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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 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

A Locally Adapting Technique for Edge Detection using Image Segmentation

Rapid growth in the field of quantitative digital image analysis is paving the way for scientific researchers to make precise measurements about objects in an image. To compute quantities from an image such as the density of compressed materials or the velocity of a shockwave, object boundaries must first be determined. Images containing regions that each have a spatial trend in intensity are of particular interest here. For edge detection, we present a supervised, statistical image segmentation method that incorporates spatial information to locate boundaries between regions with overlapping intensity histograms, specifically for images where the regions are known but precise boundary locations are unknown. The segmentation of a pixel is determined by comparing its intensity to distributions from nearby pixel intensities, and a gradient of the segmented image indicates edge locations. Because of the statistical nature of the algorithm, we use maximum likelihood estimation to quantify uncertainty about each boundary. We demonstrate the success of this algorithm at locating boundaries and providing uncertainty bands on a radiograph of a multicomponent cylinder and on an optical image of a laser-induced shockwave.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

A practical guide to unbinned unfolding

Unfolding, in the context of high-energy particle physics, refers to the process of removing detector distortions in experimental data. The resulting unfolded measurements are straightforward to use for direct comparisons between experiments and a wide variety of theoretical predictions. For decades, popular unfolding strategies were designed to operate on data formatted as one or more binned histograms. In recent years, new strategies have emerged that use machine learning to unfold datasets in an unbinned manner, allowing for higher-dimensional analyses and more flexibility for current and future users of the unfolded data. This guide comprises recommendations and practical considerations from researchers across a number of major particle physics experiments who have recently put these techniques into practice on real data.

Canelli, Florencia [Univ. of Zurich (Switzerland)]↗
DOE OSTI2012

NV: Nessus Vulnerability Visualization for the Web

Network vulnerability is a critical component of network se- curity. Yet vulnerability analysis has received relatively lit- tle attention from the security visualization community. In this paper we describe nv, a web-based Nessus vulnerability visualization. Nv utilizes treemaps and linked histograms to allow system administrators to discover, analyze, and man- age vulnerabilities on their networks. In addition to visual- izing single Nessus scans, nv supports the analysis of sequen- tial scans by showing which vulnerabilities have been fixed, remain open, or are newly discovered. Nv was also designed to operate completely in-browser, to avoid sending sensitive data to outside servers. We discuss the design of nv, as well as provide case studies demonstrating vulnerability analysis workflows which include a multiple-node testbed and data from the 2011 VAST Challenge.

information visualization↗
DOE OSTI2025

Poster Abstract: Leveraging Large Language Models to Reveal Interpretable Cooling Behaviors from Smart Thermostat Data

Frequent heatwaves and hot summers increasingly challenge occupant comfort, health, and energy grid stability. Addressing these challenges requires a detailed understanding of household cooling behaviors, such as thermostat adjustments and adaptive responses to extreme conditions. Traditional analyses often rely on aggregated numerical metrics that overlook subtle but important household-specific variations. In this study, we introduce a generalizable methodology that integrates large language models (LLMs) with vision capabilities to enable scalable and detailed analysis of residential thermostat data. Using Ecobee's Donate Your Data (DYD) dataset—which provides five-minute records of indoor temperatures, thermostat setpoints, and HVAC runtimes—we focus on two U.S. cities with contrasting summer climates : Austin (TX) and Phoenix (AZ). Because raw time-series data are not well suited for direct LLM analysis, we transform them into visual representations, such as daily indoor temperature trajectories and weekly runtime histograms, to better capture behavioral variations. Leveraging LLMs' visual interpretation, we extract descriptive behavioral features, including temperature preferences, time-of-day cooling orientation, anticipatory versus reactive heatwave responses, and behavioral consistency. These semantic features support unsupervised clustering to identify distinct occupant archetypes at scale, revealing differences—such as morning-centric anticipatory coolers versus households that shift toward warmer setpoints during heatwaves—that can inform demand response, resilience planning, and health-aware interventions. By converting raw numerical data into interpretable behavioral patterns, this methodology enables scalable and practical analysis of occupant behavior, supporting actionable insights for comfort, resilience, and energy management.

