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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 451 records · Page 25

All sources NASA NTRS DOE OSTI

NASA NTRS2011

GLM Proxy Data Generation: Methods for Stroke/Pulse Level Inter-Comparison of Ground-Based Lightning Reference Networks

In order to produce useful proxy data for the GOES-R Geostationary Lightning Mapper (GLM) in regions not covered by VLF lightning mapping systems, we intend to employ data produced by ground-based (regional or global) VLF/LF lightning detection networks. Before using these data in GLM Risk Reduction tasks, it is necessary to have a quantitative understanding of the performance of these networks, in terms of CG flash/stroke DE, cloud flash/pulse DE, location accuracy, and CLD/CG classification error. This information is being obtained through inter-comparison with LMAs and well-quantified VLF/LF lightning networks. One of our approaches is to compare "bulk" counting statistics on the spatial scale of convective cells, in order to both quantify relative performance and observe variations in cell-based temporal trends provided by each network. In addition, we are using microsecond-level stroke/pulse time correlation to facilitate detailed inter-comparisons at a more-fundamental level. The current development status of our ground-based inter-comparison and evaluation tools will be presented, and performance metrics will be discussed through a comparison of Vaisala s Global Lightning Dataset (GLD360) with the NLDN at locations within and outside the U.S.

Cummins, Kenneth L.↗

NASA NTRS2000

Remote Sensing of Wind Fields and Aerosol Distributions with Airborne Scanning Doppler Lidar

The coherent Doppler lidar, when operated from an airborne platform, offers a unique measurement capability for study of atmospheric and surface processes and feature. This is especially true for scientific objectives requiring measurements in optically-clear air, where other remote sensing technologies such as Doppler radar are at a disadvantage in terms of spatial resolution and coverage. The atmospheric lidar remote sensing groups of several US institutions, led by Marshall Space Flight Center, have developed an airborne coherent Doppler lidar capable of mapping the wind field and aerosol structure in three dimensions. The instrument consists of about a 1 Joule/pulse (eyesafe) lidar transceiver, telescope, scanner, inertial measurement unit, and operations control system to orchestrate all subsystem functions and tasks. The scanner is capable of directing the expanded lidar beam in a variety of ways, in order to extract vertically resolved wind fields. Horizontal resolution is about 1 km; vertical resolution is even finer. Winds are obtained by measuring backscattered, Doppler-shifted laser radiation from naturally-occurring aerosol particles (on an order of 1 micron in diameter). Measurement coverage depends on aerosol spatial distribution and concentration. Velocity accuracy has been verified to be about 1 m/s. A variety of applications has been demonstrated during the three flight campaigns conducted during 1995-1998. Examples will be shown during the presentation. In 1995, boundary layer winds over the ocean were mapped with unprecedented resolution. In 1996, unique measurements were made of flow over the complex terrain of the Aleutian Islands; interaction of the marine boundary layer jet with the California coastal mountain range; a weak dry line in Texas - New Mexico; an upper tropospheric jet stream; the angular dependence of sea surface scattering; and in-flight radiometric calibration using the surface of White Sands National Monument. In 1998, the first measurements of eyewall and boundary layer winds within a hurricane were made with the airborne Doppler lidar. Potential future applications, and plans for improvements, will also be identified.

Rothermel, Jeffry↗

NASA NTRS1973

Task 1: Water Depth Management, 1388

The author has identified the following significant results. ERTS-1 MSS data taken on October 10, 1972, of the Little Bahama Bank are being used to demonstrate the use of ERTS-1 data for mapping of shallow water features for the purpose of upgrading world navigation charts. Marked reflectance differences occur for the shallow water areas in bands 4, 5, and 6. Digital processing of two adjacent data tapes within the ERTS-1 frame covering an area of about 40 by 40 miles has been completed. Correlation of depth measurements to 5 meters has been successful. A mathematical model for depth measurements using ratio of voltages in band 4 and 5 has been successfully developed and is being tested for accuracy. Additional studies for areas near Puerto Rico and in northern Lake Michigan will be undertaken. Satellite data will also provide geographical evidence for verifying existence or nonexistence of doubtful shoal waters now appearing on world charts and considered to be hazardous to shipping.

Polcyn, F. C.↗

NASA NTRS1973

Mission description

The accomplishments of the Apollo 17 flight are discussed. The scientific objectives included geological surveying and sampling of materials and surface features in a preselected area of the Taurus-Littrow region, deploying and activating surface experiments, and conducting inflight experiments and photographic tasks during lunar orbit and transearth coast. The individual Apollo 17 experiments and photographic tasks are presented in outline form. Charts are developed to show the major mission events and data collection periods correlated to Greenwich Mean Time and ground elapsed time. Maps of the lunar surface ground track envelope for the Apollo 17 orbiting spacecraft for revolutions one to seventy-five is shown.

