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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 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Understanding the Response of Photosynthetic Metabolism in Tropical Forests to Seasonal Climate Variations

We propose a study focused on the basic question: What controls the response of photosynthesis in Amazonian forests to seasonal variations in climate? This question, despite its apparent simplicity, remains difficult for modern earth system models to answer, and is also the subject of continuing controversy in the remote-sensing literature. For example, in the modeling arena, four cutting-edge earth system models (ESM’s) show significant divergence in their seasonal patterns of photosynthesis from observed whole-system photosynthetic fluxes at two sites in the central Amazon. The overall objective of the project is to guide improvements in earth system models of tropical forest photosynthesis by collecting and integrating a suite of observations to 1) test several hypotheses (three core, conceptual hypotheses for explaining observed photosynthetic seasonality and a methodological hypotheses for scaling from leaves to canopy with hyperspectral cameras), and 2) perform a synthesis activity that applies our empirical work to earth system models of terrestrial carbon cycling. The project will provide an extensive suite of new and unique datasets that enable us to fill, through advanced modeling techniques and analysis, critical knowledge gaps in current understanding of what controls the response of canopy photosynthesis and related functions in Amazonian forests to seasonal variation in climate. Three major types of datasets and data products will be delivered: (1) in situ leaf and tree-scale measurements from intensive ecophysiological and ecohydological field campaigns, (2) time-series observations of leaf-to-crown scale forest reflectance properties and atmospheric radiation from two innovative, ground-based imaging sensors (respectively, the Hyperspectral Vegetation Imaging System and the High Dynamic Range All-Sky Imaging System), and (3) results from state-of-the-art models of 3-dimensional canopy processes for radiative transfer and photosynthesis that integrate and link our observations to tropical forest processes. These data products and the improved knowledge we achieve with them will contribute to testing and improving the treatment of tropical forest processes in ESMs. They will contribute data to and leverage related data from of the GOAmazon campaign, and make significant contributions to support the overall goals of GOAmazon. This work will also help establish a foundation for the Next Generation Ecosystem Experiments (NGEE) in the Tropics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Quantifying Uncertainty in All-to-All Estimates of Space Object Conjunction Probabilities using U-Statistics

Predicting space object conjunctions is inherently probabilistic due to initial state and orbit model uncertainty. A commonly considered Monte Carlo estimator of the conjunction probability is the ’all-to-all’ estimator. Given independent random samples of the trajectories of both objects, the estimator is the percentage of all pairs of trajectories that result in a conjunction. Intuitively, the all-to-all estimator is the best possible estimator of the conjunction probability since it considers all pairs of Monte Carlo samples. However, its distribution is not available in closed-form, which limits its use in practice and makes this intuition difficult to make rigorous. In this paper, the all-to-all estimator is identified as a U-statistic, which implies that it has several favorable properties. Specifically, the estimator is the minimum variance unbiased estimator of the conjunction probability and is asymptotically Gaussian distributed. An approximate confidence interval for the conjunction probability is obtained from an estimate of the asymptotic Gaussian distribution. We show how to efficiently compute the confidence interval and demonstrate that the interval has the nominal coverage level. The confidence intervals are also seen to be narrower than those based on the commonly-used each-to-each estimator. Furthermore, the all-to-all estimator is shown to allow different Monte Carlo sample sizes, whereas the each-to-each estimator requires equal sample sizes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Thermal-Mechanic Modeling of Fusion Components Using The MOOSE Framework

With fusion energy rapidly developing, there is a great need for an open source tool that can rapidly model different fusion tokamak designs. The open-source codes Multiphysics Object Oriented Simulation Environment (MOOSE), Tritium Migration Analysis Program Version 8 (TMAP8) , and Fusion ENergy Integrated Multiphysi-X (FENIX) are being used to demonstrate thermal-mechanic modeling of fusion components. In this study, an analysis is done on the fusion nuclear science facility (FNSF) blanket, and ITER toroidal magnet and diverter mono block. This study will lay the groundwork for fusion component analysis in the MOOSE framework and FENIX program.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Sensitivity Analysis, Reduced-order Modeling, and Optimization of a Gas-Cooled Pebble Bed Reactor using Equilibrium-Core and DLOFC Performance

