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XES Neo is a fitting software that was based on the already approved EXAFS Neo genetic algorithm fitting software code. Using the principles of genetics, data fitting is done for x-ray emission spectroscopy data. Using the EXAFS Neo open source code, as well as the open source xes_neo code written by other collaborators, a new final repository for XES Neo was created with several necessary changes for general user use.
This report documents the progress in preparing the ANSYS/Fluent ® and MELCOR models to perform characterization studies of aerosols dispersal and deposition within a select spent fuel cask system. Steady state thermal-hydraulic cask response is modeled with both codes, at present, while the MELCOR source code is being modified to allow imposed thermal-hydraulic conditions with aerosol physics calculations. This will allow the MELCOR model to assume the thermal-hydraulic calculation from the ANSYS/Fluent ® , while only computing the aerosol physics. Detailed results are presented on the thermal-hydraulic analysis of the MAGNASTOR ® cask for the current ANSYS/Fluent ® model, with convergent conditions observed over two fidelities. While the MELCOR computation computes the steady state conditions, they differ sufficiently from that computed with ANSYS/Fluent ® . The MELCOR analysis include a set of UO 2 sources to investigate system response for interim reporting. The airborne concentrations and evolving distributions are presented. Model development is anticipated to continue as additional components impacting flow conditions or aerosol deposition within the MAGNASTOR ® cask and Westinghouse fuel assemblies are identified.
We describe the Harvard-NASA Emission Component version 1.0 (HEMCO), a stand-alone software component for computing emissions in global atmospheric models. HEMCO determines emissions from different sources, regions, and species on a user-defined grid and can combine, overlay, and update a set of data inventories and scale factors, as specified by the user through the HEMCO configuration file. New emission inventories at any spatial and temporal resolution are readily added to HEMCO and can be accessed by the user without any preprocessing of the data files or modification of the source code. Emissions that depend on dynamic source types and local environmental variables such as wind speed or surface temperature are calculated in separate HEMCO extensions. HEMCO is fully compliant with the Earth System Modeling Framework (ESMF) environment. It is highly portable and can be deployed in a new model environment with only few adjustments at the top-level interface. So far, we have implemented HEMCO in the NASA Goddard Earth Observing System (GEOS-5) Earth system model (ESM) and in the GEOS-Chem chemical transport model (CTM). By providing a widely applicable framework for specifying constituent emissions, HEMCO is designed to ease sensitivity studies and model comparisons, as well as inverse modeling in which emissions are adjusted iteratively. The HEMCO code, extensions, and the full set of emissions data files used in GEOS-Chem are available at http: //wiki.geos-chem.org/HEMCO.
MADSpython (Model analysis and decision support tools in Python) is a code in Python that streamlines the process of using data and models for analysis and decision support using the code MADS. MADS is open-source code developed at LANL and written in C/C++ (MADS; http://mads.lanl.gov; LA-CC-11-035). MADS can work with external models of arbitrary complexity as well as built-in models of flow and transport in porous media. The Python scripts in MADSpython facilitate the generation of input and output file needed by MADS as wells as the external simulators which include FEHM and PFLOTRAN. MADSpython enables a number of data- and model-based analyses including model calibration, sensitivity analysis, uncertainty quantification, and decision analysis. MADSpython will be released under GPL V3 license. MADSpython will be distributed as a Git repo at gitlab.com and github.com. MADSpython manual and documentation will be posted at http://madspy.lanl.gov.
Leading high performance computing systems achieve their status through use of highly parallel devices such as NVIDIA graphics processing units or Intel Xeon Phi many-core CPUs. The concept of performance portability across such architectures, as well as traditional CPUs, is vital for the application programmer. In this paper we describe targetDP, a lightweight abstraction layer which allows grid-based applications to target data parallel hardware in a platform agnostic manner. We demonstrate the effectiveness of our pragmatic approach by presenting performance results for a complex fluid application (with which the model was co-designed), plus separate lattice quantum chromodynamics particle physics code. For each application, a single source code base is seen to achieve portable performance, as assessed within the context of the Roofline model. TargetDP can be combined with Message Passing Interface (MPI) to allow use on systems containing multiple nodes: we demonstrate this through provision of scaling results on traditional and graphics processing unit-accelerated large scale supercomputers.
