Mapping the Turn: An Eulerian Binormal-Axis Diagnostic for Recirculating 3D Flows

TL;DR

This paper introduces an Eulerian diagnostic that quantifies local streamline turning in 3D recirculating flows, providing detailed orientation maps that enhance traditional visualization methods.

Contribution

The work presents a novel Eulerian binormal-axis diagnostic that captures local recirculation orientation without explicit streamline integration, improving 3D flow analysis.

Findings

Diagnostic successfully visualizes recirculation orientation in vortex and separation bubble flows.

Reveals orientation changes not visible with traditional streamline visualization.

Provides a quantitative, spatially resolved measure of flow turning directions.

Abstract

Three-dimensional (3D) recirculating flows are often interpreted qualitatively from selected streamline visualizations. In separated flows, such recirculating motion is central to the drag modulation, but the local orientation of recirculation remains difficult to quantify in a field-based form. This work introduces an Eulerian binormal-axis diagnostic that locally evaluates the orientation of streamline turning at each point in the velocity field, yielding a spatially resolved field of the recirculating direction. Motivated by the Frenet-Serret binormal direction of a curved streamline, the diagnostic uses the velocity vector and its convective acceleration to extract the local streamline-turning axis without requiring explicit streamline integration. The resulting direction is encoded with barycentric RGB weights to visualize streamwise, spanwise, and wall-normal turning axis contributions. The diagnostic is first applied to Hill's spherical vortex, which provides a controlled analytic example of 3D recirculating motion for interpreting the binormal-axis direction and the associated barycentric RGB encoding. It is then applied to the mean field of a pressure-gradient-induced 3D separation bubble. The resulting visualizations show that the diagnostic reveals orientation changes that are not apparent from streamline visualization. The proposed diagnostic therefore converts qualitative streamline impressions into a spatially resolved measure of local streamline-turning orientation, providing a quantitative complement to conventional 3D flow visualization.