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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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160 records · Page 9
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Risk Reduction through Early Software Prototyping for the Mars Ascent Vehicle

Despite the critical nature flight software plays in the development of spacecraft, software development teams are often brought on to the project after initial requirements definition and design efforts have started, and well after the conceptual design phase has completed. The flight software development team for the Mars Ascent Vehicle has supported the project from project formulation and conceptual design into the formal design and development. This approach has allowed for the development of a robust software prototype that supported risk reduction in numerous areas for the project. Early software prototyping supported decision making in the areas of hardware selection and considerations, software architecture, and simulation requirements. The prototype software actively supported ongoing trade studies and analyses for the project and enable valuable collaboration across multiple subsystems as well as between MSFC and the Jet Propulsion Laboratory. This early development also allowed the team to leverage past projects’ lessons learned to make valuable process improvements.

Stefanie Justice↗
NASA NTRS2018

Self-Reliant Rover Design for Increasing Mission Productivity

Achieving consistently high levels of productivity has been a challenge for Mars surface missions. While the rovers have made major discoveries and dramatically increased our understanding of Mars, they often require a great deal of effort from the operations teams, and achieving mission objectives can take longer than anticipated. The objective of this work is to identify changes to flight software and ground operations that enable high levels of productivity with reduced reliance on ground interactions. This will enable the development of Self-Reliant Rovers: rovers that make use of high-level guidance from operators to select their own situational activities and respond to unexpected conditions, all without dependence on ground intervention. In this paper we describe the system we are developing and illustrate how it enables increased mission productivity.

Vasavada, Ashwin↗
NASA NTRS2018

Integrated Planning and Execution for a Self-Reliant Mars Rover

Planetary rovers exploring the surface of Mars face a challenging operational environment that requires close cooperation between deliberative planning and behavioral execution in order to most efficiently leverage the robot’s capabilities into science value returned to earth. The Self-Reliant Rovers project envisions future rover missions that require only occasional high-level direction from human controllers to successfully conduct detailed in-situ studies of its Martian environs. To achieve this high degree of autonomy, this work leverages a spectrum of planning and execution techniques that allow the rover to respond appropriately to both opportunity and adversity it encounters. Small perturbations are accommodated at first by behavioral adaptation, with more and more extensive disruptions handled in turn by executive administration of plan flexibility, heuristic-guided plan repair strategies, and finally comprehensive replanning from science campaign goals. The integrated system has been deployed and tested on a terrestrial rover in an environment and under scenarios that anticipate those faced by future Mars rovers. This paper recounts complexities of planning and execution coordination faced in the rover domain and the practical solutions employed to address them. Particular emphasis is given to lessons from the field and foibles ripe for remedy by future advances in planning and execution research.

Gaines, Daniel↗
NASA NTRS2018

Self-Reliant Rover Design for Increasing Mission Productivity

Achieving consistently high levels of productivity has been a challenge for Mars surface missions. While the rovers have made major discoveries and dramatically increased our understanding of Mars, they often require a great deal of effort from the operations teams, and achieving mission objectives can take longer than anticipated. The objective of this work is to identify changes to flight software and ground operations that enable high levels of productivity with reduced reliance on ground interactions. This will enable the development of Self-Reliant Rovers: rovers that make use of high-level guidance from operators to select their own situational activities and respond to unexpected conditions, all without dependence on ground intervention. In this paper we describe the system we are developing and illustrate how it enables increased mission productivity.

Vasavada, Ashwin↗
NASA NTRS2019

Evaluation of the Surface Reflectance Long-Term Data Record from AVHRR over Multiple Land Surface Types

In this work, we evaluate the performance of the AVHRR surface reflectance LTDR V4 using Landsat-5 Thematic Mapper (TM5) Collection-1 surface reflectance data over 440 globally distributed sites, which give a representative set of land surface types and climate conditions. Surface reflectance anisotropy effects were normalized using the VJB method, and spectral response differences were accounted for with spectral band adjustment factors (SBAFs) computed as a function of the Normalized Difference Vegetation Index (NDVI) from a set of over 100,000 hyperspectral spectra from Hyperion. The performance of the AVHRR record is reported in terms of the accuracy, precision, and uncertainty, as compared to the specifications of the reference product. Results show that the AVHRR record performance is close to the 0.071ρ+0.0071 specification defined in the original TM5 product evaluation.

