51-60 of 63 results

  • NSF REU Site: Swarms of Unmanned Aircraft Systems in the Age of AI/Machine Learning

    PI Houbing Song

    CO-I Richard Stansbury

    Embry-Riddle Aeronautical University establishes a new Research Experiences for Undergraduates (REU) Site to engage participants in research in drone swarms. The emerging concept of drone swarms, which is defined as the ability of drones to autonomously make decisions based on shared information, creates new opportunities with major societal implications. However, future drone swarm applications and services pose new networking challenges. A resurgence of Artificial Intelligence and machine learning research presents a tremendous opportunity for addressing these networking challenges. There is an overwhelming need to foster a robust workforce with competencies to enable future drone swarm applications and services in the age of AI/machine learning.

    The project establishes a new Research Experiences for Undergraduates (REU) Site with a focus on networking research for drone swarms in the age of AI/machine learning at Embry-Riddle Aeronautical University. The goals of the REU Site are: (1) attract undergraduate students to state-of-the-art drone swarm research, especially those from underrepresented groups, and from institutions with limited opportunities; (2) develop the research capacity of participants by guiding them to perform research on drone swarms; (3) grow the participants’ technical skills to enable a wide variety of beneficial applications of drone swarms; (4) promote the participants’ integrated AI/machine learning and drone swarm competencies; and (5) prepare participants with professional skills for careers. The focus of the REU Site is on the design, analysis and evaluation of innovative computing and networking technologies for future drone swarm applications and services. To be specific, research activities will be conducted in three focus areas, notably dynamic network management, network protocol design, and operationalizing AI/machine learning for drone swarms. Each year eight undergraduate students will participate in a ten-week summer REU program to perform networking research for drone swarms under the guidance of research mentors with rich experiences in AI/machine learning and drone swarms. This REU site is expected to foster workforce knowledge and skills about developing new computing and networking technologies for future drone swarm applications and services. This site is supported by the Department of Defense ASSURE program in partnership with the NSF REU program.

    Categories: Faculty-Staff

  • Mixing of a supercritical jet in a supercritical cross-flow

    PI Neil Sullivan

    CO-I Mark Ricklick

    This project is focused on the exploration and validation of numerical modeling techniques, for the simulation of supercritical jets in crossflow. 

    ​The injection of fuels and oxidizers into combustion chambers is often performed at near-critical or supercritical (SC) temperatures and pressures. At the critical point, the surface tension and enthalpy of vaporization of a fluid approach zero. This means there is no droplet formation in a jet, and also no density change between phases. The fluid has in effect only one supercritical phase, and has both liquid-like and gas-like properties. Physical and thermodynamic properties of the fluid have large gradients near the critical point, and this has led to complications in numerical simulation of even simple flow phenomena at this condition.

    It is desired to simulate the mixing and subsequent combustion of certain supercritical fluids for application to the design of SC-CO2­ combustion power generation. SC methane and oxygen will be burned in an atmosphere of SC carbon dioxide, allowing highly efficient power extraction using smaller turbomachinery than in traditional Brayton or Rankine cycles. The study of SC methane jets also has applications to liquid rocket propellant injection and jet impingement rocket nozzle cooling.

    Reynolds-Averaged Navier Stokes (RANS) and Large Eddy Simulation (LES) numerical studies are conducted to investigate the diffusion-driven mixing of one or more species in a SC jet, with another species in a SC cross-flow. Real-gas effects will be captured using the Peng-Robinson cubic equation of state. Benchmarking is performed against previous experimental and LES studies performed on near-critical and SC jets in quiescent fluids. The commercial code STAR-CCM+ is used for the simulation.

    Improved prediction of jet behavior at near-critical and SC pressures and temperatures will better inform combustor design, combustion efficiency and thermodynamic efficiency.

    Ideal gas axisymmetric simulation of a sub-critical nitrogen jet

    Categories: Graduate

  • A Curriculum Wide Software Development Case Study

    PI Massood Towhidnejad

    CO-I Thomas Hilburn

    This NSF funded research develops case studies of software development for use in software engineering and computing instruction.

    Products include realistic projects, complete artifacts throughout the software development life cycle, case studies decoupled from a particular textbook, and case modules designed with varying complexity allowing for use in multiple classes throughout undergraduate and graduate curricula. 

    Categories: Faculty-Staff

  • Encouraging Students to Pursue an Engineering Education and Career

    PI Massood Towhidnejad

    This NSF-sponsored project provides scholarship for engineering students pursuing degrees in computer science, computer engineering, electrical engineering, mechanical engineering and software engineering.

    Working closely with faculty and student mentors, scholarship recipients are involved in multi-disciplinary projects involving unmanned and autonomous systems throughout their four years of undergraduate study.

    Categories: Faculty-Staff

  • From Middle School to Industry Vertical Integration to Inspire Interest in Computational Thinking

    PI Massood Towhidnejad

    CO-I Thomas Hilburn

    While students typically do not see immediate advantages of the topics being studies, top down integration exposes students to larger, more complex projects, giving them better appreciation for topics as they realize the “big picture.”