Baldwin, R. R.↗

NASA NTRS1989

Calibrating a VPL DataGlove for teleoperating the Utah/MIT hand

A system able to control the Utah/MIT hand with the VPL DataGlove has been developed. To get the actual joint angles from the DataGlove sensor values, a least-squares fit is used to find the best-fit exponential curve for each sensor, and then the correlation between the sensors is reduced by the iterative correlation elimination procedure. The calibration depends both on the wearer and the particular DataGlove being used. The first-level calibration is simple and can be done under 15 min with experience. The second level is fixed and requires no adjustments. To control the hand, a mapping from the DataGlove angles to the hand angles is applied, making the hand fingertips follow the DataGlove fingertips. The hand can successfully implement various high-level tasks under the DataGlove wearer's control.

Hong, Jiawei↗

NASA NTRS2019

Usability Test of a Prototype UAS Awareness Interface for Public Users

A tool to give the public a window into Unmanned Aerial Systems (UAS) Traffic Management (UTM) operations was created from an existing data collection tool. The interface included a map and a table showing details about UAS operations that could be queried in a number of ways. Eleven participants attended the study, successfully completing a 19-item task set in about 30 minutes. They correctly found information for 87% of the non-subjective tasks at a rate of around a minute per task, and rated the usability of the tool at the end of the session above the industry benchmark. Participants gave favorable reviews of the "public portal tool", even reporting that they would be satisfied with less information that it presented. There were one or two elements of the display that users found distracting and some navigation functions that need improvement, but on balance, the public representatives liked the features they saw in, and had few criticisms of, the public portal tool. One important issue for the small Unmanned Aerial System community to resolve will be how much or how little information should be available about UTM operations to members of the public.

Martin, Lynne H.↗

NASA NTRS2019

Usability Test of a Prototype UAS (Unmanned Aircraft Systems) Awareness Interface for Public Users

Martin, Lynne H.↗

NASA NTRS2021

Classifying Forest Type in the National Forest Inventory Context with Airborne Hyperspectral and Lidar Data

Forest structure and composition regulate a range of ecosystem services, including biodiversity, water and nutrient cycling, and wood volume for resource extraction. Forest type is an important metric measured in the US Forest Service Forest Inventory and Analysis (FIA) program, the national forest inventory of the USA. Forest type information can be used to quantify carbon and other forest resources within specific domains to support ecological analysis and forest management decisions, such as managing for disease and pests. In this study, we developed a methodology that uses a combination of airborne hyperspectral and lidar data to map FIA-defined forest type between sparsely sampled FIA plot data collected in interior Alaska. To determine the best classification algorithm and remote sensing data for this task, five classification algorithms were tested with six different combinations of raw hyperspectral data, hyperspectral vegetation indices, and lidar-derived canopy and topography metrics. Models were trained using forest type information from 632 FIA subplots collected in interior Alaska. Of the thirty model and input combinations tested, the random forest classification algorithm with hyperspectral vegetation indices and lidar-derived topography and canopy height metrics had the highest accuracy (78% overall accuracy). This study supports random forest as a powerful classifier for natural resource data. It also demonstrates the benefits from combining both structural (lidar) and spectral (imagery) data for forest type classification.

random forest↗

DOE OSTI2022

Gas-mapping 3D imager measurement techniques and method of data processing

Measurement approaches and data analysis methods are disclosed for combining 3D topographic data with spatially-registered gas concentration data to increase the efficiency of gas monitoring and leak detection tasks. Here, the metric for efficiency is defined as reducing the measurement time required to achieve the detection, or non-detection, of a gas leak with a desired confidence level. Methods are presented for localizing and quantifying detected gas leaks. Particular attention is paid to the combination of 3D spatial data with path-integrated gas concentration measurements acquired using remote gas sensing technologies, as this data can be used to determine the path-averaged gas concentration between the sensor and points in the measurement scene. Path-averaged gas concentration data is useful for finding and quantifying localized regions of elevated (or anomalous) gas concentration making it ideal for a variety of applications including: oil and gas pipeline monitoring, facility leak and emissions monitoring, and environmental monitoring.

