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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 OSTI2021

“Scrutinizing entrainment and mass flux closures in shallow cumulus parameterizations using cloud-radar observations and large-eddy simulation” (Final report for DOE grant)

Warm, shallow clouds are responsible for a substantial portion of Earth System Model (ESM) uncertainty and divergence among model climate projections. A substantial amount of this uncertainty is tied to the representation of convection in ESMs, in particular assumptions underlying the convective parameterizations. This study focuses on entrainment and entrainment closures in ESM parameterizations. The completed project was a joint observational and modeling study to observationally derive estimates of entrainment and to use high-resolution modeling to scrutinize entrainment and mass flux closures in shallow convection parameterizations.

58 GEOSCIENCES↗
DOE OSTI2026

Predicting U 3 O 8 powder processing conditions: An AI/ML approach analyzing deep learning embeddings of SEM micrographs

High-resolution SEM images of uranium-oxide powders encode micro- and nanoscale clues to their synthesis route and calcination temperature. We trained a ResNet-50 model on 11 commercial-scale U₃O₈ classes, ammonium diuranate (ADU) or uranyl peroxide (H₂O₂) precursors calcined at temperatures ranging from 400 to 750 °C and added a 256-D projection head before the classifier to analyze the learned representation. The best of eight seeds reached 92.4 % accuracy on reserved testing data, but our focus is the structure of the embedding space rather than the accuracy and labels. We quantify class relatedness in the original 256-D space using centroid similarity and distributional distances, and we use Uniform Manifold Approximation Projection (UMAP) for visualization. ‘Unknown’ images from different preparation methods, SEM operators, and from the literature localized near the expected classes under a nearest-centroid analysis without retraining, as well as clustered in similar UMAP space. In conclusion, this embedding-centered workflow complements black-box classification by providing quantitative, similarity-based comparisons of U₃O₈ morphologies and reduces storage space by up to 98 % for image data used in millisecond vector search comparisons.

36 MATERIALS SCIENCE↗
NASA NTRS1996

The Aggregate Representation of Terrestrial Land Covers Within Global Climate Models (GCM)

This project had four initial objectives: (1) to create a realistic coupled surface-atmosphere model to investigate the aggregate description of heterogeneous surfaces; (2) to develop a simple heuristic model of surface-atmosphere interactions; (3) using the above models, to test aggregation rules for a variety of realistic cover and meteorological conditions; and (4) to reconcile biosphere-atmosphere transfer scheme (BATS) land covers with those that can be recognized from space; Our progress in meeting these objectives can be summarized as follows. Objective 1: The first objective was achieved in the first year of the project by coupling the Biosphere-Atmosphere Transfer Scheme (BATS) with a proven two-dimensional model of the atmospheric boundary layer. The resulting model, BATS-ABL, is described in detail in a Masters thesis and reported in a paper in the Journal of Hydrology Objective 2: The potential value of the heuristic model was re-evaluated early in the project and a decision was made to focus subsequent research around modeling studies with the BATS-ABL model. The value of using such coupled surface-atmosphere models in this research area was further confirmed by the success of the Tucson Aggregation Workshop. Objective 3: There was excellent progress in using the BATS-ABL model to test aggregation rules for a variety of realistic covers. The foci of attention have been the site of the First International Satellite Land Surface Climatology Project Field Experiment (FIFE) in Kansas and one of the study sites of the Anglo-Brazilian Amazonian Climate Observational Study (ABRACOS) near the city of Manaus, Amazonas, Brazil. These two sites were selected because of the ready availability of relevant field data to validate and initiate the BATS-ABL model. The results of these tests are given in a Masters thesis, and reported in two papers. Objective 4: Progress far exceeded original expectations not only in reconciling BATS land covers with those that can be recognized from space, but also in then applying remotely-sensed land cover data to map aggregate values of BATS parameters for heterogeneous covers and interpreting these parameters in terms of surface-atmosphere exchanges.

