SEARCH · Search NASA
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
This software contains the code artifacts for the Supercomputing 2025 paper titled "C.A.T.S.: Memory and Control Flow Tracing for Whole-Program Performance Analysis". The included code contains analysis passes, dynamic program instrumentation, runtime, examples, and tests.
Python software package for PCA analysis of ToF-SIMS spectra data
Fixed-energy Response-function Analysis with Multiple efficiency (FRAM) is a software code designed primarily for plutonium and uranium isotopic analysis. It is widely used in both the domestic and international safeguards community. FRAM can quickly and accurately determine the isotopic compositions of plutonium, uranium, and mixed oxides (MOX) when measuring with a high-purity germanium (HPGe), cadmium zinc telluride (CZT), or lanthanum bromide (LaBr 3 ) detector. The capabilities of FRAM have been enhanced to analyze the data of the pixelated CZT detector (made by H3D) and to measure the mass of plutonium, uranium, and MOX. Both the isotopic composition and mass of the item can be quickly determined with one measurement using a gamma detector with FRAM v.7.1.
This paper introduces a new approach toward characterizing local structural features of two-dimensional particle systems. The approach can accurately identify and characterize defects in high-temperature crystals, distinguish a wide range of nominally disordered systems, and robustly describe complex structures such as grain boundaries. This paper also introduces two-dimensional functionality into the open-source software program VoroTop which automates this analysis. This software package is built on a recently-introduced multithreaded version of V ORO ++, enabling the analysis of systems with billions of particles on high-performance computer architectures.
In previous years, SGTech funded enhancements to the isotopics routine in the software called Gamma Detector Response and Analysis Software-Detector Response Function (GADRAS-DRF), including the addition of peak fit customization capabilities. A project was also funded that focused on implementing a peak-based model fitting routine, allowing model fitting to be performed without dependence on export-controlled cross-sections. In FY25 significant improvements were made to the custom peak fitting interface, accompanied by several validation studies within GADRAS-DRF. These studies encompassed IsotopeID performance, distributed source analysis, isotopics validation, and activity estimation. Additionally, the peak-only model fitting option was validated using an HPGe measurement of a rotating drum with line sources.
Software supply chain risk management (SCRM) depends upon accurate information regarding the software components that comprise any given software system. The collection of components included in a software package can be organized within a software bill of materials, or SBOM. SBOMs are ideally generated when the software components are put together, such as at compile time, but for many reasons that has not and is not always possible. For example, legacy or proprietary software packages often do not have SBOMs available to downstream consumers of that software. It’s not just end users that are affected, manufacturers themselves also must deal with this problem. To answer these questions, the market has seen the rise of several commercial software composition analysis (SCA) tools. These tools aim to peer into completed software systems, automatically identifying hidden software dependencies and looking up known vulnerabilities associated with those dependencies to enable end-users to enhance their cyber supply chain risk management processes. These tools are potentially a huge boon to end users of legacy and proprietary software – and a potential bane, depending on how accurate they are. This research asks that question – how accurate are currently available binary SCA tools – and provides answers to several other questions: What does it mean to be “accurate”? What limitations do the tools have in identifying common edge cases that take place in modern software development? Can they help you avoid a devastating supply chain attack, or is it all just noise? After researching SCA tools on the market, we identified three vendors that fit our use case and would provide analysis on compiled binaries. Using these tools, we submitted firmware for critical infrastructure devices for analysis and SBOM generation. The SBOM outputs were then cross referenced with SBOMs generated through manual analysis for comparison. In addition to the firmware samples, we also submitted edge case samples based off a popular open-source library that were specifically crafted to evaluate each tools’ ability to accurately identify components. These samples were customized to be consistent with modifications we have seen in modern software development as well as a couple that are representative of supply chain attacks.
One major software update was accomplished within the Gamma Detector Response and Analysis Software-Detector Response Function (GADRAS-DRF) package. This update allows users to adjust individual peak fits for use in subsequent analysis processes within GADRAS-DRF. A graphical user interface (GUI) was implemented so users can see the effect of their fit adjustments on the spectrum. This new feature will enhance the capability of the previously funded auto-enrichment and peak-based 1D model fit feature that was implemented in FY23 (Fiscal Year). Isotopic analysis was performed using M400 data obtained from uranium standards and the isotopic assessment is given with and without manual peak adjustments.
