This project seeks to accomplish four new goals:

• Advance our ability to extract meaningful insight into the dynamics from exascale computational fluid dynamics (CFD) simulations of complex flows through data compression in both space and time;
• Advance machine learning algorithms to mine the data from exascale simulations for turbulence model improvements to benefit lower-fidelity (and therefore less computationally intensive) modeling capability;
• Extend existing work in uncertainty quantification and multi-fidelity modeling to exascale; and
• Extend these new components into our growing stable of in situ data analytics. Collectively, these objectives also benefit other areas of partial differential equation simulation through a dramatic advance in computational modeling and associated scientific and engineering insight.

This work creates a series of stepping-stone simulations that can be carried out alongside the four new objectives so as to provide testing and assessment at each stage. These stepping stones will be aligned to reach the aerodynamic flow control task which has built upon previous ESP and INCITE efforts in which computational models of a vertical tail and rudder assembly with 12 synthetic jets were validated against experiments at a Reynolds number 53 times smaller than flight conditions.

These simulations will be the first design optimization under uncertainty quantification of a full-scale aerodynamic component using detached eddy simulation. The vertical tail is sized to handle an engine-out condition which requires it to be much larger than what is needed for all other conditions in the flight envelope. The economic impact is directly related to the size of the stabilizer since it is a significant contributor to drag in cruise where much of the fuel is expended. If flow control can achieve the same side force with a vertical tail 25% smaller than current sizes, then the jet fuel usage can be substantially reduced.