Thorpe, Michael↗

DOE OSTI2023

User Manual - HydraGNN: Distributed PyTorch Implementation of Multi-Headed Graph Convolutional Neural Networks

This document serves as user manual for HydraGNN, a scalable graph neural network (GNN) architecture that allows for a simultaneous prediction of multiple target properties using multi-task learning (MTL). The HydraGNN architecture is constructed by successive superposition of three different sets of layers. The first set is made of message-passing layers to exchange information across nodes in the graph and use this to update the nodal features. The second set is made of global pooling layers that aggregate information from all the nodes in the graph and map it into a scalar, and is needed only for global target properties that are related to the entire graph. The third set of layers is dedicated to the implementation of MTL, which is enabled by forking of the architecture into separate heads, each one of them dedicated to the predictive task of one specific target property. Through an object-oriented programming paradigm, HydraGNN is templated over different message-passing policies, which allows for a user-friendly hyperparameter study to assess the sensitivity of the predictive performance of the HydraGNN architecture on a specific dataset with respect to the choice of the message-passing policy. The object-oriented paradigm used by HydraGNN also allows for a user-friendly inclusion of newly developed message passing policies within the existing framework. HydraGNN supports distributed computing capabilities for scalable data reading and scalable training on leadership-class supercomputers.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2001

Remote Sensing of Wind Fields and Aerosol Distribution with Airborne Scanning Doppler Lidar

The coherent Doppler laser radar (lidar), when operated from an airborne platform, is a unique tool for the study of atmospheric and surface processes and features. This is especially true for scientific objectives requiring measurements in optically-clear air, where other remote sensing technologies such as Doppler radar are typically at a disadvantage. The atmospheric lidar remote sensing groups of several US institutions, led by Marshall Space Flight Center, have developed an airborne coherent Doppler lidar capable of mapping the wind field and aerosol structure in three dimensions. The instrument consists of an eye-safe approx. 1 Joule/pulse lidar transceiver, telescope, scanner, inertial measurement unit, and flight computer system to orchestrate all subsystem functions and tasks. The scanner is capable of directing the expanded lidar beam in a variety of ways, in order to extract vertically-resolved wind fields. Horizontal resolution is approx. 1 km; vertical resolution is even finer. Winds are obtained by measuring backscattered, Doppler-shifted laser radiation from naturally-occurring aerosol particles (of order 1 micron diameter). Measurement coverage depends on aerosol spatial distribution and composition. Velocity accuracy has been verified to be approx. 1 meter per second. A variety of applications have been demonstrated during the three flight campaigns conducted during 1995-1998. Examples will be shown during the presentation. In 1995, boundary layer winds over the ocean were mapped with unprecedented resolution. In 1996, unique measurements were made of. flow over the complex terrain of the Aleutian Islands; interaction of the marine boundary layer jet with the California coastal mountain range; a weak dry line in Texas - New Mexico; the angular dependence of sea surface scattering; and in-flight radiometric calibration using the surface of White Sands National Monument. In 1998, the first measurements of eyewall and boundary layer winds within a hurricane were made with the airborne Doppler lidar. Potential applications and plans for improvement will also be described.

Rothermel, Jeffry↗

NASA NTRS1983

Mapping and monitoring renewable resources with space SAR

The SEASAT-A SAR and SIR-A imagery was examined to evaluate the quality and type of information that can be extracted and used to monitor renewable resources on Earth. Two tasks were carried out: (1) a land cover classification study which utilized two sets of imagery acquired by the SEASAT-A SAR, one set by SIR-A, and one LANDSAT set (4 bands); and (2) a change detection to examine differences between pairs of SEASAT-A SAR images and relates them to hydrologic and/or agronomic variations in the scene.

Ulaby, F. T.↗

DOE OSTI2022

Advanced Laboratory and Field Arrays: Debris Modeling, Detection,& Mitigation (Task 1)

The statement of project objectives for this task was: develop tools, methods and models to assess, and mitigate the risk of damage to MHK infrastructure from woody debris. Develop the capability to detect woody debris using sonar and/or physical methods for purposes of characterizing debris statistics in river (at UAF’s Tanana River Test Site) and near-shore wave (at Yakutat, AK) environments and to activate debris mitigation measures. Develop debris impact risk maps and tables using statistics on debris size, geometry, type, prevalence, mobility and location. Improve and apply the COUPi discrete element method (DEM) to develop models of debris movement and impact on MHK infrastructure to evaluate risk of damage, and interference, to operations from debris. The proposed final deliverable for the task was a set of tools or techniques for providing estimates of the probability of debris impact, and resulting impact forces, on MHK infrastructure as a function of debris size, type, wave regime, and current velocity. Such estimates are required to assess damage risks to operational MHK infrastructure.

13 HYDRO ENERGY↗

DOE OSTI2021

Beyond Visual Analytics: Human-Machine Teaming for AI-Driven Data Sensemaking

"Detect the expected, discover the unexpected" was the founding principle of the field of visual analytics. This mantra implies that human stakeholders, like a domain expert or data analyst, could leverage visual analytics techniques to seek answers to known unknowns and discover unknown unknowns in the course of the data sensemaking process. We argue that in the era of AI-driven automation, we need to recalibrate the roles of humans and machines (e.g., a machine learning model) as teammates. We posit that by realizing human-machine teams as a stakeholder unit, we can better achieve the best of both worlds: automation transparency and human reasoning efficacy. However, this also increases the burden on analysts and domain experts towards performing more cognitively demanding tasks than what they are used to. In this paper, we reflect on the complementary roles in a human-machine team through the lens of cognitive psychology and map them to existing and emerging research in the visual analytics community. We discuss open questions and challenges around the nature of human agency and analyze the shared responsibilities in human-machine teams.