Due to software fragmentation, PV system modeling teams can be limited to language specific packages, preventing cross-sectional analysis of different modeling techniques and workflows. To this end, PVplr, a popular PV performance modeling R software package, has been ported to the Python programming language. To verify and test the robustness of the port, NSRDB data has been used to simulated PV installations at native resolution (~2 million Sites), with a variety of degradation rates, degradation patterns, and modules. Performance Ratios were calculated using the ported functions from pvplr-python and compared against Rdtools YoY values. Due to the complicated nature of degradation, a new metric has been proposed to quantify the performance loss of a system. The cumulative production loss, is the total amount of energy lost due to the degrading performance of the system. Cumulative production loss alleviates the problems with fitting linear functions to non-linear degradation. Cumulative Production loss was shown to better estimate the total lost revenue for non-linear degradation patterns. $XbX + UTC$ was found to most accurately predict the total lost revenue in simulated systems.
There is currently a lack of software that meets the needs for the analysis of raw data produced by modern isotope ratio mass spectrometers for both R&D and routine use at SRNL and other US national labs • Needs to accommodate multiple isotope systems, instruments, and manufacturers • Include modern statistical methods and handling/visualization of uncertainty • Flexible software with transparent (no “black box”) and reproducible methods • This project is inspired by existing discipline-specific data analysis software (e.g., Tripoli1 , ET_Redux2 , IsoplotR3) used in the geochemical community • Our goal is to build an open source data analysis software package that focuses on flexibility, transparency, and reproducibility
• There is currently a lack of software that meets the needs for the analysis of raw data produced by modern isotope ratio mass spectrometers for both R&D and routine use at SRNL and other US national labs • Needs to accommodate multiple isotope systems, instruments, and manufacturers • Include modern statistical methods and handling/visualization of uncertainty • Flexible software with transparent (no “black box”) and reproducible methods • This project is inspired by existing discipline-specific data analysis software (e.g., Tripoli1 , ET_Redux2, IsoplotR3) used in the geochemical community • Our goal is to build an open source data analysis software package that focuses on flexibility, transparency, and reproducibility
This report documents the verification and validation (V&V) efforts of the Monte Carlo N-Particle transport code (MCNP) version 6.2 on the Sawtooth supercomputer for the Microreactor Applications Research Validation and Evaluation (MARVEL) microreactor required for the preliminary documented safety analysis. This document records V&V for a safety, hazards, analysis, and design software used for design and analysis of safety class structures, system and components (SSC)s.
The IRIS-HEP software institute, as a contributor to the broader HEP Python ecosystem, is developing scalable analysis infrastructure and software tools to address the upcoming HL-LHC computing challenges with new approaches and paradigms, driven by our vision of what HL-LHC analysis will require. The institute uses a "Grand Challenge" format, constructing a series of increasingly large, complex, and realistic exercises to show the vision of HL-LHC analysis. Recently, the focus has been demonstrating the IRIS-HEP analysis infrastructure at scale and evaluating technology readiness for production. As a part of the Analysis Grand Challenge activities, the institute executed a "200 Gbps Challenge", aiming to show sustained data rates into the event processing of multiple analysis pipelines. The challenge integrated teams internal and external to the institute, including operations and facilities, analysis software tools, innovative data delivery and management services, and scalable analysis infrastructure. The challenge showcases the prototypes - including software, services, and facilities - built to process around 200 TB of data in both the CMS NanoAOD and ATLAS PHYSLITE data formats with test pipelines. The teams were able to sustain the 200 Gbps target across multiple pipelines. The pipelines focusing on event rate were able to process at over 30 MHz. These target rates are demanding; the activity revealed considerations for future testing at this scale and changes necessary for physicists to work at this scale in the future. The 200 Gbps Challenge has established a baseline on today's facilities, setting the stage for the next exercise at twice the scale.
Current approaches for risk analysis of software vulnerabilities using manual assessment and numeric scoring do not complete fast enough to keep pace with the maintenance work rate to patch and mitigate the vulnerabilities. This paper proposes a new approach to modeling software vulnerability risk in the context of the network environment and firewall configuration. In the approach, vulnerability features are automatically matched up with networking, target asset, and adversary features to determine whether adversaries can exploit a vulnerability. The ability of adversaries to reach a vulnerability is modeled by automatically identifying the network services associated with vulnerabilities through a pipeline of machine learning and natural language processing and automatically analyzing network reachability. Our results show that the pipeline can identify network services accurately. We also find that only a small number of vulnerabilities pose real risks to a system. However, if left unmitigated, adversarial reach to vulnerabilities may extend to nullify the effect of firewall countermeasures.
