About the OFRN Research & Development Technology Demonstrations

The Ohio Federal Research Network (OFRN) project awardees are strongly encouraged to complete a live demonstration of their technologies at the end of the project period of performance. These tests and demonstrations are used to verify performance and completion of a project, and to showcase the technology to stakeholders and potential customers.

OFRN SOARING Round 3 Demonstration

The Ohio Federal Research Network’s (OFRN) Sustaining Ohio’s Aeronautical Readiness and Innovation in the Next Generation (SOARING) Round 3 initiative began in December 2018 and funds three unique UAV technologies that have the potential to significantly influence the UAS market in Ohio and the Nation. A flight test/demonstration is required as part of the initiative, which took place in September 2020.

This test/demonstration presents three OFRN-funded UAV technologies. OFRN continues to accelerate the research and development of UAV-focused technologies that address the interests of our federal partners, including the Air Force Research Laboratory, National Aeronautics and Space Administration-Glenn Research Center, National Air and Space Intelligence Center, Naval Medical Research Unit Dayton, and the Ohio National Guard. Since 2015, OFRN continues to use technology and innovation to build strategic partnerships among government, academia and industry in order to meet federal mission objectives and promote economic growth in Ohio.

SOARING Round 3 - Brushless Doubly-fed Machine and Drive Systems for Aviation Application demonstration

The Ohio State University (OSU) teamed up with Safran Electrical & Power in Ohio and the University Dayton Research Institute (UDRI) to develop high-speed brushless doubly-fed machines (BDFMs) for aviation propulsion application using a direct current distribution power system. OSU leads the electromagnetic design of the electric machine, power converter design and drive system control algorithm design. Safran leads the mechanical design and thermal design of the machine and builds the machine prototypes. UDRI leads the aircraft power system analysis and control simulation.

This new propulsion architecture based on BDFMs allows a highly efficient energy conversion from the turbines to the propulsors, i.e., 65% power flows directly from the generator to the motor and then the propulsor without creating any losses in the power converters. The turbine speed and the propulsor can still be independently controlled. The power rating of the power converters is approximately 1/3 of that of the electric machines. This allows a significant size and weight reduction of the power converters and about a 19% loss reduction for the entire electric powertrain system considering a 24-MW single-aisle aircraft. This loss reduction can result in fuel savings of ~262 kg for a single-aisle aircraft to do a five-hour flight, equivalent to ~828 kg of CO2 reduction compared to the state-of-the-art electric propulsion with a turbo-generator and a permanent magnet motor/drive.

SOARING Round 4 - Computer-Human interaction for rapid program analysis through cognitive collaboration

CHIRP2C captures and decomposes code analysis workflow and reverse engineer’s (RE’s) cognitive load using passive and active human factors capture tools such as eye, haptic and brain activity trackers. CHIRP2C employs a cognitive learning based context processor (CP) that allows humans and computers to share and learn new concepts in an open easy-to-use platform while solving complex and impactful problems for real-world applications. The CP is based on the open Soar cognitive architecture that closely mimics the human behavioral model (HBM).

This processor “learns” to abstract and conceptualize problems using both software artifacts as well as the observable behavior of the human RE in a problem-solving context to rapidly reason and propose information gap representations useful for vulnerability discovery. The overlapping similarities of human and AI’s Soar cognitive architecture substantially reduce the learning and information sharing costs while minimizing the RE’s cognitive load, thus leading to faster and more efficient vulnerability discovery.