LTDR↗
NASA NTRS2021

The Arya Crop Yield Forecasting Algorithm: Application to the Main Wheat Exporting Countries

Wheat is the most important commodity traded in the international food market. Thus, accurate and timely information on wheat production can help mitigate food price fluctuations. Within the existing operational regional and global scale agricultural monitoring systems that provide information on global crop yield and area forecasts, there are still fundamental gaps: #1. Lack of quantitative Earth Observation (EO) derived crop information, #2. Lack of global but detailed (national or subnational level) and timely crop production forecasts and #3. Lack of information on forecast uncertainties. In this study we present the Agriculture Remotely-sensed Yield Algorithm (ARYA) an EO-based method, advancing the state of EO data application and usage (addressing gap #1) to forecast wheat yield. The algorithm is based on the evolution of the Difference Vegetation Index (DVI) using MODIS data at 1km resolution and the Growing Degree Days (GDD) from reanalysis data. Additionally, we explore how Land Surface Temperature (LST) can be included into the model and whether this parameter adds any value to the model performance when combined with the optical information. ARYA is implemented at the national and subnational level to forecast winter wheat yield in the main wheat exporting countries of US, Russia, Ukraine, France, Germany, Australia and Argentina from 2001 to 2019 (covering over 70% of wheat exports globally) in a timely manner by providing daily forecasts (addressing gap #2). The results show that ARYA provides yield estimations with RMSE’s within 0.3 ± 0.1 t/ha at national level and 0.6 ± 0,1 t/ha at subnational level after Day Of the Year (DOY) 140 (mid May) in the Northern Hemisphere and DOY 280 (beginning of October) in the Southern Hemisphere. This means that ARYA can provide crop yield estimates of wheat yield with 5-15 % error at national and 7-20 % error at subnational level starting from 2 to 2.5 months prior to harvest.

Agriculture↗
NASA NTRS2021

FORECASTING WHEAT YIELD USING REMOTE SENSING: THE ARYA FORECASTING SYSTEM

In this study we present a model to forecast wheat yield based on the evolution of the Difference Vegetation Index (DVI)and the Growing Degree Days (GDD), presented in Franch et al. (2015), but adapted to Franch et al. (2019) model. Additionally, we explore how the Land Surface Temperature (LST) can be included into the model and if this parameter adds any value to the model when combined with the optical information. This study is applied toMODIS data at 1km resolution to monitor the national and state level yield of winter wheat in the United States and Ukraine from 2001 to 2019.

B. Franch↗
NASA NTRS2022

First 210 solar days of Mars 2020 Perseverance Robotic Operations – Mobility, Robotic Arm, Sampling, and Helicopter

This paper includes the summary, lessonslearned, and upcoming plans for the first 210 Mars solar days(sols) of the mission. The focus of the paper is on roboticoperations which has the primary responsibility for strategicplanning, uplink commanding and downlink analysis forrover mobility and navigation, robotic arm operation, thesampling and caching capability including coring, theadaptive caching assembly and the 2nd sample handlingrobotic arm, and interface to the Mars helicopter Ingenuity.As of Sol 210 the rover has driven 2663.65 meters, executed20764 robotic arm and sampling commands, and hassuccessfully completed 13 helicopter flights covering 2382meters horizontal distance. It includes the OperationsReadiness Tests in preparation for landing, landing and initialcheckouts, strategic route planning to the science destinationand waypoints, surface checkout of all of the roboticscapability of the rover. It also discusses the strategic planningand tactical agility needed for interleaving scienceinvestigation and technology demonstration of the Marshelicopter flights where a minimum distance had to bemaintained between the rover and helicopter during flights. Itdiscusses the challenges with planning robotic operations andaddressing anomalies with the larger uncertainty presentduring early mission operations. It also discusses the impacton robotic operations from lessons incorporated fromprevious missions.

