Date of Award
Doctor of Philosophy
Atul G Kelkar
The current state-of-the-art technologies available at the disposal of the aerospace industry lacks the ability to measure the aerodynamic forces and moments acting on an aircraft in real-time during it's flight. Since the entire flight of an aircraft is based on the balance and controlled manipulation of these forces and moments, the appropriate real-time estimation for these parameters is of utmost interest.
The work presented herein addresses the issues associated with the real-time aerodynamic load estimation problem through the use of a distributed Flush Air Data System (FADS) sensor network and the development of appropriate estimation methods. This work showcases a method to design the sensor network to capture the critical aerodynamic information in the aircraft pressure signature. It also elaborates upon a neural-network based estimation method to extract the aerodynamic load information from the pressure
information captured by the sensor network.
This research also focuses on the use of the real-time aerodynamic load estimations on building new aircraft applications for aircraft safety and control. This work shows that the incipient stall conditions can be detected using the real-time aerodynamic load information. The idea and implementation of a prognostic control is also presented in this work. It is shown here that the prognostic control based on the real-time estimates of aerodynamic forces and moments can anticipate the change in aircraft states and therefore employ appropriate control action before a traditional controller.
Goswami, Ruchir, "Real-time aerodynamic load estimation and aircraft prognostic control using distributed flush air data system (FADS) sensor network" (2020). Graduate Theses and Dissertations. 17830.
Available for download on Tuesday, December 15, 2020