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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Technology Transfer: Marketing Tomorrow's Technology

The globalization of the economy and the end of the Cold War have triggered many changes in the traditional practices of U.S. industry. To effectively apply the resources available to the United States, the federal government has firmly advocated a policy of technology transfer between private industry and government labs, in this case the National Aeronautics and Space Administration (NASA). NASA Administrator Daniel Goldin is a strong proponent of this policy and has organized technology transfer or commercialization programs at each of the NASA field centers. Here at Langley Research Center, the Technology Applications Group (TAG) is responsible for facilitating the transfer of Langley developed research and technology to U.S. industry. Entering the program, I had many objectives for my summer research with TAG. Certainly, I wanted to gain a more thorough understanding of the concept of technology transfer and Langley's implementation of a system to promote it to both the Langley community and the community at large. Also, I hoped to become more familiar with Langley's research capabilities and technology inventory available to the public. More specifically, I wanted to learn about the technology transfer process at Langley. Because my mentor is a member of Materials and Manufacturing marketing sector of the Technology Transfer Team, another overriding objective for my research was to take advantage of his work and experience in materials research to learn about the Advanced Materials Research agency wide and help market these developments to private industry. Through the various projects I have been assigned to work on in TAG, I have successfully satisfied the majority of these objectives. Work on the Problem Statement Process for TAG as well as the development of the Advanced Materials Research Brochure have provided me with the opportunity to learn about the technology transfer process from the outside looking in and the inside looking out. Because TAG covers all of the research efforts conducted at Langley, my studies with TAG were ab!e to provide me an excellent overview of Langley's contribution to the aeronautics industry.

Tcheng, Erene↗
NASA NTRS2021

Adaptive Data Screening for Multi-Angle Polarimetric Aerosol and Ocean Color Remote Sensing Accelerated by Automatic Differentiation

Remote sensing measurements from multi-angle polarimeters (MAPs) contain rich aerosol microphysical property information, and these sensors have been used to perform retrievals in optically complex atmosphere and ocean systems. Previous studies have concluded that, generally, five moderately separated viewing angles in each spectral band provide sufficient accuracy for aerosol property retrievals, with performance gradually saturating as angles are added above that threshold. The Hyper-Angular Rainbow Polarimeter (HARP) instruments provide high angular sampling with a total of 90-120 unique angles across four bands, a capability developed mainly for liquid cloud retrievals. In practice, not all view angles are optimal for aerosol retrievals due to impacts of clouds, sun glint, and other impediments. The many viewing angles of HARP can provide resilience to these effects, if the impacted views are screened from the dataset, as the remaining views may be sufficient for successful analysis. In this study, we discuss how the number of available viewing angles impacts aerosol and ocean color retrieval uncertainties, as applied to two versions of the HARP instrument. AirHARP is an airborne prototype that was deployed in the ACEPOL field campaign, while HARP2 is an instrument in development for the upcoming NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission. Based on synthetic data, we find that a total of 20-30 angles across all bands (i.e. five to eight viewing angles per band) are sufficient to achieve good retrieval performance. Following from this result, we develop an adaptive multi-angle polarimetric data screening (MAPDS) approach to evaluate data quality by comparing measurements with their best-fitted forward model. The FastMAPOL retrieval algorithm is used to retrieve scene geophysical values, by matching an efficient, deep learning-based, radiative transfer emulator to observations. The data screening method effectively identifies and removes viewing angles affected by thin cirrus clouds and other anomalies, improving retrieval performance. This was tested with AirHARP data, and we found agreement with the High Spectral Resolution Lidar-2 (HSRL-2) aerosol data. The data screening approach can be applied to modern satellite remote sensing missions, such as PACE, where a large amount of multi-angle, hyperspectral, polarimetric measurements will be collected.

multi-angle polarimeter↗
NASA NTRS2010

An Overview of My Internship with the Ecological Program at John F. Kennedy Space Center

