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Displaying 49-60 of 274 Results

C

Comparison of Grades Based on Learning Mode: How Learning Environment Impacts Grades

  • PI John Griffith

    CO-I Donna Roberts

    CO-I Beverly Wood

  • A comparison of failure rates and grade distribution will be conducted between four learning disciplines utilized by Embry-Riddle Aeronautical University-Worldwide: Eagle Vision Classroom (synchronous classroom to classroom), Eagle Vision Home (synchronous home to home), Online and traditional classroom learning environments. Researchers will examine approximately 20,000 Embry-Riddle end-of-course student grades from the 2015-2016 academic year. The study will determine if significant relationships between failing grades and learning environment (modes) exist between the English, Humanities, Economics and Mathematics disciplines. Due to the continued technological advancements in course delivery, recommendations from previous studies in this area include continued research on the relationship of student performance and learning mode.
Composite Wind Turbine Blade

  • PI Sathya Gangadharan

  • The world's primary energy needs are projected to grow by 56% between 2005 and 2030, by an average annual rate of 1.8% per year (International Energy Agency, 2012). Energy policy has confirmed the improvement of the environment sustainability of energy as a primary objective also though increasing use of renewable sources (Increasing Wind Energy's contribution to U.S. Electricity supply, 2008).
CONVECT (CONvection and water Vapor Exchange in Complex Terrain)

  • PI Curtis James

    CO-I Ronny Schroeder

  • CONVECT is a major meteorological field research project being proposed for June-August 2027 in north-central Arizona. The project is aimed at improving our understanding and ability to predict the convective development, propagation and intensification of thunderstorms during the North American Monsoon (NAM).

Cost Optimization Modeling for Airport Capacity Expansion Problems in Metropolitan Areas

  • PI Woo Jin Choi

    CO-I Dothang Truong

  • The purpose of this research was to develop a cost optimization model to identify an optimal solution to expand airport capacity in metropolitan areas in consideration of demand uncertainties. The study first analyzed four airport capacity expansion cases from different regions of the world to identify possible solutions to expand airport capacity and key cost functions which are highly related to airport capacity problems. Using mixedinteger nonlinear programming (MINLP), a deterministic optimization model was developed with the inclusion of six cost functions: capital cost, operation cost, delay cost, noise cost, operation readiness, and airport transfer (ORAT) cost, and passenger access cost. These six cost functions can be used to consider a possible trade-off between airport capacity and congestion and address multiple stakeholders’ cost concerns.

Creating Connections: Bed bugs to UAV Swarms

  • PI Bryan Watson

  • ​The overarching goal of our research is to advance our understanding of bed bug behavior and use this understanding to improve performance of aerospace swarms.
Critical Fora Discussing U.S.-Japan Space Security Cooperation

  • PI Alice Dell'Era

    CO-I Elisabeth Murray

  • The proposed project aims to bring together leading American and Japanese space security experts with students and faculty from Embry Riddle Aeronautical University (ERAU) for a dialogue on the current state of space security and the potential for cooperation between Japan and the United States.
Cross-Scale Wave Coupling Processes in Kelvin-Helmholtz Structures

  • PI Heidi Nykyri

  • Project investigates cross-scale wave coupling processes and their role on ion heating, mixing and diffusion.
CubeSats Hosting Flexible Appendages for On-Orbit Testing of Advanced Control Algorithms

  • PI Riccardo Bevilacqua

  • ​The objective of this work is to start the assembly of a CubeSat hosting specialized flexible appendages, taking inspiration from a previously designed spacecraft developed by the Advanced Autonomous Multiple Spacecraft (ADAMUS).
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. 

D

Damage Control Measures in Composites: Focus on Damage Tolerance of Aerospace Structures

  • PI Alberto Mello

    CO-I Kais Jribi

    CO-I Doug Neill (C.E.S. LLC)

    CO-I Jon Gosse (C.E.S. LLC)

  • The focus of this research is to provide advanced methods and tools to address damage tolerance in composite structure 
Data-enabled Science & Engineering Education (IUSE 1626602, 2016-2020),

  • PI Matthew Ikle

    CO-I Hong Liu

    CO-I Michael Wolyniak

    CO-I Raphael Isokpehi

  • ​T​his project will develop a virtual department across four partner campuses to provide computer science education to students at campuses that are individually too small to support this kind of department. The new department will focus on the analysis of "big data" - large sets of computational and observational data - that are becoming increasingly prevalent in STEM. Cyber-learning techniques such as recorded lectures, archived materials, blog participation, and active learning approaches will be combined to offer a set of classes in big data science spanning meteorology, environmental science, biology and chemistry. By combining students from different campuses into the same courses, problems with minimal resources and limited potential enrollments on the individual campuses can be overcome. In particular, the project will focus on developing courses in biology and earth science, areas where students are not attracted by traditional computer science classes. 
Deep-Learning-Based Unobtrusive Estimation of Pilot Adverse Interactions and Loss of Energy State Awareness

  • PI Hever Moncayo

  • ​This project aimed at gaining more insight into the mechanisms of pilot SD and LESA occurrence, capturing their dynamic fingerprint, and developing on-board intelligent schemes capable of predicting and detecting these dangerous phenomena associated to pilot behaviors.