Shuttleworth, W. James↗
NASA NTRS2019

NASA Orbital Debris Engineering Model ORDEM 3.1 - Software User Guide

This National Aeronautics and Space Administration (NASA) Orbital Debris Engineering Model (ORDEM) 3.1 Software User Guide accompanies delivery of the latest upgraded version of the model, ORDEM 3.1. The user guide also provides a top-level program description and a list of capabilities. It includes descriptions of runtime error and information codes, input/output file formats, runtimes for different orbit configurations, and how to use uncertainty files. ORDEM 3.1 supersedes the previous NASA Orbital Debris Program Office (ODPO) models – ORDEM 3.0 (Stansbery, et al. 2014) and ORDEM2000 (Liou, et al. 2002). The availability of new sensor and in situ data, re-analysis of older data, and development of new analytical techniques has enabled the construction of this more comprehensive and sophisticated model. An upgraded graphical user interface (GUI) is integrated with the software. This upgraded GUI uses project-oriented organization and provides the user with graphical representations of numerous output data products. For example, these range from the conventional flux vs. average debris size (or altitude bin) for chosen analysis orbits (or views) to the more complex color-contoured, two-dimensional (2-D) directional flux diagrams in local spacecraft elevation and azimuth. The current model, ORDEM 3.1, supports spacecraft as well as telescope/radar project assessments. ORDEM 3.1 contains updated debris populations covering low Earth orbit (LEO, up to 2000 km altitude) to geosynchronous orbit (GEO, up to 40,000 km altitude) and can assess debris calculations up to year 2050, extending coverage past the previous limit of 2035 in ORDEM 3.0. Although populations differ from its predecessor, ORDEM 3.1 is functionally the same as ORDEM 3.0 and can support ORDEM 3.0 projects through backward compatibility.

Vavrin, Andrew B.↗
NASA NTRS2023

NASA Orbital Debris Engineering Model ORDEM 3.2 – Software User Guide

This National Aeronautics and Space Administration (NASA) Orbital Debris Engineering Model (ORDEM) 3.2 Software User Guide accompanies delivery of the latest upgraded version of the model, ORDEM 3.2. The user guide also provides a top-level program description and a list of capabilities. It includes descriptions of runtime error and information codes, input/output file formats, runtimes for different orbit configurations, and how to use uncertainty files. ORDEM 3.2 supersedes the previous NASA Orbital Debris Program Office (ODPO) models – ORDEM 3.0 (Stansbery, et al. 2014) and ORDEM2000 (Liou, et al. 2002). The availability of new sensor and in situ data, re-analysis of older data, and development of new analytical techniques has enabled the construction of this more comprehensive and sophisticated model. An upgraded graphical user interface (GUI) is integrated with the software. This upgraded GUI uses project-oriented organization and provides the user with graphical representations of numerous output data products. For example, these range from the conventional flux vs. average debris size (or altitude bin) for chosen analysis orbits (or views) to the more complex color-contoured, two-dimensional (2-D) directional flux diagrams in local spacecraft elevation and azimuth. The current model, ORDEM 3.2, supports spacecraft as well as telescope/radar project assessments. ORDEM 3.2 contains updated debris populations covering low Earth orbit (LEO, up to 2000 km altitude) to geosynchronous orbit (GEO, up to 40,000 km altitude) and can assess debris calculations up to year 2050, extending coverage past the previous limit of 2035 in ORDEM 3.0. Although populations differ from its predecessor, ORDEM 3.2 is functionally the same as ORDEM 3.0 and can support ORDEM 3.0 projects through backward compatibility.

Andrew Vavrin↗
DOE OSTI2020

A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5

Abstract. Land models are essential tools for understanding and predicting terrestrial processes and climate–carbon feedbacks in the Earth system, but uncertainties in their future projections are poorly understood. Improvements in physical process realism and the representation of human influence arguably make models more comparable to reality but also increase the degrees of freedom in model configuration, leading to increased parametric uncertainty in projections. In this work we design and implement a machine learning approach to globally calibrate a subset of the parameters of the Community Land Model, version 5 (CLM5) to observations of carbon and water fluxes. We focus on parameters controlling biophysical features such as surface energy balance, hydrology, and carbon uptake. We first use parameter sensitivity simulations and a combination of objective metrics including ranked global mean sensitivity to multiple output variables and non-overlapping spatial pattern responses between parameters to narrow the parameter space and determine a subset of important CLM5 biophysical parameters for further analysis. Using a perturbed parameter ensemble, we then train a series of artificial feed-forward neural networks to emulate CLM5 output given parameter values as input. We use annual mean globally aggregated spatial variability in carbon and water fluxes as our emulation and calibration targets. Validation and out-of-sample tests are used to assess the predictive skill of the networks, and we utilize permutation feature importance and partial dependence methods to better interpret the results. The trained networks are then used to estimate global optimal parameter values with greater computational efficiency than achieved by hand tuning efforts and increased spatial scale relative to previous studies optimizing at a single site. By developing this methodology, our framework can help quantify the contribution of parameter uncertainty to overall uncertainty in land model projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Earth System Model Overestimation of Cropland Temperatures Scales With Agricultural Intensity

