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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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

SU-E-J-257: A PCA Model to Predict Adaptive Changes for Head&neck Patients Based On Extraction of Geometric Features From Daily CBCT Datasets

Purpose: Using daily cone beam CTs (CBCTs) to develop principal component analysis (PCA) models of anatomical changes in head and neck (H&N) patients and to assess the possibility of using these prospectively in adaptive radiation therapy (ART). Methods: Planning CT (pCT) images of 4 H&N patients were deformed to model several different systematic changes in patient anatomy during the course of the radiation therapy (RT). A Pinnacle plugin was used to linearly interpolate the systematic change in patient for the 35 fraction RT course and to generate a set of 35 synthetic CBCTs. Each synthetic CBCT represents the systematic change in patient anatomy for each fraction. Deformation vector fields (DVFs) were acquired between the pCT and synthetic CBCTs with random fraction-to-fraction changes were superimposed on the DVFs. A patient-specific PCA model was built using these DVFs containing systematic plus random changes. It was hypothesized that resulting eigenDVFs (EDVFs) with largest eigenvalues represent the major anatomical deformations during the course of treatment. Results: For all 4 patients, the PCA model provided different results depending on the type and size of systematic change in patient’s body. PCA was more successful in capturing the systematic changes early in the treatment course when these were of a larger scale with respect to the random fraction-to-fraction changes in patient’s anatomy. For smaller scale systematic changes, random changes in patient could completely “hide” the systematic change. Conclusion: The leading EDVF from the patientspecific PCA models could tentatively be identified as a major systematic change during treatment if the systematic change is large enough with respect to random fraction-to-fraction changes. Otherwise, leading EDVF could not represent systematic changes reliably. This work is expected to facilitate development of population-based PCA models that can be used to prospectively identify significant anatomical changes early in treatment. This work is supported in part by a grant from Varian Medical Systems, Palo Alto, CA.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Quality-Controlled Upper-Air Sounding Dataset for DYNAMO/CINDY/AMIE: Development and Corrections

The upper-air sounding network for DYNAMO (Dynamics of the Madden-Julian Oscillation or MJO) has provided an unprecedented set of observations for studying the MJO over the Indian Ocean (IO) where coupling of this oscillation with deep convection first occurs. With 72 sounding sites and dropsonde data from 13 aircraft mission, the sonde network covers the tropics from Eastern African to the West Pacific. In total nearly 26,000 sondes were collected from this network during the experiment’s 6-month extended observing period (from October 2011 to March 2012). Slightly more than half of the sondes, collected from 33 sites, are at high vertical resolution. Rigorous post-field phase processing of the sonde data included several levels of quality checks and a variety of corrections which address a number of issues (e.g., daytime dry bias, baseline surface data errors, ship deck-heating effects, artificial dry spikes in slow ascent sondes). Because of the importance of an accurate description of the moisture field in meeting the scientific goals of the experiments, particular attention is given to humidity correction and its validation. The humidity corrections, though small relative to some previous field campaigns, produced high fidelity moisture analyses in which sonde precipitable water compared well with independent estimates. An assessment of model operational analyses moisture using corrected sonde data shows an overall good agreement with the exception at upper-levels where model moisture and clouds are more abundant than the sounding data would indicate.

dataset↗
DOE OSTI2013

Non-Cooperative Facial Recognition Video Dataset Collection Plan

The Pacific Northwest National Laboratory (PNNL) will produce a non-cooperative (i.e. not posing for the camera) facial recognition video data set for research purposes to evaluate and enhance facial recognition systems technology. The aggregate data set consists of 1) videos capturing PNNL role players and public volunteers in three key operational settings, 2) photographs of the role players for enrolling in an evaluation database, and 3) ground truth data that documents when the role player is within various camera fields of view. PNNL will deliver the aggregate data set to DHS who may then choose to make it available to other government agencies interested in evaluating and enhancing facial recognition systems. The three operational settings that will be the focus of the video collection effort include: 1) unidirectional crowd flow 2) bi-directional crowd flow, and 3) linear and/or serpentine queues.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2016

Additional Model Datasets and Results to Accelerate the Verification and Validation of RELAP-7

The RELAP-7 code verification and validation activities are ongoing under the code assessment plan proposed in the previous document (INL-EXT-16-40015). Among the list of V&V test problems in the ‘RELAP-7 code V&V RTM (Requirements Traceability Matrix)’, the RELAP-7 7-equation model has been tested with additional demonstration problems and the results of these tests are reported in this document. In this report, we describe the testing process, the test cases that were conducted, and the results of the evaluation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

High-Fidelity, Large-Scale, Realistic Dataset Development

The final report summarizes the work performed for supporting the ARPA-E Grid Optimization Competition (Challenge 2 and Challenge 3) within the stated period. Challenge 2 For the challenge period, the main responsibility of the team is to investigate, gen- erate, and deliver parts of the data sets for the competition, based on the competition model for Challenge 2, existing data sets from Challenge 1, and data source supplied by other data set teams. Challenge 3 For the challenge period, the main responsibility of the team is to propose, create, deliver, and maintain the data format during the competition period. The data format will specify how the benchmark data will be represented and communicated to competitors. It will also specify how competitors should report back the solutions. The data format will be closely aligned with the problem formulation (maintained by the formulation team) and the solution validation process (maintained by the validation team). Our team is also responsible in investigating, generating, and delivering parts of the data sets for the competition. The data sets will be created based on the competition model for Challenge 3, existing data sets from Challenge 1 and Challenge 2, and data source supplied by other data set teams.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

New Particle Formation Event Dataset at the Southern Great Plains (SGP) Observatory from 2018 to 2023

