The research proposed in this document will build upon and extend the previously funded CRIAQ (AUT-1701) and MITACS (IT12130) projects on the development of a UAV platform for search and rescue activities in the ski facilities of Domain Saint Bernard in Mont Tremblant in collaboration with SII Canada. The goal of this research is to develop a synthesis methodology for a multivariable PID flight controller to steer a rescuing UAV to a person in danger using output feedback.

The primary objective of this collaborative research project is to further advance an established industrial strength framework for the aerodynamic design optimization of Advanced Aerodynamic Systems. A major expense during a three-dimensional aerodynamic shape optimization (ASO) is the cost of obtaining design sensitivities or gradients at each design iteration. The computational cost grows rapidly as the number of cost functions and design variables are added.

During interplanetary travels, space radiations are always crucial to spacecraft design due to their extreme hazards to human beings and electronics. Conventional materials, such as aluminum and other heavy metals, have been widely used on spacecraft. The cost of launching overweighed items to space is still high even reusable rockets are available now. Reducing weight of shielding structures on spacecraft will largely benefit future space missions. Nanocomposites with extreme low density but high radiation shielding properties are proposed in this project.

This research project between the University of Windsor and Pratt & Whitney Canada (P&WC) is focused on a porous composite material used by aircraft engine manufacturers in the design of fancases of turbofan engines. The objective of the project is two-fold and includes 1) experimentally investigating the behavior of the composite material at different loading conditions; and 2) identifying a model that can be used to represent this material in fan blade-off simulations.

The intelligent control of space cameras project is concerned with development of the next generation of space cameras. Currently, there is a large gap between the onboard capabilities of standard commercial cameras and those currently in space (examples include image resolution, onboard storage, advanced scene understanding and exposure control).

The evolution of aerospace technologies and automated systems has been accompanied by the phenomenon of “de-crewing”. A large body of current research focuses on how to move to single-pilot operations (SPO), but a major barrier to the implementation of SPO and other autonomous commercial aircraft operations is that advances in human-machine interactions and human factors have not kept pace with technological change. The objective of the research project that is the subject of this proposal is to develop a methodology to simulate autonomous flight in a real-time, virtual environment.

The research activity is to collect, compile, and analyze relevant information from external open sources about specific topics impacting the aerospace market. Additionally, research into management and marketing theory related to processes, frameworks, and best practices for market analysis and investment decision-making will also be part of the project.

This project seeks to explore the use of a class of artificial intelligence algorithms called reinforcement learning for the purpose of aiding the training of new pilots. In the process, we seek to “teach” an algorithm how to fly an aircraft by exposing the AI pilot to a virtual environment and providing it with flight data and a goal. Alternatively, the algorithm could learn by observing human pilots.