Results for “GAMUT” — Page 3

NASA NTRS2018

Topological Control of Polystyrene-Silica Core-Shell Microspheres

Controllable surface morphology is requisite across a gamut of processes, industries, and applications. The surface morphology of silica-coated polystyrene microspheres was controllably modified to enable generation of both smooth and bumpy, or raspberry-like, surfaces. Although smooth and raspberry-like silica shells on polystyrene templates have been demonstrated extensively, the method described here used readily available materials to produce radical changes in surface morphology from a single polystyrene template coated in silica through a facile sol-gel reaction processes. Silica shells were deposited via a sol-gel process (using tetraethyl orthosilicate as the silica precursor) onto 1 to 2 m diameter anionic polystyrene spheres, fabricated by emulsifier-free polymerization. By varying of the concentration of silica precursor and ammonium hydroxide catalyst and altering the electrostatic surface interactions via addition of a cationic polymeric brush, an array of surface topologies was generated. The resulting silica shells ranged from 100 to 200 nm in thickness, as measured by calcination of the polystyrene template. Empirical relations between reaction conditions and the resulting silica colloid diameter were utilized to understand the resultant silica shell topology. These results may serve as a guide to generate a versatile platform for research in the multitude of applications where polystyrene-silica core-shell particles are utilized.

Zane A. Grady↗

NASA NTRS2019

Interface Conductance Under a Real Electronics Box

Electronics Boxes with high heat dissipations use a thermal interface material to increase heat transfer to the radiator in a vacuum/space environment. There are lots of materials to choose from, but for Spacecraft applications, there are more than high heat transfer metrics which must be met. Contamination (both particle generation and outgassing), ease of cutting, and removal are just as important metrics in material selection. However, vendor data of material thermal conductance is usually based on a 1" X 1" piece of material under high uniform pressures. Large Electronics boxes almost never have optimal pressures, as they are bolted along the perimeter and leave gaps in the center regions. In order to characterize the relative thermal conductance for large Electronics boxes, an 8" X 8" plate was fabricated to simulate an electronics box bottom and bolted around the perimeter to a cold plate. Various thermal interface materials were inserted between the box and cold plate, and overall thermal conductance's were calculated. A table was generated which compares the full gamut of thermal interface materials for large boxes, from a dry joint to a wet joint. Materials were placed in order of high to low conductance's, so an engineer can compare the benefit of each material in a real-world scenario.

Thermal Interface Conductance↗

NASA NTRS2020

Is Structured Agile an Oxymoron? Tales from Implementing and Executing Agile in a US Government Environment

To paraphrase a famous quote, "No plan survives contact with the reality." Software (SW) development is often a classic example of this: whatever the plan was for a particular development, it often does not survive contact with technical realities, budget realities, program realities and schedule realities. Traditionally, SW development has followed a waterfall methodology with requirements being rigorously specified before the design, which was completed before the coding and unit testing started, which were in turn finished before validation and verification started. This model of SW engineering derives much from the HW engineering of large systems, and has been the standard methodology used in US government software acquisitions and systems for decades, with highly variable results. US Government SW requirements are built around Waterfall concepts, which assume that the plan will survive contact with reality, or at least that modifications to the plan are relatively small, and relatively few.Because of the inefficiencies and difficulties inherent in Waterfall, the commercial SW world started using a different SW development methodology called Agile more than 20 years ago. Agile believes that a plan should evolve and learn rapidly in response to the realities encountered. At its core, there are a few key elements of Agile:- A small team of people which is highly flexible and adaptive. The team collaborates and interoperates through sophisticated development architectures and release environments- An iterative, incremental development and release approach which is based upon the concept that knowledge comes from experience within the team, and that the team makes decisions based upon what it knows- A team culture which prizes transparency, inspection and adaptation. These values are necessary so that the team experience and decision making is transparent and responsive to the realities encountered during development and testingSo, how to use Agile in a US Government environment? GMSEC (Goddard Mission Services Evolution Center) develops satellite ground system software for NASA and other US Government agencies. The SW developed by the team contains a large code base of many applications used within satellite mission operations centers. It spans the full gamut of SW development types: from SW which is in a classic maintenance and sustainment mode, to new developments with a fairly well understood scope and approach, to new developments whose scope and approach are quite unclear and which require significant research and prototyping. Team members move between all of these different types of SW development. Waterfall was inadequate to the programmatic and technical needs of the team, as well as the various types of SW development being done. The software plan was not surviving contact with the technical and programmatic realities experienced by the team. To address this, the team started a small pilot project in 2016 to test the use of Agile within a small subset of the team for a new web services application. In early 2018, the use of Agile was expanded to the whole team and all the software, but we had to fulfill the NASA SW development requirements. And we needed to do this while still remaining true to the key Agile elements of transparency, inspection and adaption. In order to do this, the team worked very closely with the Software Process Improvement (SPI) team at NASA Goddard, as well as NASA engineering manageme