This data set contains observations of new particle formation (NPF) events collected at the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) observatory from 2018 to 2023. Measurements include particle number size distributions, radiance measurements, and associated meteorological variables from onsite instrumentation relevant for identifying and characterizing NPF events. Events were identified using standardized criteria and documented to support investigations of aerosol nucleation, growth dynamics, and their interactions with local atmospheric conditions. The data set provides a multi-year record that enables evaluation of seasonal and interannual variability in NPF occurrence and intensity at a mid-continental site. These data are intended to support studies of aerosol-cloud-climate interactions and model evaluation within both ARM and the broader atmospheric science community. More information can be found within the README_NPF_SGP_2018_2023.docx file.

event↗
DOE OSTI2015

Global hourly land surface air temperature datasets: inter‐comparison and climate change

ABSTRACT The land surface air temperature ( LSAT ) is one of the fundamental parameters to represent heat transfer and to modulate the moisture cycle between land and atmosphere. Here, we quantify the spatiotemporal characteristics of four newly developed hourly temperature products by merging reanalysis with in situ data Climatic Research Unit ( CRU ). Overall, LSATs from different hourly products are consistent with each other, and their differences are generally smaller in magnitude than biases between hourly products and monthly averaged daily maximum and minimum temperature data from CRU . While the true monthly mean (using hourly values) and the monthly mean (of daily maximum and minimum temperatures) and their seasonality [as represented by the (July–January) differences] differ, their trends agree with each other very well. The polar amplification ratio of average temperature trend north of 65°N to that over global land (excluding Greenland and Antarctica) is also similar among different products, with the annual ratio of around 1.7. The ratio in summer (June–August) is always smaller than the annual value for different periods among all products. Based on the probability distribution functions from the monthly anomalies of different variables, the coldest tenth percentile of temperature in each decade overall increases with time, while the warmest tenth percentile does not vary much from 1950–1979, followed by a rapid increase from 1980–2009. These results and additional sensitivity tests suggest that the 4‐h LSAT products can be widely used for climate analysis, model evaluation, and offline land surface modelling from 1948–2009.

Wang, Aihui↗
DOE OSTI2023

Search for heavy, long-lived, charged particles with large ionisation energy loss in $pp$ collisions at $ \sqrt{s} $ = 13 TeV using the ATLAS experiment and the full Run 2 dataset

This paper presents a search for hypothetical massive, charged, long-lived particles with the ATLAS detector at the LHC using an integrated luminosity of 139 fb –1 of proton–proton collisions at √s =13 TeV. These particles are expected to move significantly slower than the speed of light and should be identifiable by their high transverse momenta and anomalously large specific ionisation losses, dE/dx. Trajectories reconstructed solely by the inner tracking system and a dE/dx measurement in the pixel detector layers provide sensitivity to particles with lifetimes down to O(1) ns with a mass, measured using the Bethe–Bloch relation, ranging from 100 GeV to 3 TeV. Interpretations for pair-production of R-hadrons, charginos and staus in scenarios of supersymmetry compatible with these particles being long-lived are presented, with mass limits extending considerably beyond those from previous searches in broad ranges of lifetime.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

A simple climatology of westerly jet streams in global reanalysis datasets part 1: mid-latitude upper tropospheric jets

A simple closed contour object identification scheme has been applied to the zonal mean monthly mean zonal wind fields from nine global reanalysis data sets for 31 years of the satellite era (1979–2009) to identify objects corresponding to westerly jet streams. The results cluster naturally into six individual jet streams but only the mid-latitude upper-tropospheric jets are considered here. The time series of the jet properties from all reanalyses are decomposed into seasonal means and anomalies, and correlations between variables are evaluated, with the aim of identifying robust features which can form the basis of evaluation metrics for climate model simulations of the twentieth century. There is substantial agreement between all the reanalyses for all jet properties although there are some systematic differences with particular data sets. Some of the results from the object identification applied to the reanalyses are used in a simple example of a model evaluation score for the zonal mean jet seasonal cycle.

Meteorology & Atmospheric Sciences↗
DOE OSTI2018

Adaptive wavelet compression of large additive manufacturing experimental and simulation datasets

New manufacturing technologies such as additive manufacturing require research and development to minimize the uncertainties in the produced parts. The research involves experimental measurements and large simulations, which result in huge quantities of data to store and analyze. We address this challenge by alleviating the data storage requirements using lossy data compression. We select wavelet bases as the mathematical tool for compression. Unlike images, additive manufacturing data is often represented on irregular geometries and unstructured meshes. Thus, we use Alpert tree-wavelets as bases for our data compression method. We first analyze different basis functions for the wavelets and find the one that results in maximal compression and miminal error in the reconstructed data. We then devise a new adaptive thresholding method that is data-agnostic and allows a priori estimation of the reconstruction error. Finally, we propose metrics to quantify the global and local errors in the reconstructed data. One of the error metrics addresses the preservation of physical constraints in reconstructed data fields, such as divergence-free stress field in structural simulations. While our compression and decompression method is general, we apply it to both experimental and computational data obtained from measurements and thermal/structural modeling of the sintering of a hollow cylinder from metal powders using a Laser Engineered Net Shape process. The results show that monomials achieve optimal compression performance when used as wavelet bases. The new thresholding method results in compression ratios that are two to seven times larger than the ones obtained with commonly used thresholds. Overall, adaptive Alpert tree-wavelets can achieve compression ratios between one and three orders of magnitude depending on the features in the data that are required to preserve. Furthermore, these results show that Alpert tree-wavelet compression is a viable and promising technique to reduce the size of large data structures found in both experiments and simulations.

42 ENGINEERING↗