https://wse.zoom.us/j/99813484575
“Structured Input-Output Analysis of Transitional Wall-Bounded Flows”
Presented by CHANG LIU
(Adviser: Prof. Dennice Gayme)
Input-output analysis has proven to be a valuable analytical tool for identifying important flow structures and energetic motions in transitional channel flows. The traditional approach abstracts the nonlinear terms as forcing that is unstructured, in that is not directly tied to the underlying dynamics. This work instead employs a structured singular value-based approach ($mu$ analysis) that preserves the dominant properties of the nonlinear forcing function in an effort to recover the larger range of key flow features identified through nonlinear analysis of transitional flows. We first apply the method to transitional plane Couette flow. The results demonstrate that the traditional input-output approaches capture the streamwise vortices and the proposed structured approach identifies the dominant oblique flow structures predicted through experimental observations, direct numerical simulation, and nonlinear optimal perturbation approaches. In plane Poiseuille flow, the most sensitive flow structures are predicted to grow in both the streamwise and spanwise directions with channel size, which is consistent with observations of the growth of oblique turbulent bands with DNS channel size. The ability to identify these streamwise varying structures as dominant in both flow regimes suggests that employing a structured approach to model the nonlinearity maintains the nonlinear saturation of the lift-up mechanism (i.e., amplification of streamwise streaks by cross-stream forcing) known to dominate the linear operator. Capturing this key mechanism enables the prediction of the wider range of known transitional flow structures without the need for fully nonlinear analysis.
“Observation-Infused Simulations for Accurate Predictions of High-Speed Boundary-Layer Transition”
Presented by DAVID BUCHTA
(Adviser: Prof. Tamer Zaki)
High-speed boundary-layer transition is extremely sensitive to the free-stream disturbances which are often uncertain. This uncertainty compromises predictions of models and simulations. To enhance the fidelity of simulations, we directly infuse them with available observations. Our methodology is general and can be adopted with any simulation approach and is herein demonstrated using direct numerical simulations. An ensemble variational (EnVar) optimization is performed, whereby we determine the upstream flow that optimally reproduces the observations for boundary-layer transition at Mach 4.5. Without prior knowledge of the free-stream condition and using only observations of wall pressure at isolated locations from an independent computation (true flow), all of the relevant inflow disturbances are identified, and the complete flow state is faithfully reconstructed. Our ability to reconstruct the flow with limited observations is predicated on the spatio-temporal domain of sensitivity of the sensor, which is analyzed using adjoint methods.