Ono, Hiro↗
NASA NTRS2022

Wichita Climate II: Quantifying and Mapping Urban Heat to Inform Equitable and Sustainable Urban Planning Initiatives in Wichita, Kansas

Wichita, Kansas is experiencing a host of climate threats, particularly extreme heat manifested through Urban Heat Islands (UHI). Heat is unevenly distributed within cities due to factors such as income inequality, historical discriminatory practices like redlining, and divestment in neighborhoods of color. This leads to less vegetation and more heat-absorbing infrastructure in specific communities. Moreover, adverse effects of heat, including heat-related morbidity and mortality, disproportionately impact populations that experience vulnerability through social inequities and structural discrimination. Heat vulnerability is a combination of the factors of heat exposure, sensitivity, and adaptive capacity, and can be harnessed to guide urban heat interventions. This DEVELOP project partnered with the City of Wichita to understand the spatial distribution and drivers of UHIs and heat vulnerability indicators. The team modeled outcomes of tree cover interventions using Landsat 8’s Thermal Infrared Sensor (TIRS) and Operational Land Imager (OLI), Landsat 9 TIRS-2 and OLI-2, and the International Space Station’s Ecosystem Spaceborne Thermal Radiometer Experiment on the International Space Station (ECOSTRESS) sensor, along with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Cooling model. The team also leveraged statistical analysis by implementing principal component analysis to develop a heat vulnerability index (HVI) specific to Wichita. Ultimately, the project’s outputs will inform the City of Wichita’s Climate Adaptation and Mitigation Plan, identify priority areas for heat mitigation initiatives, and be used in public-facing communications to educate communities on the impacts of urban heat.

Environmental Justice↗
NASA NTRS2023

San Joaquin Valley Health & Air Quality: Evaluating the Overlap of Social Vulnerabilities and Air Quality in the San Joaquin Valley Air Pollution Control District

Little Manila Rising (LMR) is a nonprofit in Stockton, California that has been increasingly concerned by the air pollution in their city and the neighboring San Joaquin Valley (SJV). Geographical factors, climate conditions, and anthropogenic activities, such as agriculture burning and vehicle emissions, contribute to the high levels of air pollution in this region. To visualize the distribution of air pollution and social disparities across the SJV, LMR partnered with NASA DEVELOP. The DEVELOP team used Terra and Aqua MODIS, Sentinel-5P TROPOMI, and CALIPSO CALIOP to observe Aerosol Optical Depth (AOD), Nitrogen Dioxide (NO 2 ), and the vertical distribution of pollutants at varying pollution levels, respectively. Additionally, Suomi-NPP VIIRS provided active fire data. By leveraging NASA Earth observations along with sociodemographic and public health data, the DEVELOP team created maps identifying the areas experiencing the highest vulnerabilities and disparities in pollution exposure. The team found that AOD was slightly higher in agricultural regions, while NO 2 was consistently higher along transportation corridors and urban areas. Wildfires dominated the type of detected active fires, and there was high correlation (R 2 = 0.6924) between active fires and burn permits in agricultural tracts. Furthermore, the team identified both high AOD vulnerability and high NO 2 vulnerability in census tracts in South Stockton, an area that has been historically redlined and disinvested in, and where LMR resides. There was also a moderate correlation between air quality reported from the satellites and in-situ ground monitors. These results will support LMR’s organizing strategies for stricter enforcement of air pollution regulations and increased public health equity for community members.

Agricultural Fires↗
NASA NTRS2024

NPCC4: Tail Risk, Climate Drivers of Extreme Heat, and New Methods for Extreme Event Projections

We summarize historic New York City (NYC) climate change trends and provide the latest scientific analyses on projected future changes based on a range of global greenhouse gas emissions scenarios. Building on previous NPCC assessment reports, we describe new methods used to develop the projections of record for sea level rise, temperature, and precipitation for NYC, across multiple emissions pathways and analyze the issue of the “hot models” associated with the 6th phase of the Coupled Model Intercomparison Project (CMIP6) and their potential impact on NYC's climate projections. We describe the state of the science on temperature variability within NYC and explain both the large-scale and regional dynamics that lead to extreme heat events, as well as the local physical drivers that lead to inequitable distributions of exposure to extreme heat. We identify three areas of tail risk and potential for its mischaracterization, including the physical processes of extreme events and the effects of a changing climate. Finally, we review opportunities for future research, with a focus on the hot model problem and the intersection of spatial resolution of projections with gaps in knowledge in the impacts of the climate signal on intraurban heat and heat exposure.

NPCC4↗