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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

The Neutrino: A Better Understanding Through Astrophysics: Final Report

The final report for the award "The Neutrino: A Better Understanding Through Astrophysics" is given. The goals of the work were the following: to construct new theoretical approaches to the problem of neutrino propagation in media including where neutrino-neutrino interactions are important; to pioneer the use of new approaches, including super-scattering operators, for the evolution of neutrino thermal and statistical ensembles; to implement these new approaches in computer codes to study neutrino evolution in supernovae and other hot, dense environments; to increase the realism of simulated signals of a Galactic supernovae neutrino burst in current and future neutrino detectors; to study the simulated signals to determine the ability to extract information on the missing neutrino mixing parameters and the dynamics of the supernova explosion; and to study sterile neutrinos and non-standard interactions of neutrinos in supernovae and their effect upon the signal. Accomplishments made in these areas are described.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Assessing Cognitive Impacts of Errors from Machine Learning and Deep Learning Models: Final Report

Due to their recent increases in performance, machine learning and deep learning models are being increasingly adopted across many domains for visual processing tasks. One such domain is international nuclear safeguards, which seeks to verify the peaceful use of commercial nuclear energy across the globe. Despite recent impressive performance results from machine learning and deep learning algorithms, there is always at least some small level of error. Given the significant consequences of international nuclear safeguards conclusions, we sought to characterize how incorrect responses from a machine or deep learning-assisted visual search task would cognitively impact users. We found that not only do some types of model errors have larger negative impacts on human performance than other errors, the scale of those impacts change depending on the accuracy of the model with which they are presented and they persist in scenarios of evenly distributed errors and single-error presentations. Further, we found that experiments conducted using a common visual search dataset from the psychology community has similar implications to a safeguards- relevant dataset of images containing hyperboloid cooling towers when the cooling tower images are presented to expert participants. While novice performance was considerably different (and worse) on the cooling tower task, we saw increased novice reliance on the most challenging cooling tower images compared to experts. These findings are relevant not just to the cognitive science community, but also for developers of machine and deep learning that will be implemented in multiple domains. For safeguards, this research provides key insights into how machine and deep learning projects should be implemented considering their special requirements that information not be missed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Application of NEAMS Multiphysics Framework for Species Tracking in Molten Salt Reactors

This report from Idaho National Laboratory (INL) summarizes the key modeling and simulation activities conducted under the Department of Energy (DOE) Molten Salt Reactor (MSR) Campaign during the Fiscal Year 2023 (FY23). The focus of the work was to leverage state-of-the-art modeling capabilities from the DOE Nuclear Energy Advanced Modeling and Simulation (NEAMS) codes to enable novel multiphysics and multiscale modeling and simulation of MSRs. Through collaboration with NEAMS code developers, advanced multiphysics analysis capabilities for MSR systems were demonstrated by coupling depletion, thermal-hydraulics, and thermochemistry into an innovative framework for chemical species transport in MSRs. As a result, the framework can track nuclides throughout their lifetimes in the core, from production (depletion) to advection throughout the salt volume (thermal-hydraulics) and off-gassing or precipitation outside of the salt (thermochemistry). This work supports the near-term deployment of MSRs by integrating the synergistic efforts between the DOE’s MSR Campaign and NEAMS program. The resulting framework will help better connect system design modelers with experimentalists to better understand and predict complex physical behaviors in MSRs. Researchers and MSR developers alike can now leverage these new modeling and simulation capabilities to perform novel analyses with applications including: • MSR dynamics during normal operational transients and accident scenarios • Off-gas system design and performance for fuel cycle and depletion analysis • Corrosion and active chemistry control for reactor component health and lifetime determination • Source term, decay heat and activity determination in accident scenarios • Special nuclear material accountancy and chemical forensic analysis for safeguards • Digital twin development of experiments and experimental reactor demonstrations • Measurement requirements for instrumentation and control design • Uncertainty and sensitivity analysis of missing data to inform future experimental data collection.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Telomere-to-telomere assemblies of chromosome 10 reveal complex adaptive variation of 3-ketoacyl-CoA-synthases in Populus trichocarpa likely driven by Helitrons