This work presents and applies a workflow for performing design optimization on gas-cooled pebble-bed reactors. Based on previous research, a representative equilibrium core of a pebble-bed reactor and a depressurized loss-of-forced-cooling model are created. These applications are built using the Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically utilizing Griffin, Pronghorn, and Bison. After defining design-related parameters and quantities of interest regarding reactor safety and efficiency, this multiphysics model is sampled using the MOOSE stochastic tools module. The result is a comprehensive dataset of configurations, enabling sensitivity analysis and the generation of reduced-order models. Subsequently, the dataset and reduced-order models are employed in an optimization study aimed at maximizing fuel utilization while adhering to safety and operational constraints. The optimization process leads to an improvement of fuel utilization by approximately 10\%, compared to engineering-judgment-based nominal conditions.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Semiautomatic segmentation of aortic valve from sequenced ultrasound image using a novel shape-constraint GCV model

Purpose: Effective and accurate segmentation of the aortic valve (AV) from sequenced ultrasound (US) images remains a technical challenge because of intrinsic factors of ultrasound images that impact the quality and the continuous changes of shape and position of segmented objects. In this paper, a novel shape-constraint gradient Chan-Vese (GCV) model is proposed for segmenting the AV from time serial echocardiography. Methods: The GCV model is derived by incorporating the energy of the gradient vector flow into a CV model framework, where the gradient vector energy term is introduced by calculating the deviation angle between the inward normal force of the evolution contour and the gradient vector force. The flow force enlarges the capture range and enhances the blurred boundaries of objects. This is achieved by adding a circle-like contour (constructed using the AV structure region as a constraint shape) as an energy item to the GCV model through the shape comparison function. This shape-constrained energy can enhance the image constraint force by effectively connecting separate gaps of the object edge as well as driving the evolution contour to quickly approach the ideal object. Because of the slight movement of the AV in adjacent frames, the initial constraint shape is defined by users, with the other constraint shapes being derived from the segmentation results of adjacent sequence frames after morphological filtering. The AV is segmented from the US images by minimizing the proposed energy function. Results: To evaluate the performance of the proposed method, five assessment parameters were used to compare it with manual delineations performed by radiologists (gold standards). Three hundred and fifteen images acquired from nine groups were analyzed in the experiment. The area-metric overlap error rate was 6.89% ± 2.88%, the relative area difference rate 3.94% ± 2.63%, the average symmetric contour distance 1.08 ± 0.43 mm, the root mean square symmetric contour distance 1.37 ± 0.52 mm, and the maximum symmetric contour distance was 3.57 ± 1.72 mm. Conclusions: Compared with the CV model, as a result of the combination of the gradient vector and neighborhood shape information, this semiautomatic segmentation method significantly improves the accuracy and robustness of AV segmentation, making it feasible for improved segmentation of aortic valves from US images that have fuzzy boundaries.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

EERE-VTO T2M Project Final Report - Ideation of a Novel Commercialization Model

The Department of Energy Technology Transfer mission is to expand the commercial impact of DOE's portfolio of R&D activities, providing ongoing economic, security, and environmental benefits for all Americans. To advance this mission, Argonne National Laboratory has executed this Technology-to-Market (T2M) project funded by EERE-VTO. The objective of the project was to ideate an overall business model for a private entity with a mission to develop and deploy technology by identifying, de-risking, and moving scientific advances into commercial use.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Who is marginalized in energy justice? Amplifying community leader perspectives of energy transitions in Ghana

There is a divide in energy access studies, between technologically-focused modeling papers in engineering and economics, and energy justice frameworks and principles grounded in social sciences. Quantitative computational models are necessary when analyzing energy, and more specifically electricity, systems, as they are technologically-complex systems that can diverge from intuitive patterns. To assure energy justice, these models must be reflective of, and informative to, a wide range of stakeholders, including households and communities alongside utilities, governments, and others. Yet, moving from a qualitative understanding of preferences to quantitative modeling is challenging. In this perspective piece, we pilot the use of the value-focused thinking framework to inform stakeholder engagement. The result is a strategic objective hierarchy that highlights the tradeoffs and the social, economic and technological factors that need to be measured in models. We apply the process in Ghana, using a survey, stakeholder workshops, and follow-up interviews to uncover key tradeoffs and stakeholder-derived objectives. We discuss three key areas that have been rarely, if ever, well-represented in energy models: (1) the relationship between the dynamics of electricity end-use and the technology and economic structure of the system; (2) explicit tradeoffs between electricity access, cost, and reliability as defined by stakeholders; and (3) the definition of new objectives, such as minimizing hazards related to theft. We conclude that this model of engagement provides an opportunity to tie together rigorous qualitative analysis and stakeholder engagement with crucial quantitative models of the electricity system.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Development and Preliminary Evaluation of a Murine Model of Chronic Radiation-Induced Proctitis