    Funded by the National Science Foundation, this research seeks to vertically integrate software development best practices from industry to graduate, undergraduate, high school, and middle school academic programs, with the intention of increasing student interest in computing and computational thinking.

    Categories: Faculty-Staff

  • Developing Artifact Peer Review Assignment Methodologies to Maximize the Value of Peer Review for Students

    PI Matthew Verleger

    This engineering education research project seeks to develop a proof-of-concept peer review matching algorithm and demonstrate if it is a valuable and viable methodology for conducting peer review. Peer review is a proven method that has positive impact on student learning. The project will test the algorithm on Model Eliciting Activities in the engineering classroom, and investigate how changing peer review can affect student learning.



    ​The broader significance and importance of this project is the transformative potential of improving peer review processes, since peer review is used throughout STEM and medical fields. Thus this preliminary investigation can extend outside the realm of improving student learning. This project overlaps with NSF's strategic goals of transforming the frontiers through preparation of an engineering workforce with new capabilities and expertise. Additionally NSF's goal of innovating for society is enabled by supporting the development of innovative learning systems.

    ​


    Categories: Faculty-Staff

  • Platform for Investigating Concept Networks on the Instrumentality of Knowledge (PICNIK)

    PI Matthew Verleger

    This engineering education research project seeks to develop a concept network for engineering and a platform for helping students identify how concepts are connected across a curriculum.  The goal is to better understand and improve how students value the concepts being taught throughout their education.



    By data mining course materials (i.e., textbooks, course notes, syllabi, video transcripts, websites, etc.), a concept network can be developed for that course. With each additional resource, the network connectedness become more fully representative.  By mapping materials from courses throughout a curriculum, and then overlaying the resulting map on a degree plan of study, students will be able to better identify and value how concepts being taught today are connected and used throughout the rest of their education. For instructors, curricular redesign becomes significantly easier, as they will be able to more fully contextualize how other courses depend on their material.

    Categories: Faculty-Staff

  • Distributed Detection and Control of Collective Behaviors in Multi-agent Systems

    PI Tianyu Yang

    Multi-agent systems can be defined as a group of dynamical systems, in which certain emergent behaviors are exhibited through the local interaction among group members that individually have the capability of self-operating. The key issues we study include the analysis of network controllability and the design of coordination control protocol in order to achieve autonomous and optimal tasking allocation. Also, the detection and resilient control of emergent behaviors in large scale multi-agent systems are of keen interest. 

    Our analysis is conducted through modeling, detection, learning, and estimation of agent interaction dynamics and interaction topologies, and the design of resilient cooperative control protocols. The projects have been funded by Air Force Research Laboratory Information Directorate (AFRL/RI) Machine Intelligence for Mission Focused Autonomy (MIMFA) program. The projects are in collaboration with researchers from Bradley University

    Categories: Faculty-Staff

  • UAS Ground Collision Severity Evaluation

    PI Feng Zhu

    CO-I Eduardo Divo

    CO-I Victor Huayamave

    Increased use of UAS requires an in-depth understanding of the hazard severity and likelihood of UAS operations in the NAS. Due to their distinct characteristics (e.g. size, weight and shape) with manned aircraft systems, UAS operations may pose unique hazards to other aircraft and people on the ground. 

    Up to date, the studies on the UAS ground collision are still very much limited, particularly the scenario of impact between UAS and human body on the ground. Therefore, it is necessary to determine lethality thresholds for UAS using characteristic factors that affect the potential lethality of UAS in collisions with other objects, particularly human body on the ground. The objectives of this study are (1) to analyze the response and failure behavior of several typical UASs impact with human body on the ground; and (2) establish the damage threshold of UAS and its correlation with the key parameters in the crash accidents (e.g. shape, size and materials of UAS; impact energy and impulse etc.). To achieve this goal, advanced computational modeling techniques (e.g. finite element method/FEM) will be used to simulate the typical UAS/people impact scenarios.Based on the results, a design guidance can be further suggested to improve the crashworthiness of UAS and safety of personnel on the ground.

    Conventional 14 CFR system safety analyses include hazards to flight crew and occupants may not be applicable to unmanned aircraft.  It is necessary to determine the dedicated hazard severity thresholds for UAS and identify the key factors that affect the potential severity of UAS in collisions with other aircraft on the ground or in airborne encounters as well as collisions with people on the ground.  These severity thresholds will help determine acceptable corresponding system failure levels in accordance with the applicable 14 CFR requirements (for example 14 CFR 23.1309 and 14 CFR 25.1309).

    Categories: Faculty-Staff

  • Langrangian Wind Tunnel

    ERAU is supporting industry (i.e. Global Aerospace Corp.) in the development of a novel hypersonic wind tunnel by using high-fidelity computational fluid dynamcs.

    GAC is leading development of a wind tunnel in which the test article is propelled thru the test section at hypersonic speeds using a novel, proprietary approach.  Due to proprietary restrictions a simplistic version of the test article is illustrated below as it moves Mach 10 from right to left.  Shock waves may be observed reflecting off tunnel walls.  A Phase I Air Force STTR effort has been completed and Phase II is expected to begin in the near future.

    Categories: Faculty-Staff

51-60 of 63 results