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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Mapping Threats to Agriculture in East Africa: Performance of MODIS Derived LST for Frost Identification in Kenya's Tea Plantations

Increased prevalence of weather related hazards in eastern Africa including drought, floods, hail and frost threatening agricultural productivity. Kenya is heavily dependent on agriculture for economic growth (FAO 2013); (1) Agriculture contributed 23.5% and 21.5% of GDP in 2009 and 2010 respectively, (2) Employment to half a million households of smallholders and 150,000 on large tea estates. Tea growing in Kenya depends on stability of the weather; (1) Weather is unpredictable, (2) Frost has contributed 30% of tea leaf losses, (3) Drought has contributed 14-30%, (4) The losses are experienced between January and march - frost and dry season.

remote sensing↗
NASA NTRSDate not supplied

Advancement of Deep Learning and Geometric Methods for Active Terrain Relative Navigation

To enhance NASA’s precision landing capabilities, in conjunction with the development of a novel active terrain relative navigation (ATRN) and terrain mapping system, denoted SHERIF, this work performed a comparative analysis between both deep-learning (DL) based and geometric approaches to hazard detection (HD) and safe-site-identification (SSI) through hardware-in-the loop testing on the Six degree-of-freedom Tendon Actuated Robot (STAR). The Standalone Hazard Evaluation and Refinement using Instrument Findings (SHERIF) system is capable of ingesting sensor data at an asynchronous rate, stitching successive terrain scans together to yield a high-resolution digital elevation map (DEM), performing absolute and relative localization using novel 3D feature extraction and matching methods, and HD/SSI activities. The DL-based HD/SSI algorithm provides a modular alternative to classical geometric approaches which have performance times that scale with map resolution. As the adoption of AI solutions become more prevalent for autonomous system decision making, it is prudent to explore the utility of such solutions in applications where they traditionally excel, such as image classification. Along with the development of a DL-based HD system, this work performed the first comparative analysis between DL and geometric approaches to HD/SSI using real sensor data from real-time testing in a relevant environment.

Davis Adams↗
NASA NTRS2018

Identifying and Forecasting Potential Biophysical Risk Areas within a Tropical Mangrove Ecosystem Using Multi-Sensor Data

Mangroves are one of the most productive ecosystems known for provisioning of various ecosystem goods and services. They help in sequestering large amounts of carbon, protecting coastline against erosion, and reducing impacts of natural disasters such as hurricanes. Bhitarkanika Wildlife Sanctuary in Odisha harbors the second largest mangrove ecosystem in India. This study used Terra, Landsat and Sentinel-1 satellite data for spatio-temporal monitoring of mangrove forest within Bhitarkanika Wildlife Sanctuary between 2000 and 2016. Three biophysical parameters were used to assess mangrove ecosystem health: leaf chlorophyll (CHL), Leaf Area Index (LAI), and Gross Primary Productivity (GPP). A long-term analysis of meteorological data such as precipitation and temperature was performed to determine an association between these parameters and mangrove biophysical characteristics. The correlation between meteorological parameters and mangrove biophysical characteristics enabled forecasting of mangrove health and productivity for year 2050 by incorporating IPCC projected climate data. A historical analysis of land cover maps was also performed using Landsat 5 and 8 data to determine changes in mangrove area estimates in years 1995, 2004 and 2017. There was a decrease in dense mangrove extent with an increase in open mangroves and agricultural area. Despite conservation efforts, the current extent of dense mangrove is projected to decrease up to 10% by the year 2050. All three biophysical characteristics including GPP, LAI and CHL, are projected to experience a net decrease of 7.7%, 20.83% and 25.96% respectively by 2050 compared to the mean annual value in 2016. This study will help the Forest Department, Government of Odisha in managing and taking appropriate decisions for conserving and sustaining the remaining mangrove forest under the changing climate and developmental activities.

