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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 NTRS2014

Atmospheric Mining in the Outer Solar System:

Atmospheric mining in the outer solar system has been investigated as a means of fuel production for high energy propulsion and power. Fusion fuels such as Helium 3 (3He) and hydrogen can be wrested from the atmospheres of Uranus and Neptune and either returned to Earth or used in-situ for energy production. Helium 3 and hydrogen (deuterium, etc.) were the primary gases of interest with hydrogen being the primary propellant for nuclear thermal solid core and gas core rocket-based atmospheric flight. A series of analyses were undertaken to investigate resource capturing aspects of atmospheric mining in the outer solar system. This included the gas capturing rate, storage options, and different methods of direct use of the captured gases. Additional supporting analyses were conducted to illuminate vehicle sizing and orbital transportation issues. While capturing 3He, large amounts of hydrogen and 4He are produced. With these two additional gases, the potential for fueling small and large fleets of additional exploration and exploitation vehicles exists. Additional aerospacecraft or other aerial vehicles (UAVs, balloons, rockets, etc.) could fly through the outer planet atmospheres, for global weather observations, localized storm or other disturbance investigations, wind speed measurements, polar observations, etc. Deep-diving aircraft (built with the strength to withstand many atmospheres of pressure) powered by the excess hydrogen or helium 4 may be designed to probe the higher density regions of the gas giants. Outer planet atmospheric properties, atmospheric storm data, and mission planning for future outer planet UAVs are presented.

planetary exploration↗

NASA NTRS2015

Atmospheric Mining in the Outer Solar System: Resource Capturing, Exploration, and Exploitation

Atmospheric mining in the outer solar system (AMOSS) has been investigated as a means of fuel production for high-energy propulsion and power. Fusion fuels such as helium 3 (He-3) and hydrogen can be wrested from the atmospheres of Uranus and Neptune and either returned to Earth or used in-situ for energy production. 3He and hydrogen (deuterium, etc.) were the primary gases of interest, with hydrogen being the primary propellant for nuclear thermal solid core and gas core rocket-based atmospheric flight. A series of analyses were undertaken to investigate resource capturing aspects of AMOSS. These analyses included the gas capturing rate, storage options, and different methods of direct use of the captured gases. Additional supporting analyses were conducted to illuminate vehicle sizing and orbital transportation issues. While capturing 3He, large amounts of hydrogen and helium 4 (He-4) are produced. With these two additional gases, the potential exists for fueling small and large fleets of additional exploration and exploitation vehicles. Additional aerospacecraft or other aerial vehicles (UAVs, balloons, rockets, etc.) could fly through the outer-planet atmosphere to investigate cloud formation dynamics, global weather, localized storms or other disturbances, wind speeds, the poles, and so forth. Deep-diving aircraft (built with the strength to withstand many atmospheres of pressure) powered by the excess hydrogen or 4He may be designed to probe the higher density regions of the gas giants.

Palaszewski, Bryan↗

NASA NTRS2015

Internal NASA Study: NASAs Protoflight Research Initiative

The NASA Protoflight Research Initiative is an internal NASA study conducted within the Office of the Chief Engineer to better understand the use of Protoflight within NASA. Extensive literature reviews and interviews with key NASA members with experience in both robotic and human spaceflight missions has resulted in three main conclusions and two observations. The first conclusion is that NASA's Protoflight method is not considered to be "prescriptive." The current policies and guidance allows each Program/Project to tailor the Protoflight approach to better meet their needs, goals and objectives. Second, Risk Management plays a key role in implementation of the Protoflight approach. Any deviations from full qualification will be based on the level of acceptable risk with guidance found in NPR 8705.4. Finally, over the past decade (2004 - 2014) only 6% of NASA's Protoflight missions and 6% of NASA's Full qualification missions experienced a publicly disclosed mission failure. In other words, the data indicates that the Protoflight approach, in and of it itself, does not increase the mission risk of in-flight failure. The first observation is that it would be beneficial to document the decision making process on the implementation and use of Protoflight. The second observation is that If a Project/Program chooses to use the Protoflight approach with relevant heritage, it is extremely important that the Program/Project Manager ensures that the current project's requirements falls within the heritage design, component, instrument and/or subsystem's requirements for both the planned and operational use, and that the documentation of the relevant heritage is comprehensive, sufficient and the decision well documented. To further benefit/inform this study, a recommendation to perform a deep dive into 30 missions with accessible data on their testing/verification methodology and decision process to research the differences between Protoflight and Full Qualification missions' Design Requirements and Verification & Validation (V&V) (without any impact or special request directly to the project).

