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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
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 NTRSDate not supplied

Process drivers, inter-model spread, and a path forward: What do we know about Arctic Amplification?

Over the recent decades, rapid changes have been observed in the Arctic resulting from enhanced surface warming in the Arctic relative to the rest of the globe, a phenomenon called Arctic Amplification. While surface-based amplification of Arctic warming in response to increased CO2 has been established. The driving mechanisms, relevant processes, and the relative roles of local and remote mechanisms to Arctic Amplification remain unclear. Research over the last decade has made it clear that coupling between the atmosphere, sea ice, and ocean play a key role in Arctic Amplification. This presentation summarizes recent research on the fundamental role that atmosphere-sea ice-ocean interactions and energy exchanges play in driving Arctic Amplification, how these interactions manifest as radiative climate feedbacks, and how these processes influence the differences in the projected Arctic climate change across CMIP5 and 6. We hypothesize that models that simulate larger warming do so by generating a larger local atmospheric circulation response that more effectively redistributes energy drawn from the ocean in sea ice retreat regions Arctic-wide and contributing to a larger increase in downwelling long wave radiation. This process is also fundamentally tied to the Arctic lapse rate feedback.

Arctic amplification↗
NASA NTRSDate not supplied

Process drivers, inter-model spread, and a path forward: What do we know about Arctic Amplification?

Over the recent decades, rapid changes have been observed in the Arctic resulting from enhanced surface warming in the Arctic relative to the rest of the globe, a phenomenon called Arctic Amplification. While surface-based amplification of Arctic warming in response to increased CO2 has been established. The driving mechanisms, relevant processes, and the relative roles of local and remote mechanisms to Arctic Amplification remain unclear. Research over the last decade has made it clear that coupling between the atmosphere, sea ice, and ocean play a key role in Arctic Amplification. This presentation summarizes recent research on the fundamental role that atmosphere-sea ice-ocean interactions and energy exchanges play in driving Arctic Amplification, how these interactions manifest as radiative climate feedbacks, and how these processes influence the differences in the projected Arctic climate change across CMIP5 and 6. We hypothesize that models that simulate larger warming do so by generating a larger local atmospheric circulation response that more effectively redistributes energy drawn from the ocean in sea ice retreat regions Arctic-wide and contributing to a larger increase in downwelling long wave radiation. This process is also fundamentally tied to the Arctic lapse rate feedback.

Patrick C. Taylor↗
NASA NTRSDate not supplied

Machine Learning Explainability and Transferability for Path Navigation

Deep neural networks are powerful tools for machine perception. Unfortunately their decisions are difficult to explain due to the complexity and size of the networks. Previously we have alleviated this issue by using the representational portion of a deep neural network and combining it with a k-nearest neighbor (KNN) classifier. Through inspection of the decisions made by the KNN, we can directly see the training data responsible for the decisions, allowing us to determine the quality of the overall decision and the quality of the representational layer of the deep NN. While the technique worked well, it requires tens of thousands of latent vectors to be stored for classification. In addition, it lacks the ability to show how parts of an image influence the classification decision. Here we address these issues by 1) Using a radial basis function network (RBFN) in place of the KNN allowing far fewer images to be used in deployment and 2) Using an auto encoder network for explainability. In addition to these techniques, we examine the effects of transfer learning to determine that results are robust. All results are tested on a domain where an unmanned aerial vehicle (UAV) navigates a forest trail through a single camera.

explainable machine learning↗
NASA NTRS2001

45 Km horizontal path optical link demonstrations

Observations made during a mountain-top-to-mountain-top horizontal optical link demonstration are described. The optical link spans a range of 45 Km at an average altitude of 2 Km above sea level. A multi-beam beacon comprised of eight laser beams emerging from multi-mode fiber coupled lasers (780 nm) are launched through a 0.6 m diameter telescope located at the JPL Table Mountain Facility (TMF) in Wrightwood, California. The multibeam beacon is received at Strawberry Peak located in the San Bernadino Mountains of California. The NASA, JPL developed optical communications demonstrator (OCD) receives the beacon, senses the atmosphere induced motion and using an upgraded fine steering loop compensates the re-transmission of a communications laser beam (852 nm, 400 Mbps) to TMF. The eight-beam beacon allowed a four reduction in normalized irradiance or scintillation index. This in turn was sufficient to eliminate beacon fades sensed b the OCD and allowed the fine steering loop performance evaluation. The uncompensated tracking error was determined to be 2.2 - 3 prad compared to a model prediction of 3.4 prad. The paper also discusses various auxiliary observations made during the campaign

Page, Norman A.↗
NASA NTRS2018

Juno at Jupiter: The Mission and Its Path to Unveiling Secrets of the History of the Solar System

The Juno mission is described, focusing on its orbits at Jupiter, how the plan evolved, and science return so far. Juno is a NASA New Frontiers spacecraft in a near-polar highly elliptical 53-day orbit at Jupiter. Since arrival in July 2016, it has used 9 science investigations to study the planet’s atmospheric composition and structure, magnetic and gravity fields, and polar and extended magnetosphere. A radiation monitoring investigation contributes to our understanding of Jupiter’s environment. Juno’s primary science goal is to understand the origin and evolution of Jupiter, to shed light on how the Earth and other planets formed. Baseline objectives will be satisfied with 32 science orbits, a spin-stabilized solar powered spacecraft, an electronics vault for radiation shielding, and a robust payload with microwave receivers, X- and Ka-band radio science hardware, vector magnetometers, high- and low-energy charged particle detectors, radio and plasma wave antennas, UV and IR spectroscopic imagers, and a visible light camera for public outreach. Observations are made in a limited number of orientations, including Gravity Science (spin axis and main antenna pointing to Earth), and microwave atmospheric sounding (spin plane passing through Jupiter’s center). Prime science data are collected near closest approach (perijove), plus calibrations, occasional remote sensing, and continued magnetospheric observations in the outer parts of the orbit. Juno’s mission plan has evolved since the 2005 proposal due to design and ops choices, e.g., mission design (cruise or early orbital trajectory), orbit period (11, 14, then 53 days), perijove attitudes (2 or more), and DSN coverage (34- and 70-m stations). Choices were partly motivated by the effect on science return. Selected preliminary science results are summarized, including the benefits of decisions as the plan evolved. Juno has begun to unveil Jupiter – peeling apart its interior by measuring gravity and magnetic fields, using microwaves to probe its atmosphere down to 100s of km, exploring its polar and extended magnetosphere, and imaging the poles for the first time. In doing so, it is revealing secrets of the history of the Earth and solar system.

Stephens, Stuart K.↗