KSTAR_UEDGE_Dataset: UEDGE Simulation Dataset for Training the KSTAR DivControlNN Surrogate Model
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Zhao, Menglong [Lawrence Livermore National Labora↗
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Fast Multiresolution Reads of Massive Simulation Datasets
Today’s massively parallel simulation codes can produce output ranging up to many terabytes of data. Utilizing this data to support scientific inquiry requires analysis and visualization, yet the sheer size of the data makes it cumbersome or impossible to read without computational resources similar to the original simulation. We identify two broad classes of problems for reading data and present effective solutions for both. The first class of data reads depends on user requirements and available resources. Tasks such as visualization and user-guided analysis may be accomplished using only a subset of variables with a restricted spatial extent at a reduced resolution. The other class of reads requires full resolution multivariate data to be loaded, for example to restart a simulation. We show that utilizing the hierarchical multiresolution IDX data format enables scalable and efficient serial and parallel read access on a variety of hardware from supercomputers down to portable devices. Here, we demonstrate interactive view-dependent visualization and analysis of massive scientific datasets using low-power commodity hardware, and we compare read performance with other parallel file formats for both full and partial resolution data.
97 MATHEMATICS AND COMPUTING↗
SARS-CoV2 Protein-Ligand Simulation Dataset: Layer 1 (Simulation Initial Conditions and Parameters)
A set of 24 protein structures/complexes from the SARS CoV-2 proteome and inputs prepared for simulation using the CHARMM36m forcefield in PDB and gromacs formats. Each system contains a pdb and gromacs top and related input files necessary for running a temperature replica-exchange simulation. In addition, we also include: charmm PSF files (generated from the gromacs topology), a list of temperatures at which replica-exchange simulations were done (tempRamp), example gromacs run input mdp files, and initial minimized structures where available (minimized.pdb).
60 APPLIED LIFE SCIENCES↗
Material Fracturing and Failure Simulation Datasets
Fracturing is a fundamental physics phenomena with broad relevance across multiple domains, ranging from infrastructure integrity, aerospace durability, reservoir production, and seismic events. We present a diverse dataset of simulated fracture evolution and material failure generated from two numerical solvers: the phase-field method and the combined finite-discrete element method (FDEM). These solvers differ in formulation, physical fidelity, and computational efficiency. The dataset includes five materials: PBX, anisotropic shale, tungsten, aluminum, and steel. For each, phase-field simulations span 400,000 cases: 200,000 under uniaxial tension and 200,000 under biaxial tension. The computationally expensive FDEM simulations include 90,000 split evenly among PBX, shale, and tungsten under uniaxial loading. All simulations begin with randomized initial fracture patterns. Each entry includes temporal data capturing fracture propagation dynamics. This comprehensive dataset is designed to support the development of foundational or surrogate machine learning approaches for predicting material failure. While no such models are introduced here, the dataset lays a robust foundation for advancing future research and innovation in these areas.
36 MATERIALS SCIENCE↗
SARS-CoV2 Protein-Ligand Simulation Dataset: Layer 3 (Raw Temperature Replica Exchange Molecular Dynamics Trajectories)
Raw temperature replica-exchange molecular dyanmics trajectories of 23 different SARS CoV-2 Systems including S Protein ACE2-receptor binding-domain, MPro, PLPro, NSP3 ADRP (X-domain/macrodomain/phosphatase), NSP15 (endoribonuclease), NSP9, NSP10, NSP16, and N-protein N-terminus. Systems were prepared with charmm-gui and simulated using the GROMACS simulation software suite. Non-demuxed, i.e. discontinuous/constant temperature window, trajectories are provided in compressed GROMACS xtc and full trr formats. This data supplements the data releases DOI: 10.13139/OLCF/160650 'SARS-CoV2 Protein-Ligand Simulation Dataset: Layer 1 (Simulation Initial Conditions and Parameters)' and DOI: 10.13139/OLCF/1657844 'SARS-CoV2 Protein-Ligand Simulation Dataset: Layer 2 (Extracted Protein Coordinate Trajectories)'
59 BASIC BIOLOGICAL SCIENCES↗
SARS-CoV2 Protein-Ligand Simulation Dataset: Layer 2 (Lowest Temperature Extracted Protein Coordinate Trajectories)
