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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.

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At least 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Community-Driven Methods for Open and Reproducible Software Tools for Analyzing Datasets from Atom Probe Microscopy

Atom probe tomography, and related methods, probe the three-dimensional architecture of a material. The software tools that microscopists use, and how these tools are connected into workflows, makes a substantial contribution to the accuracy and precision of such a material characterization experiment. Typically, we adapt methods from other communities like mathematics, data science, computational geometry, artificial intelligence, or scientific computing. We also realize that improving on research data management is a challenge when it comes to align with the FAIR data stewardship principles. Faced with this global challenge, we are convinced that collaborating is useful. Here, we report the results and challenges with an inter-laboratory call for developing test cases for several types of atom probe software tools. The results support why defining detailed recipes of software workflows and sharing these recipes is necessary and rewarding: Open source tools and (meta)data exchange can help to make our day-to-day data processing tasks become more efficient, the training of new users and knowledge transfer become easier, and assist us with automated quantification of uncertainties to gain access to substantiated results.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Invited: Software defined accelerators from learning tools environment

Next generation systems, such as edge devices, will need to provide efficient processing of machine learning (ML) algorithms along several metrics, including energy, performance, area, and latency. However, the quickly evolving field of ML makes it extremely difficult to generate accelerators able to support a wide variety of algorithms. At the same time, designing accelerators in hardware description languages (HDLs) by hand is hard and time consuming, and does not allow quick exploration of the design space. In this paper we present the Software Defined Accelerators From Learning Tools Environment (SODALITE), an automated open source high-level ML framework-to-verilog compiler targeting ML Application-Specific Integrated Circuits (ASICs) chiplets. The SODALITE approach will implement optimal designs by seamlessly combining custom components generated through high-level synthesis (HLS) with templated and fully tunable Intellectural Properties (IPs) and macros, integrated in an extendable resource library. Through a closed loop design space exploration engine, developers will be able to quickly explore their hardware designs along different dimensions.

High-Level Synthesis, Accelerators, Hardware-Softw↗
DOE OSTI2016

OPENSTUDIO® [SWR-07-40]

OpenStudio® is a cross-platform (Windows, Mac, and Linux) collection of software tools to support whole building energy modeling using EnergyPlus and advanced daylight analysis using Radiance. OpenStudio is an open source (LGPL) project to facilitate community development, extension, and private sector adoption. OpenStudio includes graphical interfaces along with a Software Development Kit (SDK). In addition to the graphical interface, OpenStudio allows building researchers and software developers to quickly get started through its multiple entry levels, including access through C++, Ruby, and C#. Users can leverage the Ruby interface to create OpenStudio Measures that can be easily shared and applied to OpenStudio Models. OPENSTUDIO® IS A REGISTERED TRADEMARK OF ALLIANCE FOR SUSTAINABLE ENERGY, LLC, THE MANAGER AND OPERATOR OF THE NATIONAL RENEWABLE ENERGY LABORATORY.

Ball, Brian↗
DOE OSTI2019

Nonlinear Modeling of Ground Motions at Lotung LSST Site with Nested Surface Soil Constitutive Model

Seismic analyses of nuclear facilities require the use of validated numerical models that can realistically reproduce the response of soils during earthquakes. The nested surface nonlinear, hysteretic soil constitutive model is one of the soil constitutive models that is vastly used due to (i) lower number of free parameters compared to other nonlinear soil constitutive models, and (ii) because of the ease of calibrating these parameters using the commonly available soil data, i.e., G/G max and damping curves as a function of shear strain. This material model is available in commercial finite element software packages (LS-DYNA and Abaqus) as well in the open source finite element tool (Mastodon). The purpose of this study is to estimate the parameters required for this material model from the soil data available for the Lotung site and to demonstrate that this nonlinear soil constitutive model used in a time domain, finite element analysis can reasonably reproduce the actual measured soil motions recorded at Lotung during the LSST07 event on May 20, 1986. Results are introduced from all the three softwares mentioned above using the same material model.

