Optimal transitional mechanisms of incompressible separated shear layers subject to external disturbances
Flavio Savarino, Denis Sipp, Georgios Rigas

TL;DR
This paper investigates the optimal transition mechanisms of incompressible shear layers over separation bubbles, highlighting how pressure gradients influence instability modes and using advanced analysis to identify disturbances that maximize skin friction drag.
Contribution
It introduces a combined linear resolvent and nonlinear Harmonic-Balanced Navier-Stokes framework to analyze and optimize disturbance growth in separated shear layers with pressure gradients.
Findings
Pressure gradient shifts instability modes from T-S waves to K-H and centrifugal instabilities.
Centrifugal instability generates streamwise vortices leading to streaks and breakdown.
Optimal disturbances that maximize skin friction are identified through adjoint optimization.
Abstract
Optimal transitional mechanisms are analysed for an incompressible shear layer developing over a short, pressure gradient-induced laminar separation bubble (LSB) with peak reversed flow of 2%. Although the bubble remains globally stable, the shear layer destabilises due to the amplification of external time- and spanwise-periodic disturbances. Using linear resolvent analysis (RA), we demonstrate that the pressure gradient modifies boundary layer receptivity, shifting from Tollmien-Schlichting (T-S) waves and streaks in a zero pressure gradient (ZPG) environment to Kelvin-Helmholtz (K-H) and centrifugal instabilities in the presence of the LSB. To characterise the non-linear evolution of these disturbances, we employ the Harmonic-Balanced Navier-Stokes (HBNS) framework, solving the Navier-Stokes equations in spectral space with a finite number of Fourier harmonics. Additionally, adjoint…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
