Virginia Space Grant Consortium

taught for 15 years in the secondary and collegiate arena.During her years in the secondary arena, she worked on numerous curriculum committees aligning the Standards of Learning (SOLs) to the Newport News Public Schools pacing

NASA's Aeronautics Test Program (ATP) is responsible for many large, high-energy ground test facilities that accomplish the nation s most advanced aerospace research. In order to accomplish these national objectives, significant energy and resources are consumed. A select group of facilities was analyzed using life-cycle assessment (LCA) to determine carbon footprint and environmental impacts. Most of these impacts stem from electricity and natural gas consumption, used directly at the facility and to generate support processes such as compressed air and steam. Other activities were analyzed but determined to be smaller in scale and frequency with relatively negligible environmental impacts. More specialized facilities use R-134a, R-14, jet fuels, or nitrogen gas, and these unique inputs can have a considerable effect on a facility s overall environmental impact. The results of this LCA will be useful to ATP and NASA as the nation looks to identify its top energy consumers and NASA looks to maximize research output and minimize environmental impact. Keywords: NASA, Aeronautics, Wind tunnel, Keyword 4, Keyword 5

Mountain snow fields act as natural storage basins in areas that rely on snowmelt, and by understanding the long-term trends of these systems, alongside the spatial component, we can make more informed decisions on their management and conservation. However, current flight intervals make the production of continuous airborne data limited. Our aim is to demonstrate the necessity of more temporal analysis and modeling of Snow water equivalent (SWE) for the management and conservation of water resources from the Tuolumne River Basin, and globally, through Time Series Forecasting and Uni/Multivariate analysis. In association with NASA SnowEx, we aim to create two Long-Short-Term Memory (LSTM) regression models to predict SWE, by itself and how it changes based on Snow Depth and Snow Density. The relevant raster data sets were collected by the ASO (Airborne Snow Observatory), and obtained from the NSIDC (National Snow and Ice Data Center).

Summer programs are the latest trend in extracurricular STEM education programs offered by universities. Efforts are made towards residential summer programs, which have the ability to expose students not only to specially designed STEM activities but to the university campus environment and student life, as well. These types of programs are expected to have better success in getting students engaged and to capture their interest in STEM fields. This paper presents one example of designing and implementing a summer residential workshop in order to expose high school students to the field of engineering technology, specifically to electrical circuits, electrical prototyping, microprocessor based design, sensing and measuring the environment, and the Internet of Things. The camp includes other workshops that are focusing on other areas of STEM, specifically science and mathematics. The paper presents the workshop setting, the activities organized, and the feedback received from students.

This paper presents the development and delivery of educational summer intensive programs for high school students that are designed to encourage students’ interests in the STEM-related fields and the motivation to pursue a STEM-related degrees in college. BLAST (Building Leaders to Advance Science and Technology) is designed as a summer-intensive, residential, on-campus STEM-learning experience for rising ninth and tenth graders. With the intention of improving the STEM-related workforce pipeline in the Commonwealth of Virginia, Virginia Space Grant Consortium (VSGC) offers multiple BLAST programs across the Commonwealth. BLAST programs are designed as intensive three-day, STEM-related three-hour lecture-lab experiences that are reinforced by evening STEM-related events. Funded by a grant by the National Aeronautics and Space Administration (NASA), VSGC targets approximately three hundred students annually who have a C+ or better average, and who have had no previous STEM-related experience. It is surmised that if more students are exposed to STEM-related fields, they may become more interested in and motivated to one-day pursue a STEM-related discipline which would help to alleviate the STEM-related workforce shortages in Virginia. BLAST is offered at three public universities in Virginia including the University of Virginia, Virginia Tech, and Old Dominion University. Faculty and graduate students at each of the respective universities design and implement programs that draw upon their respective faculty interests and strengths. In this paper, a content analysis of the various BLAST programs and interviews with the directors and faculty involved were conducted to identify common and unique strengths across the different BLAST programs. Impacts of COVID on the development and delivery of the BLAST programs are addressed, as are suggestions for program improvements. The purpose of this paper is to share the results of perceived impacts of the BLAST programs on increasing high school students' interest in STEM-related fields and to increase their motivation in the pursuit of STEM-related college degrees. If the U.S. is to be successful at improving its STEM-ready workforce, one solution is to increase the number of high school students pursuing a STEM-related degree and career.

The MicroMAPS instrument is a nadir-viewing, gas filter-correlated radiometer which operating in the 4.67 micrometer fundamental band of carbon monoxide. Originally designed and built for a space mission, this CO remote sensor is being flown in support of satellite validation and science instrument demonstrations for potential UAV applications. The MicroMAPS instrument system, as flown on Proteus, was designed by a senior student design project in the Aerospace Engineering Department, Virginia Tech, in Blacksburg, VA. and then revised by Systems Engineers at NASA Langley. The final instrument system was integrated and tested at NASA LaRC, in partnership with Scaled Composites and Virginia Space Grant Consortium (VSGC). VSGC supervised the fabrication of the nacelle that houses the instrument system on the right rear tail boom of Proteus. Full system integration and flight testing was performed at Scaled Composites, in Mojave, in June 2004. Its successful performance enabled participation in four international science missions on Proteus: in 2004, INTEX -NA over eastern North America in July, ADRIEX over the Mediterranean region and EAQUATE over the United Kingdom region in September,and TWP-ICE over Darwin, Australia and the surrounding oceans in Jan-Feb 2006. These flights resulted in nearly 300 hours of data. In parallel with the engineering developments, theoretical radiative transfer models were developed specifically for the MicroMAPS instrument system at the University of Virginia, Mechanical Engineering Department by a combined undergraduate and graduate student team. With technical support from Resonance Ltd. in June 2005, the MicroMAPS instrument was calibrated for the conditions under which the Summer-Fall 2004 flights occurred. The analyses of the calibration data, combined with the theoretical radiative transfer models, provide the first data reduction for the science flights reported here. These early results and comparisons with profile data from the NASA DC-8, the coincident AIRS CO retrievals, and selected CO measurements from the MOZAIC program will be presented.

