Global Stability and Sensitivity Analysis of a Hypersonic Slender Cone
Abstract
In high-speed flight, increased viscous heating past the onset of transition to turbulence is a critical limiting factor in the design of hypersonic vehicles. Acoustic exponential instabilities, which are ineffective at low Mach numbers, amplify and open up additional avenues for breakdown to turbulence in the compressive boundary layer. The seeding of the instabilities is generated during the receptivity stage which governs the initiation of a disturbance field inside the shear, for instance by external disturbances. The present study seeks to rigorously analyze the receptivity process of the high-speed flow over a cone geometry by leveraging the structural sensitivity information provided by the adjoint linearized governing equations. The approach enables the direct identification of the free-stream conditions which are most effective at trigger.ng perturbation growth in the boundary layer. A second key element of the study is the direct identification of strategies for delaying transition to turbulence in high-speed flows. It is well known that for instance surface porosity can weaken the amplification of instabilities, in particular the Mack second mode. The properties such as spacing anc depth of the surface porosity which most effectively dampen the amplification of instabilities have however not been thoroughly established. As part of our study, we thus seek to rigorously identify the properties of optimal surface porosity using adjoint-based sensitivity analysis. A further element of the study, which is currently pursued, is the first identification of nonlinear optimal perturbations in high-speed flows. Nonlinear optimal perturbations can be seen as minimal seeds which induce transition to turbulence as efficiently as possible, and they can thus be interpreted as worst-case conditions for breakdown to turbulence.
Document Details
- Document Type
- Technical Report
- Publication Date
- May 24, 2021
- Accession Number
- AD1224132
Entities
People
- Parviz Moin
Organizations
- Stanford University