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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology

Central nervous system (CNS) tumors come with vastly heterogeneous histologic, molecular, and radiographic landscapes, rendering their precise characterization challenging. The rapidly growing fields of biophysical modeling and radiomics have shown promise in better characterizing the molecular, spatial, and temporal heterogeneity of tumors. Integrative analysis of CNS tumors, including clinically acquired multi-parametric magnetic resonance imaging (mpMRI) and the inverse problem of calibrating biophysical models to mpMRI data, assists in identifying macroscopic quantifiable tumor patterns of invasion and proliferation, potentially leading to improved (a) detection/segmentation of tumor subregions and (b) computer-aided diagnostic/prognostic/predictive modeling. This article presents a summary of (a) biophysical growth modeling and simulation,(b) inverse problems for model calibration, (c) these models' integration with imaging workflows, and (d) their application to clinically relevant studies. We anticipate that such quantitative integrative analysis may even be beneficial in a future revision of the World Health Organization (WHO) classification for CNS tumors, ultimately improving patient survival prospects.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

DeepBench: A simulation package for physical benchmarking data

We introduce **DeepBench**, a python library that generates simple simulated image data from first principles, such as basic geometric shapes and astronomical objects. These data are highly valuable for developing (calibration, testing, and benchmarking) statistical and machine learning models because they make it possible to connect the final data product to physically interpretable inputs. This software includes tools to curate and store the datasets to maximize reproducibility.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

CACTI Radar b1 Processing: Corrections, Calibrations, and Processing Report

The U.S. Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) user facility deployed a large number of instruments to a region nearby the Sierra de Córdobas mountains in Argentina as part of the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign (1). During this campaign, four radars were installed at a site outside of Villa Yacanto as shown in Figure 1. As part of a post-campaign effort to improve the usability of these data, a significant activity was undertaken towards the calibration, correction, and improvement of the data quality of these radar datastreams. This process in ARM nomenclature is referred to as generating a “b1” datastream. While these “b1” standards may imply different corrections or standards for various ARM instruments, for radars it refers to a datastream that has been calibrated (and cross-calibrated), including a serious effort to deliver the highest-quality (well-characterized) data possible. This report details (i) the status/quality of the original “a1” (raw) data, (ii) the corrections and calibrations that are applied to generate these b1 datastreams, (iii) the details of the applied algorithms and how radar offset/calibration numbers were determined for the eventual corrections, and (iv) the new and flexible plug-in-based processing system designed during CACTI for radar b1 activities (current, future) that interfaces with ARM’s Data Integrator (ADI) and high-performance computing (HPC) system.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Simulating PBX 9501 Gap-stick Experiment

It has previously been shown that the rate parameters of the SURF reactive burn model can be adjusted to fit the gap-stick data for PBX 9501 [Johnson et al., 2018, fig 4]. A recent recalibration of the SURF model for PBX 9501 (lot 730-010 at ρ = 1.837 g/cc 3 ) used different reactants and products EOS and an updated fitting form for the burn rate [Menikoff, 2021]. The new calibration fit gap-stick data [Hill et al., 2021] together with data from SDT experiments and curvature effect data. Here the results of gap-stick simulations using the xRage code are shown. They provide insight into the reactive flow in the gap-stick experiment.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2022

Emrgy 2022 Hydrokinetic Turbine Data - TEAMER Post Access Submission

The data herein contains all data collected and used for the Performance Characterization Testing and Model Calibration of a Vertical Axis Hydrokinetic Turbine. The data includes performance data and durability data for the Hydrokinetic Turbine. The device performance data contains shaft RPM, turbine RPM, power output, flow velocity, pressure, and pressure drop across the turbine. The mechanical durability data includes stress and strain at varied depths and velocities. There is also an FEA analysis included. This TEAMER project was awarded to Emrgy, Inc.in collaboration with Alden Research Laboratory LLC.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

A High-Granularity Approach to Modeling Energy Consumption and Savings Potential in the U.S. Residential Building Stock: Preprint

Building simulations are increasingly used in various applications related to energy efficient buildings. For individual buildings, applications include: design of new buildings, prediction of retrofit savings, ratings, performance path code compliance and qualification for incentives. Beyond individual building applications, larger scale applications (across the stock of buildings at various scales: national, regional and state) include: codes and standards development, utility program design, regional/state planning, and technology assessments. For these sorts of applications, a set of representative buildings are typically simulated to predict performance of the entire population of buildings. Focusing on the U.S. single-family residential building stock, this paper will describe how multiple data sources for building characteristics are combined into a highly-granular database that preserves the important interdependencies of the characteristics. We will present the sampling technique used to generate a representative set of thousands (up to hundreds of thousands) of building models. We will also present results of detailed calibrations against building stock consumption data.

