The Daniel Guggenheim School of Aerospace Engineering at Georgia Tech

Brian Kestner

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Dr. Brian Kestner

Research Engineer II
Weber, Rm 308
(404) 305 - 2781
brian.kestner (at) asdl.gatech.edu

Education

  • Bachelor of Science in Mechanical Engineering, Milwaukee School of Engineering, May 1998
  • Master of Science in Mechanical Engineering, University of Wisconsin, May 2001
  • Doctor of Philosophy in Aerospace Engineering, Georgia Institute of Technology, December 2009

Research

  • Technology assessment for N+2 Tube-and-Wing and Hybrid-Wing_Body configurations for simultaneous fuel burn, noise, and emissions reduction. Research sponsored by NASA Integrated System Research Program, Environmentally Responsible Aviation (ERA) project.
  • Transient and real-time simulation of combined cycle power plants
  • Airframe and mission analysis using NPSS
  • Dynamic simulation in NPSS using a volume dynamics approach
  • Transient gas turbine simulation using surrogate models
  • Gas turbine gas path diagnostics using Bayesian inference networks
  • Gas turbine performance anomaly detection using Kalman filters
  • Parametric design space exploration of variable cycle engine for supersonic business jet application

Honors and Distinctions

  • NASA University Research Engineering Technology Institute (URETI) on Aeropropulsion and Power Technology (UAPT) award for “Outstanding Doctoral Student”.

Membership and Service

  • Member, American Institute of Aeronautics and Astronautics, 2009 – Present

Selected Publications

  • Kestner, B., Tai, J.C.M., and Mavris, D.N., “Multivariable PI Control for Tip-Jet Reaction Drive Systems”, 45th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit, Denver, CO: AIAA 2009-4808, 2009
  • Kestner, B., Schutte, J., Gladin, J., and Mavris, D.N., ”Ultra High Bypass Ratio Engine sizing and Cycle Selection Study for a Subsonic Commercial Aircraft in the N+2 Timeframe”, Proceedings of ASME Turbo Expo 2011, Vancouver, Canada: GT2011-45370, 2011
  • Kestner, B.K., et.al., ” Diagnostics of Highly Degraded Industrial Gas Turbines Using Bayesian Networks”, Proceedings of ASME Turbo Expo 2011, Vancouver, Canada: GT2011-45943, 2011
  • Rezvani, R., et. al., “A Gas Turbine Engine Model of Transient Operation Across the Flight Envelope”, Proceedings of ASME Turbo Expo 2011, Vancouver, Canada: GT2011-45565, 2011
  • Kestner, B.K., Tai, J.C.M, and Mavris, D.N., “A Computationally Efficient Methodology for Generating Training Data for a Transient Neural Network of a Tip-Jet Reaction Drive System”, To be published in Journal of Engineering for Gas Turbines and Power, GTP-10-1354