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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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NASA NTRS2019

James Webb Space Telescope Optical Telescope Element / Integrated Science Instrument Module (OTIS) Cryogenic Vacuum Test Part II: Thermal Analysis

The James Webb Space Telescope (JWST), set to launch in early 2019, is currently undergoing a series of system-level environmental tests to verify its workmanship and end-to-end functionality. As part of this series, the Optical Telescope Element and Integrated Science Instrument Module (OTIS) Cryo-Vacuum (CV) test, the most complex cryogenic test executed to date by NASA, was completed in 2017 at the Johnson Space Center's Chamber A facility. The OTIS CV test was intended as a comprehensive test of the integrated instrument and telescope systems to fully understand its optical, structural, and thermal performance within its intended flight environment. Due to its complexity, extensive pre-test planning was required to ensure payload safety and compliance with all limits and constraints. A comprehensive thermal analysis campaign was undertaken to predict the behavior of the test payload during its transition to cryogenic temperatures and back to ambient, and exhaustive preparations for off-nominal scenarios were studied due to the increased possibility of unanticipated events during the 100-day test length. This current four-part online course for the NASA Engineering and Safety Center (NESC) Academy gives a thorough overview of the multiple years of preparation executed by hundreds of individuals to ensure the success of the JWST OTIS CV Test. Part I will introduce the major components of James Webb and specifically the OTIS CV Test thermal architecture. Part II will encompass the extensive thermal analysis performed to prepare for the test. Part III will talk about the preparations for off-nominal events: what analysis was done, and how steps were taken pre-test to anticipate unexpected circumstances and mitigate their impacts to hardware and test timeline. Part IV is a recap of the lessons learned from the thermal perspective for both the payload and ground support equipment (GSE) test conductors. It is hoped that the insight gained from the OTIS CV test campaign will benefit planning and execution for upcoming large cryogenic missions.

Testing↗
NASA NTRS2019

ATD-3 Dynamic Routes for Arrivals in Weather (DRAW) Operational Concept, V2.0

NASA's Airspace Technology Demonstration-3 (ATD-3) is the applied traffic flow management activity, and third in the series of ATD projects. ATD-3 provides a suite of en route automation tools, both ground and flight-deck based, that focus on improving the efficiency of en route operation from initial cruise to arrival into the Terminal Radar Approach Control (TRACON). Dynamic Routes for Arrivals in Weather (DRAW) is a technology in the ATD-3 suite that is designed to provide Traffic Managers with a capability to efficiently manage arrival traffic flow and help sustain metering operations when weather is impacting arrivals into major airports. DRAW mitigates convective weather impact on arrival metering operations by providing tools to Traffic Managers that enable efficient reroutes free of convective weather conflicts, and integrated with the arrival metering schedule(s).

terminal airspace↗
NASA NTRS2020

Great Lakes Water Resources II: A Google Earth Engine Tool to Automate Wetland Mapping Using Optical and Radar Satellite Sensors in the Great Lakes Basin for Wetland Management and Monitoring

The Great Lakes Basin is one of the world’s largest freshwater ecosystems. The Basin harbors over 200,000 acres of wetlands. These wetlands provide a variety of environmental, ecological, and recreational functions to over 30 million people in the region. Some of these functions include improving water quality, mitigating flood impacts, providing wildlife habitat, and housing recreational activities. However, due to anthropogenic activities, habitat conversion and degradation threaten to disrupt or destroy remaining wetland ecosystems. Maps of wetland distribution based on ground surveys are costly and labor-intensive, prohibiting timely evaluations of wetland loss and gain. The Great Lakes Water Resources II team at the NASA Jet Propulsion Laboratory developed the Wetlands Extent Tool 2.0 (WET 2.0) in Google Earth Engine to automate mapping of wetland distribution in the Great Lakes Basin. The team partnered with the US Fish and Wildlife Service (USFWS), Environmental Protection Agency (EPA), Minnesota Department of Natural Resources (MDNR), the National Oceanic and Atmospheric Administration (NOAA), and Ducks Unlimited (DU). WET 2.0 incorporates Landsat 8 Operational Land Imager (OLI), Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), and Sentinel-2 Multispectral Instrument (MSI) satellite data. WET 2.0 is trained to classify anywhere in the Great Lakes Basin. Utilizing a Random Forest classifier, WET 2.0 is capable of automatically mapping wetland extent in the entire Great Lakes Basin, achieving a mean overall accuracy of 80.12% when tested in Michigan. Findings and maps produced in WET 2.0 will enable our partners to identify areas of ecosystem degradation and wetland destruction in order to enact environmental practices and policy initiatives to maintain environmental and economic health in the area.