Beech, Theresa W.↗

NASA NTRSDate not supplied

Dynamic Radioisotope Power Systems Development Status and Path to Flight

Dynamic power conversion offers the potential to produce Radioisotope Power Systems (RPS) that generate higher power outputs and utilize the Pu-238 radioisotope more efficiently. Additionally, dynamic power conversion offers the potential of producing generators with minimal degradation resulting in more power at the end of the mission, when the power is needed. Dynamic power conversion technologies being developed for space applications include the Stirling and Brayton thermodynamic cycle machines. Machines can be built based on these cycles while eliminating wear mechanisms of the moving components, enabling long design life necessary for space missions. The Dynamic Radioisotope Power Systems (DRPS) project at NASA Glenn Research Center (Glenn Research Center) is pursuing the realization of this type of power source on a flight mission. The project currently has three convertor development contracts that will deliver prototype hardware in 2020. This hardware will undergo a gamut of experimental performance verification efforts at NASA GRC. In parallel, the project has also initiated generator design efforts based on these underlying convertor options, and is also on track to build an in-house version of a generator for laboratory system-level testing. The project is also funding control electronics technology, which are necessary to convert alternating current from the dynamic devices to direct current for use by a spacecraft. A lunar mission is being targeted as the first use of this new technology, as DRPS enables a wide range of high-return scientific missions on the moon, while the mission being short in duration (2 years rather than 10 years for an outer planets mission).

Salvatore M Oriti↗

NASA NTRSDate not supplied

Dynamic Radioisotope Power Systems Status and Path to Flight

Dynamic power conversion offers the potential to produce Radioisotope Power Systems (RPS) that generate higher power outputs and utilize the Pu-238 radioisotope more efficiently. Additionally, dynamic power conversion offers the potential of producing generators with minimal degradation resulting in more power at the end of the mission, when the power is needed. Dynamic power conversion technologies being developed for space applications include the Stirling and Brayton thermodynamic cycle machines. Machines can be built based on these cycles while eliminating wear mechanisms of the moving components, enabling long design life necessary for space missions. The Dynamic Radioisotope Power Systems (DRPS) project at NASA Glenn Research Center (Glenn Research Center) is pursuing the realization of this type of power source on a flight mission. The project currently has three convertor development contracts that will deliver prototype hardware in 2020. This hardware will undergo a gamut of experimental performance verification efforts at NASA GRC. In parallel, the project has also initiated generator design efforts based on these underlying convertor options, and is also on track to build an in-house version of a generator for laboratory system-level testing. The project is also funding control electronics technology, which are necessary to convert alternating current from the dynamic devices to direct current for use by a spacecraft. A lunar mission is being targeted as the first use of this new technology, as DRPS enables a wide range of high-return scientific missions on the moon, while the mission being short in duration (2 years rather than 10 years for an outer planets mission).

Salvatore Oriti↗

NASA NTRS2021

Low and High Temperature Non-Thermonuclear Fusion Approaches to Energy Production

We are responding to the RFI points A1, A2 and A3. We appreciate the need for a high density, high power, environmentally-benign and carbon-neutral power system. Although fusion has been considered for over 70 years, the nearly intractable means of maintaining a hydrogen plasma at temperatures exceeding the center of the Sun increasingly delays not only commercialization but even the scientific breakeven point. As a recent JASON report notes, the world-wide energy grid is moving towards decentralization which is counter to the large tokamak fusion de-vices necessary given their underdense plasmas. NASA has spent several years understanding and developing Lattice Confinement Fusion as an alternative to thermonuclear fusion by exploring many means of loading and triggering fusion reactions in deuterated metal lattices. Triggering has run the gamut from bremsstrahlung-initiated photo-neutrons to the role of electron screening in assisting these reactions. Electron screening may be responsible for both nuclear effects in deuterium pumped metals as well as fast neutrons observed from electrolytically driven cathodes. Electron screened fusion rate enhancement has been observed by others, including the Lawrence Berkeley National Laboratory. NASA’s need for deep space power drove this research with a goal of a ten-year lifetime for a multi-kWe power system. Previous NASA studies have explored the use of fusion systems for nuclear thermal propulsion for manned spaceflight to the outer planets. Despite these designated uses, small-scale terrestrial power applications as well as medical isotope production are possible once scaling is achieved. ARPA-E can contribute to the Nation’s Green Energy mandates by funding research in this field that builds on condensed matter nuclear science findings by the US Navy and NASA.