building stock↗
DOE OSTI2025

Comparisons of the v11.1 Orbiting Carbon Observatory‐2 (OCO‐2) X CO2 Measurements With GGG2020 TCCON

The Orbiting Carbon Observatory 2 (OCO-2) is NASA's first Earth observation satellite mission dedicated to studying the sources and sinks of carbon dioxide (CO 2 ) on a global scale. The observations of reflected sunlight are inverted in a retrieval algorithm to produce estimates of the dry air mole-fractions of CO 2 (X CO2 ). The OCO-2 Level 2 data release, version 11.1 (v11.1) retrievals from the Atmospheric Carbon Observations from Space (ACOS) algorithm, includes significant improvements in the X CO2 data product compared to older OCO-2 data versions. This work compares the v11.1 X CO2 from OCO-2 against X CO2 estimates collected from a global ground-based network known as the Total Carbon Column Observing Network (TCCON), OCO-2's primary validation source. The OCO-2 project provides a version of the Level 2 data product, called “lite” files that include calibrated and bias-corrected XCO2 values, accessible together with all OCO-2 data products through the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). This work shows that OCO-2 X CO2 observations made between September 2014 and December 2023, after quality filtering and the application of an averaging kernel correction, agree well with coincident TCCON data for all OCO-2 observational modes of land (nadir, glint, target) and ocean (glint). The aggregated, bias-corrected, and quality-filtered absolute average bias values are less than or equal to 0.20 parts per million (ppm) globally for all OCO-2 observation modes, where the biases do not indicate a statistically significant time dependence. The land nadir/glint mode has the lowest bias value of −0.03 ± 0.85 ppm.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Multi-system analysis of offshore geologic carbon storage: a review of open-source data science solutions

Geologic carbon storage projects are maturing worldwide and the footprint of deployment in the offshore is expanding. At present, there are ten projects in operation or that have been completed, more than 50 in construction and development, and dozens of characterization studies completed or underway. Offshore geologic carbon storage offers potential benefits over onshore geologic carbon storage. These offshore projects are generally remote in location, distant from population centers, and avoid complicated pore space rights while having abundant prospective storage potential. Some offshore fields targeted for carbon storage have comparatively fewer prior borehole penetrations except for areas that have been explored for petroleum production, minimizing potential issues such as pressure interference and infrastructure impacts. Yet offshore geologic carbon storage projects face distinctive technical and economic challenges, such as seafloor geohazards (e.g., seabed instability), expensive maritime transport, and meteorological-oceanographic conditions that can damage infrastructure and impact operations. Analytical capabilities and improved computational speeds have advanced engineering, earth and energy sciences in the wake of the arrival of modern data science over the last decade. These advancements have created an opportunity for integrated, multi-systems modeling approaches utilizing artificial intelligence and machine learning that are no longer limited by computational issues. Analytical tools developed alongside this advancement in data science can be leveraged to calibrate the potential advantages and challenges of carbon storage operations in the offshore. New methods and approaches that incorporate data science to analyze multiple aspects of engineered and natural systems can provide insights that complement the characterization and onsite engineering that traditional commercial and operational software addresses. These new methods and approaches can potentially improve the outcome of energy operations and carbon storage. Providing multi-system, science-driven data analytics enhances the knowledge base that offshore developers, operators, and regulatory bodies may draw from to improve offshore site selection and operational efficiency. Here, we provide a brief synopsis of geologic carbon storage efforts to date, an overview of the engineered and natural systems involved in offshore geologic carbon storage, and a review of publicly available, open-source, offshore and/or carbon storage related data- and science-driven tools developed by 2010 or later that are suitable for screening and assessing regions for offshore geologic carbon storage.

artificial intelligence↗
DOE OSTI2024

Augmenting a Simulation Campaign for Hybrid Computer Model and Field Data Experiments