We requested HPC support to continue research on the seismic waves generated by impacts. We used the following codes: (1) the Hybrid Optimization Software Suite (HOSS), developed at LANL. HOSS is based on a combined Finite and Discrete Element Method (FDEM). New material models are developed for the sedimentary rocks. HOSS has been recently benchmarked to iSale and FLAG codes. SPECFEM3D is an open-source code developed since the last 90s. It won the Gordon Bell award for best performance in 2003, was finalist again in 2008 for a run at 0.16 petaflops on 149,784 cores on the ‘Jaguar’ Cray system at Oak Ridge National Laboratory. It also won the BULL Joseph Fourier supercomputing award in 2010.; SW4 is a 4th-order finite difference code developed at LLNL which is currently actively developed to handle complex 3D models and to be ported on future exascale platforms. We assessed our need to a total of 3.9M CPU-hrs for year 1 and and 3.1 M for year 2.
This document acts as a repository of knowledge for the Monte Carlo N-Particle (MCNP) transport computer code. It is maintained alongside the source code and attempts to introduce new users and re-familiarize experienced users with the theory and practices of using the MCNP code for the wide range of particle transport analyses that it is appropriate for. The latest version of the MCNP code, version 6.3.0, provides the Monte Carlo particle transport community with the latest feature developments and bug fixes in the MCNP code. The MCNP code version 6.0 and later is also known as the MCNP6 code.
This document acts as a repository of knowledge for the Monte Carlo N-Particle (MCNP) transport computer code. It is maintained alongside the source code and attempts to introduce new users and re-familiarize experienced users with the theory and practices of using the MCNP code for the wide range of particle transport analyses that it is appropriate for. The latest version of the MCNP code, version 6.3.1, provides the Monte Carlo particle transport community with the latest feature developments and bug fixes in the MCNP code. The MCNP code version 6.0 and later is also known as the MCNP6 code.
This document acts as a repository of knowledge for the Monte Carlo N-Particle (MCNP) transport computer code. It is maintained alongside the source code and attempts to introduce new users and re-familiarize experienced users with the theory and practices of using the MCNP code for the wide range of particle transport analyses that it is appropriate for. The latest version of the MCNP code, version 6.3.2, provides the Monte Carlo particle transport community with the latest feature developments and bug fixes in the MCNP code. The MCNP code version 6.0 and later is also known as the MCNP6 code.
GPU computing has established itself as a way to accelerate parallel codes in the high performance computing world. This work focuses on speeding up APNASA, a legacy CFD code used at NASA Glenn Research Center, while also drawing conclusions about the nature of GPU computing and the requirements to make GPGPU worthwhile on legacy codes. Rewriting and restructuring of the source code was avoided to limit the introduction of new bugs. The code was profiled and investigated for parallelization potential, then OpenACC directives were used to indicate parallel parts of the code. The use of OpenACC directives was not able to reduce the runtime of APNASA on either the NVIDIA Tesla discrete graphics card, or the AMD accelerated processing unit. Additionally, it was found that in order to justify the use of GPGPU, the amount of parallel work being done within a kernel would have to greatly exceed the work being done by any one portion of the APNASA code. It was determined that in order for an application like APNASA to be accelerated on the GPU, it should not be modular in nature, and the parallel portions of the code must contain a large portion of the code's computation time.