Shanti Shrestha↗
NASA NTRS1993

Classification of high dimensional multispectral image data

A method for classifying high dimensional remote sensing data is described. The technique uses a radiometric adjustment to allow a human operator to identify and label training pixels by visually comparing the remotely sensed spectra to laboratory reflectance spectra. Training pixels for material without obvious spectral features are identified by traditional means. Features which are effective for discriminating between the classes are then derived from the original radiance data and used to classify the scene. This technique is applied to Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data taken over Cuprite, Nevada in 1992, and the results are compared to an existing geologic map. This technique performed well even with noisy data and the fact that some of the materials in the scene lack absorption features. No adjustment for the atmosphere or other scene variables was made to the data classified. While the experimental results compare favorably with an existing geologic map, the primary purpose of this research was to demonstrate the classification method, as compared to the geology of the Cuprite scene.

Hoffbeck, Joseph P.↗
DOE OSTI2022

Sulphur variations in annually layered stalagmites using benchtop micro-XRF

Variation of sulphur in annually laminated stalagmites can be used to infer the impact of past volcanic activities, anthropogenic pollution, and climate change due to increased bushfire activity. The synchrotron radiation micro-X-Ray fluorescence (SR-XRF) microprobe is a powerful tool to analyse and image sulphur recorded in stalagmites with micrometre resolution. However, access to SR-XRF beamlines can be limited, so researchers must select the most promising stalagmites for imaging. Benchtop micro-XRF is an effective tool for trace elemental analysis of speleothem samples and is a candidate for routine laboratory measurement of sulphur along stalagmite laminae and screening for SR-XRF. Here, this study describes a protocol using matrix-matched standards to measure annual variations of sulphur at trace to percent level along the laminae of two Western Australian stalagmites, one of which already having been analysed using SR-XRF. Parameters that affected quantitation include X-ray tube voltage and current, spot size of the X-ray beam and stalagmite surface roughness and porosity. The use of a 20 μm X-ray spot size provides sub-annual spatial resolution that can be completed in an overnight scan. The features in a 1000 point micro-XRF analysis of sulphur along a 20 mm transect show good consistency with SR-XRF microprobe data. Micro-XRF mapping was also performed to produce chemical images on the stalagmite and compared with Raman and X-ray diffraction (XRD) to confirm that the stalagmite is exclusively calcite, with no aragonite, and that the source of sulphur in the samples was gypsum and anhydrite. Regions of very high sulphur in the micro-XRF maps were found to be artefacts due to diffraction of the incident beam but these could be efficiently removed by using a multiple point statistics approach to produce a clean image suitable for analysis of the laminae. This work shows the potential of micro-XRF for routine analysis of sulphur in stalagmites, and to streamline sample characterisation before SR-XRF imaging.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1999

Backup Attitude Control Algorithms for the MAP Spacecraft

The Microwave Anisotropy Probe (MAP) is a follow-on to the Differential Microwave Radiometer (DMR) instrument on the Cosmic Background Explorer (COBE) spacecraft. The MAP spacecraft will perform its mission, studying the early origins of the universe, in a Lissajous orbit around the Earth-Sun L(sub 2) Lagrange point. Due to limited mass, power, and financial resources, a traditional reliability concept involving fully redundant components was not feasible. This paper will discuss the redundancy philosophy used on MAP, describe the hardware redundancy selected (and why), and present backup modes and algorithms that were designed in lieu of additional attitude control hardware redundancy to improve the odds of mission success. Three of these modes have been implemented in the spacecraft flight software. The first onboard mode allows the MAP Kalman filter to be used with digital sun sensor (DSS) derived rates, in case of the failure of one of MAP's two two-axis inertial reference units. Similarly, the second onboard mode allows a star tracker only mode, using attitude and derived rate from one or both of MAP's star trackers for onboard attitude determination and control. The last backup mode onboard allows a sun-line angle offset to be commanded that will allow solar radiation pressure to be used for momentum management and orbit stationkeeping. In addition to the backup modes implemented on the spacecraft, two backup algorithms have been developed in the event of less likely contingencies. One of these is an algorithm for implementing an alternative scan pattern to MAP's nominal dual-spin science mode using only one or two reaction wheels and thrusters. Finally, an algorithm has been developed that uses thruster one shots while in science mode for momentum management. This algorithm has been developed in case system momentum builds up faster than anticipated, to allow adequate momentum management while minimizing interruptions to science. In this paper, each mode and algorithm will be discussed, and simulation results presented.