The model woody plant Populus trichocarpa displays an atypical alkene-diverse wax cuticle likely driven by copy number variation (CNV) of 3-ketoacyl-CoA synthases ( KCS ), which has been difficult to confirm with short-read assemblies. Long-read sequencing enables the development of telomere-to-telomere resources to detect cryptic variation, including CNVs, which are currently missed. Integrating this information can improve genomic prediction for breeding and provide insights into the evolutionary basis of important traits. Our analysis of 78 long-read haplotypes from chromosome 10 identified more than twice as many KCS genes as previously reported, and numerous intragenic non-synonymous substitutions. Random Forest predictive models highlighted the importance of Potri.010G079500 in producing very long chain alkenes; however, its absence did not predict previously reported alkene-deficient phenotypes. Instead, alkene levels are best predicted by the combinations of KCS copies. Additionally, amino acid substitutions clustered around ligand and donor binding pockets, suggesting they contribute to differing wax cuticle composition. Finally, each KCS gene and copy was linked to a Helitron transposon. A phylogenetic analysis suggests Helitrons are the evolutionary mechanism for generating KCS tandem arrays. Long-read generated telomere-to-telomere assemblies of P. trichocarpa chromosome 10 revealed large-effect loci critical to genetic studies that are unattainable from short-reads. This new resource produced novel insights into genome structure and function, and a novel mechanism for generating tandem gene duplication. Our results highlight that, given current challenges in annotation and assembly, detailed and focused long-read sequences are key to interpreting complex genomic regions that contain tandem copy number variants.

09 BIOMASS FUELS↗
DOE OSTI2018

Exploring the Value of Learned Representations for Automated Syndromic Definitions

Objective: To better define and automate biosurveillance syndrome categorization using modern unsupervised vector embedding techniques. Introduction: Comprehensive medical syndrome definitions are critical for outbreak investigation, disease trend monitoring, and public health surveillance. However, because current definitions are based on keyword string-matching, they may miss important distributional information in free text and medical codes that could be used to build a more general classifier. Here, we explore the idea that individual ICD codes can be categorized by examining their contextual relationships across all other ICD codes. We extend previous work in representation learning with medical data [1] by generating dense vector embeddings of these ICD codes found in emergency department (ED) visit records. The resulting representations capture information about disease co-occurrence that would typically require SME involvement and support the development of more robust syndrome definitions.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1989

Analysis of random drop for gateway congestion control

Lately, the growing demand on the Internet has prompted the need for more effective congestion control policies. Currently No Gateway Policy is used to relieve and signal congestion, which leads to unfair service to the individual users and a degradation of overall network performance. Network simulation was used to illustrate the character of Internet congestion and its causes. A newly proposed gateway congestion control policy, called Random Drop, was considered as a promising solution to the pressing problem. Random Drop relieves resource congestion upon buffer overflow by choosing a random packet from the service queue to be dropped. The random choice should result in a drop distribution proportional to the bandwidth distribution among all contending TCP connections, thus applying the necessary fairness. Nonetheless, the simulation experiments demonstrate several shortcomings with this policy. Because Random Drop is a congestion control policy, which is not applied until congestion has already occurred, it usually results in a high drop rate that hurts too many connections including well-behaved ones. Even though the number of packets dropped is different from one connection to another depending on the buffer utilization upon overflow, the TCP recovery overhead is high enough to neutralize these differences, causing unfair congestion penalties. Besides, the drop distribution itself is an inaccurate representation of the average bandwidth distribution, missing much important information about the bandwidth utilization between buffer overflow events. A modification of Random Drop to do congestion avoidance by applying the policy early was also proposed. Early Random Drop has the advantage of avoiding the high drop rate of buffer overflow. The early application of the policy removes the pressure of congestion relief and allows more accurate signaling of congestion. To be used effectively, algorithms for the dynamic adjustment of the parameters of Early Random Drop to suite the current network load must still be developed.

Hashem, Emam Salaheddin↗
NASA NTRS1991

Aging and the complexity of cardiovascular dynamics

Biomedical signals often vary in a complex and irregular manner. Analysis of variability in such signals generally does not address directly their complexity, and so may miss potentially useful information. We analyze the complexity of heart rate and beat-to-beat blood pressure using two methods motivated by nonlinear dynamics (chaos theory). A comparison of a group of healthy elderly subjects with healthy young adults indicates that the complexity of cardiovascular dynamics is reduced with aging. This suggests that complexity of variability may be a useful physiological marker.