Every two years, the National Aeronautics and Space Administration (NASA) leads an assessment of U.S. Federal civilian agency Earth observation needs submitted through the Satellite Needs Working Group (SNWG) survey. In four survey cycles beginning in 2016, nearly 400 high-priority satellite needs have been identified, spanning Earth Science and representing a wide variety of potential applications for Earth observation data. During each assessment cycle, new data products and services (i.e., solutions) that meet the needs of multiple agencies are identified and proposed for funding. The majority of solutions being developed or currently operational are global in scope, including harmonized land surface reflectance data from Landsat and Sentinel-2; composites of cloud properties derived from MODIS, VIIRS, and five geostationary satellites; dynamic surface water extent and land surface disturbance products derived from multiple optical and radar missions; a suite of low-latency products from the ICESat-2 mission; and a soil moisture product derived from the upcoming NISAR mission. The SNWG Management Office, within the Earth Action element of NASA’s Earth Science Division, manages both the biennial SNWG survey assessment and the development of solutions starting at full capacity with the 2020 cycle. Each solution project is required to align with NASA’s open science policy, including developing source code in an open code repository, having an open-source software license, and making all data freely available via NASA’s Earthdata website. The presentation will include an overview of the SNWG process, its emphasis on open science, and highlight several operational solutions freely available to the global research and applications communities.
This paper describes the development of a custom preprocessor for the PaRAllel Thermal Hydraulics simulations using Advanced Mesoscopic methods (PRATHAM) code based on an open-source mesh generator, CartGen [1]. PRATHAM is a three-dimensional (3D) lattice Boltzmann method (LBM) based parallel flow simulation software currently under development at the Oak Ridge National Laboratory. The LBM algorithm in PRATHAM requires a uniform, coordinate system-aligned, non-body-fitted structured mesh for its computational domain. CartGen [1], which is a GNU-licensed open source code, already comes with some of the above needed functionalities. However, it needs to be further extended to fully support the LBM specific preprocessing requirements. Therefore, CartGen is being modified to (i) be compiler independent while converting a neutral-format STL (Stereolithography) CAD geometry to a uniform structured Cartesian mesh, (ii) provide a mechanism for PRATHAM to import the mesh and identify the fluid/solid domains, and (iii) provide a mechanism to visually identify and tag the domain boundaries on which to apply different boundary conditions.
MADSpython (Model analysis and decision support tools in Python) is a code in Python that streamlines the process of using data and models for analysis and decision support using the code MADS. MADS is open-source code developed at LANL and written in C/C++ (MADS; http://mads.lanl.gov; LA-CC-11-035). MADS can work with external models of arbitrary complexity as well as built-in models of flow and transport in porous media. The Python scripts in MADSpython facilitate the generation of input and output file needed by MADS as wells as the external simulators which include FEHM and PFLOTRAN. MADSpython enables a number of data- and model-based analyses including model calibration, sensitivity analysis, uncertainty quantification, and decision analysis. MADSpython will be released under GPL V3 license. MADSpython will be distributed as a Git repo at gitlab.com and github.com. MADSpython manual and documentation will be posted at http://madspy.lanl.gov.