ODonnell, James R., Jr.↗
DOE OSTI2021

Revealing causes of macroscale heterogeneity in lithium ion pouch cells via synchrotron X-ray diffraction

Heterogeneous battery performance is a critical issue for maximization of cell lifetime capacity and safety. Using high energy synchrotron X-ray diffraction, the influence of charge rate, voltage limit, uneven stack pressure, and gas generation on the lithium transport properties was quantified in single-layer graphite/LiNi 0.5 Mn 0.3 Co 0.2 O 2 pouch cells. A freshly formatted cell tracked in operando during initial fast charge cycles indicated variable position-dependent performances, while lateral mapping showed a significant fast charge (6C) heterogeneity compared to slow charge (C/2). Pressure effects were non-dominant compared to charge rate. Maps of previously aged and rested cells indicate that lateral heterogeneity slowly equilibrates at rest, but regenerates upon further cycling at fast charge rate. Furthermore, an unformatted cell was mapped at charge and discharge during its first formation cycle to analyze the effect of byproduct gases on the heterogeneous lithium transport. Gas was observed as randomly interspersed “bubbles” which locally hindered lithium intercalation and caused significant heterogeneity. Electrode architectures and charging protocols that promote homogeneous intercalation are critical for predictable high-performance and long-life batteries.

25 ENERGY STORAGE↗
DOE OSTI2024

Offshore application of landslide susceptibility mapping using gradient-boosted decision trees: a Gulf of Mexico case study

Abstract Among natural hazards occurring offshore, submarine landslides pose a significant risk to offshore infrastructure installations attached to the seafloor. With the offshore being important for current and future energy production, there is a need to anticipate where future landslide events are likely to occur to support planning and development projects. Using the northern Gulf of Mexico (GoM) as a case study, this paper performs Landslide Susceptibility Mapping (LSM) using a gradient-boosted decision tree (GBDT) model to characterize the spatial patterns of submarine landslide probability over the United States Exclusive Economic Zone (EEZ) where water depths are greater than 120 m. With known spatial extents of historic submarine landslides and a Geographic Information System (GIS) database of known topographical, geomorphological, geological, and geochemical factors, the resulting model was capable of accurately forecasting potential locations of sediment instability. Results of a permutation modelling approach indicated that LSM accuracy is sensitive to the number of unique training locations with model accuracy becoming more stable as the number of training regions was increased. The influence that each input feature had on predicting landslide susceptibility was evaluated using the SHapely Additive exPlanations (SHAP) feature attribution method. Areas of high and very high susceptibility were associated with steep terrain including salt basins and escarpments. This case study serves as an initial assessment of the machine learning (ML) capabilities for producing accurate submarine landslide susceptibility maps given the current state of available natural hazard-related datasets and conveys both successes and limitations.

Dyer, Alec S. (ORCID:0000000219813904)↗
DOE OSTI2020

Radioactive Source Localization via Bayesian Particle Filter

In the event of a misplaced radioactive source or other emergency situation, measuring a radiation field, mapping its distribution, and determining a source location are essential tasks to ameliorating the situation. However, radiation fields may be extremely hazardous to human surveyors and minimizing received radiation doses is just as essential. Robots appear to be a potential solution to these problems. Beyond simply measuring radiation, the robot's computer processing capabilities offer a way to apply complex data analysis methods to radiation measurements in real-time. Methods which predict likely source locations can then feed this information into other processes, potentially improving path planning and enabling more efficient measurements. Given a robot mounted with a gamma-ray detector, can we: develop a methodology to account for detector performance across a wide range of source angles, distances, and photon energies? operate an autonomously navigating robot to effectively survey and characterize an area of interest? implement a data analysis method, conventionally used in measurements of motion, for source localization purposes? An open-source TurtleBot 3 robot, running Robot Operating System (ROS) on Ubuntu 16.04 LTS, was fitted with a Kromek GR1{sup R} Cadmium Zinc Telluride (CZT) solid-state gamma-ray detector. As a part of ROS, the packages OpenSlam, gmapping, and amcl were used to perform Simultaneous Localization and Mapping (SLAM), determining the robot's position and mapping the surrounding area. Data was acquired via Lidar mounted on top the TurtleBot 3. Detector Calibration Fit: The equation was fit to 365 counts of various energies, distances, and angles. A MATLAB{sup R} program was written to simulate measurements taken a robot on a random walk, with count data and positions discretized into finite element pixels. Using this program, a sample of 100 runs was performed on a map with a simulated source at the center, with a total of 200 of 2 pixels each. Similarly, multiple runs of the filter were performed on recorded robot measurement data. In both simulation and real tests, when corrected for errors (particles placed outside of bounds or on the robot, and simulation-specific errors), corresponding t-tests of predicted x and y-coordinates were within a 95% confidence interval of the actual position. For the real trial, these positions are slightly skewed right in the x-axis as the robot remained mainly to the left side of the source within the sample area. These simulations demonstrate potential validity for the usage of a particle filter as method of radioactive source localization. In the future, true real-time implementation and data fusion may further augment the performance of the robot to localize lost sources. Additionally, identification of multiple sources, determination of source types, and usage of a collimator are areas to potentially be explored.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

