Research Projects

2NextGen Task Q: Implementation of NextGen Air Traffic Management system. Airborne Execution of Flow Strategies (AEFS). Modeling, Simulation and Data Analysis

PI Vitaly Guzhva

We work with Metron Aviation, Lockheed Martin, Mosaic ATM and CSSI in development of AEFS concept, designing Modeling and Simulation, collecting data and conducting sensitivity and statistical analyses for concept evaluation.

Airborne Execution of Flow Strategies (AEFS) promotes increased collaboration among National Airspace System (NAS) stakeholders. AEFS recognizes the following current problem: Air Traffic Controllers are aware of the overall air traffic and flight conditions, but lack the capability to collaboratively communicate handling preferences based on flight operators’ business needs, whereas flight operators have limited awareness of Air Traffic Control (ATC) constraints and their potential impacts on flights. By promoting methods to increase collaboration between flight operators and ATC, AEFS targets improvements in Traffic Flow Management (TFM) efficiency, situational awareness among stakeholders, and flexibility in the usage of the NAS.

The FAA conducted research and development of the AEFS concept at the Florida NextGen Test Bed (FTB) at Embry Riddle Aeronautical University (ERAU) in Daytona FL. The research team from ERAU, Metron Aviation, Lockheed Martin, Mosaic ATM, and CSSI demonstrated the AEFS operational scenarios and corresponding use cases through laboratory demonstration as well as Modeling and Simulation (M&S).

The research team conducted two M&S demonstrations: one with Future Concept Team (FCT) members as active participants in the demonstration, and second with FAA stakeholders. Airborne/Airborne; Airborne/Pre-departure; Pre-departure/Airborne; and Pre-departure/pre-departure use cases were recorded and demonstrated to the participants including one airborne/airborne use case with Trial Planning functionality. In all of the use cases, DAL flights originally had default priority 5 that was changed to the highest priority 1 for one of the flights about 10 minutes before that flight would cross the freeze horizon. Based on stakeholder feedback received during the first M&S Demonstration, the FAA demonstrated an additional use case for the second demonstration where one of the flights was instead assigned the lowest priority of 10 to observe the results.

All use cases clearly demonstrated that AEFS would act based on assigned priority reducing sequencing delay for the High Priority Flights (HPF) and increasing it for Low Priority Flights LPF(s). In most cases, more than two flights were involved: a delay from an HPF was distributed among several LPFs, or by increasing delay for an LPF, several higher priority flights were able to decrease their delays. Moreover, the demonstration showed that AEFS successfully altered the arrival sequence based on assigned priorities. Both M&S demonstrations received positive feedback from key stakeholders indicating it was helpful in providing a clearer understanding of the concept.

Categories: Faculty-Staff

NextGen Task J: Implementation of NextGen Air Traffic Management system. Integrated Airport Initiative. Benefit-Cost Analysis of Aircraft Arrival Management Systems (AAMS)

PI Vitaly Guzhva

CO-I Ahmed Abdelghany

The main task was to evaluate AAMS implemented at CLT (with US Airways) and MSP (with Delta Airlines) and quantify its costs and benefits.

We collected six months of data before AAMS implementation and 6 months of data after the implementation, conducted statistical analysis and delivered the cost benefit analysis to the FAA.

Categories: Faculty-Staff

Resolving Physical Conditions of Diffuse Ionized Gas throughout the Milky Way-Magellanic System

PI Lawrence Haffner

CO-I Edwin Mierkiewicz

We use a dedicated, sensitive spectroscopic facility in Chile, the Wisconsin H-Alpha Mapper (WHAM), to study the physical conditions of the diffuse ionized gas (DIG) in the Milky Way and Magellanic System.

