171-180 of 268 results
-
Turbulence and Structure in the Magnetospheric Cusps: Cluster spacecraft observations
PI Heidi Nykyri
Project analyzes the structure, origin of fluctuations and high-energy particles in the high-altitude cusp regions
Project uses Cluster data and high-resolution local 3-D MHD simulations with test particles to determine the structure and origin of high-energy particles in the high-altitude cuspCategories: Faculty-Staff
-
Magnetospheric Multi-Scale (MMS) Observations and simulations of high-energy electrons in the dayside magnetosheath
PI Heidi Nykyri
CO-I Brandon Burkholder
CO-I Xuanye Ma
The key objective of this study is to better understand the source and cause of high-energy electrons observed by the MMS in the dayside magnetosheath.
The key objective of this study is to better understand the source and cause of high-energy electrons observed by the MMS in the dayside magnetosheath. The Magnetospheric Multi-Scale (MMS) mission is a four-spacecraft constellation orbiting in formation around Earth with a main goal to study the microphysics of magnetic reconnection at the dayside magnetopause. Recent MMS observations showed high energy (40 keV) electrons leaking into the magnetosheath. However, the dominant leaking mechanism has not been fully understood. Global Lyon-Fedder-Mobarry (LFM) with test particle simulations suggest that low latitude reconnection and the nonlinear Kelvin-Helmholtz (KH) instability can cause the leak of high energy electrons into the magnetosheath. But it is important to notice that many of the electrons leaking events were observed close to Fall Equinox when the MMS orbit has a significant y-component and the z_GSM coordinate can be substantial (up to ~7 R_E). Therefore, MMS high-energy electron events may have a high-latitude source. For instance, it is well demonstrated that magnetic reconnection between the Interplanetary Magnetic Field (IMF) and Earth's magnetic field surrounding the cusps can lead to the formation of cusp diamagnetic cavities (Nykyri et al., JGR 2011a,b; Adamson et al., angeo 2011), extended regions of decreased magnetic field, which can be filled with higher energy (>30 keV) electrons, protons and O+ ions. Cluster observations revealed 90-degree pitch angle electrons in the cavity, strongly suggestive of a local acceleration mechanism (Walsh, angeo 2010; Nykyri et al, JASTP 2012). Test particle simulations in a high-resolution 3D cusp model uncovered that trapped particles in the diamagnetic cavities can be accelerated when their drift paths go through regions of "reconnection quasi-potential" (Nykyri et al, JASTP 2012). Once the IMF orientation changes it is possible for trapped particles in the cavity to end up into the loss cone and "leak out" of the cavity. A systematic approach to our science objective addresses the following compelling science questions by synergy using MMS observational data and numerical simulation.
Categories: Faculty-Staff
-
Science and engineering proof of concept study for the Next generation Space Weather Prediction mission and space weather model development
PI Heidi Nykyri
Project analyzes astrodynamics (transfer trajectories) and spacecraft constellation stability about all Lagrange points for Mercury, Venus, Earth, Mars system for the "next generation" space weather prediction mission, and develops a solar wind model which will use data from this mission
Project analyzes astrodynamics and constellation stability for the "next generation" space weather prediction mission, and develops a solar wind model which will use data from this missionCategories: Faculty-Staff
-
On The Origin and Transport of Energetic Particles
PI Heidi Nykyri
CO-I Xuanye Ma
Understanding the properties, origin and dynamics of energetic particles in the solar wind and magnetosphere is crucial for safe unmanned and manned space operations. This project will unravel the birth-mechanism of the source population of the Earth's radiation belts.
