Numerical & Experimental Studies in Developing Turbulent Flows Via Multi-Scale Similarity

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CBET-0829020CastilloThis project seeks to perform numerical simulations of complex flows subject to adverse pressure gradient with eventual separation to better understand how the turbulent flows develop over smooth/rough surfaces at different Reynolds numbers. The PIs plan the integration of theoretical, experimental and computational Large Eddy Simulation (LES) and Direct Navier-Stokes (DNS) computations to increase the understanding of pressure gradient flows. The main objective is to demonstrate the suitability of LES/DNS methods to strong adverse pressure gradient (APG) flows with eventual separation by using the multi-scale similarity method. If successful, this will be the first time that the LES technique works on APG flows at moderate Reynolds numbers. Validation of the numerical solution will be confirmed with the smooth and rough surface experiments carried out by means of LDA. The PI's multi-scale similarity method is an improvement because it uses different scales for the inner and outer flow. For the first time the rescaling method developed by Lund et al. (1998) for zero pressure gradient flows works on APG, although the single scaling approach fails. The PIs seek to developed analytical tools to calculate the skin friction law and a separation criterion for rough boundary layers. This research will be catalyzed by the Rensselaer's Computational Center for Nanotechnology Innovations, a 70 TFLOP supercomputer, to carry out one of the most extensive simulations available for the turbulence community. Experiments will be carried out at the new Boundary Layer Wind Tunnel Facility equipped with unique optical instrumentation and low turbulence intensity (less than 0.1%). The research proposed herein has been designed for two students from underrepresented groups to dedicate full time to this project. Some additional outreach activities for students in the proposed project will include engaging research experiences for undergraduate students, providing one-to-one mentoring for the participant postdoctoral fellow and undergraduate students, and having summer workshops on multi-scales, data analysis and parallel computing for participating students. The project will interface and use the existing Alliances for Graduate Education in the Professoriate (AGEP) infrastructure and know-how at RPI. For example, seminars and summer workshops will be recorded and uploaded on a website. This will facilitate dissemination and student recruitment. This award is also funded at the Office of Naval Research (R. Joslin PD).