WHAM can reveal emission nearly a 100-million times fainter than the Orion Nebula, making it unsurpassed for collecting high-resolution, optical-line spectra from faint, diffuse sources. Here, we embark on a diverse observational program using multiple optical emission lines with this powerful, remotely-controlled, Fabry-Perot instrument to substantially advance our understanding of interstellar matter and processes that shape it. In previous work, we released the first spectral survey of the Galaxy's DIG with observations of the Balmer-alpha optical emission line of hydrogen. This effort, the WHAM Sky Survey (WHAM-SS), complements neutral gas surveys of the 21-cm radio emission line. The WHAM-SS reveals ionized gas that can be seen in every direction from our location inside the Galaxy and offers a comprehensive view of the distribution and dynamics of the Milky Way's ionized gas. Using different instrument configurations, we are now surveying the southern sky in other emission lines, allowing us to measure physical conditions within the same ionized component.

NSF AST-2009276

Categories: Faculty-Staff

Information Systems (IS) and Information Security & Assurance (ISA) Curriculum Development and Design: A DACUM Approach.

PI Leila Halawi

PI Wendi Kappers

PI Aaron Glassman

Issues associated with information security are numerous and diverse. Since the majority of organizational actions rely greatly on information and communication technologies, Information Systems (IS) security and Information Security & Assurance (ISA) is now a main concern for firms, governments, institutes, and society as a whole. As a result, a plethora of graduate programs have been created, covering nearly every aspect of IS security. The purpose of this project is to document the findings for using a particularly inventive and extremely efficient technique of job skill analysis known as a DACUM, which stands for “developing a curriculum.” A DACUM begins with an identification of an industry pool that is further reduced to an expert panel, culminating in a daylong workshop to identify new job skill statements and skill needs.

Due to limited DACUM application within the Information Systems (IS) and Information Security & Assurance (ISA) fields of study, DACUM curriculum development milestones and outcomes experienced during the process will be shared to benefit future industry and academic collaborations with regard to curriculum development. Additionally, within the workshop process, the identification of job skill need versus curriculum assessment and activity inclusion is planned and will be documented to produce an end of process expert field survey that expects to support future IS and ISA course development to best support employment opportunities. Furthermore, a discussion of Embry‑Riddle Aeronautical University and its unique relationship with Microsoft will also be included as a possible model for future university-industry partnerships.

Categories: Faculty-Staff

Implementing Active Learning Techniques in an Undergraduate Aviation Meteorology Course

PI Daniel Halperin

PI Joseph Keebler

CO-I Robert Eicher

CO-I Thomas Guinn

CO-I Kim Chambers

Student feedback from end-of-course evaluations repeatedly indicated a desire to change the format of the course by de-emphasizing the PowerPoint-based lectures. The goal of the present study was to determine whether including a set of new active-learning techniques in an Aviation Weather course would result in better student understanding (as measured by exam scores) and make the course more engaging (as measured by end-of-course evaluations). During 2018-19, three instructors implemented five different active-learning techniques into their classes (i.e., the experimental group), while two instructors continued to use the unrevised course materials (i.e., the control group). The new active-learning techniques, described below, included daily quizzes, polling questions, flipped classroom sessions, in-class activities, and assertion-evidence-based lectures. All sections used the same assignments and exams, allowing for direct assessment of the effectiveness of the active-learning techniques. Analyses of Variance (ANOVA) tables were used to determine the statistical significance of the differences in exam scores. Indirect assessments in the form of end-of-course evaluations were also examined.

Categories: Faculty-Staff

Adding Tropical Cyclone Verification Capabilities to the Model Evaluation Tools – Tropical Cyclone (MET-TC) Software

PI Daniel Halperin

Producing reliable tropical cyclone (TC) genesis forecasts is an operational priority. The National Hurricane Center uses several TC genesis guidance products for their Tropical Weather Outlook. Furthermore, global model output is used in many TC genesis guidance products and is considered an important source of deterministic TC genesis forecast guidance. This project creates a standard framework for verifying deterministic and probabilistic TC genesis forecasts using the TC-Gen tool in the Model Evaluation Tools software package.

Accurately predicting tropical cyclone (TC) genesis is an important component of providing the public with the information they need to protect life and property in the event of a landfalling storm. The National Hurricane Center (NHC) issues probabilistic forecasts of TC genesis within two and five days in their Tropical Weather Outlook product. There are several guidance products available to the forecasters, many of which rely at least in part on global forecast models. It is important to understand how accurate these guidance products and the global models are at forecasting TC genesis.

