DOE CSGF Research Statement

I just finished applying for the DOE Computational Science Graduate Fellowship. In March they tell me if I got it. One of the short responses the CSGF application required was the first time I'd sat down and really thought through my research goals. With the addition of some background material links, here's the prompt and my response...

Essay prompt 1: Field of Interest

"Computational science" involves the innovative and essential use of high-performance computation, and/or the development of high performance computational technologies, to advance knowledge or capabilities in a scientific or engineering discipline. Please describe (in no more than 300 words) your specific research interest paying particular attention to how computational science will spur advances.

Essay response 1

My research uses direct numerical simulation (DNS) of the Navier-Stokes equations to investigate the compressible, turbulent boundary layers present in atmospheric reentry vehicle applications. My work is motivated by both its long-term value and an immediate need at the University of Texas' Center for Predictive Engineering and Computational Sciences (PECOS). PECOS is developing new uncertainty quantification techniques through the study of reentry vehicle thermal protection systems (TPS). Due to its order one impact on vehicle heating rate uncertainty, improving hypersonic turbulent boundary layer science is crucial for PECOS' success.

I have three interwoven research thrusts. First, by combining scalable spectral numerical techniques with a multiscale formulation, I will advance understanding of how TPS ablation products interact with turbulent boundary layers. Second, through a simulation-based characterization of the ablative conditions that may sustain turbulence, I will improve understanding of turbulence transition phenomena in transpired-wall compressible flows. Lastly, I will produce high quality data fields for use in compressible turbulence model development and calibration. This novel data will also fuel PECOS' Bayesian-inspired uncertainty quantification activities.

Improvements in computational science will aid all three of these endeavors. The scientific need to attain higher, more realistic Reynolds numbers and to obtain finer-grained, larger datasets drives DNS towards ever higher performance computing platforms. Increases in compute node availability, compute node memory, and interconnect speed will all boost the quality of the science I produce. Spectral turbulence simulations walk the line between being CPU and communications bound. Accordingly, my research stands to benefit from either improvements to low-level building block performance (e.g. BLAS) or algorithmic advances in high-level scalability (e.g. communication topology optimization). Finally, in turn, my application's stringent requirements will help me drive new computational developments for others to use.