To aid in WIPP PA calculations, the US DOE has undertaken a broad modernization effort that includes: 1) updating the hardware and operating system used; 2) moving the current PA (performance assessment) codes to the new system; and 3) adding a database to store results essential for the analysis of assessments; 4) integrating run-control scripts with a web site to facilitate monitoring of performance assessment calculations; and 5) adding new capabilities via implementation of the PFLOTRAN code to replace multiple codes currently in use. The majority of codes were migrated from an Open VMS/Alpha processor system to a Unix/Intel processor system. Most codes were upgraded to use the dynamic allocation features of Fortran 95 and double precision variables. A few codes were migrated from Linux/Intel systems to the Unix/Intel systems and these code required nearly no changes to the source code. All of the migrated codes were re-qualified under Sandia's WIPP QA program. The implementation of some of the conceptual models using PFLOTRAN is being driven by the expectation that changes in the configuration of the repository will require a 3-dimensional representation of the mined areas. Symmetry in the current configuration of the repository made a 2-D representation adequate. PFLOTRAN is an open-source, state-of-the-art massively parallel subsurface flow and reactive transport code. A version of this code is being developed specifically for WIPP PA in order to maintain the capabilities of the current flow and transport codes, as well as add the capabilities of a parallel code that will treat additional processes, including: 1) miscible multi-phase flow; and 2) heat transfer. PFLOTRAN will replace the two codes (i.e. BRAGFLO and NUTS) currently used for flow and transport calculations, simplifying the PA code structure and speeding up calculations by simultaneously solving for flow and transport with a single code. To ensure that PFLOTRAN reproduces the results of the current code suite, existing test cases have been used for verification purposes. Additional test cases have been developed to establish the new capabilities for WIPP PA. The result of the modernization effort will be a state-of-the-art subsurface flow and transport capability that will serve WIPP PA into the future. (authors)
We present the SuperNova Explosion Code (SNEC), an open-source Lagrangian code for the hydrodynamics and equilibrium-diffusion radiation transport in the expanding envelopes of supernovae. Given a model of a progenitor star, an explosion energy, and an amount and distribution of radioactive nickel, SNEC generates the bolometric light curve, as well as the light curves in different broad bands assuming blackbody emission. As a first application of SNEC, we consider the explosions of a grid of 15 M{sub ⊙} (at zero-age main sequence, ZAMS) stars whose hydrogen envelopes are stripped to different extents and at different points in their evolution. The resulting light curves exhibit plateaus with durations of ∼20–100 days if ≳1.5–2 M{sub ⊙} of hydrogen-rich material is left and no plateau if less hydrogen-rich material is left. If these shorter plateau lengths are not seen for SNe IIP in nature, it suggests that, at least for ZAMS masses ≲20 M{sub ⊙}, hydrogen mass loss occurs as an all or nothing process. This perhaps points to the important role binary interactions play in generating the observed mass-stripped supernovae (i.e., Type Ib/c events). These light curves are also unlike what is typically seen for SNe IIL, arguing that simply varying the amount of mass loss cannot explain these events. The most stripped models begin to show double-peaked light curves similar to what is often seen for SNe IIb, confirming previous work that these supernovae can come from progenitors that have a small amount of hydrogen and a radius of ∼500 R{sub ⊙}.
MGPT is an unclassified source code for the fast evaluation and application of quantum-based MGPT interatomic potentials for mrtals. The present version of MGPT has been developed entirely at LLNL, but is specifically designed for implementation in the open-source molecular0dynamics code LAMMPS maintained by Sandia National Laboratories. Using MGPT in LAMMPS, with separate input potential data, one can perform large-scale atomistic simulations of the structural, thermodynamic, defeat and mechanical properties of transition metals with quantum-mechanical realism.
MGPT is an unclassified source code for the fast evaluation and application of quantum-based MGPT interatomic potentials for mrtals. The present version of MGPT has been developed entirely at LLNL, but is specifically designed for implementation in the open-source molecular0dynamics code LAMMPS maintained by Sandia National Laboratories. Using MGPT in LAMMPS, with separate input potential data, one can perform large-scale atomistic simulations of the structural, thermodynamic, defeat and mechanical properties of transition metals with quantum-mechanical realism.
Sandia National Laboratories has modified Simulating WAves Nearshore (SWAN), the Delft University of Technology code,, to include functionality to better model wave energy converters (WECs). The code modified by SNL has been named SNL-SWAN. SNL-SWAN includes a frequency dependent WEC Module that builds upon the traditional obstacle module in SWAN. In SNL-SWAN, a frequency dependent transmission coefficient is calculated to model power absorbed by WEC devices. The power transmission coefficients are calculated based on the WEC's power performance, which is defined by the user in the input text files. Two methods of defining WEC power performance have been implemented: a user-specified power matrix, and a user-specified relative capture width. These methods allow the user to define the WEC's frequency dependent energy absorption as a function of period and/or wave height. SWAN test cases were run to verify the compiled code's functionality, and verification of the frequency dependent WEC Module in SNL-SWAN has also been performed. SNL-SWAN code is will be released as open source code and will be made publicly available.