Nihar, Kopal↗
DOE OSTI2019

Troubleshooting deep-learner training data problems using an evolutionary algorithm on Summit

Architectural and hyper-parameter design choices can influence deep-learner (DL) model fidelity but can also be affected by malformed training and validation data. However, practitioners may spend significant time refining layers and hyper-parameters before discovering that distorted training data was impeding training progress. We found that an evolutionary algorithm (EA) can be used to diagnose this kind of DL problem. An EA evaluated thousands of DL configurations on Summit that yielded no overall improvement in DL performance, which suggested problems with the training and validation data. We suspected that Contrast Limited Adaptive Histogram Equalization (CLAHE) enhancement that was applied to previously generated digital surface models (DSMs), for which we were training DLs to find errors, had damaged the training data. Subsequent runs with an alternative global normalization yielded significantly improved DL performance. However, the DL Intersection Over Union (IOU) still exhibited consistent sub-par performance, which suggested further problems with the training data and DL approach. Nonetheless, we were able to diagnose this problem within a 12-hour span via Summit runs, which prevented several weeks of unproductive trial-and-error DL configuration refinement, and allowed for a more timely convergence on an ultimately viable solution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Observation of a single protein by ultrafast X-ray diffraction

* runs/ - The raw data from the pnCCD * histograms.h5 - Histogram of the values recorded by each pixel throughout the data acquisition. Used for pedestal and gain correction. * mask.h5 - Detector bad pixels mask. * 1SS8.pdb - PDB model of the assembled GroEL 14-mer, from the 1SS8 PDB entry. * 1SS8_H2O.pdb - Hydrated version of the assembled GroEL 14-mer, derived from the 1SS8 PDB entry. The various hydrated models are created by removing water from this initial model. * rotations.h5 - Quaternions of the rotations used for template matching

European XFEL↗
DOE OSTI2014

Sandia PUF Analysis Tool

This program is a graphical user interface for measuring and performing inter-active analysis of physical unclonable functions (PUFs). It is intended for demonstration and education purposes. See license.txt for license details. The program features a PUF visualization that demonstrates how signatures differ between PUFs and how they exhibit noise over repeated measurements. A similarity scoreboard shows the user how close the current measurement is to the closest chip signatures in the database. Other metrics such as average noise and inter-chip Hamming distances are presented to the user. Randomness tests published in NIST SP 800-22 can be computed and displayed. Noise and inter-chip histograms for the sample of PUFs and repeated PUF measurements can be drawn.

Source record↗
DOE OSTI2012

DAQMAN - A flexible configurable data acquisition system

DAQMAN is a flexible configurable interface that allows the user to build and operate a VME-based data acquisition system on a Linux workstation. It consists of two parts: a Java-based Graphical User Interface to configure the system, and a C-based utility that reads out the data and creates the output ASCII data file, with two levels of diagnostic tools. The data acquisition system requires a CAEN CONET-VME Bridge to communicate between the hardware in the VME crate and the Linux workstation. Data acquisition modules, such as ADCs, TDC, Scalers, can be loaded into the system, or removed easily. The GUI allows users to activate modules, and channels within modules by clicking on icons. Running configurations are stored; data are collected and can be viewed either as raw numbers, or by charts and histograms that update as the data are accumulated. Data files are written to disk in ASCII format, with a date and time stamp.

Sivertz, Michael↗
DOE OSTI2019

numericalFunctions

The numericalFunction C library is designed to handle point-wise interpolative data representing a mathematical function. The library currently handles 1-d functions (i.e., functions of the format f(x)). The main object is the ptwXYPoints C struct that holds list of x,y pairs sorted by ascending x-values with an interpolation rule. That is, it contains the 1-d function f(x) = y(x). Functions are included that support common unitary and binary operations (e.g., negation, addition, multiplication). As example, if f(x) and g(x) are two ptwXYPoints instances, then routines to calculate f(x) + g(x) and f(x) * g(x) are included. The library support storage of data with lin-lin, lin-log, log-lin, log-log and flat (or histogram) interpolation. Functions to convert any of the supported interpolation to lin-lin are included. However, many of the binary functions only support lin-lin interpolation. The library also contains a C struct for storing a Legendre series representation of a 1-d function, and functions to convert Legendre series to and from a ptwXYPoints instance. A python interface to the library is included, with the ptwXYPoints C struct treated as a class. This class has many of the standard python math methods (e.g., __add__, __mul__).

Beck, Bret↗
DOE OSTI2020

XRD-PUAT (X-Ray Diffraction - Parameter Uncertainty Analysis Toolkit)

This XRD uncertainty toolkit was built to investigate uncertainty and local least squares topology over a specified parameter space on a single histogram in a gpx GSAS-II file. The parameter uncertainty can be investigated using either a frequentist F-test approach or a Bayesian Inference statistical inversion method on a weighted least squares or peak fit refinements.SAND Number: SAND2020-12227 M 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.