Water Resources↗
NASA NTRS2020

Great Lakes Water Resources II: A Google Earth Engine Tool to Automate Wetland Extent Mapping Using Optical and Radar Satellite Sensors in the Great Lakes Basin for Wetland Management and Monitoring

The Great Lakes Basin is one of the world’s largest freshwater ecosystems. The Basin harbors over 200,000 acres of wetlands that provide a variety of environmental, ecological, and recreational functions to over 30 million people in the region. Some of these functions include improving water quality, mitigating flood impacts, providing wildlife habitat, and housing recreational activities. However, due to anthropogenic activities, habitat conversion and degradation threaten to disrupt or destroy remaining wetland ecosystems. Maps of wetland distribution based on ground surveys are costly and labor-intensive, prohibiting timely evaluations of wetland loss and gain. The Great Lakes Water Resources II team at the NASA Jet Propulsion Laboratory developed the Wetlands Extent Tool 2.0 (WET 2.0) in Google Earth Engine to automate mapping of wetland distribution in the Great Lakes Basin. The team partnered with the US Fish and Wildlife Service, Environmental Protection Agency, Minnesota Department of Natural Resources, the National Oceanic and Atmospheric Administration, and Ducks Unlimited. WET 2.0 incorporates Landsat 8 Operational Land Imager (OLI), Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), and Sentinel-2 Multispectral Instrument (MSI) data. Utilizing a Random Forest classifier, WET 2.0 is capable of automatically mapping wetland extent in the entire Great Lakes Basin, achieving a mean overall accuracy of 80.12% when tested in Michigan.Findings and maps produced by WET 2.0 will enable our partners to identify areas of ecosystem degradation and wetland destruction in order to enact environmental practices and policy initiatives to maintain environmental and economic health in the area.

Water Resources↗
NASA NTRS2020

Exploring Spatiotemporal Relations Between Soil Moisture, Precipitation, and Streamflow for a Large Set of Watersheds Using Google Earth Engine

An understanding of streamflow variability and its response to changes in climate conditions is essential for water resource planning and management practices that will help to mitigate the impacts of extreme events such as floods and droughts on agriculture and other human activities. This study investigated the relationship between precipitation, soil moisture, and streamflow over a wide range of watersheds across the United States using Google Earth Engine (GEE). The correlation analyses disclosed a strong association between precipitation, soil moisture, and streamflow, however, soil moisture was found to have a higher correlation with the streamflow relative to precipitation. Results indicated different strength of the association depends on the watershed classes and lag times assessments. The perennial watersheds showed higher coherence compared to intermittent watersheds. Previous month precipitation and soil moisture have a stronger influence on the current month streamflow, particularly in the snow-dominated watersheds. Monthly streamflow forecasting models were developed using an autoregressive integrated moving average (ARIMA) and support vector machine (SVM). The results showed that the SVM model generally performed better than the ARIMA model. Overall streamflow forecasting model performance varied considerably among watershed classes, and perennial watersheds tend to exhibit better predictably compared to intermittent watersheds due to lower streamflow variability. The SVM models with precipitation and streamflow inputs performed better than those with streamflow input only. Results indicated that the inclusion of antecedent root-zone soil moisture improved the streamflow forecasting in most of the watersheds, and the largest improvements occurred in the intermittent watersheds. In conclusion, this work demonstrated that knowing the relationship between precipitation, soil moisture, and streamflow in different watershed classes will enhance the understanding of the hydrologic process and can be effectively utilized in improving streamflow forecasting for better satellite-based water resource management strategies.

Nazmus Sazib↗
NASA NTRSDate not supplied

Creating Formal Characterizations ofRoutine Contingency Management inCommercial Aviation

Traditional approaches to safety management focus on collection of data describing unwanted states (i.e., accidents and incidents) and analysis of undesired behaviors (i.e., faults and errors) that precede those states. Thus, in the traditional view of safety, safety is both defined and measured by its absence, namely the lack of safety. In extremely high confidence systems like commercial air transport, however, opportunities to measure the absence of safety are relatively rare. Ironically, a critical barrier to measuring safety and the impact of mitigation strategies in commercial aviation is the lack of opportunities for measurement.