In the past two years, two separate facility-specific life cycle assessments (LCAs) have been performed as summer student projects. The first project focused on 13 facilities managed by NASA s Aeronautics Test Program (ATP), an organization responsible for large, high-energy ground test facilities that accomplish the nation s most advanced aerospace research. A facility inventory was created for each facility, and the operational-phase carbon footprint and environmental impact were calculated. The largest impacts stemmed from electricity and natural gas used directly at the facility and to generate support processes such as compressed air and steam. However, in specialized facilities that use unique inputs like R-134a, R-14, jet fuels, or nitrogen gas, these sometimes had a considerable effect on the facility s overall environmental impact. The second LCA project was conducted on the NASA Ames Arc Jet Complex and also involved creating a facility inventory and calculating the carbon footprint and environmental impact. In addition, operational alternatives were analyzed for their effectiveness at reducing impact. Overall, the Arc Jet Complex impact is dominated by the natural-gas fired boiler producing steam on-site, but alternatives were provided that could reduce the impact of the boiler operation, some of which are already being implemented. The data and results provided by these LCA projects are beneficial to both the individual facilities and NASA as a whole; the results have already been used in a proposal to reduce carbon footprint at Ames Research Center. To help future life cycle projects, several lessons learned have been recommended as simple and effective infrastructure improvements to NASA, including better utility metering and data recording and standardization of modeling choices and methods. These studies also increased sensitivity to and appreciation for quantifying the impact of NASA s activities.

Autonomous formation control of multi-agent dynamic systems has a number of applications that include ground-based and aerial robots and satellite formations. For air vehicles, formation flight (flocking) has the potential to significantly increase airspace utilization as well as fuel efficiency. This presentation addresses two main problems in multi-agent formations: optimal role assignment to minimize the total cost (e.g., combined distance traveled by all agents); and maintaining formation geometry during flock motion. The Kuhn-Munkres (Hungarian) algorithm is used for optimal assignment, and consensus-based leader-follower type control architecture is used to maintain formation shape despite the leader s independent movements. The methods are demonstrated by animated simulations.

The Snow Water Equivalent Synthetic Aperture Radar and Radiometer (SWESARR) is a dual microwave instrument meant to fill in information gaps in the remote sensing data of Snow Water Equivalent (SWE). The aim of this work is to improve and validate SWESARR measurements of SWE for areas with tree canopy using unsupervised machine learning methods. This information is critical to NASA’s SnowEx mission for understanding the spatial and temporal variability of snow. SWE is an integral part of the climate system and affects many other climate-related processes, thus an accurate understanding of how SWE is changing with climate change is crucial for future water resource management. We have made use of a suite of parameters to help in identifying features most important in predicting SWE in areas that have missing satellite data due to vegetation. Our aim is to validate and improve SWESARR measurements using unsupervised machine learning clustering algorithms with the goal of being able to better quantify spatial and temporal changes of SWE due to climate change.Other relevant datasets that have been central to identifying parameters for predicting SWE and data validation have been from the ASO (Airborne Snow Observatory), NSIDC (National Snow and Ice Data Center), and ground snow pit observations taken by the SnowEx field work team. This work is a result of NASA’s SnowEx team based out of Goddard Space Flight Center within the Climate Change Research Initiative at NASA GISS.

Satellites are particularly well-suited to provide spatially distributed observations of global snow. For hydrological research and applications, Snow Water Equivalent (SWE) is the most important observation, but it is also our biggest gap in snow remote sensing. Currently, no satellite sensor has the ability to measure SWE globally at the accuracy, resolution and frequency needed, because of a number of factors that impact the signals such as forests, mountains, clouds and the snow characteristics themselves. The NASA Climate Change Research Initiative (CCRI) SnowEx team uses machine learning approaches to attempt to address the unanswered questions of snow science. The team has collaborated with other similar NASA wide programs and leveraged skills and resources which led to the formation of a community machine learning (ML) working group.

The SWESARR instrument produces unreliable results when flying over terrain populated by foliage. As a result, our team used a modified version of the LOF algorithm to detect subtle and extreme disturbances in the SWESARR data. We were only able to work with the VV polarization of the X band and the VH polarization of the Ku band as those were the sets of data where we found a significant correlation between the LOF data and the original data. Currently, we can only use the LOF data to make predictions as to where the foliage might be. But in the future, we aim to overlay the two sets of data to find precise locations and remove the need for ground truth measurements. NASA’s SWESARR airborne instrument collected the relevant data used in this research experiment.