Moore, Alexander↗
DOE OSTI2021

HUNTRESS: a fast heuristic for reconstructing phylogenetic trees of tumor evolution (HUNTRESS) v0.1

We introduce HUNTRESS (Histogrammed UNion Tree REconStruction heuriStic), a computational method for tumor phylogeny reconstruction from noisy genotype matrices derived from single-cell sequencing data, whose running time is linear with the number of cells and quadratic with the number of mutations. Provided that the input genotype matrix includes no false positives, each cellular subpopulation is at least a user defined fraction of the total number of cells, and the number of cells are much bigger than the number of mutations considered, HUNTRESS computes the ground truth tumor phylogeny with high probability. On simulated data HUNTRESS is faster than available alternatives with comparable or better accuracy. Additionally, the phylogenies reconstructed by HUNTRESS on two single-cell sequencing data sets agree with the best known evolutionary scenarios for the associated tumors.

Buluc, Aydin↗
DOE OSTI2016

PeakWorks

The PeakWorks software is designed to assist in the quantitative analysis of atom probe tomography (APT) generated mass spectra. Specifically, through an interactive user interface, mass peaks can be identified automatically (defined by a threshold) and/or identified manually. The software then provides a means to assign specific elemental isotopes (including more than one) to each peak. The software also provides a means for the user to choose background subtraction of each peak based on background fitting functions, the choice of which is left to the users discretion. Peak ranging (the mass range over which peaks are integrated) is also automated allowing the user to chose a quantitative range (e.g. full-widthhalf- maximum). The software then integrates all identified peaks, providing a background-subtracted composition, which also includes the deconvolution of peaks (i.e. those peaks that happen to have overlapping isotopic masses). The software is also able to output a 'range file' that can be used in other software packages, such as within IVAS. A range file lists the peak identities, the mass range of each identified peak, and a color code for the peak. The software is also able to generate 'dummy' peak ranges within an outputted range file that can be used within IVAS to provide a means for background subtracted proximity histogram analysis.

Source record↗
DOE OSTI2020

Tidal Disruption Event Galaxy Binner

This software simulates astronomical survey detections of tidal disruptions of stars by super-massive black holes. It begins with the synthetic galaxy catalogue described in van Velzen 2008 (https://arxiv.org/abs/1707.03458). The stellar disruption rate in each galaxy is estimated based on Stone & Metzger 2016 (https://arxiv.org/abs/1410.7772). Based on these rates, and the present-day stellar mass function in the galaxy, disruptions are randomly sampled, and the properties of the resulting flares are sampled based on empirical distributions. The code also accounts for obscuration by dust in the host galaxy. Finally, the survey selection effects are applied. The detectable simulated flares are stored in a database, allowing histograms of their properties to be created.

Roth, NathanielJ.↗
DOE OSTI2023

PRESSURE-FILM OPEN SOURCE SCANNING AND MAPPING

SF-22-099 Pressure-film Open Source Scanning and Mapping (POSSM) is software that analyses images of pressure-film (scans or photographs). POSSM collects data from that analysis in order to produce a number of visual aides, including pressure maps, histograms, and 3d surface plots. POSSM is designed to be able to analyze an entire folder full of images, exporting numeric data as .csv files and visual graphics as .png files.

Grider, Patrick↗
DOE OSTI2025

Simulation-Based Inference for Neutrino Interaction Model Tuning

This project demonstrates, for the first time, the application of simulation-based inference (SBI) techniques to tune neutrino–nucleus interaction models. Using a mock dataset based on the MicroBooNE tuning of the GENIE event generator, our approach employs a Neural Posterior Estimator (NPE) with Masked Autoregressive Flows (MAF) to infer the posterior distributions of key GENIE parameters directly from simulated histograms. The workflow provides a scalable and amortized framework for performing likelihood-free inference in high-dimensional parameter spaces, offering a pathway to more efficient and uncertainty-aware model tuning for next-generation neutrino experiments such as DUNE and SBND.

