Balaji Shankar Venkatachari

Accurate modeling of laminar-to-turbulent transition is crucial for the design of hypersonic flight systems. However, the current transition models used in production CFD codes are insufficient for high-speed flows. Many extensions to low-speed models have been suggested; however, a thorough verification and validation effort is needed before these models can be used in design settings. A meaningful assessment of the generalization capability of these models is also hindered by a lack of information regarding the specific flow configurations and grids employed for model calibration. In this work, we present an independent assessment of two models for high-speed transition, namely, a one-equation model within the shear-stress-transport [ ] framework and a two-equation model based on the [ ] equations. These models are implemented in the NASA OVERFLOW 2.3e solver, and a thorough description of the models, numerical parameters, and coupled turbulence modeling parameters is given. Additionally, a formal grid convergence study is conducted for six test cases, covering first-mode and second-mode transitions, such that grid-independent predictions for each model for each case are found. These contributions represent a critical step in the characterization of the models and provide a basis for future users to verify their predictions.

The accurate, robust, and efficient prediction of transition in viscous flows is a significant challenge in computational fluid dynamics. We present a coupled high-fidelity iterative approach that leverages the FUN3D flow solver and the LASTRAC stability code to predict transition in low-disturbance environments, initiated by the linear growth of boundary-layer instability modes. Our method integrates the ability of FUN3D to compute mixed laminar–transitional–turbulent mean flows via transition-sensitized Reynolds-Averaged Navier–Stokes equations with the ability of LASTRAC to perform linear stability analysis, all within an automated framework that requires no intermediate user involvement. Unlike conventional frameworks that rely on classical stability theory or reduced-order metamodels, our approach employs parabolized stability equations to provide more accurate and reliable estimates of disturbance growth for multiple instability mechanisms, including Tollmien–Schlichting, Kelvin–Helmholtz, and crossflow modes. By accounting for the effects of mean-flow nonparallelism as well as the surface curvature, this approach lays the foundation for improved N-factor correlations for transition onset prediction in a broad class of flows. We apply this method to three distinct flow configurations: (1) flow over a zero-pressure-gradient flat plate, (2) the NLF-0416 airfoil with both natural and separation-induced transition, and (3) a 6:1 prolate spheroid, where transition is primarily driven by crossflow instability. For two-dimensional cases, a formulated intermittency distribution is used to model the transition zone between the laminar and fully turbulent flows. The results include comparisons with experimental measurements, similar numerical approaches, and transport-equation-based models, demonstrating good agreement in surface pressure coefficients, transition onset locations, and skin-friction coefficients for all three configurations. In addition to contributing a couple of new insights into boundary-layer transition in these canonical cases, this study presents a powerful tool for transition modeling in both research and design applications in aerodynamics.

The findings of the recent AIAA and NATO workshops on transition modeling have underscored the importance of verifying the transition models that are based on Reynolds-averaged Navier–Stokes. To that end, the present work aims to verify the Langtry–Menter (LM2009) transition model coupled with Menter’s shear-stress transport (SST) turbulence model. Specifically, the SST-2003-LM2009 model, as implemented in the OVERFLOW structured overset solver and the FUN3D unstructured solver, is applied to both natural and bypass transition scenarios on a flat plate and to natural and separation-bubble-induced transition on the NLF-0416 airfoil. The Richardson extrapolation method was used to analyze and report the grid convergence of integrated and local load coefficients upstream, within, and downstream of the transition region, based on solutions obtained using the two solvers on identical grids. Reasonable agreement is obtained between the two codes for both global and local surface load coefficients when further refining the workshop mesh series, although with a grid-convergence index for the local friction coefficient that may exceed 1% in certain cases. The study also highlights the solution sensitivity to the choice of turbulence inflow boundary conditions and model options, along with the necessity to unambiguously specify this information for benchmark exercises.

Transition prediction is an important aspect of aerodynamic design because of its impact on skin friction and potential coupling with flow separation characteristics. Traditionally, the modeling of transition has relied on correlation-based empirical formulas based on integral quantities such as the shape factor of the boundary layer. However, in many applications of computational fluid dynamics, the shape factor is not straightforwardly available or not well-defined. We propose using the complete velocity profile along with other quantities (e.g., frequency, Reynolds number) to predict the perturbation amplification factor. While this can be achieved with regression models based on a classical fully connected neural network, such a model can be computationally more demanding. We propose a convolutional neural network inspired by the underlying physics as described by the stability equations. Specifically, convolutional layers are first used to extract integral quantities from the velocity profiles, and then fully connected layers are used to map the extracted integral quantities, along with frequency and Reynolds number, to the output (amplification ratio). Numerical tests on classical boundary layers clearly demonstrate the merits of the proposed method. More importantly, we demonstrate that, for Tollmien-Schlichting instabilities in two-dimensional, low-speed boundary layers, the proposed network encodes information in the boundary-layer profiles into an integral quantity that is strongly correlated to a well-known, physically defined parameter—the shape factor.