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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Lessons Learned in Fabrication of a High-Specific-Torque Concentric Magnetic Gear

Magnetic gearing is being investigated at NASA as a replacement to conventional mechanical gearing in aerospace applications. Some potential benefits of magnetic gears over mechanical gearing are torque transmission without mechanical contact, decreased transmission noise, and no required lubrication. However, in order to be a viable alternative for aerospace applications, magnetic gearing must be shown to provide high enough specific torque (torque per unit mass). NASA's second magnetic gearing prototype (PT-2) was able to achieve promising specific torque on par with low torque mechanical gearboxes. This work will briefly review the electromagnetic and structural design of PT-2, provide detailed information on fabrication and assembly, examine build errors, walk through rebuild efforts to improve operation, and conclude with remarks on build difficulties and opportunities for improvement in future prototypes.

Magnetic Gearing↗
NASA NTRS2019

NASA Undergraduate Student Instrument (USIP) Project--Lessons Learned

NASA offers real-world experiences, with the goal of developing students’ skills and capabilities in science, technology, engineering and math, skills critical to building a STEM-literate workforce and achieving the nation's exploration goals. In 2016, the NASA Science Mission Directorate (SMD) and the Office of Education National Space Grant Program (OE Space Grant) awarded $8 million through the Undergraduate Student Instrument Project (USIP) to 47 student teams to conduct hands-on research with 23 of the projects being CubeSat projects. The USIP student teams proposed science or technology experiments that are relevant to NASA missions, and the platform to fly their payload. The platforms include sounding rockets, scientific and hand-held balloons, aircraft, suborbital reusable and commercial launch vehicles, and CubeSats launched as a secondary payload on an orbital vehicle. The mission management for USIP was the responsibility of NASA Goddard Space Flight Centers Wallops Flight Facility (WFF) in Virginia. The university faculty supporting USIP attest to the benefits students are receiving in the hands-on aspects of the projects, as well as the real-world problem resolution. The USIP university teams are receiving an authentic NASA educational experience that will encourage the students to be part of NASA’s or the Nations’ future workforce and leaders.

Winterton, Joyce L.↗
NASA NTRS2019

Benefits and Challenges of Model-based Software Engineering: Lessons Learned based on Qualitative and Quantitative Findings

Even though Model-based Software Engineering (MBSwE) techniques and Autogenerated Code (AGC) have been increasingly used to produce complex software systems, there is only anecdotal knowledge about the state-of-thepractice. Furthermore, there is a lack of empirical studies that explore the potential quality improvements due to the use of these techniques. This paper presents in-depth qualitative findings about development and Software Assurance (SWA) practices and detailed quantitative analysis of software bug reports of a NASA mission that used MBSwE and AGC. The mission’s flight software is a combination of handwritten code and AGC developed by two different approaches: one based on state chart models (AGC-M) and another on specification dictionaries (AGC-D). The empirical analysis of fault proneness is based on 380 closed bug reports created by software developers. Our main findings include: (1) MBSwE and AGC provide some benefits, but also impose challenges. (2) SWA done only at a model level is not sufficient. AGC code should also be tested and the models and AGC should always be kept in-sync. AGC must not be changed manually. (3) Fixes made to address an individual bug report were spread both across multiple modules and across multiple files. On average, for each bug report 1.4 modules, that is, 3.4 files were fixed. (4) Most bug reports led to changes in more than one type of file. The majority of changes to auto-generated source code files were made in conjunction to changes in either file with state chart models or XML files derived from dictionaries. (5) For newly developed files, AGC-M and handwritten code were of similar quality, while AGC-D files were the least fault prone.

Goseva-Popstojanova, Katerina↗
NASA NTRS2020

Enabled International Collaborations in EDL: Lessons Learned and Recommendations

The modern day National Airspace System (NAS) is powered by System Wide Information Management (SWIM) which is a real-time digital data sharing infrastructure that provides a high fidelity view of the lifecycle of a flight. The newly available data within the SWIM feeds can be leveraged to help drive efficiencies in the NAS. In this talk, we investigate the gate conflict prediction problem as a concrete use case which could help drive efficiencies. We begin with a high level description of NASA's Airspace Technology Demonstration 2 which is built upon the real-time SWIM feeds and produces the data used in our investigation. We model gate conflicts as a regression problem and describe the iterative process of model building, model validation, and evaluation used to assess the efficacy of our approach.