Understanding the properties, origin and dynamics of energetic particles in the solar wind and magnetosphere is crucial for safe unmanned and manned space operations. Therefore, energetic particles have attracted attention from the space physics community for decades. However, different regions and energy ranges of energetic particles may have their own unique origin and role for magnetospheric dynamics, which have not been fully explored and deserve to be investigated case by case. For instance, MMS recently observed dispersionless micro-injections in the 30-300 keV electrons accompanied by strong anisotropic ion temperature at the high-latitude magnetospheric boundary layer in the vicinity of the exterior southern cusp. Due to the different magnetic field geometry, these high-latitude microinjections could have a totally different origin than the typical low-latitude microinjections. Because this region is close to the radiation belts, ionosphere, and magnetosheath, these high-latitude microinjections could be the ~ tens to hundreds of keV seed population of the radiation belts, as well as leak into the ionosphere or into the magnetosheath. This project will unravel the birth-mechanism of the source population of the Earth's radiation belts.Categories: Faculty-Staff
-
Experimental Identification of Plasma Wave Modes
PI Heidi Nykyri
CO-I Rachel Rice
Project uses MMS data to identify plasma wave modes contributing to the heating of the magnetospheric boundary layer
Projects uses single and multi-spacecraft data-analysis techniques to experimentally identify various plasma modes at different frequencies and assess their contribution to plasma heatingCategories: Faculty-Staff
-
CyberCorps Scholarship for Service: High-skilled Workforce Development for the Aviation and Aerospace Cybersecurity Domains
PI Omar Ochoa
CO-I Keith Garfield
CO-I Laxima Niure Kandel
CO-I Krishna Sampigethaya
This project promotes workforce development in this vital sector by building on undergraduate and graduate cybersecurity programs at Embry-Riddle Aeronautical University (ERAU), where both ERAU campuses (Daytona Beach, FL and Prescott, AZ) have a history of collaborative education and research activities within the aviation and aerospace cybersecurity domain.
Aviation and aerospace cybersecurity is of critical importance to the Nation. As a key component of the overall U.S. transportation infrastructure, it protects people and contributes to American prosperity and leadership. This project promotes workforce development in this vital sector by building on undergraduate and graduate cybersecurity programs at Embry-Riddle Aeronautical University (ERAU), where both ERAU campuses (Daytona Beach, FL and Prescott, AZ) have a history of collaborative education and research activities within the aviation and aerospace cybersecurity domain. Known locally as "Cyber Eagles," the project will advance the collaboration ecosystem across education programs and research centers to prepare students for productive cybersecurity careers and leadership roles in federal and state agencies. The program will recruit diverse scholars and create a supportive environment through effective mentorship, a well-developed curriculum, student involvement activities, and research experiences. These project components will help establish a pathway that enables students to participate in an environment where they can excel and enter a rewarding career in government aviation and aerospace administration agencies.
The project aims to develop a high-skilled workforce to cover the Nation’s needs in the area of aviation and aerospace cybersecurity, focusing on the safety-criticality aspects of airborne systems and the protection of associated hardware and software assets. The project will fund 20 scholarships to students over a five-year period. Student scholars will benefit from the strong ties that ERAU has with Federal and state aviation and transportation administration agencies and the aviation and aerospace industry. Scholars will have the opportunity to meet and learn from top cybersecurity engineers and managers from government and industry through aviation and aerospace-themed projects, events, and symposia hosted by ERAU. Furthermore, the project will take advantage of on-site expertise at ERAU in all computation and communication services related to flight operations, including airborne hardware and software, avionics equipment, and network and communication data links among aircraft, ground stations, radar systems, and satellite systems. This expertise places the scholarship students in a unique position to contribute to cybersecurity protection during the design, development, and operation stages of systems specific for the aviation and aerospace domain.
This project is supported by the CyberCorps® Scholarship for Service (SFS) program, which funds proposals establishing or continuing scholarship programs in cybersecurity and aligns with the U.S. National Cyber Strategy to develop a superior cybersecurity workforce. Following graduation, scholarship recipients are required to work in cybersecurity for a Federal, state, local, or tribal Government organization for the same duration as their scholarship support.
Categories: Faculty-Staff
-
Expanding the Nation’s STEM Talent Pool by Accelerating Graduate Degree Completion in Computer, Software, and Cybersecurity Engineering
PI Omar Ochoa
CO-I Massood Towhidnejad
CO-I Debarati Basu
The project will increase student persistence in STEM fields by linking scholarships with a newly created effective ecosystem that combines evidence-based practices such as faculty mentoring, academic advising, participation in the learning community, professional development activities, guidance in acquiring internships and research opportunities.