This project seeks to create a standard framework for verifying TC genesis forecasts from various sources. This will allow a straightforward comparison between official forecasts, the experimental guidance products, and global model forecasts. These verification capabilities are developed within the existing Model Evaluation Tools (MET) software package from the National Center for Atmospheric Research (NCAR). As such, the user will have considerable flexibility when setting up a verification task. They will also be provided numerous output statistics for deterministic and probabilistic forecasts.

Categories: Faculty-Staff

Demonstration of an Electrostatic Dust Shield on the Lunar Surface

PI Troy Henderson

This project will demonstrate the capability of an electrostatic dust shield, developed by NASA/KSC engineers, to remove dust from the lens of a camera after impact on the lunar surface.

This project, which is funded by NASA Kennedy Space Center, will demonstrate the capability of an electrostatic dust shield, developed by NASA/KSC engineers, to remove dust from the lens of a camera after impact on the lunar surface. Laboratory tests will confirm the experiment design, followed by a flight to the lunar surface in early 2022

Categories: Faculty-Staff

Hazard Detection and Avoidance for Lunar Landing

PI Troy Henderson

This project develops and demonstrates algorithms for detecting and avoiding areas of large rocks and high slopes for a lunar lander

This project, funded by Intuitive Machines, develops and demonstrates algorithms for detecting and avoiding areas of large rocks and high slopes for a lunar lander. Preliminary work uses an optical camera and future work will include a lidar sensor. These algorithms will be tested in simulation, tested in laboratory experiments and demonstrated on a lunar lander flight mission.

Categories: Faculty-Staff

Improved Image Processing for Orbit Estimation

PI Troy Henderson

This project seeks to improve orbit estimation methods using advanced image processing techniques applied to images from ground and space-based telescopes.

This project, funded by Air Force Research Laboratory, seeks to improve orbit estimation methods using advanced image processing techniques applied to images from ground and space-based telescopes. Additional work uses RF signals to estimate orbits of transmitting spacecraft.

Categories: Faculty-Staff

Using Machine Learning to Improve Forecasting of Deep Convection

PI Christopher Hennon

CO-I Ronny Schroeder

CO-I Curtis James

CO-I Abd AlRahman AlMomani

We are working to train a neural network to forecast the initiation time, location, and intensity of thunderstorms. These results will support operations during the proposed CONVECT project and could ultimately aid operational forecasting during the North American Monsoon (NAM).

This research, funded through an NSF EAGER grant, seeks to improve forecast accuracy of monsoon thunderstorm activity and precipitation amounts in the Southwest. The project creates an innovative machine learning tool trained using regional numerical weather model output and satellite remote sensing data (the predictors) with respect to known thunderstorm cell locations and intensities detected by radar (the targets). The tool will be designed to extract important fundamental relationships between the predictors and targets that help explain the development and evolution of thunderstorms. After an intense training, validation and testing phase, the relationships will then be leveraged to generate better forecasts of the timing, severity and location of future thunderstorm events in the Southwest. The tool will be shared with the National Weather Service tto help forecasters predict thunderstorm-related hazards such as large hail, flash flooding or wildfire ignition. This innovative approach will also provide a framework for improving operational meteorological and geophysical prediction systems and for guiding scientific field studies.

The project develops a probabilistic model to predict convective initiation, rain rates, and convective cell tracks during the wet phase of the North American Monsoon (NAM). Predictors of convection (e.g., relative humidity, convective available potential energy, precipitable water) will be collected from dynamic mesoscale model (High Resolution Rapid Refresh, University of Arizona-Weather Research Forecast model) analyses and forecasts and combined with new satellite-derived observations of soil moisture and surface temperature to produce a unique prediction tool. A novel machine learning approach – causality informed learning – will be applied to identify the most suitable predictors for further training in a neural network and to gain insight into the processes governing convective initiation and evolution. Hourly forecasts of precipitation occurrence, nature, and categorical rain rates will be produced operationally to guide forecasters and field research.

Categories: Faculty-Staff