Protein-only trajectories of the lowest-temperature window (310K) extracted from temperature replica-exchange molecular dynamics simulations of 23 different SARS CoV-2 systems, including S Protein ACE2-receptor binding-domain, MPro, PLPro, NSP3 ADRP (X-domain/macrodomain/phosphatase), NSP15 (endoribonuclease), NSP9, NSP10, NSP16, and N-protein N-terminus. Systems were prepared with charmm-gui and simulated using the GROMACS simulation software suite. Trajectories are provided in compressed dcd format with accompanying coordinate/topology files in pdb and psf formats. This data supplements the data release, DOI: 10.13139/OLCF/1650650 ('SARS-CoV2 Protein-Ligand Simulation Dataset: Layer 1 (Simulation Initial Conditions and Parameters)')
59 BASIC BIOLOGICAL SCIENCES↗
A priori examination of reduced chemistry models derived from canonical stirred reactors using three-dimensional direct numerical simulation datasets
Data-driven approaches to construct reduced chemical kinetic models, that rely heavily on thermo-chemical datasets with full chemical kinetics, have been gaining popularity. Datasets from direct numerical simulations (DNS) under three-dimensional (3-D) realistic turbulent flow conditions are desirable but limited to carefully designed parametric conditions due to the computational cost. Constructing datasets from a large ensemble of zero-dimensional stirred reactors like perfectly stirred reactor (PSR) and partially stirred reactor (PaSR) is a computationally efficient solution to consider the turbulence-chemistry interactions and cover a broad range of parametric conditions. In this paper, we derive reduced chemistry models from solutions of a large number of PSR and PaSR reactors using autoencoder (AE) neural networks and principal component analysis (PCA), and conduct a priori examination of the reduced models in three temporally evolving 3-D DNS jet flames featuring local extinction and re-ignition. The results show that the reduced models derived from PaSR datasets, i.e., AE-PaSR and PCA-PaSR, generally show significant improvement over the ones derived from PSR datasets. Among all the reduced models, AE-PaSR shows the best agreement with DNS results on the reconstruction accuracy and the representation of temporally evolving local extinction and re-ignition events.
Zhang, Pei↗
deeplenstronomy: A dataset simulation package for strong gravitational lensing
Automated searches for strong gravitational lensing in optical imaging survey datasets often employ machine learning and deep learning approaches. These techniques require more example systems to train the algorithms than have presently been discovered, which creates a need for simulated images as training dataset supplements. This work introduces and summarizes deeplenstronomy, an open-source Python package that enables efficient, large-scale, and reproducible simulation of images of astronomical systems. A full suite of unit tests, documentation, and example notebooks are available at https://deepskies.github.io/deeplenstronomy/ .
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Quasi-Distributed Fiber Sensor-Based Approach for Pipeline Health Monitoring: Generating and Analyzing Physics-Based Simulation Datasets for Classification
This study presents a framework for detecting mechanical damage in pipelines, focusing on generating simulated data and sampling to emulate distributed acoustic sensing (DAS) system responses. The workflow transforms simulated ultrasonic guided wave (UGW) responses into DAS or quasi-DAS system responses to create a physically robust dataset for pipeline event classification, including welds, clips, and corrosion defects. This investigation examines the effects of sensing systems and noise on classification performance, emphasizing the importance of selecting the appropriate sensing system for a specific application. The framework shows the robustness of different sensor number deployments to experimentally relevant noise levels, demonstrating its applicability in real-world scenarios where noise is present. Overall, this study contributes to the development of a more reliable and effective method for detecting mechanical damage to pipelines by emphasizing the generation and utilization of simulated DAS system responses for pipeline classification efforts. The results on the effects of sensing systems and noise on classification performance further enhance the robustness and reliability of the framework.
36 MATERIALS SCIENCE↗
A hydrological simulation dataset of the Upper Colorado River Basin from 1983 to 2019
Abstract This article presents a hydrological reconstruction of the Upper Colorado River Basin with an hourly temporal resolution, and 1-km spatial resolution from October 1982 to September 2019. The validated dataset includes a suite of hydrologic variables including streamflow, water table depth, snow water equivalent (SWE) and evapotranspiration (ET) simulated by an integrated hydrological model, ParFlow-CLM. The dataset was validated over the period with a combination of point observations and remotely sensed products. These datasets provide a long-term, natural-flow, simulation for one of the most over-allocated basins in the world.