L. P. Forsley↗

NASA NTRS2021

Microgravity Science Database Development

Throughout NASA’s history, the agency has developed a plethora of complex systems, such as the International Space Station and the space shuttle, and performed research in several fields spanning the gamut from psychology to welding and materials research. Throughout these studies, an extensive amount of data has been generated and unfortunately at times regenerated. As Barend Mons states “Huge sums of taxpayer funds go to waste because such data cannot be reused.”[2] While his comments were directed at the state of data management in the European Union, it is no less valid for data management practices in the United States. The issues surrounding data management, including storage, retrieval, and analysis, will continue to be of utmost importance as the agency aims to responsibly utilize funds and gather the maximum benefit from flight and ground experiments.

Data↗

NASA NTRS2022

NASA's Responsible AI Use Cases

This submission consists of NASA's Responsible Artificial Intelligence (RAI) Use Cases. These RAI Use Cases are to be made public, pursuant to the Presidential Executive Order 13960, Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government. They were collected from NASA's practicing AI research community and cover the gamut of NASA's AI activities. These Use Cases will be updated annually as required by the Executive Order. The information included consists of the NASA Center, a summary of the goal, the AI techniques being applied, information on training data, and information on source code.

Artificial Intelligence↗

NASA NTRSDate not supplied

A Strategic Approach for Dense, Integrated, Vehicle Navigation

Drone usage has been on the rise in recent years with applications that include parcel delivery, wildlife protection, precision farming, law enforcement, and industrial inspection, just to name a few. Once regulations and safety policies are put in place to allow for the widespread use of unmanned drones, the number of aircraft in the National Airspace System (NAS) is expected to skyrocket to millions, potentially congesting the airspace which increases the likelihood of separation violations and possibly incidents. Currently, flight infrastructure can only support a few thousand aircraft flying over the United States National Airspace System (NAS) at any given time. A delay at one airport can send ripple effects throughout the system, causing more delays and missed connections. In air traffic control, separation is the concept of keeping an “ownship” aircraft outside a minimum distance from “intruder” aircraft to reduce the risk of the aircraft colliding, as well as preventing accidents due to secondary factors, such as wake turbulence. Maintaining proper separation is often a safety critical property for fixed-wing drones in the airspace. This paper addresses drone separation in time and distance for high volume corridors (en route) and lanes (on ground), merging as well as crossing intersections of multiple corridors/lanes. In this paper, the term drone is applied to both Unmanned Aerial Vehicle (UAV) and small Unmanned Aircraft System (UAS) vehicles operating autonomously. There exists a gamut of approaches to the merging and crossing problems. At one end of the extreme are the conservative yet low cost and verifiable solutions of today that deal with two drones at a time. At the other end are complex Machine Learning-based solutions with high computing requirements for fully autonomous drones of the future that are expected to handle all contentions. This paper presents a feasible and verifiable strategic approach to these problems that is based on distributed cooperation between the drones and the infrastructure. Three phases of the strategic approach (Prepare, Adjust, Commit) are presented. Simulation results are presented that show the proposed approach is stable and resilient to induced perturbations and guarantees a set of fixed-wing drones to merge and cross intersections by adjusting their speed based on their distance to the aircraft in front of them while remaining in the equilibrium state. The equilibrium state is defined as the state when a set of n aircraft move at a relatively constant speed and uniform spacing from each other in a congested system. A congested system is defined as the state when at least one aircraft cannot move at its maximum allowed speed. Unlike existing centralized and pre-planned approaches, the proposed solution is fully distributed and enables autonomous aircraft to decide to adjust their speed and distance with respect to the preceding aircraft, dynamically. Simulation results are presented that assess the feasibility of the approach.

