Summary
The Automated Prediction Aided by Quantized Simulators (APAQuS) Disruption Opportunity seeks to develop data-informed techniques to predictively model turbulence, one of the most intractable and challenging problems in physics.
Examples of particular interest to this effort include high-Reynolds number turbulent flows (e.g. airflow over an aircraft wing at hypersonic speeds or high-speed water flow over a propeller on a fast-moving vessel), energy cascades in compressible turbulent systems, and shock wave - boundary layer interactions.
The APAQuS effort will harness the tunability and wide dynamic range of tabletop turbulence simulators, so called “quantum wind tunnels,” using ultracold quantum fluids or other quantum systems to generate experimental data that will guide AI-based models to better understand turbulence in various regimes.
These quantum wind tunnels would enable more precise testing over much wider parameter spaces and at much lower cost than existing wind tunnels designed to measure turbulence in extreme conditions.
Teams within APAQuS will combine expertise in table-top experimental simulations of turbulent behavior, AI-based modeling and automated discovery of governing laws, and computational fluid dynamics to develop new approaches to modeling turbulence.