42 ENGINEERING↗
DOE OSTI2023

R “SHINY” GUI DEVELOPMENT FOR URANIUM ISOTOPIC ANALYSIS WITH MATRIX-ASSISTED IONIZATION MASS SPECTROMETRY

The international nuclear safeguards community continues to seek rapid, accurate, and precise characterization capabilities for the in-field measurement of uranium isotopic compositions in nuclear facilities. Mass spectrometry (MS) is considered the “gold standard” for analysis of relatively long-lived actinides such as uranium (U) and plutonium; however, conventional MS analysis often requires time consuming sample preparation and complex analytical methodologies that are difficult to perform in-field or in-facility. Matrix assisted ionization (MAI) is a novel ambient ionization MS technique (i.e., MAI-MS) that potentially addresses these challenges due to the relative simplicity of the ionization phenomenon and ruggedness of ambient MS instrumentation. Savannah River National Laboratory (SRNL, USA) has demonstrated this technique for nanogram-level 235U/238U isotope ratio measurements within seconds, with percent-level analytical uncertainties capable of discriminating depleted, natural, and low-enriched uranium. Current experimental work on developing MAI methods for uranium isotopic analysis has been enabled by parallel development of a comprehensive MAI-MS data analysis suite at SRNL. Development of this bespoke data analysis software was necessary because commercially available ambient MS software is poorly suited for uranium isotope ratio measurement. The effort leverages the power of R, a popular open-source programming language, and Shiny, an R package providing tools for graphical user interface (GUI) and web interface coding. This software allows researchers without any programming experience to harness and utilize R’s considerable data analysis/visualization power.

LaBone, Elizabeth D.↗
DOE OSTI2023

Machine Learning Full NMR Chemical Shift Tensors of Silicon Oxides with Equivariant Graph Neural Networks

The nuclear magnetic resonance (NMR) chemical shift tensor is a highly sensitive probe of the electronic structure of an atom and furthermore its local structure. Recently, machine learning has been applied to NMR in the prediction of isotropic chemical shifts from a structure. Current machine learning models, however, often ignore the full chemical shift tensor for the easier-to-predict isotropic chemical shift, effectively ignoring a multitude of structural information available in the NMR chemical shift tensor. Here we use an equivariant graph neural network (GNN) to predict full 29 Si chemical shift tensors in silicate materials. The equivariant GNN model predicts full tensors to a mean absolute error of 1.05 ppm and is able to accurately determine the magnitude, anisotropy, and tensor orientation in a diverse set of silicon oxide local structures. When compared with other models, the equivariant GNN model outperforms the state-of-the-art machine learning models by 53%. The equivariant GNN model also outperforms historic analytical models by 57% for isotropic chemical shift and 91% for anisotropy. The software is available as a simple-to-use open-source repository, allowing similar models to be created and trained with ease.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Domain-specific compilers for dynamic simulations of quantum materials on quantum computers

Abstract Simulation of the dynamics of quantum materials is emerging as a promising scientific application for noisy intermediate-scale quantum (NISQ) computers. Due to their high gate-error rates and short decoherence times, however, NISQ computers can only produce high-fidelity results for those quantum circuits smaller than some given circuit size. Dynamic simulations, therefore, pose a challenge as current algorithms produce circuits that grow in size with each subsequent time-step of the simulation. This underscores the crucial role of quantum circuit compilers to produce executable quantum circuits of minimal size, thereby maximizing the range of physical phenomena that can be studied within the NISQ fidelity budget. Here, we present two domain-specific (DS) quantum circuit compilers for the Rigetti and IBM quantum computers, specifically designed to compile circuits simulating dynamics under a special class of time-dependent Hamiltonians. The compilers outperform state-of-the-art general-purpose compilers in terms of circuit size reduction by around 25%–30% as well as wall-clock compilation time by around 40% (dependent on system size and simulation time-step). Drawing on heuristic techniques commonly used in artificial intelligence, both compilers scale well with simulation time-step and system size. Code for both compilers is open-source and packaged into a full-stack quantum simulation software with tutorials included for ease of use for future researchers wishing to perform dynamic simulations of quantum materials on quantum computers. As our DS compilers provide significant improvements in both compilation time and simulation fidelity, they provide a building block for accelerating progress toward physical quantum supremacy.