Protoflight↗

NASA NTRS2017

Controllable Buoys and Networked Buoy Systems

Buoyant sensor networks are described, comprising floating buoys with sensors and energy harvesting capabilities. The buoys can control their buoyancy and motion, and can organize communication in a distributed fashion. Some buoys may have tethered underwater vehicles with a smart spooling system that allows the vehicles to dive deep underwater while remaining in communication and connection with the buoys.

Davoodi, Faranak↗

NASA NTRS2016

Benefits of Mars ISRU Regolith Water Processing: A Case Study for the NASA Evolvable Mars Campaign

ISRU of Mars resources was baselined in 2009 Design Reference Architecture (DRA) 5.0, but only for Oxygen production using atmospheric CO2. The Methane (LCH4) needed for ascent propulsion of the Mars Ascent Vehicle (MAV) would need to be brought from Earth. However: Extracting water from the Martian Regolith enables the production of both Oxygen and Methane from Mars resources: Water resources could also be used for other applications including: Life support, radiation shielding, plant growth, etc. Water extraction was not baselined in DRA5.0 due to perceived difficulties and complexity in processing regolith. The NASA Evolvable Mars Campaign (EMC) requested studies to look at the quantitative benefits and trades of using Mars water ISRUPhase 1: Examined architecture scenarios for regolith water retrieval. Completed October 2015. Phase 2: Deep dive of one architecture concept to look at end-to-end system size, mass, power of a LCH4/LO2 ISRU production system

Mars Exploration↗

NASA NTRS2017

An ISRU Propellant Production System to Fully Fuel a Mars Ascent Vehicle

ISRU of Mars resources was base lined in 2009 Design Reference Architecture (DRA) 5.0, but only for Oxygen production using atmospheric CO2The Methane (LCH4) needed for ascent propulsion of the Mars Ascent Vehicle (MAV) would need to be brought from Earth. HOWEVER: Extracting water from the Martian Regolith enables the production of both Oxygen and Methane from Mars resources Water resources could also be used for other applications including: Life support, radiation shielding, plant growth, etc. Water extraction was not base lined in DRA5.0 due to perceived difficulties and complexity in processing regolith. The NASA Evolvable Mars Campaign (EMC) requested studies to look at the quantitative benefits and trades of using Mars water ISRU Phase 1: Examined architecture scenarios for regolith water retrieval. Completed October 2015Phase 2: Deep dive of one architecture concept to look at end-to-end system size, mass, power of a LCH4LO2 ISRU production system.Evolvable Mars CampaignPre-deployed Mars ascent vehicle (MAV)4 crew membersPropellants: Oxygen MethaneGenerate a system model to roll up mass power of a full ISRU system and enable parametric trade studies. Leverage models from previous studies and technology development programs Anchor with mass power performance from existing hardware. Whenever possible used reference-able (published) numbers for traceability.Modular approach to allow subsystem trades and parametric studies. Propellant mass needs taken from most recently published MAV study:Polsgrove, T. et al. (2015), AIAA2015-4416MAV engines operate at mixture ratios (oxygen: methane) between 3:1 and 3.5:1, whereas the Sabatier reactor produces at a 4:1 ratio. Therefore:Methane production is the driving requirement-Excess Oxygen will be produced.

Mars surface↗

NASA NTRS2016

Status and Path Forward for the Large Ultraviolet Optical Infrared Surveyor (LUVOIR) Mission Concept Study

In preparation of the 2020 Astrophysics Decadal Survey, National Aeronautics and Space Administration (NASA) has commenced a process for the astronomical community to study several large mission concepts leveraging the lessons learned from past Decadal Surveys. This will enable the Decadal Survey committee to make more informed recommendations to NASA on its astrophysics science and mission priorities with respect to cost and risk. Four astrophysics large mission concepts were identified. Each of them had a Science and Technology Definition Teem(STDT) chartered to produce scientifically compelling, feasible, and executable design reference mission (DRM)concepts to present to the 2020 Decadal Survey. In addition, The Aerospace Corporation will perform an independent cost and technical evaluation (CATE) of each of these mission concept studies in advance of the 2020 Decadal Survey by interacting with the STDTs to provide detailed technical details on certain areas for which deep dives are appropriate. This paper presents the status and path forward for one of the four large mission concepts, namely, the Large UltraViolet, Optical, InfraRed surveyor (LUVOIR).

