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Contains data for paper on nonlinear pedestal electron-temperature-gradient turbulence simulations: *.py # python plotting scripts for generating paper plots. *.txt, *.npy # post-processed data required for plotting scripts. *.png, *.eps, *.pdf # paper plots. *.in # gs2 and stella input files for simulations in this paper. stella used for nonlinear simulations (https://github.com/mabarnes/stella)
This Modeling Archive is in support of a NGEE Arctic publication in review "Drying of tundra landscapes will limit subsidence-induced acceleration of permafrost thaw". The study used a cryohydrology model to assess the potential risk of abrupt permafrost thaw triggered by melting ground ice, a key open question associated with permafrost response to a warming Arctic. The spatially resolved simulations are for a small catchment 465 ice-wedge polygons in polygonal tundra near Utqiagvik, Alaska in the high-emissions RCP8.5 climate scenario. The simulations are compared to runoff, evapotranspiration and subsidence in the current climate and agree well. The study used the ATS code configured as an intermediate-scale cryohydrology model (Advanced Terrestrial Simulator, Version v2). The archive includes input files for spinup (1985 to 2005) and projections (2006 to 2100 - the manuscript reports 2006 to 2098). Three of the projections include the effects of subsidence and microtopography change with different depth profiles of ice content corresponding to the median, 20th percentile and 80th percentile. The fourth projection has subsidence turnoff and uses the reference case (median) ice content. Spatially averaged or aggregated output variables are included for the projections. Selected checkpoint files for the projections and postprocessing scripts are also included. Files included are *.xml; *.h5; *.exo; *.py; *.sh, *. nb, mesh files, and one file in the original Excel format plus *.csv and *.pdf to conserve formatting contained in the original file. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
This repository contains call source code and input files that are part of the DayCent_CABBI BNI model experiments described in the manuscript below. Hartman et al. 2022. In Silico Evaluation of Plant Nitrification Suppression Effects on Agroecosystem Nitrogen Loss. Ecosphere . In Review.
The equivalent fundamental-mode source refers to a “source” which is identically distributed in space, energy, and angle as that of a fundamental-mode fission source distribution. It has previously been shown that the system fixed source multiplication ($M_{fs}$) relates to the effective multiplication factor ($\kappa_{eff}$) as $M_{fs} = \frac{g^*}{1-k_{eff}}$ The term $\mathcal{g}$* therefore relates the system source distribution to the system equivalent fundamental-mode source. As previously shown, the $\mathcal{g}$* term is approximately 1 for deeply subcritical systems and then diverges as the system multiplication increases. For a system with a point source located in the center of an assembly (such a sphere), $\mathcal{g}$* will be greater than 1; this happens because there is a higher probability of induced fission when all of the starter neutrons originate from the center of the sphere. For a uniform source, $\mathcal{g}$* is less than 1, but does not diverge from 1 as dramatically as seen by a point source. One reason that $\mathcal{g}$* is an important parameter is that it is used in measurement methods to infer reactor kinetics parameters ($β_{eff}$ being one example). This work will discuss multiple ways to simulate $\mathcal{g}$* using MCNP®6.2, which include use of traditional methods versus a single input file method that was recently published. A discussion related to methods to simulate $M_{fs}$ will be presented. Spherical systems of Highly Enriched Uranium (HEU) will be compared with previous works. In addition, results of plutonium including multiple reflector materials will be investigated. Simulated systems will include both parametric studies as well as configurations used in recent experiments performed at the National Criticality Experiments Research Center (NCERC). These studies will include subtleties associated with system geometry that have not been previously shown. Last, this work will investigate the relationship between $\mathcal{g}$* and reactor kinetics parameters (such as $β_{eff}$).
To support advanced reactor demonstrations, the Virtual Test Bed (VTB) [1] repository hosts a wide range of challenge problems for showcasing modeling and simulation capabil- ities in support of advanced reactor demonstrations. This document presents a coupled multiphysics model of the Mark 1 pebble-bed fluoride-salt-cooled high-temperature reactor (PB-FHR). The analysis leverages NEAMS tools (Griffin [2], SAM [3], Pronghorn [4], and the MOOSE [5] heat con- duction module) for core neutronics, thermal hydraulics of the core and primary loop, and multiscale fuel performance simulations. The analysis was entirely created by coupling standalone simulations of the reactor that were previously available on the VTB. All input files and documentation de- veloped for this example are available on the VTB website: mooseframework.inl.gov/ virtual_test_bed/ . This model was featured in the National Reactor Innovation Cen- ter Tech Talk presented in December 2021.
