domains
No full textThis dataset contains a parallelized Lattice Boltzmann code, along with an example input domain and input file containing the necessary parameters for a test run. A MATLAB script to visualize and process the simulation results is also provided.
Among the uploaded files, Lb7mpi_281124.f90 is a computational code implementing a single phase Lattice Boltzmann (LB) model originally proposed by Guo et al. (2002). The code was developed by Prof. Andreas Yiotis (TUC) and Dr. Michael Kainourgiakis (NCSR Demokritos) based on the Fortran 95 computing language during 2005-2014. It is a highly parallel, scalable and computationally efficient code that relies on the MPI 2.0 (Message Passing Interface) protocol in order to take advantage of the computational capabilities of distributed memory supercomputers. As such, it has been thoroughly tested in terms of its efficiency and scalability in several large-scale supercomputers, including MareNostrum (BSC, Spain), IDRIS (France) and ARIS (Greece), exhibiting excellent scalability up to 2048+ cpu cores.
Lb7mpi is optimized for numerical simulations of single phase flows in 3D digital porous media in order to study the pore scale characteristics on the permeability of the digital porous domain. Flow is driven using a standard body force scheme and it is assumed to be periodic in all three directions in space. The digital domain is an ascii file that represents the 3D pore space as a sequence of 2D matrices. Each matrix corresponds to a fixed z value and contains the x-y conductivity of the medium. The hydraulic conductivity is denoted as zero (0) for solid lattice voxels where no flow is possible, and as one (1) for pore (void) voxels where flow takes place.
All computational parameters, including the name of the digital domain, the value of the body force, the LB relaxation parameters, as well as the load balancing scheme for parallel computation are declared by the user in a single input file named input.txt. The source code should be compiled using the standard mpif90 compiler wrapper that produces an executable linked to the installed mpi libraries available on your computational system.
The code is distributed as open-source and can be used explicitly for academic, educational and research purposes, as long as an appropriate reference to the original authors (see above) is provided. It should not be used as part of a commercial product. Furthermore, the code is distributed as is for the academic and research community without any warranty regarding the accuracy of the produced results. Very limited support can be provided by the authors. Such inquiries should be addressed to [email protected] (Prof. Andreas Yiotis)
Replication Data for: mmcFoam-v2306
No full textData for mmcFoam v2306 Test Runs
This data set contains different cases for validation of the mmcFoam-v2306 version. While tested for mmcFoam-2306 and OpenFOAM-v2306 they serve as a test basis for future code development and validation.
Requirements
To run the simulations, you need to download and compile the mmcFoam software based on OpenFOAM v2306. This software is open-source upon registration. If you wish to use mmcFoam please contact:
Prof. Andreas Kronenburg: [email protected]
Prof. Matthew Cleary: [email protected]
Content
mmcFoam-particleMatchingAlgorithm-greedySearch:
Compares the particle mixing distances in reference and physical
space for two different particle pairing algorithms.
samplingInletData:
Tool to sample inlet data (or any data)
Required to regenerate the inlet data for the sandiaFlame
sandiaFlame:
Setup for the Sandia sooting flame case. However, the inlet data
has to be regenerated as it is too large for this repository > 80GB
--> See samplingInletData tool
shearLayerCase:
Provides setup for the double shear layer with mmcFoam 5.x and
mmcFoam v2212. Different decompositions or particle pairing methods
can be tested.
</ol
Replication Data for: Solvothermal Template-Induced Hierarchical Porosity in Covalent Organic Frameworks: A Pathway to Enhanced Diffusivity
No full textAll primary data files of the journal article mentioned can be found here. The data is structured by analysis technique. All files are tagged with the corresponding material. The chronoamperometry (CA) and cyclic voltammetry (CV) data are located in the "Electrochemistry" folder for the corresponding materials. The characterization and catalysis data are recorded experimentally. The XRPD files tagged with "sim" were generated via BIOVIA Materials Studio 2017 software based on models created in ChemDraw 22.0.0. The data can be used to replicate the experiments, to evaluate and compare the materials' properties with others and to investigate the materials' structures