Sensemaking, human-machine teaming, agency, artifi↗

DOE OSTI2026

Neural Scaling Laws of Deep ReLU and Deep Operator Network: A Theoretical Study

Neural scaling laws play a pivotal role in the performance of deep neural networks and have been observed in a wide range of tasks. However, a complete theoretical framework for understanding these scaling laws remains underdeveloped. In this paper, we explore the neural scaling laws for deep operator networks, which involve learning mappings between function spaces, with a focus on the Chen and Chen style architecture. These approaches, which include the popular Deep Operator Network (DeepONet), approximate the output functions using a linear combination of learnable basis functions and coefficients that depend on the input functions. We establish a theoretical framework to quantify the neural scaling laws by analyzing its approximation and generalization errors. We articulate the relationship between the approximation and generalization errors of deep operator networks and key factors such as network model size and training data size. Moreover, we address cases where input functions exhibit low-dimensional structures, allowing us to derive tighter error bounds. These results also hold for deep ReLU networks and other similar structures. Our results offer a partial explanation of the neural scaling laws in operator learning and provide a theoretical foundation for their applications.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2009

A Framework for Parallel Unstructured Grid Generation for Complex Aerodynamic Simulations

A framework for parallel unstructured grid generation targeting both shared memory multi-processors and distributed memory architectures is presented. The two fundamental building-blocks of the framework consist of: (1) the Advancing-Partition (AP) method used for domain decomposition and (2) the Advancing Front (AF) method used for mesh generation. Starting from the surface mesh of the computational domain, the AP method is applied recursively to generate a set of sub-domains. Next, the sub-domains are meshed in parallel using the AF method. The recursive nature of domain decomposition naturally maps to a divide-and-conquer algorithm which exhibits inherent parallelism. For the parallel implementation, the Master/Worker pattern is employed to dynamically balance the varying workloads of each task on the set of available CPUs. Performance results by this approach are presented and discussed in detail as well as future work and improvements.

Zagaris, George↗

NASA NTRSDate not supplied

Combinatorial Reasoning: Selecting Reasons in Generative AI Pipelines via Combinatorial Optimization

Recent Large Language Models (LLMs) have demonstrated impressive capabilities at tasks that require human intelligence and are a significant step towards human-like artificial intelligence (AI). Yet the performance of LLMs at reasoning tasks have been subpar and the reasoning capability of LLMs is a matter of significant debate. While it has been shown that the choice of the prompting technique to the LLM can alter its performance on a multitude of tasks, including reasoning, the best performing techniques require human-made prompts with the knowledge of the tasks at hand. We introduce a framework for what we call Combinatorial Reasoning (CR), a fully-automated prompting method, where reasons are sampled from an LLM pipeline and mapped into a Quadratic Unconstrained Binary Optimization (QUBO) problem. The framework investigates whether QUBO solutions can be profitably used to select a useful subset of the reasons to construct a Chain-of-Thought style prompt. We explore the acceleration of CR with specialized solvers. We also investigate the performance of simpler zero-shot strategies such as linear majority rule or random selection of reasons. Our preliminary study indicates that coupling a combinatorial solver to generative AI pipelines is an interesting avenue for AI reasoning and elucidates design principles for future CR methods.

combinatorial reasoning↗

NASA NTRS2004

Antarctic Ultraviolet Radiation Climatology from Total Ozone Mapping Spectrometer Data

This project has successfully produced a climatology of local noon spectral surface irradiance covering the Antarctic continent and the Southern Ocean, the spectral interval 290-700 nm (UV-A, UV-B, and photosynthetically active radiation, PAR), and the entire sunlit part of the year for November 1979-December 1999. Total Ozone Mapping Spectrometer (TOMS) data were used to specify column ozone abundance and UV-A (360- or 380-nm) reflectivity, and passive microwave (MW) sea ice concentrations were used to specify the surface albedo over the Southern Ocean. For this latter task, sea ice concentration retrievals from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) and its successor, the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager (SSM/I) were identified with ultraviolet/visible-wavelength albedos based on an empirical TOMS/MW parameterization developed for this purpose (Lubin and Morrow, 2001). The satellite retrievals of surface albedo and UV-A reflectivity were used in a delta-Eddington radiative transfer model to estimate cloud effective optical depth. These optical depth estimates were then used along with the total ozone and surface albedo to calculate the downwelling spectral UV and PAR irradiance at the surface. These spectral irradiance maps were produced for every usable day of TOMS data between 1979-1999 (every other day early in the TOMS program, daily later on).

Lubin, Dan↗