This work introduces a completely rewritten version of the programRMCProfile(version 7), big-box, reverse Monte Carlo modelling software for analysis of total scattering data. The major new feature ofRMCProfile7is the ability to refine multiple phases simultaneously, which is relevant for many current research areas such as energy materials, catalysis and engineering. Other new features include improved support for molecular potentials and rigid-body refinements, as well as multiple different data sets. An empirical resolution correction and calculation of the pair distribution function as a back-Fourier transform are now also available.RMCProfile7is freely available for download at https://rmcprofile.ornl.gov/.
The embedded call to a special version of the web-based Bilbao Crystallographic Server tool k-SUBGROUPSMAG from within GSAS-II to form a list of all possible commensurate magnetic subgroups of a parent magnetic grey group is described. It facilitates the selection and refinement of the best commensurate magnetic structure model by having all the analysis tools including Rietveld refinement in one place as part of GSAS-II. It also provides the chosen magnetic space group as one of the 1421 possible standard Belov–Neronova–Smirnova forms or equivalent non-standard versions.
In applications where efficiency is critical, developers may examine their compiled binaries, seeking to understand how the compiler transformed their source code and what performance implications that transformation may have. This analysis is challenging due to the vast number of disassembled binary instructions and the many-to-many mappings between them and the source code. These problems are exacerbated as source code size increases, giving the compiler more freedom to map and disperse binary instructions across the disassembly space. Interfaces for disassembly typically display instructions as an unstructured listing or sacrifice the order of execution. Here, we design a new visual interface for disassembly code that combines execution order with control flow structure, enabling analysts to both trace through code and identify familiar aspects of the computation. Central to our approach is a novel layout of instructions grouped into basic blocks that displays a looping structure in an intuitive way. We add to this disassembly representation a unique block-based mini-map that leverages our layout and shows context across thousands of disassembly instructions. Finally, we embed our disassembly visualization in a web-based tool, DisViz, which adds dynamic linking with source code across the entire application. DizViz was developed in collaboration with program analysis experts following design study methodology and was validated through evaluation sessions with ten participants from four institutions. Participants successfully completed the evaluation tasks, hypothesized about compiler optimizations, and noted the utility of our new disassembly view. Our evaluation suggests that our new integrated view helps application developers in understanding and navigating disassembly code.
SAND2026-23986O EFBMC performs elastic Bayesian model calibration by applying Bayesian statistics and functional analysis. The software provides Python, R, and MATLAB scripts that enable users to calibrate models. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
US Special Operations Command (USSOCOM) was seeking a technological leap in small firearms weapon suppressor technology, because anticipated enemy capabilities are requiring the operators to have smaller detection cross sections to ensure the safe execution of missions. Suppressors have been developed almost exclusively through trial-and-error methods since the time of the original design by Hiram Maxim over one hundred years ago. Consequently USSOCOM deemed it prudent to perform a physicsbased study of weapon suppression to understand performance limits and possibly identify breakthrough technologies. Lawrence Livermore National Laboratory’s (LLNL’s) high performance production level computational tool called ALE3D (Arbitrary Lagrangian-Eulerian 3D and 2D) has unique physics models and numerical algorithms for modeling suppressor dynamics. The flexible and extendable code framework supports fully integrated hydrodynamics, heat transfer, solid and fluid dynamics, and chemistry that can be applied to simulating propellant-driven motion of a bullet down a gun barrel, the transfer of heat from the burning propellant to the barrel and suppressor, the chemistry of muzzle flash, and the shock/acoustic/optical signatures in the near-field. LLNL’s originally anticipated role was to augment ALE3D for this task, by developing the software and analysis methodologies specific to the simulation of blast and muzzle flash phenomena. It was believed that insights provided by our ALE3D simulations in tandem with a coordinated experimental component by our other team members from Oak Ridge National Laboratory (ORNL) and the U. S. Army Armament, Research, Development and Engineering Center (ARDEC), would have excellent prospects of yielding useful suppressor design improvements that could be transitioned to industry and utilized by US Special Operations Command. The three year effort has come to fruition with the development of revolutionary suppressor designs that far outperform any previous or current design by anyone outside this multi-lab team.