54 ENVIRONMENTAL SCIENCES↗
TGW Hydrology, River Routing, and Hydropower Simulation Datasets
he current dataset contains data upload links to the following hydrologic (water balance), river routing (water management), and hydropower simulation data over CONUS: Climate Forcing: TGW (https://tgw-data.msdlive.org/) Simulation Scenario: Historical, Mid-Century, End-Century Simulation Period: 1980-2024, 2020-2059, 2060-2099 Simulation Models: VIC (Variable Infiltration Capacity), mosartwmpy (Model for Scale Adaptive River Transport-Water Management in Python), and PNNL B1Hydro Output Format: NetCDF and CSV
Tidwell, Vincent C [Pacific Northwest National Lab↗
Adaptive wavelet compression of large additive manufacturing experimental and simulation datasets
New manufacturing technologies such as additive manufacturing require research and development to minimize the uncertainties in the produced parts. The research involves experimental measurements and large simulations, which result in huge quantities of data to store and analyze. We address this challenge by alleviating the data storage requirements using lossy data compression. We select wavelet bases as the mathematical tool for compression. Unlike images, additive manufacturing data is often represented on irregular geometries and unstructured meshes. Thus, we use Alpert tree-wavelets as bases for our data compression method. We first analyze different basis functions for the wavelets and find the one that results in maximal compression and miminal error in the reconstructed data. We then devise a new adaptive thresholding method that is data-agnostic and allows a priori estimation of the reconstruction error. Finally, we propose metrics to quantify the global and local errors in the reconstructed data. One of the error metrics addresses the preservation of physical constraints in reconstructed data fields, such as divergence-free stress field in structural simulations. While our compression and decompression method is general, we apply it to both experimental and computational data obtained from measurements and thermal/structural modeling of the sintering of a hollow cylinder from metal powders using a Laser Engineered Net Shape process. The results show that monomials achieve optimal compression performance when used as wavelet bases. The new thresholding method results in compression ratios that are two to seven times larger than the ones obtained with commonly used thresholds. Overall, adaptive Alpert tree-wavelets can achieve compression ratios between one and three orders of magnitude depending on the features in the data that are required to preserve. Furthermore, these results show that Alpert tree-wavelet compression is a viable and promising technique to reduce the size of large data structures found in both experiments and simulations.
42 ENGINEERING↗
Simulation dataset for 'Chemical and Morphological Structure of Transgenic Switchgrass Organosolv Lignin Extracted by Ethanol, Tetrahydrofuran, and γ-Valerolactone Pretreatments'
Contains the initial configurations and output results for the simulations reported in the paper Chemical and Morphological Structure of Transgenic Switchgrass Organosolv Lignin Extracted by Ethanol, Tetrahydrofuran, and γ-Valerolactone Pretreatments
09 BIOMASS FUELS↗
Open dynamic simulation dataset of the Texas 2000-bus synthetic power system on ANDES [SWR-25-110]
An open-source power system model (Texas2k), running on an open-source dynamic simulator (ANDES), designed to lower barriers for machine-learning studies of power systems.
Peng, Jiangkai [National Renewable Energy Laborato↗
IM3/HyperFACETS Thermodynamic Global Warming (TGW) Simulation Datasets
Publication For a thorough description of the methods, see the peer-reviewed paper: Jones, A.D., Rastogi, D., Vahmani, P. et al. Continental United States climate projections based on thermodynamic modification of historical weather. Sci Data 10, 664 (2023). https://doi.org/10.1038/s41597-023-02485-5 Overview The IM3 / HyperFACETS climate simulations provide 40-year historical (1980-2019) as well as four 80-year future simulations (2020-2099) over the U.S. The future simulations are split into near (2020-2059) and far future (2060-2099) segments. The future scenarios span a range of plausible changes in future climate (both Global Circulation Model (GCM) and Representative Concentration Pathways/Shared Socioeconomic Pathway (RCP/SSP) dimensions). The simulations provide climate variables with high spatiotemporal resolution (25 hourly variables and 207 3-hourly variables at 12 km2). The datasets are generated using dynamical downscaling with the WRF (Weather Research and Forecasting) model (version 4.2.1) and therefore preserve physical consistency across variables. WRF is a state-of-the-art, fully compressible, non-hydrostatic, mesoscale numerical weather prediction model. WRF is coupled with an urban canopy model (UCM), which resolves urban surfaces. The future scenarios were developed using a thermodynamic global warming approach where past events are replayed under a range of future warming conditions. These scenarios therefore provide a perspective on potential increases in extreme event intensity, geographic scope, and duration, with previously non-extreme conditions potentially crossing new thresholds to be considered extreme by today's standards. This approach is not intended to estimate future changes in extreme event frequency that might result from changes in large-scale atmospheric dynamics. This dataset has NOT been bias corrected. A bias corrected version of selected variables is under development and will be released here when available. Scenarios Files Data for each scenario is provided in weekly NetCDF files. 