Intelligent Contingency Management,↗
NASA NTRS2020

Improvements of On-orbit Characterization of Terra MODIS Short-wave Infrared Spectral Bands Out-of-Band Responses

The short-wave infrared (SWIR) bands (5-7, 26) of Terra MODIS, which are co-located with the mid-wav e infrared (MWIR) bands (20-25) on the short and mid-wave infrared (SMIR) Focal Plane Assembly (FPA),have a known issue related to 5.3 μm out-of-band (OOB) thermal leak and electronic crosstalk that was identified prelaunch. As a result, a crosstalk correction algorithm was designed and implemented in the MODIS Level 1B (L1B) calibration. Shortly after the Terra launch, extensive efforts were under taken to help characterize and mitigate the impact due to the OOB response and crosstalk on the SWIR on-orbit calibration and, consequently, the associated L1Bdata products. In addition, special night time day mode (NTDM) operations have been regularly scheduled to derive the crosstalk correction coefficients. Since MODIS does not have a spectral band centered at 5.3 μm, its band 28 (7.325 μm) was chosen as the surrogate sending band to simulate the OOB radiances at 5.3 μm. This was largely based on the measurements from the MODIS Airborne Simulator (MAS) spectrometer field campaigns in the early months after the Terra launch. In the case of Aqua MODIS, the magnitude of the SWIR crosstalk was much smaller and band 25 (4.52 μm) was found to be more effective as the sending band for the crosstalk correction. In recent years, the Terra MODIS photovoltaic (PV) long-wave infrared (LWIR) bands (27-30)electronic crosstalk has increased considerably, especially after the spacecraft safe-mode event occurred in February2016. This accentuated degradation in the PV LWIR performance has also impacted the performance of the SWIR crosstalk correction and thus its calibration and data quality. In this paper, we examine the use of band 25 as the sending band for the Terra MODIS SWIR crosstalk correction and compare its performance with that based on band 28 as the sending band. Results indicate an improvement of on-orbit gain stability for the SWIR calibration and reduced detector to detector and sub-frame to sub-frame striping in the calibrated L1B imagery, especially during the period when the PV LWIR electronic crosstalk has become more severe. This approach has been implemented in the forward production of Terra MODIS Collection 6 and Collection 6.1,starting from July 2019, and is planned to be used for the future reprocessing of MODIS L1B and to help improve the mission-long reflectance calibration and trending stability of the SWIR bands.

MODIS, Terra, Aqua, MODIS, SWIR, calibration, out-↗
NASA NTRS2017

Contract-based byzantine resilience in spacecraft swarms

This paper discusses an extension to the probabilistic underpinnings of flexible resiliency contracts that mitigate the impact of Byzantine faults without requiring additional redundancy typically employed for Byzantine fault tolerance.

Madni, Azad M.↗
NASA NTRS2018

Spacecraft Block Scheduling for NASA’s Deep Space Network

Currently, NASA’s Deep Space Network (DSN) is responsible for uplink to, downlink from, and/or tracking of dozens of missions for space agencies across the world. The DSN scheduling process starts about four months prior to the start of the schedule week, a process in which requirements are defined and then the schedule is created, de-conflicted, and negotiated over the next 2–3 weeks with a team of mission representatives. Now scheduled for late 2019, Exploration Mission1 (EM-1) will deploy upwards of 12 SmallSat missions that will be served by the DSN. This will increase the DSN’s actively serviced spacecraft by up to 30%, further increasing the difficulty of meeting all mission needs via the over subscribed network. To mitigate their impact on DSN scheduling, a block scheduling process is proposed for scheduling the SmallSats. Block scheduling consists of aggregating spacecraft together into larger “pseudo-spacecraft” based on geometric alignment that then follow the same process as any other DSN mission to receive segments of track time. These tracks are then decomposed into tracks for individual users based on their specific requirements. This paper describes a full novel scheduling tool set for building candidate blocks, evaluating the efficacy of these blocks, and optimal and suboptimal de-blocking schemes. To demonstrate these developed tools, results from three simulations are presented: a blocking example with lunar SmallSats, blocking potential in the greater DSN spacecraft catalog, and opportunistic multiple spacecraft per aperture potential for the DSN spacecraft catalog. Block scheduling has the potential to reduce overhead and scheduling resources for the EM-1 SmallSats while also providing them with a better means to meet their mission requirements.

