Data driven low-bandwidth intelligent control of a jet engine combustor
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.
King, Purdue University.
Engineering|Mechanical engineering|Artificial intelligence
Off-Campus Purdue Users:
To access this dissertation, please log in to our