Propulsive Performance

Propulsive performance research focuses on optimizing the efficiency and effectiveness of systems that generate thrust, encompassing diverse applications from underwater vehicles to jet engines. Current investigations utilize computational modeling, including deep learning and reinforcement learning algorithms (like asynchronous parallel training), to analyze and improve propulsion systems across various scales and environments. These efforts aim to enhance efficiency, reduce energy consumption, and ultimately improve the performance and range of vehicles and machines, impacting fields ranging from robotics to aerospace engineering. The development of novel figures of merit and predictive models is crucial for comparing and optimizing different designs and control strategies.