Radiotherapy (RT) is commonly used to treat most pelvic malignancies. While treatment is often effective, curative radiation doses to the rectum can result in chronic radiation-induced proctitis, which is characterized by diarrhea, tenesmus, and/or rectal bleeding, recently termed pelvic radiation disease. An animal model of chronic radiation-induced proctitis would be useful to test both preventative and therapeutic strategies to limit this morbidity but has been elusive because of the high rodent mortality associated with acute bowel RT injury. The objective of this research was to develop a novel mouse model of chronic radiation-induced proctitis using advanced technology.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Understanding the extreme luminosity of DES14X2fna

We present DES14X2fna, a high-luminosity, fast-declining Type IIb supernova (SN IIb) at redshift z = 0.0453, detected by the Dark Energy Survey (DES). DES14X2fna is an unusual member of its class, with a light curve showing a broad, luminous peak reaching M r ≃ –19.3 mag 20 d after explosion. This object does not show a linear decline tail in the light curve until ≃60 d after explosion, after which it declines very rapidly (4.30 ± 0.10 mag 100 d –1 in the r band). By fitting semi-analytic models to the photometry of DES14X2fna, we find that its light curve cannot be explained by a standard 56 Ni decay model as this is unable to fit the peak and fast tail decline observed. Inclusion of either interaction with surrounding circumstellar material or a rapidly-rotating neutron star (magnetar) significantly increases the quality of the model fit. Furthermore, we also investigate the possibility for an object similar to DES14X2fna to act as a contaminant in photometric samples of SNe Ia for cosmology, finding that a similar simulated object is misclassified by a recurrent neural network (RNN)-based photometric classifier as an SN Ia in ~1.1–2.4 per cent of cases in DES, depending on the probability threshold used for a positive classification.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

CLASH: A census of magnified star-forming galaxies at z ∼ 6-8

We utilize 16 band Hubble Space Telescope (HST) observations of 18 lensing clusters obtained as part of the Cluster Lensing And Supernova survey with Hubble (CLASH) Multi-Cycle Treasury program to search for z ∼ 6-8 galaxies. We report the discovery of 204, 45, and 13 Lyman-break galaxy candidates at z ∼ 6, z ∼ 7, and z ∼ 8, respectively, identified from purely photometric redshift selections. This large sample, representing nearly an order of magnitude increase in the number of magnified star-forming galaxies at z ∼ 6-8 presented to date, is unique in that we have observations in four WFC3/UVIS UV, seven ACS/WFC optical, and all five WFC3/IR broadband filters, which enable very accurate photometric redshift selections. We construct detailed lensing models for 17 of the 18 clusters to estimate object magnifications and to identify two new multiply lensed z ≳ 6 candidates. The median magnifications over the 17 clusters are 4, 4, and 5 for the z ∼ 6, z ∼ 7, and z ∼ 8 samples, respectively, over an average area of 4.5 arcmin{sup 2} per cluster. We compare our observed number counts with expectations based on convolving 'blank' field UV luminosity functions through our cluster lens models and find rough agreement down to ∼27 mag, where we begin to suffer significant incompleteness. In all three redshift bins, we find a higher number density at brighter observed magnitudes than the field predictions, empirically demonstrating for the first time the enhanced efficiency of lensing clusters over field surveys. Our number counts also are in general agreement with the lensed expectations from the cluster models, especially at z ∼ 6, where we have the best statistics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

High Temperature Viscosity Model For Glassy Slags Produced From The Air Products Municipal Waste Gasification Process

Air Products, Inc. (AP) is developing a technology that uses plasma-enhanced gasification to create syngas from Municipal Solid Waste (MSW), for the purpose of generating electricity at a large scale. The process is intended to convert the inorganic components in MSW to a slag that flows easily through and from the gasifier. AP and the Savannah River National Laboratory (SRNL) have established a Cooperative Research and Development Agreement (CRADA) to improve the properties of the slag material produced from MSW gasification. A primary objective for AP is the development of a viscosity model, which can be used to predict the viscosity of the slag produced in the gasification process as a function of its composition in order to ensure that the incoming feeds will ultimately yield slag that can effectively be poured from the gasifier. In addition, the development of such a model could allow AP to determine the type and amount of additives to mix with the incoming MSW to target a specific viscosity range as well as identify waste compositions that could lead to difficulties during processing.