The Virtual Test Bed (VTB) repository hosts a wide range of challenge problems to showcase modeling and simulation capabilities to support advanced reactor demonstration. A coupled multiphysics model of the Mk1 pebble-bed fluoride-salt-cooled high-temperature reactor (PB-FHR) is presented here. The analysis leverages NEAMS tools (Griffin), SAM, Pronghorn, and the MOOSE heat conduction module) for core neutronics, thermal hydraulics of the core and the primary loop, and multiscale fuel performance simulations. The analysis was entirely created by coupling standalone simulations of the reactor previously available on the VTB. All input files and documentation developed for this example are available on the VTB website: \url{mooseframework.inl.gov/virtual_test_bed/}}. This model was featured in the NRIC Tech Talk presented in December 2021.
The National Reactor Innovation Center (NRIC) accelerates the demonstration and deployment of advanced nuclear energy through inspiring stakeholders and the public, empowering innovators, and delivering successful outcomes. The Virtual Test Bed (VTB) supports this mission by providing an open-source repository of advanced test reactor models for industry, academia, and the public to utilize. During my time at Idaho National Laboratory (INL), I: • Verified the input files of the High Temperature Engineering Test Reactor • Generated computational results • Created user documentation for the HTTR • Uploaded a steady-state HTTR model to the Virtual Test Bed • Authored a conference publication on the model
Brief presentation on recent work to enable analysis of arrays of fuel rods in Bison. Mesh generation is available in the input file. Simplified syntax is available to ease descriptions common to all fuel rods. The capability allows contact between neighboring rods.
TAPsolver is a python package, which automates TAP simulation and analysis routines. TAPsolver is built around the python packages FEniCS and Dolfin-Adjoint, which help take advantage of model adjoints to provide automatic derivatives. TAPsolver is flexible, with reaction mechanisms and rate constants that can be set through input files that allow users to take advantage of the different functionalities, which include sensitivity analyses, parameter optimization and uncertainty quantification.
SAS4A/SASSYS-1 is a safety analysis software package already under copyright by Argonne National Laboratory. There is interest from third-party users of the software to gain access to our internal verification and validation test suite. Therefore we are seeking to establish copyright over the test suite, which includes input files and reference results for hundreds of test cases that demonstrate functionality of the software. Making the test suite available to licensed users allows them to utilize the broad range of tests for commercial grade dedication activities needed to qualify the SAS4A/SASSYS-1 software under their own Software Quality Assurance program for use in a regulatory environment (e.g. with the Nuclear Regulatory Commission).
This dataset contains input and result files of computational simulations with the SCALE code system. The simulations cover radionuclide inventory and reactivity analyses of a fluoride salt-cooled high temperature pebble-bed reactor (PB-FHR), specifically the Hermes low-power PB-FHR demonstration reactor. Users wanting to reproduce results from this dataset are required to obtain a license to the SCALE code system for which details on the distribution can be found here: https://www.ornl.gov/scale/releases
This dataset contains input, output, and sensitivity data files for computational simulations with the SCALE code system as part of the Verified, Archived Library of Inputs and Data (VALID). The simulations cover critical benchmark experiments from the International Criticality Safety Benchmark Evaluation Project. The files are to be housed in a public directory for distribution. The information contained in the files have been approved for release by the Organisation for Economic Co-operation and Development Nuclear Energy Agency (NEA). Users wanting to reproduce results from this dataset are required to obtain a license to the SCALE code system for which details on the distribution can be found here: https://www.ornl.gov/scale/releases.
