Project Description

The purpose of the research is to leverage recent advances in the fidelity of Direct Numerical Simulations (DNS) and Large Eddy Simultion (LES) tools to improve understanding and modeling of the complex unsteady physics that occur in the high-turbulence environment of gas turbines. Utilizing the HiPSTAR code that has been optimized for HPCs in previous programs, the research aims to identify opportunities to increase turbine aero-thermal efficiency by 2-4% and extend hot-gas-path durability, translating into combined cycle efficiency gains of 0.4%-0.8%. Using novel machine learning tools the data will also be used to develop affordable turbulence simulation methodologies that embody a step-change in predictive accuracy.

Allocation History

Source Hours Start Date End Date
ALCC30,000,0002017-07-012018-06-30