The Kennedy and O’Hagan (KOH) calibration framework uses coupled Gaussian processes (GPs) to meta-model an expensive simulator (first GP), tune its “knobs” (calibration inputs) to best match observations from a real physical/field experiment and correct for any modeling bias (second GP) when predicting under new field conditions (design inputs). There are well-established methods for placement of design inputs for data-efficient planning of a simulation campaign in isolation, that is, without field data: space-filling, or via criterion like minimum integrated mean-squared prediction error (IMSPE). Analogues within the coupled GP KOH framework are mostly absent from the literature. Here, in this study, we derive a closed form IMSPE criterion for sequentially acquiring new simulator data for KOH. We illustrate how acquisitions space-fill in design space, but concentrate in calibration space. Closed form IMSPE precipitates a closed-form gradient for efficient numerical optimization. We demonstrate that our KOH-IMSPE strategy leads to a more efficient simulation campaign on benchmark problems, and conclude with a showcase on an application to equilibrium concentrations of rare earth elements for a liquid–liquid extraction reaction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Inference of strength and phase transition kinetics in dynamically-compressed tin

Dynamic compression experiments in condensed matter are of interest in part because they provide opportunities to examine material response under extreme conditions; however, the inference of material behavior from dynamic experiments is challenging in the presence of phase transitions exhibiting kinetic processes. Demonstrating an approach to quantitative interpretation of such dynamic experiments, here we present a Bayesian model calibration of strength and phase transformation parameters to data drawn from pulsed power and gas gun shot experiments. The posterior predictions of the Bayesian model capture the experimental measurements and account for the various uncertainties in the experimental configurations. This holistic approach to model calibration utilizing multiple types of experimental data identifies important cross correlations among kinetics, strength, and the phase boundary. Improved insight into potential sources of current model form error is provided by comparing the differences between calibrations against different subsets of the experimental data.

42 ENGINEERING↗
DOE OSTI2021

A combined experimental and computational analysis of failure mechanisms in open-hole cross-ply laminates under flexural loading

In this work, integrated experimental tests and computational modeling are proposed to investigate the failure mechanisms of open-hole cross-ply carbon fiber reinforced polymer (CFRP) laminated composites. In particular, we propose two effective methods, which include width-tapered double cantilever beam (WTDCB) and fixed-ratio mixed-mode end load split (FRMMELS) tests, to obtain the experimental data more reliably. We then calibrate the traction-separation laws of cohesive zone model (CZM) used among laminas of the composites by leveraging these two methods. The experimental results of fracture energy, i.e. G Ic and G Tc , obtained from WTDCB and FRMMELS tests are generally insensitive to the crack length thus requiring no effort to accurately measure the crack tip. Moreover, FRMMELS sample contains a fixed mixed-mode ratio of G IIc /G Tc depending on the width taper ratio. Examining comparisons between experimental results of FRMMELS tests and failure surface of B–K failure criterion predicted from a curve fitting, good agreement between the predictions and experimental data has been found, indicating that FRMMELS tests are an effective method to determine mixed-mode fracture criterion. In addition, a coupled experimental-computational modeling of WTDCB, edge notched flexure, and FRMMELS tests are adopted to calibrate and validate the interfacial strengths. Finally, failure mechanisms of open-hole cross-ply CFRP laminates under flexural loading have been studied systematically using experimental and multi-scale computational analyses based on the developed CZM model. The initiation and propagation of delamination, the failure of laminated layers as well as load-displacement curves predicted from computational analyses are in good agreement with what we have observed experimentally.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Bayesian State-Space Modeling Framework for Understanding and Predicting Golden Eagle Movements Using Telemetry Data

Predicting raptor movements through a wind power plant under given atmospheric and topographical conditions is a crucial first step in the overall goal of quantifying the risk of turbine-related collisions and mortalities. Extracting behavioral traits of golden eagles (Aquila chrysaetos) from telemetry data requires the fusion of noisy and sparse movement data (location, heading, velocity) with a stochastic mathematical representation of the eagles' decision-making processes. In this study, we framed this problem in a Bayesian state-space framework where both observations and decision-making are assumed to be stochastic processes connected through hidden states (mode of flight, intent), and the unknown model parameters are assumed to be random variables that are calibrated using the available telemetry data. This framework allowed for rigorous consideration of underlying uncertainties while allowing for both data and prior biological knowledge to contribute to a probabilistic and predictive agent-based movement model. We implemented and applied the Bayesian framework to understand movement behavior of 23 GPS-tagged golden eagles travelling in the western US for years 2019 and 2020. Our preliminary findings show that the Bayesian state-space framework provides a robust inverse modeling apparatus to decode eagle behavioral characteristics from telemetry data. This study was primarily aimed at verifying and validating the framework with selected golden eagle tracks (both long- and short-ranged), with future research aimed at extending the framework to include multi-mode flight, consideration of atmospheric data and uplift mechanisms, eagle-to-eagle interaction, and eagle-to-turbine interaction.

