Project Description
A better understanding of turbulent unsteady flows in turbomachinery is necessary for breakthroughs in the design of jet engines that will enable lower fuel burn. Owing to relatively high Reynolds numbers and complex wall-bounded geometries, the flow physics in turbomachinery are hard to predict. While the most popular technique to model the flows in turbomachinery is based on a Reynolds–averaged Navier–Stokes (RANS) approach, these models are not accurate for complex turbulent flows. However, direct numerical simulation (DNS) of turbulent flows at high Reynolds numbers will remain intractable for a long time.
With the increase in computing power, large eddy simulation (LES) emerges as a promising technique to improve both knowledge of complex flow physics and reliability of flow predictions. The central premise of LES is that large scales dominate the turbulent transport and energy budget, while strategies for dealing with the small scales include explicit subgrid-scale (SGS) models or implicit numerical dissipation referred to as implicit LES (ILES).
The team proposes a high-order computational fluid dynamics (CFD) code based on the hybridized discontinuous Galerkin (HDG) method for wall-resolved LES of complex turbulent flows in turbomachinery. Their approach will focus on comparing high-order LES solvers against an industrial code, evaluating ILES and explicit SGS models, analyzing numerical errors due to the grid inadequacy, improving in the scalability and robustness of the flow solvers, and analyzing wall-resolved LES predictions of flows for advancing compressor and fan designs.
The team plans to advance the state-of-the-art of CFD simulations for complex turbo machinery configurations at realistic Reynolds numbers starting with tens of thousands and then extending the size of simulations. Success in their LES computations will provide a strong case to accelerate investments in transformative high-fidelity simulations through the partnership of industries, universities, and the US government.
Allocation History
| Source | Hours | Start Date | End Date |
|---|---|---|---|
| DOE INCITE PROGRAM | 2,350,000 | 2017-01-01 | 2018-01-31 |
| DOE INCITE PROGRAM | 2,350,000 | 2017-01-01 | 2018-01-31 |
| DOE INCITE PROGRAM | 5,000 | 2017-01-01 | 2018-01-31 |
| DOE INCITE PROGRAM | 5,000 | 2017-01-01 | 2018-01-31 |
| DOE INCITE PROGRAM | 35,000,000 | 2017-01-01 | 2018-01-31 |
| DOE INCITE PROGRAM | 35,000,000 | 2017-01-01 | 2018-01-31 |
| DOE INCITE PROGRAM | 2,350,000 | 2017-01-01 | 2018-01-31 |
| DOE INCITE PROGRAM | 2,350,000 | 2017-01-01 | 2018-01-31 |
| DOE INCITE PROGRAM | 5,000 | 2017-01-01 | 2018-01-31 |
| DOE INCITE PROGRAM | 5,000 | 2017-01-01 | 2018-01-31 |
| DOE INCITE PROGRAM | 35,000,000 | 2017-01-01 | 2018-01-31 |
| DOE INCITE PROGRAM | 35,000,000 | 2017-01-01 | 2018-01-31 |