high temperature↗
DOE OSTI2016

Quantifying model-structure- and parameter-driven uncertainties in spring wheat phenology prediction with Bayesian analysis

Recent international efforts have brought renewed emphasis on the comparison of different agricultural systems models. Thus far, analysis of model-ensemble simulated results has not clearly differentiated between ensemble prediction uncertainties due to model structural differences per se and those due to parameter value uncertainties. Additionally, despite increasing use of Bayesian parameter estimation approaches with field-scale crop models, inadequate attention has been given to the full posterior distributions for estimated parameters. The objectives of this study were to quantify the impact of parameter value uncertainty on prediction uncertainty for modeling spring wheat phenology using Bayesian analysis and to assess the relative contributions of model-structure-driven and parameter-value-driven uncertainty to overall prediction uncertainty. This study used a random walk Metropolis algorithm to estimate parameters for 30 spring wheat genotypes using nine phenology models based on multi-location trial data for days to heading and days to maturity. Across all cases, parameter-driven uncertainty accounted for between 19 and 52% of predictive uncertainty, while model-structure-driven uncertainty accounted for between 12 and 64%. Here, this study demonstrated the importance of quantifying both model-structure- and parameter-value-driven uncertainty when assessing overall prediction uncertainty in modeling spring wheat phenology. More generally, Bayesian parameter estimation provided a useful framework for quantifying and analyzing sources of prediction uncertainty.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Using both qualitative and quantitative data in parameter identification for systems biology models

Abstract In systems biology, qualitative data are often generated, but rarely used to parameterize models. We demonstrate an approach in which qualitative and quantitative data can be combined for parameter identification. In this approach, qualitative data are converted into inequality constraints imposed on the outputs of the model. These inequalities are used along with quantitative data points to construct a single scalar objective function that accounts for both datasets. To illustrate the approach, we estimate parameters for a simple model describing Raf activation. We then apply the technique to a more elaborate model characterizing cell cycle regulation in yeast. We incorporate both quantitative time courses (561 data points) and qualitative phenotypes of 119 mutant yeast strains (1647 inequalities) to perform automated identification of 153 model parameters. We quantify parameter uncertainty using a profile likelihood approach. Our results indicate the value of combining qualitative and quantitative data to parameterize systems biology models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

INTERFEROMETRY OF ϵ AURIGAE: CHARACTERIZATION OF THE ASYMMETRIC ECLIPSING DISK

We report on a total of 106 nights of optical interferometric observations of the ϵ Aurigae system taken during the last 14 years by four beam combiners at three different interferometric facilities. This long sequence of data provides an ideal assessment of the system prior to, during, and after the recent 2009–2011 eclipse. We have reconstructed model-independent images from the 10 in-eclipse epochs which show that a disk-like object is indeed responsible for the eclipse. Using new three-dimensional, time-dependent modeling software, we derive the properties of the F-star (diameter, limb darkening), determine previously unknown orbital elements (Ω, i), and access the global structures of the optically thick portion of the eclipsing disk using both geometric models and approximations of astrophysically relevant density distributions. These models may be useful in future hydrodynamical modeling of the system. Finally, we address several outstanding research questions including mid-eclipse brightening, possible shrinking of the F-type primary, and any warps or sub-features within the disk.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Local models for scatter estimation and descattering in polyenergetic X-ray tomography

We propose a new modeling approach for scatter estimation and descattering in polyenergetic X-ray computed tomography (CT) based on fitting models to local neighborhoods of a training set. X-ray CT is widely used in medical and industrial applications. X-ray scatter, if not accounted for during reconstruction, creates a loss of contrast in CT reconstructions and introduces severe artifacts including cupping, shading, and streaks. Even when these qualitative artifacts are not apparent, scatter can pose a major obstacle in obtaining quantitatively accurate reconstructions. Our approach to estimating scatter is, first, to generate a training set of 2D radiographs with and without scatter using particle transport simulation software. To estimate scatter for a new radiograph, we adaptively fit a scatter model to a small subset of the training data containing the radiographs most similar to it. We compared local and global (fit on full data sets) versions of several X-ray scatter models, including two from the recent literature, as well as a recent deep learning-based scatter model, in the context of descattering and quantitative density reconstruction of simulated, spherically symmetrical, single-material objects comprising shells of various densities. Our results show that, when applied locally, even simple models provide state-of-the-art descattering, reducing the error in density reconstruction due to scatter by more than half.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Estimation of Fission Product Transport Parameters for Cesium in the AGR-3/4 TRISO Fuel Experiment