Tame-Narvaez, KarlaMaria [Fermi National Accelerat↗
DOE OSTI2011

Tropical and subtropical cloud transitions in weather and climate prediction models: the GCSS/WGNE Pacific Cross-Section Intercomparison (GPCI)

A model evaluation approach is proposed where weather and climate prediction models are analyzed along a Pacific Ocean cross-section, from the stratocumulus regions off the coast of California, across the shallow convection dominated trade-winds, to the deep convection regions of the ITCZ: the GCSS/WGNE Pacific Cross-section Intercomparison (GPCI). The main goal of GPCI is to evaluate, and help understand and improve the representation of tropical and sub-tropical cloud processes in weather and climate prediction models. In this paper, a detailed analysis of cloud regime transitions along the cross-section from the sub-tropics to the tropics for the season JJA of 1998 is presented. This GPCI study confirms many of the typical weather and climate prediction model problems in the representation of clouds: underestimation of clouds in the stratocumulus regime by most models with the corresponding consequences in terms of shortwave radiation biases; overestimation of clouds by the ECMWF Re-Analysis (ERA40) in the deep tropics (in particular) with the corresponding impact in the outgoing longwave radiation; large spread between the different models in terms of cloud cover, liquid water path and shortwave radiation; significant differences between the models in terms of vertical crosssections of cloud properties (in particular), vertical velocity and relative humidity. An alternative analysis of cloud cover mean statistics is proposed where sharp gradients in cloud cover along the GPCI transect are taken into account. This analysis shows that the negative cloud bias of some models and ERA40 in the stratocumulus regions (as compared to ISCCP) is associated not only with lower values of cloud cover in these regimes, but also with a stratocumulus-to-cumulus transition that occurs too early along the trade-wind Lagrangian trajectory. Histograms of cloud cover along the cross-section differ significantly between models. Some models exhibit a quasi-bimodal structure with cloud cover being either very large (close to 100%) or very small, while other models show a more continuous transition. The ISCCP observations suggest that reality is in-between these two extreme examples. These different patterns reflect the diverse nature of the cloud, boundary layer, and convection parameterizations in the participating weather and climate prediction models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Simulations of ENSO Phase-locking in CMIP5 and CMIP6

The characteristics of El-Niño-Southern Oscillation (ENSO) phase-locking in observations and CMIP5 and CMIP6 models are examined in this study. Two metrics based on the peaking month histogram for all El Niño and La Niña events are adopted to delineate the basic features of ENSO phase-locking in terms of the preferred calendar month and strength of this preference. It turns out that most models are poor at simulating the ENSO phase-locking, either showing little peak strengths or peaking at the wrong seasons. By deriving ENSO’s linear dynamics based on the conceptual recharge oscillator (RO) framework through the seasonal linear inverse model (sLIM) approach, various simulated phase-locking behaviors of CMIP models are systematically investigated in comparison with observations. In observations, phase-locking is mainly attributed to the seasonal modulation of ENSO’s SST growth rate. In contrast, in a significant portion of CMIP models, phase-locking is co-determined by the seasonal modulations of both SST growth and phase-transition rates. Further study of the joint effects of SST growth and phase-transition rates suggests that for simulating realistic winter peak ENSO phase-locking with the right dynamics, climate models need to have four key factors in the right combination: (1) correct phase of SST growth rate modulation peaking at the fall; (2) large enough amplitude for the annual cycle in growth rate; (3) amplitude of semi-annual cycle in growth rate needs to be small; and (4) amplitude of seasonal modulation in SST phase-transition rate needs to be small.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Evaluation of and Suggested Improvements to the WSM6 Microphysics in WRF- ARW Using Synthetic and Observed GOES-13 Imagery

Synthetic satellite imagery can be employed to evaluate simulated cloud fields. Past studies have revealed that the Weather Research and Forecasting (WRF) WRF Single-Moment 6-class (WSM6) microphysics in WRF-ARW produces less upper level ice clouds within synthetic images compared to observations. Synthetic Geostationary Operational Environmental Satellite (GOES)-13 imagery at 10.7 μm of simulated cloud fields from the 4 km National Severe Storms Laboratory (NSSL) WRF-ARW is compared to observed GOES-13 imagery. Histograms suggest that too few points contain upper level simulated ice clouds. In particular, side-by-side examples are shown of synthetic and observed convective anvils. Such images illustrate the lack of anvil cloud associated with convection produced by the NSSL WRF-ARW. A vertical profile of simulated hydrometeors suggests that too much cloud water mass may be converted into graupel mass, effectively reducing the main source of ice mass in a simulated anvil. Further, excessive accretion of ice by snow removes ice from an anvil by precipitation settling. Idealized sensitivity tests reveal that a 50% reduction of the conversion of cloud water mass to graupel and a 50% reduction of the accretion rate of ice by snow results in a significant increase in anvil ice of a simulated storm. Such results provide guidance as to which conversions could be reformulated, in a more physical manner, to increase simulated ice mass in the upper troposphere.

GOES-13 imagery↗