Study Office: science analyses↗

NASA NTRS2018

Data, Meet Compute: NASA's Cumulus Ingest Architecture

NASA's Earth Observing System Data and Information System (EOSDIS) houses nearly 30PBs of critical Earth Science data and with upcoming missions is expected to balloon to between 200PBs-300PBs over the next seven years. In addition to the massive increase in data collected, researchers and application developers want more and faster access - enabling complex visualizations, long time-series analysis, and cross dataset research without needing to copy and manage massive amounts of data locally. NASA has looked to the cloud to address these needs, building its Cumulus system to manage the ingest of diverse data in a wide variety of formats into the cloud. In this talk, we look at what Cumulus is from a high level and then take a deep dive into how it manages complexity and versioning associated with multiple AWS Lambda and ECS microservices communicating through AWS Step Functions across several disparate installations

EOSDIS↗

NASA NTRS2016

Analyzing Double Delays at Newark Liberty International Airport

When weather or congestion impacts the National Airspace System, multiple different Traffic Management Initiatives can be implemented, sometimes with unintended consequences. One particular inefficiency that is commonly identified is in the interaction between Ground Delay Programs (GDPs) and time based metering of internal departures, or TMA scheduling. Internal departures under TMA scheduling can take large GDP delays, followed by large TMA scheduling delays, because they cannot be easily fitted into the overhead stream. In this paper we examine the causes of these double delays through an analysis of arrival operations at Newark Liberty International Airport (EWR) from June to August 2010. Depending on how the double delay is defined between 0.3 percent and 0.8 percent of arrivals at EWR experienced double delays in this period. However, this represents between 21 percent and 62 percent of all internal departures in GDP and TMA scheduling. A deep dive into the data reveals that two causes of high internal departure scheduling delays are upstream flights making up time between their estimated departure clearance times (EDCTs) and entry into time based metering, which undermines the sequencing and spacing underlying the flight EDCTs, and high demand on TMA, when TMA airborne metering delays are high. Data mining methods (currently) including logistic regression, support vector machines and K-nearest neighbors are used to predict the occurrence of double delays and high internal departure scheduling delays with accuracies up to 0.68. So far, key indicators of double delay and high internal departure scheduling delay are TMA virtual runway queue size, and the degree to which estimated runway demand based on TMA estimated times of arrival has changed relative to the estimated runway demand based on EDCTs. However, more analysis is needed to confirm this.

traffic management advisor↗

NASA NTRS2018

An Analysis of the Lightning Jump Algorithm Using Geostationary Lightning Mapper Flashes

Lightning's relation to severe weather has been studied since the 1980's. The invention of the Lightning Mapping Array allowed for total lightning measurements in a 125 km operational range. This brought forth an automated lightning Jump Algorithm (LIA) that predicted severe weather based on two-sigma increases in total lightning. The LIA's biggest downfall is being restrained to the limited field of view (FOV) of LMA's. The launch of the Geostationary Lightning Mapper (GLM) aboard the GOES-16 satellite now gives us hemispheric total lightning measurements. The wide FOV makes the GLM a good candidate to apply the LIA to. However the GLM and LMA have some differences. One being the coarser spatial resolution of GLM. Another being that LMA measures very high frequency (VHF) electromagnetic radiation while GLM measures optical radiation. These differences suggest an extensive study must be done on using the LIA with GLM to understand potential differences in the LIA and to maximize its operational skill. Four deep dive cases are conducted showcasing the differences between the GLM and LMA and their jumps.

Curtis, Nathan↗

NASA NTRS2018

An Analysis of the Lightning Jump Algorithm Using Geostationary Lightning Mapper Flashes

This project aims to implement the two-sigma lightning jump algorithm (LJA) developed using Lightning Mapping Arrays (LMAs), with GOES-16 Geostationary Lightning Mapper (GLM) flashes, evaluate its performance, and identify any needed adjustments to the algorithm to optimize operational skill. The GLM is projected to have lower detection efficiency (DE) (70-90 percent) than operational LMAs (95-99 percent). The reduced GLM DE coupled with the coarser spatial resolution of the GLM could have impacts on flash rates and trends that could affect the LJA in various ways. Deep dives are conducted on four separate cases. Three of four cases show LMAs seeing two to three times as many flashes as the GLM. Only fifteen of twenty five GLM jumps saw increases in radar intensity while fourteen of nineteen LMA jumps did. These results suggest a larger sample sized study must be conducted to determine how to implement the LJA with the GLM.