A one-dimensional (1D) finite-element model of fission product transport in the AGR-3/4 experiment has been developed using the Multiphysics Object Oriented Simulation Environment (MOOSE) framework and implemented in the fuel performance code, BISON. The model resolves capsule-specific geometries, materials, and temperature histories and simulates radial migration of fission products from the fuel compact through the inner ring, outer ring, and into the sink ring. Model parameters governing diffusion and sorption were estimated for key fission products – cesium (Cs), and europium (Eu) – by simultaneously fitting modeled isotopic concentration profiles and total ring inventories to a post-irradiation experimental measurement. These data include gamma scanning, liquid scintillation for Sr-90, radial deconsolidation leach-burn-leach analysis, tomographic reconstructions, and destructive physical sampling. A mortar-based interfacial sorption framework was implemented to enforce physically consistent mass transfer and flux conservation across gas gaps. Two classes of parameter sets were derived: a least-squares best-fit, and a safety-oriented conservative-fit, what applies strong penalties for underprediction of sink inventories. Across all twelve capsules, the model successfully reproduces the dominant radial transport trends for Cs, Sr, with decreasing concentrations from the compact outward through successive rings. Cs behavior is captured most consistently, while strontium predictions reveal systematic trade-offs between compact accuracy and conservative sink-ring bounding. The results demonstrate that sink ring weighted calibration provides conservative, safety relevant bounds on low temperature fission product transport, but at the cost of underpredicting compact inventories for Sr isotopes. These discrepancies highlight the need for additional physics, including fast-slow diffusion model, incorporating trapping mechanism in the transport behavior. Overall, this work establishes a robust, capsule-specific modeling framework for AGR-3/4 fission product transport and provides a defensible basis for parameter selection in source-term and fuel performance analyses for high temperature gas-cooled reactors.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Selective Amnesia using Contrastive Subnet Erasure for Class Level Unlearning in Vision Models

We study concept-level forgetting in pretrained vision models: removing an entire semantic category so the system no longer recognizes that object in unseen images and contexts, rather than merely forgetting specific training examples. Prior work either applies blunt global projections or fine-tunes parameters, which can introduce collateral damage to unrelated features, add compute, and become unstable as forgetting strength increases. We introduce Contrastive Subnet Erasure (CSE), a training-free, encoder-centric edit that targets a compact set of channels most responsible for the class and attenuates them in a calibrated manner. The modification is algebraically folded into the subsequent layer, yielding no inference-time overhead and leaving task heads unchanged. To evaluate whether forgetting generalizes beyond the data used to specify the class, we introduce a cross dataset protocol in which the class is defined on a source dataset and performance is measured on a disjoint target dataset drawn from a different distribution with no shared images. This setup tests whether the model still fails to recognize the object when it looks different or appears in new scenes, and it helps avoid overfitting to patterns in the source dataset. Across CIFAR 10, CIFAR 100, and ImageNet under this protocol, CSE achieves stronger forgetting of the target class while better preserving non target utility than existing baselines in both single class and multi class settings. Overall, CSE provides a simple, stable, and deployment-ready mechanism for class-level unlearning in vision.

Kotevska, Olivera [ORNL] (ORCID:0000000316772243)↗
DOE OSTI2022

Kinetic modeling of neutral transport for a continuum gyrokinetic code

In this work we present the first-of-its-kind coupling of a continuum full- f gyrokinetic turbulence model with a 6D continuum model for kinetic neutrals, carried out using the Gkeyll code. Our objective is to improve the first-principle understanding of the role of neutrals in plasma fueling, detachment, and their interaction with edge plasma profiles and turbulence statistics. Our model includes only atomic hydrogen and incorporates electron-impact ionization, charge exchange, and wall recycling. These features have been successfully verified with analytical predictions and benchmarked with the DEGAS2 Monte Carlo neutral code. We carry out simulations for a scrape-off layer (SOL) with simplified geometry and National Spherical Torus Experiment parameters. We compare these results to a baseline simulation without neutrals and find that neutral interactions reduce the normalized density fluctuation levels and associated skewness and kurtosis, while increasing auto-correlation times. A flatter density profile is also observed, similar to the SOL density shoulder formation in experimental scenarios with high fueling.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