OptiBench: An Optimization Benchmark Tool for Renewable Energy Problems

We propose a benchmark framework and visualization tool, OptiBench, for analyzing the performance of state-of-the-art optimization solvers across a variety of optimization problems in renewable energy research. Our framework is designed from the ground up in the Julia programming language and enables analysis at scale on high performance computing (HPC) systems. Our visualization tool allows effortless evaluation of optimization solver performance, robustness, and accuracy through intuitive plots, e.g., performance profiles, heat maps, and distribution plots. We have tested three benchmark suites relevant to the modeling of renewable energy systems, viz., CUTEst, PGLib-OPF, and WaterTAP water treatment optimization problems. We illustrate benchmarking of CUTEst using OptiBench on the National Renewable Energy Laboratory's (NREL) HPC Kestrel. Our findings indicate that MA57 HSL linear solver demonstrated the best overall performance for an experimental IPOPT implementation. Our work is ongoing and we intend to add support for more optimization solvers and benchmark test suites in the future.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

OptiBench: An Optimization Benchmark Tool for Renewable Energy Problems

We propose a benchmark framework and visualization tool, OptiBench, for analyzing the performance of state-of-the-art optimization solvers across a variety of optimization problems in renewable energy research. Our framework is designed from the ground up in the Julia programming language and enables analysis at scale on high performance computing (HPC) systems. Our visualization tool allows effortless evaluation of optimization solver performance, robustness, and accuracy through intuitive plots, e.g., performance profiles, heat maps, and distribution plots. We have tested three benchmark suites relevant to the modeling of renewable energy systems, viz., CUTEst, PGLib-OPF, and WaterTAP water treatment optimization problems. We illustrate benchmarking of CUTEst using OptiBench on the National Laboratory of the Rockies's (NLR) HPC Kestrel. Our findings indicate that MA57 HSL linear solver demonstrated the best overall performance for an experimental IPOPT implementation. Our work is ongoing and we intend to add support for more optimization solvers and benchmark test suites in the future.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Integrated System for Autonomous and Adaptive Caretaking (ISAAC): Phase 1 Low-Fidelity Demo

This presentation describes the ISAAC phase 1 low-fidelity demonstration results. The demonstration satisfied the milestone from the Gateway-ISAAC Memorandum of Understanding to "Demonstrate spatial and logical data registration between robotics and spacecraft". It integrated many new ISAAC components, including a spatially linked model, Astrobee multi-sensor mapping, and an integrated data interface. It advanced ISAAC key performance parameters related to mapping, in a lab setting. Areas for phase 1 forward work include: improve maturity toward the high-fidelity demo on the ISS; demonstrate mapping with more sensor modalities; expand initial anomaly detection implementation into a flexible framework with multiple detection algorithms for different tasks; begin open source software release process for releasable ISAAC components.

robotics↗
NASA NTRS2011

Hyperspectral Mapping of the Invasive Species Pepperweed and the Development of a Habitat Suitability Model