During my internship with Innovative Health Applications, I participated in numerous longterm research projects involving the study of various plant and animal life at the Kennedy Space Center (KSC). I observed the monitoring of nesting sea turtles. I learned about the transfer of egg clutches from the northern Gulf Coast in an effort to help the hatchlings avoid the oil spill in the Gulf of Mexico. I gained knowledge of tracking the movements of important sport fish and sharks in this area using a hydro-acoustic tag and receiver system. This effort included routinely taking water quality data at multiple sites around KSC. Alligator population and nesting assessments was another part of my internship. I observed the biologists take morphometric measurements, blood, urine and tissue samples from alligators found in KSC waterways. I assisted in taking photosynthesis and reflectance measurements on various scrub oaks and palmettos. I participated in Florida Scrub-Jay surveys in an effort to monitor their population trends and was involved in Southeastern beach mouse trapping and identification. I also assisted in seagrass surveys monitoring the health of the seagrass beds.

Owen, Samantha↗
NASA NTRS2016

Design and Execution of Dawn HAMO to LAMO Transfer at Ceres

On October 23, 2015, the Dawn spacecraft left the High Altitude Mapping Orbit (HAMO) around Ceres and began its final decent to the Low Altitude Mapping Orbit (LAMO), arriving on December 15. The transfer between the two science orbits, a tight spiraling trajectory with over 100 revolutions, required the operations team to perform weekly maneuver designs for a period of 50 days. While the first six weeks of the transfer executed as planned, unexpectedly the spacecraft incurred a multi-sigma delivery error to the final science orbit that was subsequently clean-up at the first orbit maintenance maneuver. In this paper we discuss the design architecture for the transfer in detail, including challenges the team faced in flying the transfer and lessons learned.

Grebow, Daniel J.↗
NASA NTRS2016

Design and Execution of Dawn HAMO to LAMO Transfer at Ceres

On October 23, 2015, the Dawn spacecraft left the High Altitude Mapping Orbit (HAMO) around Ceres and began its final decent to the Low Altitude Mapping Orbit (LAMO), arriving on December 15. The transfer between the two science orbits, a tight spiraling trajectory with over 100 revolutions, required the operations team to perform weekly maneuver designs for a period of 50 days. While the first six weeks of the transfer executed as planned, unexpectedly the spacecraft incurred a multi-sigma delivery error to the final science orbit that was subsequently clean-up at the first orbit maintenance maneuver. In this paper we discuss the design architecture for the transfer in detail, including challenges the team faced in flying the transfer and lessons learned.

Grebow, Dan↗
NASA NTRS2018

New Neural Network Cloud Mask Algorithm Based on Radiative Transfer Simulations

Cloud detection and screening constitute critically important first steps required to derive many satellite data products. Traditional threshold-based cloud mask algorithms require a complicated design process and fine tuning for each sensor, and they have difficulties over areas partially covered with snow/ice. Exploiting advances in machine learning techniques and radiative transfer modeling of coupled environmental systems, we have developed a new, threshold-free cloud mask algorithm based on a neural network classifier driven by extensive radiative transfer simulations. Statistical validation results obtained by using collocated CALIOP and MODIS data show that its performance is consistent over different ecosystems and significantly better than the MODIS Cloud Mask (MOD35 C6) during the winter seasons over snow-covered areas in the mid-latitudes. Simulations using a reduced number of satellite channels also show satisfactory results, indicating its flexibility to be configured for different sensors. Comparedto threshold-based methods and previous machine-learning approaches, this new cloud mask (i) does not rely on thresholds, (ii) needs fewer satellite channels, (iii) has superior performance during winter seasons in mid-latitude areas, and (iv) can easily be applied to different sensors.

cloud mask algorithms↗
NASA NTRSDate not supplied

Exploring Transfers Between Earth-Moon Halo Orbits via Multi-Objective Reinforcement Learning

