Data driven low-bandwidth intelligent control of a jet engine combustor

<p>This thesis introduces a low-bandwidth control architecture for navigating the input space of an un-modeled combustor system between desired operating conditions while avoiding regions of instability and blow-out. An experimental procedure is discussed for identifying regions of instability and gathering sufficient data to build a data-driven model of the system's operating modes. Regions of instability and blow-out are identified experimentally and a data-driven operating point classifier is designed. This classifier acts as a map of the operating space of the combustor, indicating regions in which the flame is in a "good" or "bad" operating mode. A data-driven predictor is also designed that monitors the combustion process in real time and provides a prediction of what operating mode the flame will be in for the next measurement. A path planning algorithm is then discussed for planning an input trajectory from the current operating condition to the desired operating condition that avoids regions of instability or blow-out in the input space. An adaptive layer is incorporated into the path planning algorithm to ensure that the path planner can update its trajectory when new information about the operating space becomes available.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Mechanical Engineering

Year

2016

Author dc:creator

• Toner, Nathan L

Contributors dc:contributor

• Galen B. King
• George Chiu
• Inseok Hwang
• Peter Meckl

• Applied sciences
• Characterization
• Combustion
• Control
• Intelligent
• Machine learning
• Thermoacoustic instability
• Artificial Intelligence and Robotics
• Engineering
• Mechanical Engineering

Repository record dc:identifier

https://docs.lib.purdue.edu/open_access_dissertations/866

OAI identifier oai:identifier

oai:docs.lib.purdue.edu:open_access_dissertations-2058