This data package supports the publication “Fire-altered Carbon Pools Create Disturbance Memory in Stream Dissolved Organic Carbon” by Li et al. (2026). The package contains processed model inputs, configuration files, restart files, simulation outputs, scripts, and visualization products used to evaluate post-fire dissolved organic carbon (DOC) dynamics in the Naches River Watershed, Washington, USA, following the 2021 Schneider Springs Fire. The modeling workflow couples ELM-BGC, the biogeochemistry-enabled Energy Exascale Earth System Model Land Model; ATS, the Advanced Terrestrial Simulator for integrated surface-subsurface hydrology; and PFLOTRAN, a reactive transport model for multicomponent aqueous geochemistry. Together, these models simulate how wildfire-induced changes in vegetation, litter, coarse woody debris, and soil organic matter influence DOC production, transport, and reaction from burned hillslopes to stream networks. The archive includes preprocessed meteorological, geospatial, hydrologic, and biogeochemical forcing data; ELM-BGC-derived DOC source terms; ATS mesh files; PFLOTRAN reactive-transport inputs; model configuration files; spin-up and transient restart files; watershed-scale diagnostic outputs; stream concentration time series; and figures or visualization files used to inspect and reproduce key results. File types include Hierarchical Data Format 5 (HDF5) files for gridded forcing and model-coupling data, model input and configuration files for ELM-BGC, ATS, and PFLOTRAN, restart and simulation-output files generated by the modeling workflow, tabular or time-series diagnostic outputs, scripts for post-processing and figure generation, and image or visualization products associated with the manuscript. Use of the package depends on the intended task. Re-running the simulations requires the relevant modeling software, including ELM-BGC, ATS, and PFLOTRAN as ATS's geochemical engine. Inspecting outputs and reproducing figures requires Python with scientific plotting libraries such as Matplotlib, and three-dimensional model outputs may be viewed with ParaView. Geographic information system files or maps may be inspected with ArcGIS Pro or comparable GIS software. The data package is intended to enable traceability, reuse, and partial reproduction of the coupled land-to-watershed hydro-biogeochemical modeling workflow used to test how wildfire disturbance affects terrestrial carbon pools and downstream DOC dynamics.
The Environmental Protection and Compliance-Compliance Programs (EPC-CP) group at Los Alamos National Laboratory (LANL) uses the Clean Air Act Assessment Package 1988 (CAP-88, Littleton 2020) PC model (Version 4.1) to estimate radiological doses for a set of areal sectors surrounding a release location, in order to satisfy the Environmental Protection Agency (EPA) National Emission Standards for Hazardous Air Pollutants (NESHAP) dose calculation requirement in 40 CFR 61 Subpart H. Among several types of data that must be prepared for CAP-88 input is a text file of meteorological data (“WIND” file), consisting of the joint frequency of wind direction, wind speed, and atmospheric stability categories. EPC-CP produces customized WIND files by running a CAP-88 utility program on a user generated text file of wind data in a different format, known as a STability ARray (STAR) file (Turner, 1964). At LANL, EPC-CP meteorologists prepare customized STAR files with data over desired time periods at selected meteorological towers. A custom program written in Precision Visuals -Workstation Analysis and Visualization Environment (PV-WAVE), a commercial Fortran-like language, is used to read LANL meteorological data and write a STAR file; the executable filename is “Star.out”. However, the outdated PV-WAVE utility program is being phased out by EPC-CP, due to the inefficient process to run it and an inability to modify the code. To preserve the ability to create customized meteorological data for CAP-88 in a way that will be easy to use and maintain, a new replacement utility program, written in the Python programming language, has been developed. The new, improved program reads a data file from any LANL meteorological tower, and at each desired observation time, determines the wind direction, wind speed, and stability categories defined in the CAP-88 documentation. The frequencies of all combinations of the three sets of categories are calculated and written to a file in the STAR format, which can later be converted to a WIND file for input into CAP-88.