Multi-Reward Proximal Policy Optimization, a multi-objective deep reinforcement learning algorithm, is used to examine the design space of low-thrust trajectories for a SmallSat transferring between two libration point orbits in the Earth-Moon system. Using Multi-Reward Proximal Policy Optimization, multiple policies are simultaneously and efficiently trained on three distinct trajectory design scenarios. Each policy is trained to create a unique control scheme based on the trajectory design scenario and assigned reward function: a unique combination of weights scaling competing objectives that guide the spacecraft to the target mission orbit, incentivize faster flight times, and penalize propellant mass usage. Then, the policies are evaluated on the same set of perturbed initial conditions in each scenario to generate the propellant mass usages, flight times, and state discontinuities from a reference trajectory for each control scheme. This solution space of low-thrust trajectories for a SmallSat is used to examine the multi-objective trade space for the trajectory design scenario. By autonomously constructing the solution space, insights into the required propellant mass, flight time, and transfer geometry are rapidly achieved.

Christopher J Sullivan↗
NASA NTRS2021

Exploring Transfers Between Earth-Moon Halo Orbits via Multi-Objective Reinforcement Learning

Multi-Reward Proximal Policy Optimization, a multi-objective deep reinforcement learning algorithm, is used to examine the design space of low-thrust trajectories for a SmallSat transferring between two libration point orbits in the Earth- Moon system. Using Multi-Reward Proximal Policy Optimiza- tion, multiple policies are simultaneously and efficiently trained on three distinct trajectory design scenarios. Each policy is trained to create a unique control scheme based on the trajectory design scenario and assigned reward function: a unique combination of weights scaling competing objectives that guide the spacecraft to the target mission orbit, incentivize faster flight times, and penalize propellant mass usage. Then, the policies are evaluated on the same set of perturbed initial conditions in each scenario to generate the propellant mass usages, flight times, and state discontinuities from a reference trajectory for each control scheme. This solution space of low-thrust trajectories for a SmallSat is used to examine the multi-objective trade space for the trajectory design scenario. By autonomously constructing the solution space, insights into the required propellant mass, flight time, and transfer geometry are rapidly achieved.

Mashiku, Alinda K.↗
NASA NTRS1985

IPAD: A unique approach to government/industry cooperation for technology development and transfer

A key element to improved industry productivity is effective management of Computer Aided Design / Computer Aided Manufacturing (CAD/CAM) information. To stimulate advancement, a unique joint government/industry project designated Integrated Programs for Aerospace-Vehicle Design (IPAD) was carried out from 1971 to 1984. The goal was to raise aerospace industry productivity through advancement of computer based technology to integrate and manage information involved in the design and manufacturing process. IPAD research was guided by an Industry Technical Advisory Board (ITAB) composed of over 100 representatives from aerospace and computer companies. The project complemented traditional NASA/DOD research to develop aerospace design technology and the Air Force's Integrated Computer Aided Manufacturing (ICAM) program to advance CAM technology. IPAD had unprecedented industry support and involvement and served as a unique approach to government industry cooperation in the development and transfer of advanced technology. The IPAD project background, approach, accomplishments, industry involvement, technology transfer mechanisms and lessons learned are summarized.

Fulton, Robert E.↗
NASA NTRS1992

An evaluation of training effectiveness of an intelligent tutoring system

The study evaluated the training effectiveness of an intelligent tutoring system (ITS) for the Remote Manipulator System (RMS). The study examined how well individuals learn the training content and skills from the RMS ITS and to what extent the content and skills learned using the ITS transfer to RMS task performance in the SES, a high fidelity simulator. Three astronauts completed 8 2-hour ITS sessions addressing movement in three coordinate systems, grapple, ungrapple, berth, and unberth procedures, and singularities and reach limits. Their performance was also observed in an SES training session. Performance data were collected using multiple measures: ITS task performance, transfer performance on the SES, a conceptual knowledge test, an opinion survey completed by astronauts, and comments and observations from astronauts and trainers. Results indicated the RMS ITS to be moderately effective and provided evidence of the efficacy of ITS's, in general. Comments and suggestions are provided relating to how the ITS could be improved and to enable decision makers to judge the effectiveness of the RMS ITS.