25 variables are available at hourly resolution, and 207 variables are available at three-hourly resolution. Spatial resolution is 12km and spans the conterminous United States (CONUS), including some areas of Canada and Mexico, resulting in a grid of 424 by 299 cells. The spatial projection is a Lambert Conformal Conic with the following proj-string: "+proj=lcc +lat_0=40.0000076293945 +lon_0=-97 +lat_1=30 +lat_2=45 +x_0=0 +y_0=0 +R=6370000 +units=m +no_defs". The available scenarios and simulation periods are listed below: historical | 1980 - 2019 rcp45cooler | 2020 - 2059 rcp45cooler | 2060 - 2099 rcp45hotter | 2020 - 2059 rcp45hotter | 2060 - 2099 rcp85cooler | 2020 - 2059 rcp85cooler | 2060 - 2099 rcp85hotter | 2020 - 2059 rcp85hotter | 2060 - 2099 * The first year (1979, 2019, and 2059) of data within each scenario represents a model warmup period and should not be used. These are located in the `spinup_files` directory. Historical year 2020 is considered an extra year of data beyond the simulation period and can be found in the `additional_files` directory. For information on specific variables and a more in-depth discussion of methodology, please refer to the data landing page at https://tgw-data.msdlive.org. Delta Warming Files The global and CONUS warming deltas for each scenario are provided in degrees Celsius annually and monthly. Restart Files Yearly restart files are provided for each scenario which can be used to restart the WRF model at a particular point in time. Spinup Files The first year of data within each simulation period represents a model warmup period and should not be used. The files are provided here for the sake of reproducibility. Additional Files Additional years of data are provided as an extension of the historic simulation.
Jones, Andrew D.↗
Autonomie Simulation Datasets in Support of U.S. DOT-NHTSA Advanced Vehicle Technology Research
Understanding how new vehicle technologies affect fuel economy and energy use is critical to the regulatory work performed by the U.S. Department of Transportation’s National Highway Traffic Safety Administration (NHTSA), which sets Corporate Average Fuel Economy (CAFE) standards under the Energy Policy and Conservation Act of 1975. In order to support this work, Argonne National Laboratory uses Autonomie, a full-vehicle simulation tool, to evaluate advanced powertrain architectures and their effects on vehicle energy consumption and performance. A wide range of vehicle classes has been assessed (i.e., internal combustion engine vehicles, hybrid electric vehicles, plug-in hybrid electric vehicles, battery-electric vehicles, and fuel cell electric vehicles), as well as the effects of various technology improvements such as lightweighting, aerodynamic refinements, and low-rolling-resistance tires. Simulations have been run across multiple drive cycles to capture fuel and electricity use under realistic operating conditions. The resulting datasets include detailed vehicle-level results, model assumptions, and validation reports, all of which have been made publicly available through NHTSA in support of the 2023 notice of proposed rulemaking covering light-duty vehicles for model years 2027 to 2035. These data are critical to stakeholders working in fuel economy regulation, vehicle technology assessment, and energy policy analysis.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Detailed Simulation Datasets Quantifying U.S. DOE VTO/HFTO R&D Benefits Across Light- to Heavy-Duty Vehicles
For more than 20 years, Argonne National Laboratory’s Vehicle & Mobility Systems Department has assessed how R&D investments by the U.S. Department of Energy’s Transportation Technologies Office and Alternative Fuels and Feedstocks Office affect vehicle energy use and cost. The analyses are performed using Autonomie, Argonne’s full-vehicle simulation tool for energy consumption, performance, and cost. The study covers five time frames ranging from present day through 2050, with more than 30 vehicle classes and applications (10 light duty and >20 medium and heavy duty), as well as six powertrain configurations (conventional, start-stop, hybrid electric vehicle, plug-in hybrid electric vehicle, battery-electric vehicle, and fuel cell electric vehicle) and five fuels (gasoline, diesel, natural gas, hydrogen, and electricity). Low and high technology uncertainty scenarios have been considered to capture a realistic range of outcomes. The resulting datasets include the assumptions used (i.e., efficiency, $/kWh), vehicle-level data (power, energy, weight, and cost), and outputs such as energy consumption, manufacturer’s suggested retail price, and total cost of ownership. These data are critical to stakeholders working in transportation, technology assessment, and long-term R&D planning.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