Non-NASA Center↗
NASA NTRSDate not supplied

TPSAS-NF1676L-27237-DND

Objective - It is observed that 5.6% of all CERES Terra/Aqua data contains missing cloud cover information or insufficient imager data for a reliable scene identification (some times it can reach up to 50% of data for a specific scene type) - The unavailability of imager data lead to gaps in global radiation budget dataset. - In this study, our objective is to develop a Machine learning methodology for the improved determination of CERES scene type and subsequent clear-sky TOA flux estimation using standalone CERES TOA radiance measurements (without any MODIS/Imager data).

CERES↗
DOE OSTI2017

Updated Radiological Inventory of G1 Reactor Thanks to a Strengthened Data Processing - 17271

The radiological inventory of G1 reactor is under refinement and consolidation. This reactor, located at the CEA Marcoule center in France, presents an issue for Chlorine-36 as graphite was used as moderator. The improvement of the 3D physical description and a better understanding of historical events enable the computation of activation levels thanks to a numerical model. These results are compared to old samples and more recent ones to quantify nuclide activity levels as well as their spatial distributions. In particular a spatial and statistical correction of sample bias leads to an update of the radiological inventory with an expected reduction of average activity levels. Another part of the work consists in identifying missing pieces of information and still uncertain parts of the reactor (sample analysis of activated concrete for instance) to reinforce the radiological inventory of G1 reactor. This will be the basis of future investigation works. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Peri-Net-Pro: the neural processes with quantified uncertainty for crack patterns

Abstract This paper develops a deep learning tool based on neural processes (NPs) called the Peri-Net-Pro, to predict the crack patterns in a moving disk and classifies them according to the classification modes with quantified uncertainties. In particular, image classification and regression studies are conducted by means of convolutional neural networks (CNNs) and NPs. First, the amount and quality of the data are enhanced by using peridynamics to theoretically compensate for the problems of the finite element method (FEM) in generating crack pattern images. Second, case studies are conducted with the prototype microelastic brittle (PMB), linear peridynamic solid (LPS), and viscoelastic solid (VES) models obtained by using the peridynamic theory. The case studies are performed to classify the images by using CNNs and determine the suitability of the PMB, LBS, and VES models. Finally, a regression analysis is performed on the crack pattern images with NPs to predict the crack patterns. The regression analysis results confirm that the variance decreases when the number of epochs increases by using the NPs. The training results gradually improve, and the variance ranges decrease to less than 0.035. The main finding of this study is that the NPs enable accurate predictions, even with missing or insufficient training data. The results demonstrate that if the context points are set to the 10th, 100th, 300th, and 784th, the training information is deliberately omitted for the context points of the 10th, 100th, and 300th, and the predictions are different when the context points are significantly lower. However, the comparison of the results of the 100th and 784th context points shows that the predicted results are similar because of the Gaussian processes in the NPs. Therefore, if the NPs are employed for training, the missing information of the training data can be supplemented to predict the results.

Mathematics↗
DOE OSTI2014

Detecting differential protein expression in large-scale population proteomics

Mass spectrometry-based high-throughput quantitative proteomics shows great potential in clinical biomarker studies, identifying and quantifying thousands of proteins in biological samples. However, methods are needed to appropriately handle issues/challenges unique to mass spectrometry data in order to detect as many biomarker proteins as possible. One issue is that different mass spectrometry experiments generate quite different total numbers of quantified peptides, which can result in more missing peptide abundances in an experiment with a smaller total number of quantified peptides. Another issue is that the quantification of peptides is sometimes absent, especially for less abundant peptides and such missing values contain the information about the peptide abundance. Here, we propose a Significance Analysis for Large-scale Proteomics Studies (SALPS) that handles missing peptide intensity values caused by the two mechanisms mentioned above. Our model has a robust performance in both simulated data and proteomics data from a large clinical study. Because varying patients’ sample qualities and deviating instrument performances are not avoidable for clinical studies performed over the course of several years, we believe that our approach will be useful to analyze large-scale clinical proteomics data.

