PREDICTING TRANSITION FROM LAMINAR TO TURBULENT FLOW OVER A SURFACE USING MODE-SHAPE PARAMETERS

摘要

In accordance with embodiments disclosed herein, there are provided methods, systems, and apparatuses for predicting whether a point on a computer-generated aircraft or vehicle surface is adjacent to laminar or turbulent flow is made using a transition prediction technique. A plurality of boundary-layer properties at the point are obtained from a steady-state solution of a fluid flow in a region adjacent to the point. Included in the list of boundary-layer properties are computed coefficients or weights of mode shapes that describe the boundary-layer profiles. A plurality of instability modes are obtained, each defined by one or more mode parameters. A vector of regressor weights is obtained for the known instability growth rates in a training dataset. For each instability mode in the plurality of instability modes, a covariance vector is determined, which is the covariance of a predicted local growth rate with the known instability growth rates. Each covariance vector is used with the vector of regressor weights to determine a predicted local growth rate at the point. Based on the predicted local growth rates, an n-factor envelope at the point is determined.

主权项

1. A computer-implemented method for predicting whether a point on a computer-generated aircraft or vehicle surface is adjacent to laminar or turbulent fluid flow, the method comprising:
(a) obtaining a plurality of boundary-layer properties, including coefficient weights of mode-based profile descriptions, at the point on the computer-generated aircraft or vehicle surface using a steady-state solution of a fluid flow in a region adjacent to the point; (b) obtaining a plurality of instability modes, wherein one or more mode parameters define each instability mode; (c) obtaining a vector of regressor weights of known instability growth rates in a training dataset; (d) for each instability mode in the plurality of instability modes:
i. determining a covariance vector comprising the covariance of a predicted local growth rate for the point with respect to each of the known instability growth rates in the training dataset;ii. determining a predicted local growth rate at the point for the instability mode using the vector of regressor weights and the covariance vector; and (e) determining an n-factor envelope at the point for the plurality of instability modes using the predicted local growth rates, wherein the n-factor envelope is indicative of whether the point is adjacent to laminar or turbulent flow.