This dataset contains the output 6,000, 3-dimensional reactive multi-phase flow and transport aquifer simulations of brine and CO2 leakage into a protective aquiver in California’s San Joaquin Valley and input data files detailing the geologic mesh, aquifer physical properties and CO2 and brine injection rates. This data set was generated as an ongoing effort with the US DOE National Risk Assessment Partnership (NRAP) to evaluate the effectiveness of monitoring techniques to detect brine and CO2 leakage from legacy wells into underground sources of drinking water overlaying a CO2 storage reservoir. Each simulation contains a unique set of input parameters, generated stochastically. The outputs consist of these upper three geologic layers (from top): the Etchegoin, Macoma-Chanac, Santa Margarita-McLure formations. These simulations span the several distances (1, 3 and 6 km or wells W31-0.2, W31-0.5 and W31-1.0, respectively) from the CO2 injector, initiated from bottom hole pressure and saturation to calculate wellbore leakage from the storage reservoir, with low and high regional groundwater gradients and wellbore leakage into 5 leaky nodes. The dataset includes 1,000 unique simulations for each distance, which each contain a unique aquifer heterogeneity, aquifer and caprock permeability, and two model generations are included with a high permeability (prod07) and hybrid permeability (prod09). The range of permeability distributions is listed in Table 1. Each model generation consists of 3,000 simulations. Included in the dataset are the leakage rates determined from 2D wellbore models which utilize the pressure and CO2 saturation from LBL's reservoir simulations, NUFT mesh files with distributed lithology, NUFT rocktab files which describe the material properties for the geologic layers and the NUFT input files and post-processed output 'ntab' files. Each ntab file contains spatial (rows) and temporal (columns) model output tables for each model cell, the locations (x,y,z) and dimensions for each cells (dx, dy, dz). Table 1. Permeability distribution ranges for prod07 and prod09 model generations Geologic Layer: Permeability Range (log10 m^2) prod07 prod09 Etchegoin -12.92 to -10.92 -13.70 to -11.44 Macoma-Chanac -12.72 to -10.72 -13.50 to -11.24 Santa Margarita-McLure -12.70 to -10.70 -13.48 to -11.22 The input files used to generate the model include which are included in the dataset are: Time series of CO2 leakage input into the model (ex: Q_brn.W31-0.2.sim1000.layers123.tab) Time series of CO2 leakage input into the model (ex: Q_CO2.W31-0.2.sim1000.layers123.tab) Physical properties of the aquifer materials detailing the aquifer porosity, solid density, partitioning coefficients, permeabilities and van-Genuchten parameters detailed in a NUFT rocktab file: (ex: sim1000.usnt.rocktab) Numerical mesh and geologic data assigned to each model cell detailed in a NUFT genmsh format (ex: sim1000.mesh_k16.prod07.trans.genmsh) The primary output parameters are: pH (use absolute value) Change in TDS (mg/kg) Change in Pressure (Pa) Change CO2 gas saturation (fraction range 0.0-1.0) for example, the directory /p/lscratchh/mansoor1/nrap/kimberlina/prod09/mainfiles/sim1000/W31- 0.2 contains: sim1000.W31-0.2.trans.pH.red.ntab sim1000.W31-0.2.no_bg.trans.TDS.red.ntab sim1000.W31-0.2.usnt.P.deltabg.red.ntab sim1000.W31-0.2.usnt.CO2_sat.deltabg.red.ntab Each row in the NTAB files consist of model output per numerical grid cell. Each output file contains 33 columns (variables), including the information of numerical records, geologic location and sizes and the simulated parameter values over time. The first 13 variables are about numerical records and relative geologic information for a simulation grid: 1. index: simulation index 2. i: the ith grid of x-axis 3. j: the ith grid of y-axis 4. k: the ith grid of z-axis 5. element_ref: element reference 6. nuft_ind: nuft index 7. x: grid location in the x axis direction 8. y: grid location in the y axis direction 9. z: grid location in the z axis direction 10. dx: grid length in the x axis direction 11. dy: grid length in the y axis direction 12. dz: grid length in the z axis direction 13. volume: volume of the simulation grid The remainder (14, 15, 16...) variables are the simulated parameter values over time, take Pressure as an example, are: 14. 0.0y: initial pressure per cell. 15. 10.0y: simulated pressure at the end of the 10th year. 16. 20.0y: simulated pressure at the end of the 20th year. ... (repeated for every 10 years until 200 years)... The model extends 10,000 m, 5,000 m and 1,411 m in the x,y and z dimensions, respectively. The mesh consists of 164,832 cells with mesh dimensions of 101 x 51 x 32 (nx, ny, nz), with cell dimensions ranging from 100 m laterally (along x and y-axis) and model layers are as designated in the z-axis: Layer 1: atmosphere (1e-30 m thick) Layer 2: upper caprock (10 m thick) Layers 3-13: Etchegoin (536.23 m thck) Layers 14-27: Macoma-Chanac (679.04 m thick) Layers 28-32: Santa Margarita-McLure (185.94 m thick) The wellbore is placed along node i=51, j=26, and extends vertically along 5 nodes from the top to the bottom of the model. Special instructions when extracting files: Each Gzip archive (ex: prod07.sim1000-sim00099.tar.gz) contains 100 simulations. Gzip archives should be transferred into base directories (ie. In Linux: mkdir prod07; mv prod07.