Computational Enhancements for Direct Numerical Simulations of Statistically Stationary Turbulent Premixed Flames
AdvisorsIm, Hong G.
KAUST DepartmentPhysical Science and Engineering (PSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/623456
MetadataShow full item record
AbstractCombustion at extreme conditions, such as a turbulent flame at high Karlovitz and Reynolds numbers, is still a vast and an uncertain field for researchers. Direct numerical simulation of a turbulent flame is a superior tool to unravel detailed information that is not accessible to most sophisticated state-of-the-art experiments. However, the computational cost of such simulations remains a challenge even for modern supercomputers, as the physical size, the level of turbulence intensity, and chemical complexities of the problems continue to increase. As a result, there is a strong demand for computational cost reduction methods as well as in acceleration of existing methods. The main scope of this work was the development of computational and numerical tools for high-fidelity direct numerical simulations of premixed planar flames interacting with turbulence. The first part of this work was KAUST Adaptive Reacting Flow Solver (KARFS) development. KARFS is a high order compressible reacting flow solver using detailed chemical kinetics mechanism; it is capable to run on various types of heterogeneous computational architectures. In this work, it was shown that KARFS is capable of running efficiently on both CPU and GPU. The second part of this work was numerical tools for direct numerical simulations of planar premixed flames: such as linear turbulence forcing and dynamic inlet control. DNS of premixed turbulent flames conducted previously injected velocity fluctuations at an inlet. Turbulence injected at the inlet decayed significantly while reaching the flame, which created a necessity to inject higher than needed fluctuations. A solution for this issue was to maintain turbulence strength on the way to the flame using turbulence forcing. Therefore, a linear turbulence forcing was implemented into KARFS to enhance turbulence intensity. Linear turbulence forcing developed previously by other groups was corrected with net added momentum removal mechanism to prevent mean velocity drift. Also, dynamic inlet control was implemented which retained flame inside of a domain even at very high fuel consumption fluctuations. Last part of this work was to implement pseudospectral method into KARFS. Direct numerical simulations performed previously are targeting real engines and turbines conditions as an ultimate goal. These targeted simulations are prohibitively computationally expensive. This work suggested and implemented into KARFS a pseudospectral method for reacting turbulent flows, as an attempt to decrease computational cost. Approximately four times computational CPU hours savings were achieved.