Environmental Molecular Sciences Laboratory↗
NASA NTRS1984

Pilot's Desk Flight Station

Aircraft flight station designs have generally evolved through the incorporation of improved or modernized controls and displays. In connection with a continuing increase in the amount of information displayed, this process has produced a complex and cluttered conglomeration of knobs, switches, and electromechanical displays. The result was often high crew workload, missed signals, and misinterpreted information. Advances in electronic technology have now, however, led to new concepts in flight station design. An American aerospace company in cooperation with NASA has utilized these concepts to develop a candidate conceptual design for a 1995 flight station. The obtained Pilot's Desk Flight Station is a unique design which resembles more an operator's console than today's cockpit. Attention is given to configuration, primary flight controllers, front panel displays, flight/navigation display, approach charts and weather display, head-up display, and voice command and response systems.

Sexton, G. A.↗
DOE OSTI2016

FY2017 Pilot Project Plan for the Nuclear Energy Knowledge and Validation Center Initiative

To prepare for technical development of computational code validation under the Nuclear Energy Knowledge and Validation Center (NEKVAC) initiative, several meetings were held by a group of experts of the Idaho National Laboratory (INL) and the Oak Ridge National Laboratory (ORNL) to develop requirements of, and formulate a structure for, a transient fuel database through leveraging existing resources. It was concluded in discussions of these meetings that a pilot project is needed to address the most fundamental issues that can generate immediate stimulus to near-future validation developments as well as long-lasting benefits to NEKVAC operation. The present project is proposed based on the consensus of these discussions. Analysis of common scenarios in code validation indicates that the incapability of acquiring satisfactory validation data is often a showstopper that must first be tackled before any confident validation developments can be carried out. Validation data are usually found scattered in different places most likely with interrelationships among the data not well documented, incomplete with information for some parameters missing, nonexistent, or unrealistic to experimentally generate. Furthermore, with very different technical backgrounds, the modeler, the experimentalist, and the knowledgebase developer that must be involved in validation data development often cannot communicate effectively without a data package template that is representative of the data structure for the information domain of interest to the desired code validation. This pilot project is proposed to use the legendary TREAT Experiments Database to provide core elements for creating an ideal validation data package. Data gaps and missing data interrelationships will be identified from these core elements. All the identified missing elements will then be filled in with experimental data if available from other existing sources or with dummy data if nonexistent. The resulting hybrid validation data package (composed of experimental and dummy data) will provide a clear and complete instance delineating the structure of the desired validation data and enabling effective communication among the modeler, the experimentalist, and the knowledgebase developer. With a good common understanding of the desired data structure by the three parties of subject matter experts, further existing data hunting will be effectively conducted, new experimental data generation will be realistically pursued, knowledgebase schema will be practically designed; and code validation will be confidently planned.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1995

NASA/DOD Aerospace Knowledge Diffusion Research Project. Paper 51: Workplace communications skills and the value of communications and information-use skills instruction: Engineering students' perspectives

Studies indicate that communications and information-related activities take up a substantial portion of an engineer's work week; therefore, effective communications and information use skills are one of the key engineering competencies that recent graduates of engineering programs are expected to possess. Feedback from industry rates communications and information use skills of entry-level engineers low. Missing from current discussions of communications and information use skills and competencies for engineering students is a clear explanation from the professional engineering community about what constitutes 'acceptable and desirable communications and information norms' within that community. To gather adequate and generalizable data about communications and information skills instruction and to provide a student perspective on the communications skills of engineers, we undertook a national study of aerospace engineering students in March 1993. The study included questions about the importance of certain communications and information skills to professional success, the instruction students had received in these skills, and perceived helpfulness of the instruction. Selected results from the study study are reported in this paper.