Distributed↗

NASA NTRSDate not supplied

A Strategic Approach for Dense, Integrated, Vehicle Navigation

Drone usage has been on the rise in recent years with applications that include parcel delivery, wildlife protection, precision farming, law enforcement, and industrial inspection, just to name a few. Once regulations and safety policies are put in place to allow for the widespread use of unmanned drones, the number of aircraft in the National Airspace System (NAS) is expected to skyrocket to millions, potentially congesting the airspace which increases the likelihood of separation violations and possibly incidents. Currently, flight infrastructure can only support a few thousand aircraft flying over the United States National Airspace System (NAS) at any given time. A delay at one airport can send ripple effects throughout the system, causing more delays and missed connections. In air traffic control, separation is the concept of keeping an “ownship” aircraft outside a minimum distance from “intruder” aircraft to reduce the risk of the aircraft colliding, as well as preventing accidents due to secondary factors, such as wake turbulence. Maintaining proper separation is often a safety critical property for fixed-wing drones in the airspace. This paper addresses drone separation in time and distance for high volume corridors (en route) and lanes (on ground), merging as well as crossing intersections of multiple corridors/lanes. In this paper, the term drone is applied to both Unmanned Aerial Vehicle (UAV) and small Unmanned Aircraft System (UAS) vehicles operating autonomously. There exists a gamut of approaches to the merging and crossing problems. At one end of the extreme are the conservative yet low cost and verifiable solutions of today that deal with two drones at a time. At the other end are complex Machine Learning-based solutions with high computing requirements for fully autonomous drones of the future that are expected to handle all contentions. This paper presents a feasible and verifiable strategic approach to these problems that is based on distributed cooperation between the drones and the infrastructure. Three phases of the strategic approach (Prepare, Adjust, Commit) are presented. Simulation results are presented that show the proposed approach is stable and resilient to induced perturbations and guarantees a set of fixed-wing drones to merge and cross intersections by adjusting their speed based on their distance to the aircraft in front of them while remaining in the equilibrium state. The equilibrium state is defined as the state when a set of n aircraft move at a relatively constant speed and uniform spacing from each other in a congested system. A congested system is defined as the state when at least one aircraft cannot move at its maximum allowed speed. Unlike existing centralized and pre-planned approaches, the proposed solution is fully distributed and enables autonomous aircraft to decide to adjust their speed and distance with respect to the preceding aircraft, dynamically. Simulation results are presented that assess the feasibility of the approach.

Distributed↗

NASA NTRSDate not supplied

A Strategic Approach for Dense, Integrated, Vehicle Navigation

Drone usage has been on the rise in recent years with applications that include parcel delivery, wildlife protection, precision farming, law enforcement, and industrial inspection, just to name a few. This paper addresses drone separation in time and distance for high volume corridors (en route) and lanes (on ground), merging as well as crossing intersections of multiple corridors/lanes. There exists a gamut of approaches to solving merging and intersection crossing problems. At one end of the extreme are the conservative yet low cost and verifiable solutions of today that deal with two drones at a time. At the other end are complex Machine Learning-based solutions with high computing requirements for fully autonomous drones of the future that are expected to handle all contentions. This paper presents a feasible and verifiable strategic approach to solving these problems that is based on distributed cooperation between the UAVs/UASs and the infrastructure. Unlike existing centralized and pre-planned approaches, the proposed solution is fully distributed and enables autonomous aircraft to decide to adjust their speed and distance with respect to the preceding aircraft, dynamically. Three phases of the strategic approach (Prepare, Adjust, Commit) are presented. Simulation results are presented that show the proposed approach is stable and resilient to induced perturbations and guarantees a set of fixed-wing UAVs/UASs to merge and cross intersections by adjusting their speed based on their distance to the aircraft in front of them.

Distributed↗

NASA NTRS2023

NASA's Responsible AI Use Cases

This submission consists of summary use cases for NASA's Responsible Artificial Intelligence (RAI). These RAI Use Cases are to be made public, pursuant to the Presidential Executive Order 13960, Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government. They were collected from NASA's practicing AI research community and cover the gamut of NASA's AI activities. These Use Cases will be updated annually as required by the Executive Order. The information included consists of the NASA Center, a summary of the goal, the AI techniques being applied, information on training data, and information on source code.

Artificial Intelligence↗