Publications

High-fidelity simulations are performed to characterize the turbulence-induced wall pressure fluctuations on a sharp cone at a 5.5° angle-of-attack in a Mach 8 flow. Wall-resolved large-eddy simulation (LES) and wall-modeled large-eddy simulation (WMLES) results are compared to measurements at several locations on the cone body, where comparisons with high-frequency PCB sensors are good, while comparison with low-frequency kulite sensors varies depending on the location. For a given streamwise location, simulation results show significant azimuthal variation in the wall pressure fluctuation statistics. Comparisons between LES and WMLES results indicate that WMLES can accurately reproduce the low-frequency component of the autospectra quite well, while slight sensitivity is apparent in the coherence. A grid sensitivity study for WMLES further demonstrated sensitivity in the wall pressure fluctuation coherence, but not in the autospectra.

The near wake flow field associated with hypersonic blunt bodies is characterized by complex physical phenomena resulting in both steady and time dependent pressure loadings on the base of the vehicle. Here, we focus on the unsteady fluid dynamic pressure fluctuation behavior as a vibratory input loading. Typically, these flows are characterized by a locally low-pressure, separated flow region with an unsteady formation of vortical cells that are locally produced and convected downstream into the far-field wake. This periodic production and transport of vortical elements is very-well known from classical incompressible fluid mechanics and is usually termed as the (Von) Karman vortex street. While traditionally discussed within the scope of incompressible flow, the periodic vortex shedding phenomenon is known for compressible flows as well. To support vehicle vibratory loading design computations, we examine a suite of analytical and high-fidelity computational models supported by dedicated experimental measurements. While large scale simulation approaches offer very high-quality results, they are impractical for design-level decisions, implying that analytically derived reduced order models are essential. The major portions of this effort include an examination of the DeChant-Smith Power Spectral Density (PSD) [1] model to better understand both overall Root Mean Square (RMS) magnitude and functional maximum associated with a critical vortex shedding phenomenon. The critical frequency is examined using computational, experiments and an analytical shear layer frequency model. Finally, the PSD magnitude maximum is studied using a theory-based approach connecting the PSD to the spatial correlation that strongly supports the DeChant-Smith PSD model behavior. These results combine to demonstrate that the current employed PSD models provide plausible reduced order closures for turbulent base pressure fluctuations for high Reynolds number flows over range of Mach numbers. Access to a reliable base pressure fluctuation model then permits simulation of bluff body vibratory input.

A wind tunnel test from AEDC Tunnel 9 of a hypersonic turbulent boundary layer is analyzed using several fidelities of numerical simulation including Wall-Modeled Large Eddy Simulation (WMLES), Large Eddy Simulation (LES), and Direct Numerical Simulation (DNS). The DNS was forced to transition to turbulence using a broad spectrum of planar, slow acoustic waves based on the freestream spectrum measured in the tunnel. Results show the flow transitions in a reasonably natural process developing into turbulent flow. This is due to several 2nd mode wave packets advecting downstream and eventually breaking down into turbulence with modest friction Reynolds numbers. The surface shear stress and heat flux agree well with a transitional RANS simulation. Comparisons of DNS data to experimental data showreasonable agreement with regard to mean surface quantities aswell as amplitudes of boundary layer disturbances. The DNS does show early transition relative to the experimental data. Several interesting aspects of the DNS and other numerical simulations are discussed. The DNS data are also analyzed through several common methods such as cross-correlations and coherence of the fluctuating surface pressure.

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