Intensive crop growth can modify regional climate by partitioning energy to latent heating through transpiration, cooling growing season temperatures. Recent work shows that cooling associated with agriculture can dampen anthropogenic warming over breadbasket regions. However, it is unknown whether climate models reproduce crop influences on regional climate, and thus the future risk of extreme climate events over global breadbasket regions. Here, we show that models overestimate growing season temperatures and underestimate evapotranspiration (ET) over global croplands, and that these differences increase with cropped area. We trace this warm and dry difference through each model's representation of the surface energy budget, showing that model differences in transpiration, leaf area index, and the ratio of transpiration to total ET drive the overall effect. While the implications of these model deficiencies for future projections are uncertain, they point to the importance of improving representations of crop-climate processes to better assess breadbasket vulnerability to climate change.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2010

The Graphical Representation of the Digital Astronaut Physiology Backbone

This report summarizes my internship project with the NASA Digital Astronaut Project to analyze the Digital Astronaut (DA) physiology backbone model. The Digital Astronaut Project (DAP) applies integrated physiology models to support space biomedical operations, and to assist NASA researchers in closing knowledge gaps related to human physiologic responses to space flight. The DA physiology backbone is a set of integrated physiological equations and functions that model the interacting systems of the human body. The current release of the model is HumMod (Human Model) version 1.5 and was developed over forty years at the University of Mississippi Medical Center (UMMC). The physiology equations and functions are scripted in an XML schema specifically designed for physiology modeling by Dr. Thomas G. Coleman at UMMC. Currently it is difficult to examine the physiology backbone without being knowledgeable of the XML schema. While investigating and documenting the tags and algorithms used in the XML schema, I proposed a standard methodology for a graphical representation. This standard methodology may be used to transcribe graphical representations from the DA physiology backbone. In turn, the graphical representations can allow examination of the physiological functions and equations without the need to be familiar with the computer programming languages or markup languages used by DA modeling software.

Briers, Demarcus↗
DOE OSTI2025

A review of low-rank methods for time-dependent kinetic simulations

Time-dependent kinetic models are ubiquitous in computational science and engineering. The underlying integro-differential equations in these models are high-dimensional, comprised of a six–dimensional phase space, making simulations of such phenomena extremely expensive. In this article we demonstrate that in many situations, the solution to kinetics problems lives on a low dimensional manifold that can be described by a low-rank matrix or tensor approximation. We then review the recent development of so-called low-rank methods that evolve the solution on this manifold. The two classes of methods we review are the dynamical low-rank (DLR) method, which derives differential equations for the low-rank factors, and a Step-and-Truncate (SAT) approach, which projects the solution onto the low-rank representation after each time step. Thorough discussions of time integrators, tensor decompositions, and method properties such as structure preservation and computational efficiency are included. We further show examples of low-rank methods as applied to particle transport and plasma dynamics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Large Divergence in Tropical Hydrological Projections Caused by Model Spread in Vegetation Responses to Elevated CO 2

Increasing atmospheric CO 2 and associated global warming are expected to alter the global hydrological cycle, thereby posing widespread threats to freshwater availability. However, future hydrological projections differ greatly between models, particularly over the tropical regions. The large difference between model projections directly limits policy planning efforts, and the responsible modeling processes remain unclear. Here, we identify the primary processes accounting for model differences in tropical hydrological changes using multiple CO 2 sensitivity experiments in the Coupled Model Intercomparison Project. We show that differences in projected changes to tropical evapotranspiration, precipitation, and surface water availability mainly arise from model representations of vegetation cover and stomatal conductance responses to elevated CO 2 and associated changes in atmospheric moisture and circulation. Atmospheric responses to sea surface warming contribute additionally to the divergence in hydrological projections. Given the importance of vegetation responses to elevated CO 2 and associated atmosphere feedbacks, our results underscore the need to improve representations of the vegetation physiological response to rising CO 2 and its coupling to the atmosphere, to provide reliable tropical hydrological projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Causally‐Informed Deep Learning to Improve Climate Models and Projections

Abstract Climate models are essential to understand and project climate change, yet long‐standing biases and uncertainties in their projections remain. This is largely associated with the representation of subgrid‐scale processes, particularly clouds and convection. Deep learning can learn these subgrid‐scale processes from computationally expensive storm‐resolving models while retaining many features at a fraction of computational cost. Yet, climate simulations with embedded neural network parameterizations are still challenging and highly depend on the deep learning solution. This is likely associated with spurious non‐physical correlations learned by the neural networks due to the complexity of the physical dynamical system. Here, we show that the combination of causality with deep learning helps removing spurious correlations and optimizing the neural network algorithm. To resolve this, we apply a causal discovery method to unveil causal drivers in the set of input predictors of atmospheric subgrid‐scale processes of a superparameterized climate model in which deep convection is explicitly resolved. The resulting causally‐informed neural networks are coupled to the climate model, hence, replacing the superparameterization and radiation scheme. We show that the climate simulations with causally‐informed neural network parameterizations retain many convection‐related properties and accurately generate the climate of the original high‐resolution climate model, while retaining similar generalization capabilities to unseen climates compared to the non‐causal approach. The combination of causal discovery and deep learning is a new and promising approach that leads to stable and more trustworthy climate simulations and paves the way toward more physically‐based causal deep learning approaches also in other scientific disciplines.