Bayesian modeling↗
DOE OSTI2025

RTN-099: Photometric Transformation Relations for the LSST Data Preview 1

This technical note provides photometric transformation relations between the Vera C. Rubin Observatory's LSSTCam and LSSTComCam systems and other photometric systems. These transformations are derived using both synthetic and empirical data and are intended to support calibration and comparison across survey systems. We present both polynomial equations and lookup-table-based methods, depending on the available data and desired accuracy. The transformations are generally valid for stars with typical spectral energy distributions (SEDs), and caution should be used when applying them to objects with strong emission lines or atypical colors.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

RTN-125: Photometric Transformation Relations for the LSST Data Preview 2

This technical note provides photometric transformation relations between the NSF-DOE Vera C. Rubin Observatory's Data Preview 2 (DP2) and other photometric systems. These transformations are derived using both synthetic and empirical data and are intended to support calibration and comparison across survey systems. We present both polynomial equations and lookup-table-based methods, depending on the available data and desired accuracy. The transformations are generally valid for stars with typical spectral energy distributions (SEDs), and caution should be used when applying them to objects with strong emission lines or atypical colors.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Breaking the degeneracy between polarization efficiency and cosmological parameters in CMB experiments

Accurate cosmological parameter estimates using polarization data of the cosmic microwave background (CMB) put stringent requirements on map calibration, as highlighted in the recent results from the Planck satellite. In this paper, we point out that a model-dependent determination of polarization calibration can be achieved by the joint fit of the temperature-E-mode cross-power spectrum (TE) and E-mode auto-power spectrum (EE). This provides a valuable cross-check to band-averaged polarization efficiency measurements determined using other approaches. We demonstrate that, in ΛCDM, the combination of the TE and EE constrain polarization calibration with sub-percent uncertainty with Planck data and 2% uncertainty with sptpol data. We arrive at similar conclusions when extending ΛCDM to include the amplitude of lensing A L , the number of relativistic species N eff , or the sum of the neutrino masses Σm ν . Furthermore, the uncertainties on cosmological parameters are minimally impacted when marginalizing over polarization calibration, except, as can be expected, for the uncertainty on the amplitude of the primordial scalar power spectrum ln(10 10 A s ), which increases by 20–50%. However, this information can be fully recovered by adding temperature auto-power spectrum (TT) information. For current and future ground-based experiments, SPT-3G and CMB-S4, we forecast the cosmological parameter uncertainties to be minimally degraded when marginalizing over polarization calibration parameters. In addition, CMB-S4 could constrain its polarization calibration at the level of ~0.2% by combining TE and EE, and reach ~0.06% by also including TT. We therefore conclude that relying on calibrating against Planck polarization maps, whose statistical uncertainty is limited to ~0.5%, would be insufficient for upcoming experiments.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Neutron tagging following atmospheric neutrino events in a water Cherenkov detector

We present the development of neutron-tagging techniques in Super-Kamiokande IV using a neural network analysis. The detection efficiency of neutron capture on hydrogen is estimated to be 26%, with a mis-tag rate of 0.016 per neutrino event. The uncertainty of the tagging efficiency is estimated to be 9.0%. Measurement of the tagging efficiency with data from an Americium-Beryllium calibration agrees with this value within 10%. The tagging procedure was performed on 3,244.4 days of SK-IV atmospheric neutrino data, identifying 18,091 neutrons in 26,473 neutrino events. The fitted neutron capture lifetime was measured as 218±9 μs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

AI for Experimental Controls at Jefferson Lab

We report the AI for Experimental Controls project is developing an AI system to control and calibrate detector systems located at Jefferson Laboratory. Currently, calibrations are performed offline and require significant time and attention from experts. This work would reduce the amount of data and the amount of time spent calibrating in an offline setting. The first use case involves the Central Drift Chamber (CDC) located inside the GlueX spectrometer in Hall D. We use a combination of environmental and experimental data, such as atmospheric pressure, gas temperature, and the flux of incident particles as inputs to a sequential Neural Network (NN) to recommend a high voltage setting and the corresponding calibration constants in order to maintain consistent gain and optimal resolution throughout the experiment. Utilizing AI in this manner represents an initial shift from offline calibration towards near real time calibrations performed at Jefferson Laboratory.

47 OTHER INSTRUMENTATION↗