This project will contribute to the national need for well-educated scientists, mathematicians, engineers, and technicians by fostering student success and supporting the retention and graduation of domestic, high-achieving, low-income students with demonstrated financial need at the Embry-Riddle Aeronautical University, a non-profit private institution. Over its six-year duration, this project will fund scholarships to 25 undergraduate students to pursue four-year bachelor’s degrees in Computer Science, Software Engineering, or Computer Engineering. Subsequently the scholars will pursue a one-year accelerated master’s degree in one of the following areas: Software Engineering, Electrical, and Computer Engineering, or Cybersecurity Engineering. First-year students will receive up to five years of scholarship support. The project will increase student persistence in STEM fields by linking scholarships with a newly created effective ecosystem that combines evidence-based practices such as faculty mentoring, academic advising, participation in the learning community, professional development activities, guidance in acquiring internships and research opportunities. With the help of mentors, the scholars will create individual development plans outlining their career goals and steps toward achieving those goals. The project will also include the evaluation of the impact of the ecosystem on supporting the academic success of scholars and the identification of best practices and lessons learned. This project will significantly contribute towards creating a model that actively engages students from groups underrepresented in STEM fields of study, broadens participation in STEM, and infuses 25 talented and diverse engineers with advanced degrees in engineering into the American workforce.
The overall goal of this project is to increase undergraduate and graduate STEM degree completion of domestic, low-income, high-achieving undergraduates with demonstrated financial need in STEM field. Three specific aims guide the project. First is to deliver financial support to domestic, low-income, high-achieving students who will pursue an undergraduate and accelerated master’s degree in engineering. Second is to leverage evidence-based practices to foster student success, increase retention and degree attainment. Third, and finally, is to evaluate the impact of the newly created ecosystem in supporting the academic success of scholars in engineering, and disseminate best practices and lessons learned. Little is known about the factors that affect the academic success of domestic, low-income, high-achieving undergraduate students in engineering fields at a private institution, and how factors such as gender, ethnic background and discipline impact their success, which is the focus of this project. Two research questions will be investigated in this project: (a) Does the academic success of scholars improve across the years by being part of this project? (b) What were the factors effecting the academic success of the scholars, and what are the accomplishments, best practices, and lessons learned from implementing the ecosystem for the scholars? This project is funded by NSF’s Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of low-income academically talented students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers, and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income students.Categories: Faculty-Staff
-
Machine Learning Engineering: Infusing Software Engineering through the Semantic Web
PI Omar Ochoa
The Semantic Web provides a wealth of high-quality, structured, and contextual data, which can be used to train machine learning models.
The Semantic Web provides a wealth of high-quality, structured, and contextual data, which can be used to train machine learning models. This can lead to the creation of models, i.e., the engineering of Machine Learning, that adhere to non-functional requirements, which include considerations such as safety, security, and reliability, which are key elements of Software Engineering. These requirements do not concern a system's functionality, but rather its quality attributes. By incorporating these concepts into the engineering of machine learning models, one can strive to create models that are secure, reliable, and exhibit the desired quality attributes. Furthermore, Verification and Validation, or V&V, is integral to successful software engineering, by ensuring that a system is implemented correctly and meets specified requirements. In engineering Machine Learning, it's equally important to define processes and methods to thoroughly test and validate models to ensure they're performing as expected and providing accurate results. Together, the fusion of Software Engineering principles into Machine Learning Engineering, aided by the Semantic Web's capabilities, can bolster trustworthiness in machine learning systems. This trustworthiness ensures that the systems can be relied upon to behave as expected. In essence, by combining these fields, one can develop machine learning systems that are reliable, secure, interpretable, and trustworthy, upholding the core principles of Software Engineering. Our research group focuses on the most recent developments in these areas, i.e., Knowledge Graphs and Large Language Models, to accomplish these goals.
