Stability-Based Mach Zero to Four Longitudinal Transition Prediction Criterion

“…The non-local AHD transition criterion [19] was developed as a simpler alternative to the e N method [20,21], based on stability analysis of Falkner-Skan attached self-similar boundary-layer velocity profiles. The criterion was extended to include the stabilising effect of compressibility on Tollmien-Schlichting instabilities based on stability analysis of compressible local similarity solutions first up to Mach 1.6 [22] and, more recently, up to Mach 4 [23]. However, the AHD transition criterion requires non-local flow quantities, which can complicate its integration in highly parallel and unstructured solution algorithms.…”

Section: Introductionmentioning

confidence: 99%

Compressibility corrections to extend a smooth local correlation-based transition model to transonic flows

Piotrowski

Zingg

2023

Aeronaut. j.

View full text Add to dashboard Cite

This paper presents progress towards a transition modelling capability for use in the numerical solution of the Reynolds-averaged Navier-Stokes equations that provides accurate predictions for transonic flows and is thus suitable for use in the design of wings for aircraft flying at transonic speeds. To this end, compressibility corrections are developed and investigated to extend commonly used empirical correlations to transonic flight conditions while retaining their accuracy at low speeds. A compressibility correction for Tollmien-Schlichting instabilities is developed and applied to a smooth local correlation-based transition model and a stationary crossflow instability compressibility correction is included by adding a new crossflow source term function. Two- and three-dimensional transonic transition test cases demonstrate that the Tollmien-Schlichting compressibility correction produces substantially improved agreement with the experimental transition locations, particularly for higher Reynolds number applications where the effects of flow compressibility are expected to be more significant, such as the NASA CRM-NLF wing-body configuration, while the crossflow compressibility correction prevents an inaccurate, upstream transition front. The compressibility corrections and modifications do not significantly affect the numerical behaviour of the model, which provides an efficient alternative to non-local and higher-fidelity approaches, and can be applied to other transport-equation-based transition models with low-speed empirical correlations without affecting their predictive capability in the incompressible regime.

show abstract

“…Several methods have been proposed as simplifications or surrogate models of the 𝑒 𝑁 methods, including database query techniques (van Ingen, 2008;Drela and Giles, 1987;Perraud and Durant, 2016) and data fitting techniques (Dagenhart, 1981;Stock and Degenhart, 1989;Gaster and Jiang, 1995;Langlois et al, 2002;Krumbein, 2008;Rajnarayan et al, 2013;Begou et al, 2017;Pinna et al, 2018). These methods are generally based on a small set of scalar input parameters representing the mean flow parameters and relevant disturbance characteristics.…”

Section: Introductionmentioning

confidence: 99%

Recurrent Neural Network for End-to-End Modeling of Laminar-Turbulent Transition

Zafar¹,

Choudhari²,

Paredes³

et al. 2021

Preprint

View full text Add to dashboard Cite

Accurate prediction of laminar-turbulent transition is a critical element of computational fluid dynamics simulations for aerodynamic design across multiple flow regimes. Traditional methods of transition prediction cannot be easily extended to flow configurations where the transition process depends on a large set of parameters. In comparison, neural network methods allow higher dimensional input features to be considered without compromising the efficiency and accuracy of the traditional data driven models. Neural network methods proposed earlier follow a cumbersome methodology of predicting instability growth rates over a broad range of frequencies, which are then processed to obtain the N-factor envelope, and then, the transition location based on the correlating Nfactor. This paper presents an end-to-end transition model based on a recurrent neural network, which sequentially processes the mean boundary-layer profiles along the surface of the aerodynamic body to directly predict the Nfactor envelope and the transition locations over a two-dimensional airfoil. The proposed transition model has been developed and assessed using a large database of 53 airfoils over a wide range of chord Reynolds numbers and angles of attack. The sequence-to-sequence transduction model proposed herein provides a more direct approach for accurate predictions of the transition location than the earlier neural network methods, which predict the local amplification rate of a single instability mode at a fixed location along the airfoil. The large universe of airfoils encountered in various applications causes additional difficulties. As such, we provide further insights on selecting training datasets from large amounts of available data. Impact StatementThe recurrent neural network (RNN) proposed here represents a significant step toward an end-to-end prediction of laminar-turbulent transition in boundary-layer flows. The general yet greatly simplified workflow should allow even nonexperts to apply a physics-based strategy for predicting transition due to a variety of instability mechanisms, which is a significant advantage over traditional direct computations of the stability theory. The sequence-to-sequence mapping enabled by the RNN faithfully represents the amplification of flow instability along the surface, exemplifying the embedding of physics in scientific machine learning. Finally, we use a very large dataset of stability characteristics of airfoil boundary layers for the training process and present an extensive study into the selection of training data and its effects on the prediction accuracy. These investigations provide important insights and best-practice guidance toward the practical deployment of neural-network-based transition models in engineering environments.

show abstract

Stability-Based Mach Zero to Four Longitudinal Transition Prediction Criterion

Cited by 17 publications

References 10 publications

Implementation of stability-based transition models by means of transport equations

Implementation of stability-based transition models by means of transport equations

Compressibility corrections to extend a smooth local correlation-based transition model to transonic flows

Recurrent Neural Network for End-to-End Modeling of Laminar-Turbulent Transition

Contact Info

Product

Resources

About