Curtis, Nathan↗

NASA NTRS2018

Controllable Buoys and Networked Buoy Systems

Davoodi, Faranak↗

NASA NTRS2016

Analyzing Double Delays at Newark Liberty International Airport (EWR)

When weather or congestion impacts the National Airspace System, multiple different Traffic Management Initiatives can be implemented, sometimes with unintended consequences. One particular perceived inequity that is commonly identified is in the interaction between Ground Delay Programs (GDPs) and time based scheduling of internal departures by the Traffic Management Advisor (TMA) (now operationally superseded by the FAA's the Time-Based Flow Management system). Internal departures under TMA scheduling can take large GDP delays, followed by large TMA scheduling delays, because they cannot easily fit into the arrival flow at the runway. In this paper we examine the causes of these double delays through an analysis of arrival operations at Newark Liberty International Airport (EWR) from June to August 2010. TMA scheduling delays are found to be generally higher than TMA airborne metering delays, regardless of prior GDP delays. Depending on how the double delay is defined, between 42 and 62 of all internal departures in GDP and TMA scheduling experienced double delays in this period. A deep dive into the data reveals that contributors to double delays include upstream flights departing before their Expect Departure Clearance Times (EDCTs); differences in the rates used for setting EDCTs and TMA Scheduled Times of Arrival; differences in the arrival demand expected based on EDCTs and the arrival demand entering TMA; and shorter en route times between takeoff and entry into TMA than assumed in the calculation of flight EDCTs, all of which undermine the sequencing and spacing underlying flight EDCTs. Double delays are also found to coincide with periods in which the virtual runway arrival queue being served by a TMA is large, there are periods of high demand relative to capacity, and there are high airborne metering delays. Data mining techniques are used to confirm that each of these factors contribute to the occurrence of double delay andor high internal departure scheduling delay across three months of data from June to August 2010. Predictors of the occurrence of double delay and high TMA scheduling delay are built using logistic regression, providing prediction accuracies of 69 and 73, respectively.

traffic flow management↗

NASA NTRS2016

The Last Orbit: Planning Cassini's Plummet into Saturn

Cassini’s final orbit around Saturn will culminate in a dramatic ending as the spacecraft plunges into the ringed planet’s atmosphere, never to escape or be heard from again. The last hours of the mission prior to the final loss of signal have some of the most unique and valuable science to date. Cassini will take a unique trajectory to dive deep into the atmosphere on its approach to final disposal and no spacecraft, Cassini included, has entered these depths of Saturn’s atmosphere. The science community has placed heavy emphasis onthis once-in-a-lifetime opportunity to inspect these deeper regions of Saturn’s atmosphere. The Cassini project specifically aims to collect the very last bits of data during the final plunge to get samples of the deepest regions before the spacecraft is lost forever. The desire to collect the final bits of data presents several challenges. Cassini’s Mission Planning (MP) team has developed an End of Mission (EOM) scenario to tackle these demands. The EOM scenario outlines the framework for the entire last orbit of the mission and details thestrategy for data collection and transmission. Attaining near real-time transmission is key for the acquisition of the very last bits of data. The Cassini spacecraft will use a new mode of operations to successfully achieve this real-time transmission. In addition to this primary investigation and planning for telecommunications, key risks have been studied within the realm of the last orbit. Ultimately, this paper shows how the Cassini Project plans to ensurethe return of every last bit of data before the spacecraft is consumed by Saturn forever.

Bittner, Molly E.↗

NASA NTRS2019

"Sensor Web Evolution - Webs of Webs for NASA Science - Focus on small Uninhabited Aerial Systems (sUAS)"

This paper will describe the evolution of information collection, derivation and delivery mechanisms in webs of NASA sensor webs, with a focus on recent advancements in small Uninhabited Aerial Systems (sUAS). I will discuss the movement to "Fog Computing", also known as Edge Computing. Fog Computing facilitates the distribution of common operations and networking between edge devices and cloud computing facilities, optimizing the production of actionable intelligence. Initially, sUASs utilized onboard data collection as standard, with minimal data downloaded directly. Information products were derived in conventional computational environments, generally desk top computers, and information products made available to the Science Community in weeks or months. With the increased availability, and increasingly lower costs, of beyond line of sight (BLOS) satellite based communication, transmission rates and data volumes increased, and processing migrated to Cloud based services. Contemporary sUASs are moving some of that information product derivation to on vehicle services, and are creating a distributed Cloud/Fog environment. I will describe the technological advances that have made this possible, including low power multi-core Central Processing Units (CPU), and, more recently, the availability of high end Graphical Processing Units (GPU) that consume only a few watts. Intelligent system software, leveraging these hardware advances, finally allows for information product generation on-board, rather than simple data collection. Additionally, intelligent flight control systems now support mutual vehicle to vehicle collaboration, allowing sUASs to create ad-hoc sensor webs on demand, as required. Also discussed will be the lessons learned by the Authors' development of data systems for NASA's large High Altitude Long Endurance (HALE) UASs like Predator and Global Hawk, and how those lessons are being applied to sUAS development. This paper will focus on application, rather a deep dive into the technology, and will highlight improving data management through these new technologies.

