arXiv subjects

Flavio Savarino

Publications and source records attributed to Flavio Savarino.

5 recordsLinked to original sources

arXiv2026

Learning 3D Hypersonic Flow with Physics-Enhanced Neural Fields: A Case Study on the Orion Reentry Capsule

We develop a 3D aerothermodynamic simulator for the Orion reentry capsule at hypersonic speeds, a timely case study given its role in upcoming lunar missions. The large computational meshes required for these scenarios make traditional computational fluid dynamics impractical for full-mission performance prediction and control. In this work, we propose physics-enhanced 3D neural fields for predicting steady hypersonic flow around aerodynamic bodies. The model maps spatial coordinates and angle of attack to pressure, temperature, and velocity components. We enhance the base model with Fourier positional feature mappings, which allow it to capture the sharp discontinuities typical of hypersonic flows, and further constrain the solution by imposing no-slip and isothermal wall conditions. We compare our proposed approach to other surrogate alternatives, such as graph neural networks, and demonstrate its superior performance in capturing the steep gradients ubiquitous in this regime. Our formulation yields a continuous and computationally efficient aerothermodynamic surrogate that supports rapid exploration of operating conditions based on angle of attack variation under realistic flight profiles. While we focus on Orion, the proposed framework provides a general methodology for data-driven simulation in 3D hypersonic aerothermodynamics.

physics.flu-dyn↗

arXiv2026

Optimal non-linear mechanisms for laminar-turbulent transition of a shock-induced separated shear layer

Laminar-turbulent transition in shock wave-boundary-layer interactions (SWBLI) remains a major challenge for hypersonic vehicle design, with implications for drag, heat transfer, and structural loads. Linear optimal perturbation analyses can identify candidate instabilities, but the full route to breakdown in SWBLI requires nonlinear optimisation. Here, we characterise the optimal transition pathway in a globally stable yet convectively unstable Mach 2.15 oblique SWBLI using a nonlinear input-output optimisation framework based on the space-time spectral Navier-Stokes formulation of Poulain et al. (Comput. Fluids, 2024). The nonlinear frequency-domain approach captures mean-flow distortion, resolves triadic energy transfers, and extracts intrinsic nonlinear stresses that activate additional instability mechanisms. We identify a four-stage pathway: (1) optimal forcing of oblique first Mack mode waves at moderate frequencies; (2) nonlinear self-interaction of counter-propagating Mack waves, generating streamwise Gortler-like vortices in the reattachment region where streamline curvature peaks; (3) lift-up of streamwise velocity streaks by these vortices; and (4) subharmonic sinuous secondary instability leading to streak breakdown. Optimisation across forcing amplitudes from infinitesimal to transitional levels yields quasi-invariant optimal forcing structures, showing that exciting the oblique first Mack mode alone can trigger the turbulent cascade. Parametric studies over frequency-wavenumber space and forcing configurations confirm this preferential pathway. By resolving nonlinear energy transfers with a finite number of harmonics, this work provides a tractable framework for transition prediction and control strategy development in high-speed separated flows, bridging linear stability theory and fully turbulent simulation.

physics.flu-dyn↗

arXiv2025

Optimal transitional mechanisms of incompressible separated shear layers subject to external disturbances

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 optimisation is incorporated to identify forcing disturbances that maximise the mean skin friction drag, conveniently chosen as the cost function for the optimisation problem since it is commonly observed to increase in the transitional stage. Compared to attached boundary layers, this transition scenario exhibits both similarities and differences. While oblique T-S instability is replaced by oblique K-H instability, both induce streamwise rotational forcing through the quadratic non-linearity of the N-S equations. However, in separated boundary layers, centrifugal instability first generates strong streamwise vortices due to multiple centrifugal resolvent modes, which then develop into streaks via lift-up. Finally, we show that the progressive distortion and disintegration of K-H rollers, driven by streamwise vortices, lead to the breakdown of large coherent structures.

physics.flu-dyn↗

arXiv2023

Aerothermodynamic Simulators for Rocket Design using Neural Fields

The typical size of computational meshes needed for realistic geometries and high-speed flow conditions makes Computational Fluid Dynamics (CFD) impractical for full-mission performance prediction and control. Reduced-Order Models (ROMs) in low-speed aerodynamics have come a long way in terms of reconstructing coherent flow patterns, thus enabling aerodynamic performance prediction. While many examples of ROMs exist for low-speed aerodynamics, there is no such broad literature for high-speed flows. We propose to use physics-enhanced neural fields for prediction of the steady, supersonic flow around a rocket for resolving: the bow shock profile, the boundary layer gradients and the wake over a range of incidences. This approach can lead to the construction of a computationally inexpensive, continuous aerothermodynamic model of a rocket at realistic flight conditions with applications to aerodynamic design and mission analysis and control. The use of neural fields allows to quickly and efficiently sweep the Angle of Attack (AoA) in a continuous manner, as compared to traditional CFD which requires running simulations for each discrete incidence value.

physics.flu-dyn↗

arXiv2022

Reconstruction of irregular flow dynamics around two square cylinders from sparse measurements using a data-driven algorithm

We propose a data-driven algorithm for reconstructing the irregular, chaotic flow dynamics around two side-by-side square cylinders from sparse, time-resolved, velocity measurements in the wake. We use Proper Orthogonal Decomposition (POD) to reduce the dimensionality of the problem and then explore two different reconstruction approaches: in the first approach, we use the subspace system identification algorithm n4sid to extract a linear dynamical model directly from the data (including the modelling and measurement error covariance matrices) and then employ Kalman filter theory to synthesize a linearly optimal estimator. In the second approach, the estimator matrices are directly identified using n4sid. A systematic study reveals that the first strategy outperforms the second in terms of reconstruction accuracy, robustness and computational efficiency. We also consider the problem of sensor placement. A greedy approach based on the QR pivoting algorithm is compared against sensors placed at the POD mode peaks; we show that the former approach is more accurate in recovering the flow characteristics away from the cylinders. We demonstrate that a linear dynamic model with a sufficiently large number of states and relatively few measurements, can recover accurately complex flow features, such as the interaction of the irregular flapping motion of the jet emanating from the gap with the vortices shed from the cylinders as well as the convoluted patterns downstream arising from the amalgamation of the individual wakes. The proposed methodology is entirely data-driven, does not have tunable parameters, and the resulting matrices are unique (to within a linear coordinate transformation of the state vector). The method can be applied directly to either experimental or computational data.

physics.flu-dyn↗