William J. Coupe↗
NASA NTRSDate not supplied

Lessons Learned in the Application of Machine Learning Techniques to Air Traffic Management

There is an increasing interest in applying methods based on Machine Learning Techniques (MLT) to problems in Air Traffic Management (ATM). The current interest is based on developments in Cloud Computing, the availability of open software and the success of MLT in automation, consumer behavior and finance involving large databases. This paper reviews the current-state-of-the art in applying MLT to aviation operations, its promises and challenges. Historically aviation operations have been analyzed using physics-based models and provide information for making operational decisions. Aviation operations involving many decision makers, multiple objectives, poor or unavailable physics-based models and a rich historical database are prime candidates for analysis using data-driven methods. The promises and challenges in applying MLT to ATM is traced through three examples based on the authors’ experience, each separated by a decade, to show the influence of data and feature selection in the successful application of MLT to ATM. As always, the best approach depends on the task, the physical understanding of the problem and the quality and quantity of the available data.

Machine Learning Techniques↗
NASA NTRS2020

Air Quality Monitoring Case Study Using Mobile Low-cost Sensors Mounted on Trash-Trucks: Methods Development and Lessons Learned

Air quality monitoring (AQM) is crucial for cities to develop management plans supporting population health. However, there is a dearth of measurements due to the high cost of standard reference instruments. Mobile AQM using low-cost sensors deployed on routine fleets of vehicles can enable the continuous detection of fine-scale pollutant variations in cities at a lower cost. New methods need to be developed to interpret these measurements. This paper presents three such methods. First, we propose a technique to identify aerosol hotspots. Second, we employ techniques published previously to assess the generalizable map of fine and coarse particle number concentrations, to understand qualitatively the contribution of local and regional sources across the region sampled. By using the raw number concentration of differently sized particles from the Optical Particle Counters (OPCs) instead of the noisier mass concentrations, we obtain more robust results. Third, in order to evaluate source signatures in cities, we propose another technique, in which we cluster the entire range of aerosol size-distribution measurements acquired. The properties of each cluster provide insight into the aerosol source characteristics in the sampling environment. We test these methods using a dataset we collected by mounting OPCs on two trash-trucks in Cambridge, Massachusetts.

Drive-by sensing↗
NASA NTRS2020

Air Quality Monitoring Case Study Using Mobile Low-cost Sensors mounted on Trash-Trucks: Methods Development and Lessons Learned

Air quality monitoring (AQM) is crucial for cities to develop management plans supporting population health. However, there is a dearth of measurements due to the high cost of standard reference instruments. Mobile AQM using low-cost sensors deployed on routine fleets of vehicles can enable the continuous detection of fine-scale pollutant variations in cities at a lower cost. New methods need to be developed to interpret these measurements. This paper presents three such methods. First, we propose a technique to identify aerosol hotspots. Second, we employ techniques published previously to assess the generalizable map of fine and coarse particle number concentrations, to understand qualitatively the contribution of local and regional sources across the region sampled. By using the raw number concentration of differently sized particles from the Optical Particle Counters (OPCs) instead of the noisier mass concentrations, we obtain more robust results. Third, in order to evaluate source signatures in cities, we propose another technique, in which we cluster the entire range of aerosol size-distribution measurements acquired. The properties of each cluster provide insight into the aerosol source characteristics in the sampling environment. We test these methods using a dataset we collected by mounting OPCs on two trash-trucks in Cambridge, Massachusetts.

Drive-by sensing↗
NASA NTRSDate not supplied

Demonstration of A Long Duration Crop in the Advanced Plant Habitat Engineering Demonstration Unit: Key Factors to Consider Prior to Testing and Lessons Learned

The Advanced Plant Habitat (APH) is a fully enclosed, closed-loop, plant growth facility currently deployed on the International Space Station (ISS). It is equipped with complete environmental control (i.e.spectral qualityandlight intensity, temperature, relative humidity, and CO2),root module which containsa growth substrate(typically arcillite) and a porous tube based automated water delivery system. Two APH units, one ground unitidentical to the on-orbit versionand an Engineering Development Unit (EDU; a high fidelity test version), currently reside at Kennedy Space Center. Prior to conducting tests on-orbit, these APH ground units areutilized to conductbothScience Verification Tests(SVT; primarilyinthe EDU) and Experiment Verification Tests (EVT; primarily in the Ground Unit). In February 2020, a 122 day experiment was concluded in the APH EDU where four 'Numex Española Improved' pepper plants (Capsicum annuum) were successfully grown from seed to harvestyieldingover 50 fruit, demonstrating the capabilityand versatilityof this APH EDU system to support plant growth for long life cycle flowering/fruiting plants.

L. E. Spencer↗