Bilén, Sven G.↗
NASA NTRSDate not supplied

Overview of the Translational Radiation Research and Countermeasures (TRRaC) Project in Space Radiation

The Translational Radiation Research and Countermeasures (TRRaC) Project was initiated by the Space Radiation (SR) Element within NASA’s Human Research Program (HRP) to support the SR mission to understand and characterize the space radiation environment, understand and quantify radiation-associated risks, and mitigate the impacts of radiation-induced adverse health outcomes to enable human space exploration. TRRaC’s mission is to translate radiation research results from experimental studies and epidemiological data to humans and astronauts using bioinformatics and computational modeling. TRRaC will leverage existing datasets such as those available from NASA’s GeneLab repository and human medical radiation exposure registries, along with relevant data generated from ground-based research at molecular, cellular, tissue, and system levels, to: (1) refine the radiation dose rate effectiveness factor, (2) characterize radiation quality effects to improve estimates of the risk of exposure-induced death (REID) from space-relevant radiation exposures, and (3) identify pathways and biomarkers for cancer, cardiovascular disease, and central nervous system changes that are impacted by space radiation exposure.

Parastou Eslami↗
NASA NTRS2021

Contextual Segmentation of Fire Spotting Regions Through Satellite-Augmented Autonomous Modular Sensor Image

Globally, forest fires remain a significant threat to human and environmental wellbeing. Towards mitigating the impacts of forest fires, it is critical that accurate and updated information regarding not only the fire line, but also nearby human settlements, vegetation, and water sources is reported quickly to emergency services. However, while existing UAS-based fire detection methods are effective, they largely do not report the contextual environmental information necessary to best serve nearby communities in disaster response. Additionally, modern advancements in deep learning offer new approaches for image segmentation which may improve classification accuracy beyond current pixel-wise indices. In this work, we benchmark the performance of these modern segmentation techniques in locating both fire lines and environmental features in historical Autonomous Modular Sensor imagery. Furthermore, we augment these outputs with satellite imagery segmentation towards developing a robust contextual mapping tool for rapid emergency fire response and decision making.

Nikhil Behari↗
NASA NTRSDate not supplied

Collaborative Weather Research and Development for Urban Air Mobility

NASA recently collaborated with National Center for Atmospheric Research (NCAR) and AvMet Application Inc. to identify the weather information, existing aviation weather products, and the novel approaches that are required to model and mitigate various impacts of uncertain atmospheric conditions to support safe and efficient Urban Air Mobility (UAM) operations. This presentation will provide updates on the collaborative efforts of weather impacts modelling and analysis for future evaluation of UAM concepts of operations in the presence of weather.

Urban Air Mobility↗
NASA NTRS2022

Design, Control, and Simulation of Tensegrity Based Kites

Due to lowering price and increased functionality, the future could bring in a huge number of UAVs flying in urban areas. In order to safely accommodate them, they should be fundamentally safe to fly. Unfortunately, current rigid designs could cause significant damage in case of a crash. To mitigate this problem, this report proposes constructing soft UAVs using tensegrity structures in order to mitigate the impact of collisions, effectively improving the safety of the vehicles and increasing public confidence in the use of UAVs. In this paradigm, the frame of the vehicle is designed using a tensegrity structure, a geometric structure composed of suspended elements of pure compression and pure tension, which during a collision can better absorb high stresses through elastic deformation. As a feasibility study we test this concept on an actuated tensegrity kite in a custom physics simulator, designed to simulate tensegrity structures with non-rigid rods, along with basic simulations of lift. Results show that this concept is feasible and that the kite can change orientation by actively changing the shape of the tensegrity structure.

Tensegrity↗
NASA NTRSDate not supplied

Developing a Spectral Correlation based Method for mitigating Angular Mismatch Effects in Intercalibration Using a Benchmark Hyperspectral Sensor