Johnson, Debra Steele↗
NASA NTRSDate not supplied

Exploring Informal Learning at the Airlines

Airline pilot training is extensive, highly structured, and defined by aircraft and airspace system operating requirements, yet pilots describe a tradition of between-pilot knowledge transfer and self-directed learning. This learning supplements their approved training programs. While industry and regulators focus on “formal learning” systems, pilots report relying on “informal learning” to build operational expertise. The persistence of informal learning suggests gaps in how successfully formal learning prepares pilots to handle operational complexities. The community that researches learning has extensively studied informal learning, and its characteristics seem to align with how pilots report increasing their skills and knowledge informally. However, no research into informal learning practices among airline pilots seems to exist. In this paper we provide examples of informal learning in commercial aviation, how they fit into two existing frameworks for workplace learning, and propose that researching informal learning might help identify opportunities to improve formal aviation learning systems.

pilot learning↗
NASA NTRS2018

Systems and Methods for Transfer Function Estimation Using Membership Functions

An apparatus, method, and computer program that can learn a linear or non-linear transfer function are disclosed. In one embodiment, for example, a computer-implemented method may include creating a plurality of combinations for at least one input, and calculating an output for the plurality of combinations. The method may also include calculating an error based on the calculated output.

Krasowski, Michael J↗
NASA NTRS1972

General and specific factors in the intersensory transfer of form.

This study assessed the relative contributions of specific and nonspecific components to intersensory transfer between vision and touch. A paired-associate paradigm was used in which visual metric figures and their tactual analogs served as stimuli, and familiar adjectives were the responses. Positive intersensory transfer, characterized by symmetry across modalities was obtained. The contribution of nonspecific learning to this effect was negligible. Intersensory transfer was found to be less efficient than the empirically determined maximum level of intrasensory transfer possible in this task.

Clark, J. L.↗
NASA NTRS1990

The effects of high free-stream turbulence on heat transfer in turbulent boundary layer

A series of heat transfer measurements utilizing a variety of flows, each characterized by a high level of turbulence, is evaluated. This research is conducted to aid in predicting the heat transfer of first stage turbine blades and vanes. Instead of establishing a particular high turbulence flow field and measuring its effect on heat transfer, a set of flow fields with very aggressive heat transfer characteristics was determined in order to learn which aspects of these flows were responsible for the high heat transfer. Results of these tests were utilized to identify the most aggressive heat transfer environments, and then the turbulence properties of those flows were studied.

Maciejewski, Paul K.↗
NASA NTRSDate not supplied

Pushing the Limits of Aquatic Remote Sensing: Synthetic Data and Deep Learning for Fast Inverse Emulation of A Coupled Ocean-Atmosphere Radiative Transfer Model

The inversion of electromagnetic information to physical and biological properties of the water column is a notoriously difficult problem, yet fundamental to our ability of understanding aquatic processes on large time and space scales. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This presentation will discuss research integrating advanced biological and radiative modeling, high-end computation, and machine learning to develop a portable global processor for simultaneous retrieval of atmosphere and water optics for diverse aquatic systems from the open and coastal ocean to optically extreme inland waters and harmful algal blooms. We will discuss some of the basic concepts behind the forward modeling approach including DEAP, the novel Distributed Equivalent Algal Populations model, for developing large spectral libraries of aquatic particle optics to aid in our ability to distinguish phytoplankton functional types (PFTs) and inorganic material, as well as other factors which enable comprehensive modeling from the benthos to top-of-atmosphere (TOA). This information is being used to understand how we can leverage next-generation deep learning methods for maximum information retrieval and rapid image processing, while also providing capabilities to identify minimum sensor spectral requirements necessary for certain aquatic applications. Further, I will touch on how we envision this research to enable the aquatic community for science discovery and how we are moving closer towards the capability for high-fidelity global analysis of aquatic ecosystems.

Jeremy Alan Kravitz↗