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A Model Predictive Framework for Thermal Management of Aircraft

[+] Author Affiliations
Timothy O. Deppen, Andrew G. Alleyne

University of Illinois at Urbana-Champaign, Urbana, IL

Joel E. Hey, Timothy S. Fisher

Purdue University, West Lafayette, IN

Paper No. DSCC2015-9771, pp. V001T08A001; 9 pages
doi:10.1115/DSCC2015-9771
From:
  • ASME 2015 Dynamic Systems and Control Conference
  • Volume 1: Adaptive and Intelligent Systems Control; Advances in Control Design Methods; Advances in Non-Linear and Optimal Control; Advances in Robotics; Advances in Wind Energy Systems; Aerospace Applications; Aerospace Power Optimization; Assistive Robotics; Automotive 2: Hybrid Electric Vehicles; Automotive 3: Internal Combustion Engines; Automotive Engine Control; Battery Management; Bio Engineering Applications; Biomed and Neural Systems; Connected Vehicles; Control of Robotic Systems
  • Columbus, Ohio, USA, October 28–30, 2015
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5724-3
  • Copyright © 2015 by ASME

abstract

The challenge of managing heat dissipation and enforcing operational constraints on temperature within a high-performance tactical aircraft is considered. For these systems, power density of the electrical equipment and the associated thermal loads are quickly outpacing the means of conventional thermal management systems (TMS) to provide on-demand cooling and in order to prevent thermal run away. The next generation of tactical aircraft is projected to include an order of magnitude greater thermal and electrical power magnitudes, and the time scale over which thermal loads will change is expected to shrink. To meet this rapidly evolving challenge, designing a TMS for the “worst case” scenario based on a steady-state thermal analysis will be infeasible. Rather, a holistic systems perspective is needed with new control methodologies that capture and even exploit the transient thermal behavior. To this end, a model predictive control strategy is presented that utilizes preview of upcoming loads and disturbances to prevent violation of temperature constraints. A simulation case study demonstrates that the predictive thermal controller can dramatically reduce constraint violations while reducing the work required by the TMS when compared to a cascaded PI feedback controller.

Copyright © 2015 by ASME

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