Mapping and predicting the spatial distribution of invasive plant species is central to habitat management, however difficult to implement at landscape and regional scales. Remote sensing techniques can reduce the impact field campaigns have on these ecologically sensitive areas and can provide a regional and multi-temporal view of invasive species spread. Invasive perennial pepperweed (Lepidium latifolium) is now widespread in fragmented estuaries of the South San Francisco Bay, and is shown to degrade native vegetation in estuaries and adjacent habitats, thereby reducing forage and shelter for wildlife. The purpose of this study is to map the present distribution of pepperweed in estuarine areas of the South San Francisco Bay Salt Pond Restoration Project (Alviso, CA), and create a habitat suitability model to predict future spread. Pepperweed reflectance data were collected in-situ with a GER 1500 spectroradiometer along with 88 corresponding pepperweed presence and absence points used for building the statistical models. The spectral angle mapper (SAM) classification algorithm was used to distinguish the reflectance spectrum of pepperweed and map its distribution using an image from EO-1 Hyperion. To map pepperweed, we performed a supervised classification on an ASTER image with a resulting classification accuracy of 71.8%. We generated a weighted overlay analysis model within a geographic information system (GIS) framework to predict areas in the study site most susceptible to pepperweed colonization. Variables for the model included propensity for disturbance, status of pond restoration, proximity to water channels, and terrain curvature. A Generalized Additive Model (GAM) was also used to generate a probability map and investigate the statistical probability that each variable contributed to predict pepperweed spread. Results from the GAM revealed distance to channels, distance to ponds and curvature were statistically significant (p < 0.01) in determining the locations of suitable pepperweed habitats.

Pepperweed↗
NASA NTRSDate not supplied

Creating A Consistent Historical NASA POWER Solar Radiation Dataset to Support Renewable Energy, Building Energy Efficiency and Agro-Climatology Decisions

Prediction of Worldwide Energy Resources (POWER) project provides irradiance dataset to support renewable energy, building energy efficiency and agricultural needs. These datasets are derived from Global Energy and Water Cycle Experiment Surface Radiation Budget (GEWEX SRB) and Clouds and the Earth’s Radiant Energy System (CERES SYN1Deg). A systematic bias has been reported between these two datasets for the years with overlapping observations. For obtaining a consistent climate data record spanning the entire time record of observations, it is crucial to understand and remove the bias in the irradiance dataset. Inconsistency in solar radiation data can lead to inaccurate conclusions about solar energy potential and obscure real trends in solar radiation patterns that would impact energy availability assessments. In this study, we adapt quantile mapping approach to remove the systematic bias and to improve reliability of shortwave and longwave irradiance data. We present a validation of the bias corrected data against ground truth. For each 1° latitude and 1° longitude grid box across the globe, we match the CDFs of the reference dataset (CERES SYN1Deg) to that of the SRB dataset, thereby, adjusting the irradiance values to match the empirical distribution of two different measurements. The performance of quantile mapping is evaluated by using the metrics such as Mean Absolute Deviation (MAD). The results indicate that the quantile mapping significantly improves the accuracy and reliability of solar irradiance dataset especially for the weather conditions associated with high cloud cover and extreme irradiance values. The initial range of MAD for the studied sites for daily data was 4 to 19 Wm-2. After correction these reduced to 3 to 7 Wm-2. The findings from this study have important implications for solar energy system design, agricultural planning, and climate modeling community. Reducing the inconsistency and biases in solar irradiance dataset can enable better planning and operation of solar energy systems, leading to increased efficiency and cost-effectiveness. Additionally, this work also contributes to the statistical post-processing techniques in the renewable energy domain and highlights the potential of historical and near-real-time NASA POWER dataset as a valuable resource for solar energy research and applications.

POWER↗
DOE OSTI2023

Comparing the Effectiveness of Low Temperature Bake in EP and BCP Cavities

Electropolishing (EP) and buffered chemical polishing (BCP) are conventional surface preparation techniques for superconducting radiofrequency (SRF) cavities. Both EP and BCP treated SRF cavities display high field Q-slope (HFQS) which degrades performance at high gradients. While high gradient performance in EP cavities can be improved by introducing oxygen via a low temperature bake (LTB) of 120^{°}C by 48 hours, LTB does not consistently remove HFQS in BCP cavities. There is no consensus as to why LTB is not effective on BCP prepared cavities. We examine quench in EP, BCP, EP+LTB, and BCP+LTB treated 1.3 GHz single-cell Nb cavities by studying the heating behavior with field using a temperature mapping system. Cavity performance is correlated to characterizations of surface impurity profile obtained via time of flight secondary ion mass spectrometry studies. We observe a difference in near surface hydrogen concentration following BCP compared to EP that may suggest that the causes of quench in EP and BCP cavities are different.

