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Author Background

Kristoffer Borgen is a graduate school in the School of Aviation and Transportation Technology at Purdue University. A graduate of Purdue University with a Bachelor’s in Aerospace Engineering Technology and a Masters in Aviation and Aerospace Management. He also possess an FAA Airframe & Powerplant certification and a Remote Pilot Certificate. Kristoffer’s research is focused on methods of UAS implementation and technologies to be used in the National Airspace.

Dr. John H. Mott is a Professor in the School of Aviation and Transportation Technology at Purdue University. A summa cum laude graduate of the University of Alabama with Bachelor's and Master's Degrees in Electrical Engineering, and of Purdue University with a Ph.D. in Civil Engineering, he also possesses an FAA Commercial Pilot Certificate with Instrument and Multiengine ratings, an FAA Flight Instructor Certificate with Airplane Single-and Multiengine and Instrument ratings, and an FAA Ground Instructor Certificate with Advanced and Instrument ratings. He holds type ratings in the Beech King Air 300, Hawker HS-125, and Canadair Challenger 600. He has worked as a flight instructor, a charter pilot, chief pilot, and director of training for a FAR 135 operator, an airline pilot flying the SA-226TC Metroliner, and a corporate pilot in many different models of aircraft.

Dr. Mott serves as the Director of the Advanced Aviation Analytics Center of Research Excellence (A3IR-CORE), and was the founding editor of the Journal of Aviation Technology & Engineering, where he served as executive editor through 2018. Dr. Mott’s research is focused primarily on the aggregation of distributed data related to various operational aspects of transportation systems, the analysis of that data using deterministic and stochastic mathematical modeling, and the development of related tools to facilitate improvements to the safety and efficiency of those systems. These tools ultimately improve the quality of life of those who utilize transportation services.

Abstract

Bridge inspections are an expensive and time-consuming process, varying significantly with a bridge’s style, height, width, and length. Inspections create interruptions that interfere with bridge use, as the examination requires partial or total closure, causing traffic delays. Unmanned aerial system (UAS) use has increased significantly over the past decade, including assistance and coordination during bridge inspections. However, the impact on a UAS from high winds and turbulent airflows induced by a bridge’s structure can decrease flight safety during inspections. Visualization of these hazards is difficult for UAS operators; therefore, a process to estimate the velocity and locations of these hazardous flows was created. The process begins by generating a simplified 3D model using the structural elements of a concrete and steel girder bridge based on the parameters and characteristics of the bridge. The model is then processed by a computational fluid dynamics application that estimates the locations and velocities of the wind flows around the structure. Finally, the results are converted into a standard computer model file type that is either an augmented reality or computer application to display so to assist the UAS operator.

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