The CLARREO Pathfinder (CPF) mission will implement a state-of-the-art intercalibration method for transferring CPF’s in-orbit Système Internationale (SI)-traceable reference to the shortwave channel of the Clouds and the Earth’s Radiance Energy System (CERES) and the reflective solar bands of the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the NOAA-20 satellite platform with a targeted intercalibration methodology uncertainty of 0.3% (k=1). In order to achieve such a high intercalibration accuracy, the CPF intercalibration measurements to be scheduled need to closely match those from CERES and VIIRS in time, space, angles, and wavelength. Despite’s CPF’s two-axis pointing capability to match its boresight line of sight to that of a target sensor, there will be finite residual differences in angular samplings of the two instruments. The potential angular mismatch between CPF and the target sensors can introduce systematic errors in the intercalibration results, and therefore needs to be corrected using a rigorously designed algorithm. The CPF intercalibration team has been developing a correction method for mitigating the impact of these angular anisotropic effects in the CPF-VIIRS and CPF-CERES intercalibration. The method explores the spectral correlation relationship between the reflected solar radiances from the same surface target that would be measured by CPF at two adjacent angles. Our studies have shown that the spectral information based on CPF measurements can be used to accurately predict the spectral radiance or reflectance difference due to a given mismatch in the viewing and solar geometry. The hyper-spectral information can also be used to provide scene stratification without using any auxiliary data, which is critical to reduce the angular correction uncertainty. The angular correction relationship can be well established using simulated CPF-like top-of-atmosphere spectral radiances observed at all sorts of viewing geometry and solar angles and for different scenes. Intensive radiative transfer simulations have been completed using a state-of-art radiative transfer model developed by the CPF team members. The implementation of the algorithm on high-fidelity event simulation data and the characterization for the angular adjustment uncertainty will be presented.

Wan Wu↗
NASA NTRSDate not supplied

Coronado Ecological Conservation: Assessing Vegetation Change Due to Border Wall Construction and Shifting Social Trails

Species monitoring is essential in mitigating the impacts of plant invasion, such as radical changes in an area’s ecosystem, degraded soil health, increased wildfire severity, landslides, and increased flooding. NASA DEVELOP partnered with the National Park Service (NPS) to investigate invasive species in disturbed lands: specifically, areas affected by off-trail walking and US-Mexico border construction activities. The team assessed how construction has impacted the distribution of Lehmann’s lovegrass and Russian thistle invasives throughout Coronado National Memorial, AZ from 1986 to 2022. Using data from Landsat 5 and 8, Sentinel-2, the National Agriculture Imagery Program, and PlanetScope, the team computed vegetation indices including the Normalized Difference Vegetation Index, Normalized Difference Moisture Index, Modified Soil Adjusted Vegetation Index 2, Enhanced Vegetation Index, and Tasseled Cap Wetness, Brightness, and Greenness transformations as vegetation health indicators to input into various machine learning algorithms. To minimize noise, the team conducted Principal Component Analysis on the vegetation indices and spectral bands before running k-means++ clustering and random forest classification algorithms. Between all datasets, we found the median area fully overtaken by invasive plants was 5.37% of the park’s total area in 2022. The NPS will use the end products to help increase restoration efforts in disturbed areas with high concentrations of invasive plants. The NPS’s collection of ground data for 2022–2023, in conjunction with future data collection, will notably improve the accuracy of classification models, leading to more precise monitoring of invasive spread over time.

Carson Schuetze↗
NASA NTRS2023

Coronado Ecological Conservation: Assessing Vegetation Change Due to Border Wall Construction and Shifting Social Trails

Species monitoring is essential for mitigating the impacts of plant invasion, such as radical changes in an area’s ecosystem, degraded soil health, increased wildfire severity, landslides, and increased flooding. For this project, NASA DEVELOP partnered with the National Park Service (NPS) to investigate invasive species in disturbed lands: specifically, areas affected by off-trail travel and U.S.-Mexico border construction activities. The team assessed how construction has impacted the distribution of Lehmann’s lovegrass and Russian thistle invasives throughout Coronado National Memorial, AZ from 1986-2022. Using data from Landsat 5 and 8, Sentinel-2, NAIP, and PlanetScope, the team computed NDVI, NDMI, MSAVI2, EVI, and Tasseled Cap Wetness, Brightness, and Greenness transformations as vegetation health indicators to input into various machine learning algorithms. To minimize noise, the team conducted Principal Component Analysis on vegetation indices and spectral bands before running k-means clustering and random forest classification algorithms. Between all datasets, the team found that the median area fully overtaken by invasive plants was 5.37% of the park’s total area in 2022. The NPS will use end products to help increase restoration efforts in disturbed areas with high concentrations of invasive plants, and this project can serve as a jumping off point for future invasive species monitoring. The NPS’s collection of ground data for 2022-2023, in conjunction with future data collection, will notably improve the accuracy of classification models, leading to more precise monitoring of invasive species spread over time.

Coronado National Memorial↗