Physics↗
DOE OSTI2019

A Novel Sparse Compositional Technique Reveals Microbial Perturbations

ABSTRACT The central aims of many host or environmental microbiome studies are to elucidate factors associated with microbial community compositions and to relate microbial features to outcomes. However, these aims are often complicated by difficulties stemming from high-dimensionality, non-normality, sparsity, and the compositional nature of microbiome data sets. A key tool in microbiome analysis is beta diversity, defined by the distances between microbial samples. Many different distance metrics have been proposed, all with varying discriminatory power on data with differing characteristics. Here, we propose a compositional beta diversity metric rooted in a centered log-ratio transformation and matrix completion called robust Aitchison PCA. We demonstrate the benefits of compositional transformations upstream of beta diversity calculations through simulations. Additionally, we demonstrate improved effect size, classification accuracy, and robustness to sequencing depth over the current methods on several decreased sample subsets of real microbiome data sets. Finally, we highlight the ability of this new beta diversity metric to retain the feature loadings linked to sample ordinations revealing salient intercommunity niche feature importance. IMPORTANCE By accounting for the sparse compositional nature of microbiome data sets, robust Aitchison PCA can yield high discriminatory power and salient feature ranking between microbial niches. The software to perform this analysis is available under an open-source license and can be obtained at https://github.com/biocore/DEICODE ; additionally, a QIIME 2 plugin is provided to perform this analysis at https://library.qiime2.org/plugins/q2-deicode .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

The SODA Approach: Leveraging High-Level Synthesis for Hardware/Software Co-design and Hardware Specialization: Invited

Novel "converged" applications combine phases of scientific simulation with data analysis and machine learning. Each computational phase can benefit from specialized accelerators. However, algorithms evolve so quickly that mapping them on existing accelerators is suboptimal or even impossible. This paper presents the SODA (Software Defined Accelerators) framework, a modular, multi-level, open-source, no-human-in-the-loop, hardware synthesizer that enables end-to-end generation of specialized accelerators. SODA is composed of SODA-Opt, a high-level frontend developed in MLIR that interfaces with domain-specific programming frameworks and allows performing system level design, and Bambu, a state-of-the-art high-level synthesis engine that can target different device technologies. The framework implements design space exploration as compiler optimization passes. We show how the modular, yet tight, integration of the high-level optimizer and lower-level HLS tools enables the generation of accelerators optimized for the computational patterns of converged applications. We then discuss some of the research opportunities that such a framework allows, including system-level design, profile driven optimization, and supporting new optimization metrics.

Bohm Agostini, Nicolas↗
DOE OSTI2023

DEPDOSE

DEPDOSE is an open-source computer application which combines the KDEP respiratory tract deposition software with committed equivalent dose coefficient tables from the DC_PAK internal dosimetry package. DEPDOSE allows the user to rapidly produce tables of dose coefficients to workers and members of the public inhaling a precisely defined, user-specified aerosols using the ICRP 60 series methodology. Combined with a plume dispersion modeling system such as QUIC, this makes it possible to predict radiation doses downstream from an accidental or intentional release of radioactive materials. For this work, a radioactive plume was calculated to members of the public downstream from a plausible release of radionuclides into the atmosphere.

Klumpp, John↗
DOE OSTI2021

A NICER View of the Massive Pulsar PSR J0740+6620 Informed by Radio Timing and XMM-Newton Spectroscopy

We report on Bayesian estimation of the radius, mass, and hot surface regions of the massive millisecond pulsar PSR J0740+6620, conditional on pulse-profile modeling of Neutron Star Interior Composition Explorer X-ray Timing Instrument event data. We condition on informative pulsar mass, distance, and orbital inclination priors derived from the joint North American Nanohertz Observatory for Gravitational Waves and Canadian Hydrogen Intensity Mapping Experiment/Pulsar wideband radio timing measurements of Fonseca et al. We use XMM-Newton European Photon Imaging Camera spectroscopic event data to inform our X-ray likelihood function. The prior support of the pulsar radius is truncated at 16 km to ensure coverage of current dense matter models. We assume conservative priors on instrument calibration uncertainty. We constrain the equatorial radius and mass of PSR J0740+6620 to be ${12.39}_{-0.98}^{+1.30}$ km and ${2.072}_{-0.066}^{+0.067}$ M ⊙ respectively, each reported as the posterior credible interval bounded by the 16% and 84% quantiles, conditional on surface hot regions that are non-overlapping spherical caps of fully ionized hydrogen atmosphere with uniform effective temperature; a posteriori, the temperature is ${\mathrm{log}}_{10}(T\,[{\rm{K}}])={5.99}_{-0.06}^{+0.05}$ for each hot region. All software for the X-ray modeling framework is open-source and all data, model, and sample information is publicly available, including analysis notebooks and model modules in the Python language. Furthermore, our marginal likelihood function of mass and equatorial radius is proportional to the marginal joint posterior density of those parameters (within the prior support) and can thus be computed from the posterior samples.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Sandbox for Mac Malware v 1.0