Sensor Web↗

NASA NTRSDate not supplied

A Quantitative Analysis on the Use of Supervised Machine Learning in Earth Science

Recent review papers (Ball et al., 2017; Reichstein et al., 2019) have investigated the opportunities and challenges in applying supervised machine learning (ML) techniques to Earth science problems. A common challenge is the lack of training (or labeled) data. Supervised ML, and especially deep learning (DL), require large training datasets. While there are large, open access Earth science archives, the data typically require preprocessing in preparation for supervised ML, frequently including manual labeling. Our objective is to understand the landscape of supervised ML in the Earth sciences, including which research communities have most rapidly adopted supervised ML, which algorithms are applied, and what data are used to train these algorithms. We conducted a literature survey of Earth science papers published during the last 10 years in journals from the American Geophysical Union (AGU), American Meteorological Society (AMS), the Institute of Electrical and Electronics Engineers(IEEE), and the Society of Photo-Optical Instrumentation Engineers (SPIE). We identified papers containing the terms ML, DL, or the names of individual supervised ML algorithms. "Earth science" is an additional required search term for IEEE and SPIE. We investigate trends in supervised ML usage during the 10-year study period, and manually analyzed AGU papers from 2018-2019 to enable deep-dive statistics.

Katrina S Virts↗

NASA NTRSDate not supplied

UASs in the VOG/Edge/FOG Sensor Web Environment

This paper will describe the evolution of information collection, derivation and delivery mechanisms in sensor webs utilizing Uninhabited Aerial Systems (UAS).We will discuss the movement to "Fog Computing", also known as Edge Computing. Fog Computing facilitates the distribution of common operations and networking between edge devices and cloud computing facilities, optimizing the production of actionable intelligence. Initially, UASs utilized onboard data collection as standard, with minimal data downloaded directly. Information products were derived in conventional computational environments, generally desk top computers, and information products made available to the Science Community in weeks or months. With the increased availability, and increasingly lower costs, of beyond line of sight (BLOS) satellite based communication, transmission rates and data volumes increased, and processing migrated to Cloud based services. Contemporary UASs are moving some of that information product derivation to on vehicle services, and are creating a distributed Cloud/Fog environment. The Author will describe the technological advances that have made this possible, including low power multi-core Central Processing Units (CPU), and, more recently, the availability of high end Graphical Processing Units (GPU) that consume only a few watts. Intelligent system software, leveraging these hardware advances, finally allows for information product generation on-board, rather than simple data collection. Additionally, intelligent flight control systems now support mutual vehicle to vehicle collaboration, allowing UASs to create ad-hoc sensor webs on demand, as required. Also discussed will be the lessons learned by the Authors' development of data systems for NASA's large High Altitude Long Endurance (HALE) UASs like Predator and Global Hawk, and how those lessons are being applied to other UAS development This paper will focus on applications, rather a deep dive into the technology, and will highlight improving data management through these new technologies.

UAS↗

NASA NTRSDate not supplied

New Developments In NASA’s Entry Systems Modeling Project

This paper describes recent developments for modeling entry, descent, and landing (EDL)of spacecraft in support of NASA’s exploration missions. Mission-specific research and model development for entry systems occurs across the Agency (e.g., within flight programs like Artemis/Orion and Mars Sample Return), however the aim of this paper is to discuss the research conducted by NASA’s Entry Systems Modeling (ESM) Project, which serves as the Agency’s only effort dedicated to advancing modeling capabilities that cross-cut multiple technical disciplines, missions and destinations. The ESM portfolio is developed to address the specific needs expressed by a cross-section of NASA stakeholders, including flight and research projects, technical leadership, and subject matter experts. Technology development in ESM is organized and prioritized from a system-level perspective, resulting in four broad technical areas of investment: (1) Thermal Protection System (TPS) material modeling, (2) Shock layer kinetics and radiation, (3) Aerosciences, and (4) Guidance, navigation, and control. In addition to the core technical areas, special topics are rolled into the project portfolio as specific demands arise. Current special topics include TPS Certification by Analysis, improving understanding of woven TPS material performance; Hypersonic Wake Flows, assessing and improving predictions of base flows; and the MEDLI2 Deep Dive, furthering analysis of data obtained during Mars2020 mission’s entry and descent at Mars. Key results from each of these areas are presented in this paper, along with associated references to serve as a roadmap for other EDL researchers to access NASA’s publications.

Aaron M Brandis↗