Meteorology & Atmospheric Sciences↗
DOE OSTI2021

FY2020 Savannah River Site Composite Analysis Annual Summary Review

This document provides the Department of Energy (DOE) Order 435.1 and its manual, Radioactive Waste Management (DOE 2021a, 2021b) required Annual Review for the Savannah River Site (SRS) Composite Analysis (CA). Progress made to-date toward addressing the secondary issue from the LFRG review of the 2010 SRS CA has focused primarily upon inventory estimate improvements. Inventory impacts dose in a linear fashion and reduces the uncertainty with the CA conclusions. Maintenance items are addressed, as funding allows, based on the relative risk associated with meeting the performance objectives. Currently, there is minimal risk in exceeding the DOE 100 mrem/yr CA primary dose limit or the DOE 30 mrem/yr dose constraint (administrative limit). Proposed activities, discoveries, new information and changes potentially affecting the 2010 SRS CA are documented in this and earlier Annual Summary reports, and a consolidated list of changes since the 2010 CA is documented in this report. The impact to the CA of changes arising from updated performance assessment (PA) baselines [i.e., Saltstone Disposal Facility (SDF), E-Area Low-Level Waste Facility (ELLWF), and F & H Tank Farm (FTF & HTF) closures] is expected to be minor for the following reasons: The primary contributors to the SRS CA dose impact at the Upper Three Runs (UTR) point of assessment (POA) are the H-Canyon and Mixed Waste Management Facility (MWMF), contributing 68% and 9%, respectively, to the dose impact at that POA. The combined contribution to the UTR dose impact from all PA’s (SDF, ELLWF, FTF and HTF) is ~2% of this total. The 2010 SRS CA model validation performed indicates that the CA projected dose, while generally conservative, provides a reasonable representation of the maximum annual doses. Doses evaluated are well below the SRS established 15 mrem/yr administrative limit (Crapse et al. 2011). Based on the assessment presented within this annual review and collective engineering judgement, the conclusions of the 2010 SRS CA remain valid and there is reasonable assurance that SRS will meet the performance objectives delineated in DOE Manual 435.1-1. The 2010 SRS CA should be updated to incorporate PA changes, proposed changes to inventories and sources and model improvements accumulated since the 2010 CA. The timing will be dependent on the completion of the ongoing E-Area PA revision.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Graph Analytics for CEBAF Operations

We report on the progress achieved during a 2-year Laboratory Directed Research and Development (LDRD) project titled “Graph Analytics for CEBAF Operations”. The objective of this project is to leverage deep learning on graph representations of CEBAF’s injector beamline in order to create a tool for improving the efficiency of beam tuning tasks. Specifically, we use graphs to represent the injector beamline at any arbitrary date and time and invoke a graph neural network (GNN) to extract a low-dimensional, informative representation that can be visualized in two-dimensions. By analyzing years of operational data from the CEBAF archiver, good and bad regions of parameter space can be identified. The goal is to exercise this framework as a real-time tool to aid beam tuning, which represents the dominant source of machine downtime.

43 PARTICLE ACCELERATORS↗
NASA NTRS1981

The role of zonally asymmetric heating in the vertical and temporal structure of the global scale flow fields during FGGE SOP-1

By examining the vertical structure of the low order spherical harmonics of the divergence and vorticity fields, the relative contribution of tropical and monsoonal circulations upon the global wind fields was estimated. This indicates that the overall flow over North America and the Pacific between January and February is quite distinct both in the lower and upper troposphere. In these longitudes there is a stronger tropical overturning and subtropical jet stream in January than February. The divergent flow reversed between 850 and 200 mb. Poleward rotational flow at upper levels is associated with an equatorward rotational flow at low levels. This suggests that the monsoon and other tropical circulations project more amplitude upon low order (global scale) representations of the flow than do the typical midlatitude circulations and that their structures show conspicuous changes on a time scale of a week or less.