43 PARTICLE ACCELERATORS↗
NASA NTRS1988

Design and predicted performance of the GPS demonstration receiver for the NASA Topex satellite

NASA's Ocean Topography Experiment (Topex) satellite will perform topographic ocean mapping. Topex will carry an experimental tracking system based on the use of GPS (Global Positioning System). An onboard GPS Demonstration Receiver (GPSDR) will be used with a network of up to ten receivers at precisely known ground sites. Following a brief overview of the Topex mission and its derived accuracy requirements, the authors describe the design of the GPSDR. The major hardware and software components are reviewed and discussed. Projected receiver tracking and navigation performance measures are presented and discussed.

Carson, Lance↗
NASA NTRS2016

Automated Segmentation and Classification of Coral using Fluid Lensing from Unmanned Airborne Platforms

In recent years, there has been a growing interest among biologists in monitoring the short and long term health of the world's coral reefs. The environmental impact of climate change poses a growing threat to these biologically diverse and fragile ecosystems, prompting scientists to use remote sensing platforms and computer vision algorithms to analyze shallow marine systems. In this study, we present a novel method for performing coral segmentation and classification from aerial data collected from small unmanned aerial vehicles (sUAV). Our method uses Fluid Lensing algorithms to remove and exploit strong optical distortions created along the air-fluid boundary to produce cm-scale resolution imagery of the ocean floor at depths up to 5 meters. A 3D model of the reef is reconstructed using structure from motion (SFM) algorithms, and the associated depth information is combined with multidimensional maximum a posteriori (MAP) estimation to separate organic from inorganic material and classify coral morphologies in the Fluid-Lensed transects. In this study, MAP estimation is performed using a set of manually classified 100 x 100 pixel training images to determine the most probable coral classification within an interrogated region of interest. Aerial footage of a coral reef was captured off the coast of American Samoa and used to test our proposed method. 90 x 20 meter transects of the Samoan coastline undergo automated classification and are manually segmented by a marine biologist for comparison, leading to success rates as high as 85%. This method has broad applications for coastal remote sensing, and will provide marine biologists access to large swaths of high resolution, segmented coral imagery.

algorithms segments classifications↗
NASA NTRS2015

Automated Segmentation and Classification of Coral Using Fluid Lensing from Unmanned Airborne Platforms

In recent years, there has been a growing interest among biologists in monitoring the short and long term health of the worlds coral reefs. The environmental impact of climate change poses a growing threat to these biologically diverse and fragile ecosystems, prompting scientists to use remote sensing platforms and computer vision algorithms to analyze shallow marine systems. In this study, we present a novel method for performing coral segmentation and classification from aerial data collected from small unmanned aerial vehicles (sUAV). Our method uses Fluid Lensing algorithms to remove and exploit strong optical distortions created along the air-fluid boundary to produce cm-scale resolution imagery of the ocean floor at depths up to 5 meters. A 3D model of the reef is reconstructed using structure from motion (SFM) algorithms, and the associated depth information is combined with multidimensional maximum a posteriori (MAP) estimation to separate organic from inorganic material and classify coral morphologies in the Fluid-Lensed transects. In this study, MAP estimation is performed using a set of manually classified 100 x 100 pixel training images to determine the most probable coral classification within an interrogated region of interest. Aerial footage of a coral reef was captured off the coast of American Samoa and used to test our proposed method. 90 x 20 meter transects of the Samoan coastline undergo automated classification and are manually segmented by a marine biologist for comparison, leading to success rates as high as 85. This method has broad applications for coastal remote sensing, and will provide marine biologists access to large swaths of high resolution, segmented coral imagery.

algorithms segments classifications↗