*.tar.gz prod07/.) before extracting, or files will be overwritten. Each sub-simulation tree should have the following file structure pattern (using the linux 'tree' command): |-- prod07 | |-- sim0001 | |-- W31-0.2 | | |-- Q_brn.W31-0.2.sim0001.layers123.tab | | |-- Q_co2.W31-0.2.sim0001.layers123.tab | | |-- sim0001.W31-0.2.no_bg.trans.TDS.red.ntab | | |-- sim0001.W31-0.2.trans.pH.red.ntab | | |-- sim0001.W31-0.2.usnt.CO2_sat.deltabg.red.ntab | | |-- sim0001.W31-0.2.usnt.P.deltabg.red.ntab | |-- W31-0.5 | | |-- Q_brn.W31-0.5.sim0001.layers123.tab | | |-- Q_co2.W31-0.5.sim0001.layers123.tab | | |-- sim0001.W31-0.5.no_bg.trans.TDS.red.ntab | | |-- sim0001.W31-0.5.trans.pH.red.ntab | | |-- sim0001.W31-0.5.usnt.CO2_sat.deltabg.red.ntab | | |-- sim0001.W31-0.5.usnt.P.deltabg.red.ntab | |-- W31-1.0 | | |-- Q_brn.W31-1.0.sim0001.layers123.tab | | |-- Q_co2.W31-1.0.sim0001.layers123.tab | | |-- sim0001.W31-1.0.no_bg.trans.TDS.red.ntab | | |-- sim0001.W31-1.0.trans.pH.red.ntab | | |-- sim0001.W31-1.0.usnt.CO2_sat.deltabg.red.ntab | | |-- sim0001.W31-1.0.usnt.P.deltabg.red.ntab | |-- sim0001.mesh_k16.prod07.trans.genmsh Disclaimer This document was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor Lawrence Livermore National Security, LLC, nor any of their employees makes any warranty, expressed or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or Lawrence Livermore National Security, LLC. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States government or Lawrence Livermore National Security, LLC, and shall not be used for advertising or product endorsement purposes. Lawrence Livermore National Laboratory is operated by Lawrence Livermore National Security, LLC, for the U.S. Department of Energy, National Nuclear Security Administration under Contract DE-AC52-07NA27344. This report was reviewed and released as LLNL-MI-753464.
The MelMACCS User Guide is intended to assist analysts in how to use the MelMACCS application to create an interface file that can be used in a MACCS calculation. MelMACCS combines MELCOR results, in the form of a MELCOR plot file, with user input. This information is used to create a MelMACCS output file that is in a format compatible with MACCS. It can then be imported as an input file in WinMACCS and used for MACCS calculations.
This presentation will describe the numerical techniques, programming paradigms, verification, and performance of a non-equilibrium plasma fluid solver that can effectively utilize current and upcoming central processing and graphics processing unit (CPU+GPU) architectures. Our plasma fluid model solves the conservation equations for self-consistent electrostatic Poisson, electron and heavy species transport, and electron temperature on adaptive Cartesian grids. Our solver is written using performance portable adaptive mesh management library, AMReX (Zhang et al., JOSS, 4 (37) 1370, 2019), and can be built and run on widely available vendor specific GPU architectures (NVIDIA/AMD/Intel). We utilize a non-subcycled second order semi-implicit time-stepping method where all adaptive mesh refinement (AMR) levels are advanced with the same time step. The composite multi-level multigrid solver from within AMReX is used for each of the governing equations that are cast into a Helmholtz equation form. We have also developed a python based chemical mechanism parser framework that uses a similar format as CANTERA (Goodwin et al., Zenodo, 2018) yaml files as input. Our custom parser reads the yaml file and provides C++ files with transport and production rate functions that can be executed on both host (CPU) and device (GPU). We present verification of our solver using method of manufactured solutions that indicate formal second order accuracy with central diffusion and fifth order weighted-essentially-non-oscillatory (WENO) advection scheme. We also verify our solver with published literature on low-pressure capacitive and high-pressure streamer discharges. Our initial performance studies indicate 10X speed-up using 20 NVIDIA GPUs versus 200 CPUs for an atmospheric streamer discharge problem solved on a 512 x 1024 x 512 grid.
Here, this work develops a predictive density tool in Python, named Plutonium Nitrate Solutions (PuNS), to reduce bias and uncertainty in nuclear criticality safety calculations for plutonium nitrate systems. The Pitzer method and an empirical method were implemented into the PuNS tool to generate atom densities for use in MCNP6 material cards. These material cards are directly prepared into an MCNP6 input text file and are calculated based on customizable user inputs of plutonium content, nitric acid content, temperature, and plutonium isotope weight percentages. The PuNS tool is validated and verified against the International Criticality Safety Benchmark Evaluation Project Handbook experiments and is observed to predict densities within a root mean square error of 0.89% for the Pitzer method and 1.82% for the empirical method. These errors in density lead to up to 1569 pcm difference in MCNP6 calculated k eff for the Pitzer method and up to a 1751 pcm difference for the empirical method when compared to experimental benchmarks. Simultaneous work is also being performed at Los Alamos National Laboratory and the University of New Mexico to create a similar tool for plutonium chloride solutions, named Plutonium Chloride Solution, which aims to provide the accreditation of the chlorine absorption. These capabilities will not only provide more accurate models but also facilitate an improved understanding of solution systems and a potential relaxation in the conservatism of current aqueous plutonium processing criticality safety limits.