Categories: Faculty-Staff
-
Predicting Pilot Misperception of Runway Excursion Risk Through Machine Learning Algorithms of Recorded Flight Data
PI Edwin Odisho
CO-I Dothang Truong
The research used predictive models to determine pilot misperception of runway excursion risk associated with unstable approaches. The Federal Aviation Administration defined runway excursion as a veer-off or overrun of the runway surface. The Federal Aviation Administration also defined a stable approach as an aircraft meeting the following criteria: (a) on target approach airspeed, (b) correct attitude, (c) landing configuration, (d) nominal descent angle/rate, and (e) on a straight flight path to the runway touchdown zone. Continuing an unstable approach to landing was defined as Unstable Approach Risk Misperception in this research. A review of the literature revealed that an unstable approach followed by the failure to execute a rejected landing was a common contributing factor in runway excursions.
Flight Data Recorder data were archived and made available by the National Aeronautics and Space Administration for public use. These data were collected over a four-year period from the flight data recorders of a fleet of 35 regional jets operating in the National Airspace System. The archived data were processed and explored for evidence of unstable approaches and to determine whether or not a rejected landing was executed. Once identified, those data revealing evidence of unstable approaches were processed for the purposes of building predictive models.
SAS™ Enterprise MinerR was used to explore the data, as well as to build and assess predictive models. The advanced machine learning algorithms utilized included: (a) support vector machine, (b) random forest, (c) gradient boosting, (d) decision tree, (e) logistic regression, and (f) neural network. The models were evaluated and compared to determine the best prediction model. Based on the model comparison, the decision tree model was determined to have the highest predictive value.
The Flight Data Recorder data were then analyzed to determine predictive accuracy of the target variable and to determine important predictors of the target variable, Unstable Approach Risk Misperception. Results of the study indicated that the predictive accuracy of the best performing model, decision tree, was 99%. Findings indicated that six variables stood out in the prediction of Unstable Approach Risk Misperception: (1) glideslope deviation, (2) selected approach speed deviation (3) localizer deviation, (4) flaps not extended, (5) drift angle, and (6) approach speed deviation. These variables were listed in order of importance based on results of the decision tree predictive model analysis.
The results of the study are of interest to aviation researchers as well as airline pilot training managers. It is suggested that the ability to predict the probability of pilot misperception of runway excursion risk could influence the development of new pilot simulator training scenarios and strategies. The research aids avionics providers in the development of predictive runway excursion alerting display technologies.
Categories: Graduate
-
Searching For Subdwarf B Long Orbital Period Binary Systems And Single Stars Using The Light Time Delay Method
PI Tomomi Otani
Our group, which includes both graduate and undergraduate students, studies the formation of subdwarf B (sdB) stars by investigating their binarity through stellar pulsations. We use photometry data from the TESS space telescope to measure precise pulsation frequencies, distinguish between binary and single-star systems, and determine orbital solutions for candidate binaries. This research is supported by NASA funding.
Subdwarf B (sdB) stars are very hot, compact stars that have already left the main sequence, but how they form is still not fully understood. The main ideas are: (1) a common-envelope (CE) phase, which makes close sdB binaries with white dwarfs (WDs) or main-sequence (MS) stars; (2) Roche-lobe overflow (RLOF), which creates wider sdB+MS binaries; and (3) white-dwarf mergers, which leave a single sdB star. About 200 CE systems have been found, but only ~26 RLOF systems are well studied because their long orbital periods and small radial-velocity signals are difficult to measure, and mergers are even harder to confirm. GAIA data help in some cases, but most sdB stars are too hot or faint for their spectroscopic solutions. Another way to look for companions is through pulsation timing, since about 30% of sdBs show stable pulsations. As the star moves in its orbit, the pulses arrive a little early or late, revealing unseen companions—including planets. This method needs two types of data: long, continuous observations to measure pulsations precisely, and repeated observations over years to find orbits. The TESS space telescope provides the first type of data, while ground-based telescopes can add the second. By combining these, we can find more sdB binaries and singles, test how they form, and better understand the stars that contribute to the ultraviolet light in galaxies.
Categories: Faculty-Staff
171-180 of 268 results