Pinelli, Thomas E.↗
DOE OSTI2018

Resolving environmental effects of wind energy

Concerns for potential wildlife impacts resulting from land-based and offshore wind energy have created challenges for wind project development. Research is not always adequately supported, results are neither always readily accessible nor are they satisfactorily disseminated, and so decisions are often made based on the best available information, which may be missing key findings. The potential for high impacts to avian and bat species and marine mammals have been used by wind project opponents to stop, downsize, or severely delay project development. The global nature of the wind industry - combined with the understanding that many affected species cross-national boundaries, and in many cases migrate between continents - also points to the need to collaborate on an international level. The International Energy Agency (IEA) Wind Technology Collaborative Programs facilitates coordination on key research issues. IEA Wind Task 34 - WREN: Working Together to Resolve Environmental Effects of Wind Energy-is a collaborative forum to share lessons gained from field research and modeling, including management methods, wildlife monitoring methods, best practices, study results, and successful approaches to mitigating impacts and addressing the cumulative effects of wind energy on wildlife.

17 WIND ENERGY↗
DOE OSTI2021

Synthetic Evaluation of Infrasonic Multipole Waveform Inversion

Acoustic source inversions estimate the mass flow rate of volcanic explosions or yield of chemical explosions and provide insight into potential source directionality. However, the limitations of applying these methods to complex sources and their ability to resolve a stable solution have not been investigated in detail. We perform synthetic infrasound waveform inversions that use 3-D Green’s functions for a variety of idealized and realistic deployment scenarios using both a flat plane and Yasur volcano, Vanuatu as examples. We investigate the ability of various scenarios to retrieve the input source functions and relative amplitudes for monopole and multipole (monopole and dipole) inversions. Infrasound waveform inversions appear to be a robust method to quantify mass flow rates from simple sources (monopole) using deployments of infrasound sensors placed around a source, but care should be taken when analyzing and interpreting results from more complex acoustic sources (multipole) that have significant directional components. In the examples we consider the solution is stable for monopole inversions with a signal-to-noise ratio greater than five and the dipole component is small. For most scenarios investigated, the vertical dipole component of the multipole explosion source is poorly constrained and can impact the ability to recover the other source term components. Because multipole inversions are ill-posed for many deployments, a low residual does not necessarily mean the proper source vector has been recovered. Synthetic studies are able to help investigate the limitations and place bounds on information that may be missing using monopole and multipole inversions for potentially directional sources.

58 GEOSCIENCES↗
DOE OSTI2014

Weak lensing corrections to tSZ-lensing cross correlation

The cross correlation between the thermal Sunyaev-Zeldovich (tSZ) effect and gravitational lensing in wide field has recently been measured. It can be used to probe the distribution of the diffuse gas in large scale structure, as well as inform us about the missing baryons. As for any lensing-based quantity, higher order lensing effects can potentially affect the signal. Here, we extend previous higher order lensing calculations to the case of tSZ-lensing cross correlations. We derive terms analogous to corrections due to the Born approximation, lens-lens coupling, and reduced shear up to order ℓ ∼> 3000.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

A change language for ontologies and knowledge graphs

Ontologies and knowledge graphs (KGs) are general-purpose computable representations of some domain, such as human anatomy, and are frequently a crucial part of modern information systems. Most of these structures change over time, incorporating new knowledge or information that was previously missing. Managing these changes is a challenge, both in terms of communicating changes to users and providing mechanisms to make it easier for multiple stakeholders to contribute. To fill that need, we have created KGCL, the Knowledge Graph Change Language (https://github.com/INCATools/kgcl), a standard data model for describing changes to KGs and ontologies at a high level, and an accompanying human-readable Controlled Natural Language (CNL). This language serves two purposes: a curator can use it to request desired changes, and it can also be used to describe changes that have already happened, corresponding to the concepts of “apply patch” and “diff” commonly used for managing changes in text documents and computer programs. Another key feature of KGCL is that descriptions are at a high enough level to be useful and understood by a variety of stakeholders—e.g. ontology edits can be specified by commands like “add synonym ‘arm’ to ‘forelimb’” or “move ‘Parkinson disease’ under ‘neurodegenerative disease’.” We have also built a suite of tools for managing ontology changes. These include an automated agent that integrates with and monitors GitHub ontology repositories and applies any requested changes and a new component in the BioPortal ontology resource that allows users to make change requests directly from within the BioPortal user interface. Overall, the KGCL data model, its CNL, and associated tooling allow for easier management and processing of changes associated with the development of ontologies and KGs.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