Paegle, J.↗
NASA NTRS2001

Neural correlates for angular head velocity in the rat dorsal tegmental nucleus

Many neurons in the rat lateral mammillary nuclei (LMN) fire selectively in relation to the animal's head direction (HD) in the horizontal plane independent of the rat's location or behavior. One hypothesis of how this representation is generated and updated is via subcortical projections from the dorsal tegmental nucleus (DTN). Here we report the type of activity in DTN neurons. The majority of cells (75%) fired as a function of the rat's angular head velocity (AHV). Cells exhibited one of two types of firing patterns: (1) symmetric, in which the firing rate was positively correlated with AHV during head turns in both directions, and (2) asymmetric, in which the firing rate was positively correlated with head turns in one direction and correlated either negatively or not at all in the opposite direction. In addition to modulation by AHV, some of the AHV cells (40.1%) were weakly modulated by the rat's linear velocity, and a smaller number were modulated by HD (11%) or head pitch (15.9%). Autocorrelation analyses indicated that with the head stationary, AHV cells displayed irregular discharge patterns. Because afferents from the DTN are the major source of information projecting to the LMN, these results suggest that AHV information from the DTN plays a significant role in generating the HD signal in LMN. A model is proposed showing how DTN AHV cells can generate and update the LMN HD cell signal.

Non-NASA Center↗
DOE OSTI2021

Nuclear Energy Model Intercomparison Project

This document summarizes the current status and future plans of the Nuclear Model Intercomparison Project. The objectives of this project are to understand how issues central to nuclear energy are modeled in long-term capacity expansion models, to investigate how model structures and input assumptions impact projections for nuclear’s role, to refine model representations of nuclear energy, and to communicate findings to the research community and decision-makers. High-level goals are discussed for each of the four participating model groups: the U.S. Energy Information Administration (EIA), U.S. Environmental Protection Agency (EPA), Electric Power Research Institute (EPRI), and National Renewable Energy Laboratory (NREL). This document summarizes scenarios and assumptions for the model comparison, outcomes from the first workshop, and next steps.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Representation of Modes of Variability in Six U.S. Climate Models

We compare the performance of several modes of variability across six U.S. climate modeling groups, with a focus on identifying robust improvements in recent models [including those participating in phase 6 of the Coupled Model Intercomparison Project (CMIP)] compared to previous versions. In particular, we examine the representation of the Madden–Julian oscillation (MJO), El Niño–Southern Oscillation (ENSO), the Pacific decadal oscillation (PDO), the quasi-biennial oscillation (QBO) in the tropical stratosphere, and the dominant modes of extratropical variability, including the southern annular mode (SAM), the northern annular mode (NAM) [and the closely related North Atlantic Oscillation (NAO)], and the Pacific–North American pattern (PNA). Where feasible, we explore the processes driving these improvements through the use of “intermediary” experiments that utilize model versions between CMIP3/5 and CMIP6 as well as targeted sensitivity experiments in which individual modeling parameters are altered. We find clear and systematic improvements in the MJO and QBO and in the teleconnection patterns associated with the PDO and ENSO. Some gains arise from better process representation, while others (e.g., the QBO) from higher resolution that allows for a greater range of interactions. Our results demonstrate that the incremental development processes in multiple climate model groups lead to more realistic simulations over time.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2010

Project M: Scale Model of Lunar Landing Site of Apollo 17: Focus on Lighting Conditions and Analysis

This document captures the research and development of a scale model representation of the Apollo 17 landing site on the moon as part of the NASA INSPIRE program. Several key elements in this model were surface slope characteristics, crater sizes and locations, prominent rocks, and lighting conditions. This model supports development of Autonomous Landing and Hazard Avoidance Technology (ALHAT) and Project M for the GN&C Autonomous Flight Systems Branch. It will help project engineers visualize the landing site, and is housed in the building 16 Navigation Systems Technology Lab. The lead mentor was Dr. Timothy P. Crain. The purpose of this project was to develop an accurate scale representation of the Apollo 17 landing site on the moon. This was done on an 8'2.5"X10'1.375" reduced friction granite table, which can be restored to its previous condition if needed. The first step in this project was to research the best way to model and recreate the Apollo 17 landing site for the mockup. The project required a thorough plan, budget, and schedule, which was presented to the EG6 Branch for build approval. The final phase was to build the model. The project also required thorough research on the Apollo 17 landing site and the topography of the moon. This research was done on the internet and in person with Dean Eppler, a space scientist, from JSC KX. This data was used to analyze and calculate the scale of the mockup and the ratio of the sizes of the craters, ridges, etc. The final goal was to effectively communicate project status and demonstrate the multiple advantages of using our model. The conclusion of this project was that the mockup was completed as accurately as possible, and it successfully enables the Project M specialists to visualize and plan their goal on an accurate three dimensional surface representation.

Vanik, Christopher S.↗