This software is an analyzer for automated sandbox analysis of malware on the OS X operating system. It runs inside an OS X virtual machine to collect data about what happens when a given file is opened or run. As of August 2014, there was no sandbox software for Mac OS X malware, as it requires different methods from those used on the Windows OS (which most sandboxes are written for). This software adds OS X analysis capabilities to an existing open-source sandbox, Cuckoo Sandbox (http://cuckoosandbox.org/), which previously only worked for Windows. The analyzer itself can take many different types of files as input: the traditional Mach-O and FAT executables, .app files, zip files, Python scripts, Java archives, and web pages, as well as PDFs and other documents. While the file is running, the analyzer also simulates rudimentary human interaction with clicks and mouse movements in order to bypass the tests some malware use to see if they are being analyzed. The analyzer outputs several different kinds of data: function call traces, network captures, screenshots, and all created and modified files. This work also includes a static analysis Cuckoo module for Mach-O binary files. It extracts file structures, code library imports and exports, and signatures. This data can be used along with the analyzer results to create signatures for malware.

Walkup, Elizabeth↗
DOE OSTI2023

MOOSE: A Modular Platform for Fission and Fusion Multiphysics

The Multiphysics Object-Oriented Simulation Environment (MOOSE) Framework, as well as MOOSE-based simulation tools, have accelerated the development of fission energy and advanced reactor technologies through the United States Department of Energy, Office of Nuclear Science, Nuclear Energy Advanced Modeling & Simulation (NEAMS) Program. MOOSE contains a complete platform of multiphysics simulation capabilities, capable of running on massively parallel systems, and is developed in an open-source manner with great attention paid to high-quality software quality assurance practices. This overall approach could greatly benefit the fusion energy community, which requires rapid design iteration and improvement in order to facilitate the successful development of fusion as an alternative energy source to fossil fuels. In the first half of this talk, applications of MOOSE and MOOSE-based tools for advanced reactor designs will be showcased, as well as MOOSE ecosystem infrastructure (such as the NEAMS Virtual Test Bed) that enables and accelerates fission reactor design. In the second half, a discussion of how the MOOSE approach to modeling and simulation is currently being applied internationally in fusion energy research and development at the United Kingdom Atomic Energy Authority will be discussed, and ongoing/future domestic research efforts will be highlighted.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Open Source Suite for Advanced Synchrophasor Analysis

The report presents the results of the development of the open-source suite of applications for synchrophasor analysis. The suite includes several software tools for oscillation analysis, power plant model validation, and frequency response analysis using synchrophasor measurements. All tools are based on the common framework and data sources. The developed tools have been used by different electrical utilities for synchrophasor analysis. The report includes several use cases based on the actual system PMU data.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2018

Open-Source Suite for Advanced Synchrophasor Analysis

The paper presents the results of the development of the open-source suite of applications for synchrophasor analysis. The suite includes several software tools for oscillation analysis, power plant model validation, and frequency response analysis using synchrophasor measurements. All tools are based on the common framework and data sources. The developed tools have been used by the Bonneville Power Administration and other utilities for synchrophasor analysis. The paper includes several use cases based on the actual system PMU data.

PMU, model validation, Oscillation Modes, Open Sou↗
DOE OSTI2015

Augmenting Traditional Static Analysis With Commonly Available Metadata

Developers and security analysts have been using static analysis for a long time to analyze programs for defects and vulnerabilities with some success. Generally a static analysis tool is run on the source code for a given program, flagging areas of code that need to be further inspected by a human analyst. These areas may be obvious bugs like potential bu er over flows, information leakage flaws, or the use of uninitialized variables. These tools tend to work fairly well - every year they find many important bugs. These tools are more impressive considering the fact that they only examine the source code, which may be very complex. Now consider the amount of data available that these tools do not analyze. There are many pieces of information that would prove invaluable for finding bugs in code, things such as a history of bug reports, a history of all changes to the code, information about committers, etc. By leveraging all this additional data, it is possible to nd more bugs with less user interaction, as well as track useful metrics such as number and type of defects injected by committer. This dissertation provides a method for leveraging development metadata to find bugs that would otherwise be difficult to find using standard static analysis tools. We showcase two case studies that demonstrate the ability to find 0day vulnerabilities in large and small software projects by finding new vulnerabilities in the cpython and Roundup open source projects.

97 MATHEMATICS AND COMPUTING↗