Assimilation of wall-pressure measurements in high-speed flow over a cone

TL;DR

This paper applies nonlinear ensemble-variational data assimilation to estimate high-speed flow over a cone at Mach-6 from wall-pressure data, revealing the importance of 3D effects and flow nonlinearity.

Contribution

It introduces a robust data assimilation method that accounts for flow nonlinearity and three-dimensional effects in high-speed flow estimation from limited measurements.

Findings

Flow predictions show rope-like structures similar to schlieren images.

Including 3D waves is essential for accurate pressure measurement reproduction.

The study emphasizes the significance of 3D flow features and base-state distortion.

Abstract

A nonlinear ensemble-variational (EnVar) data assimilation is performed in order to estimate the unknown flow field over a slender cone at Mach-6, from isolated wall-pressure measurements. The cost functional accounts for discrepancies in wall-pressure spectra and total intensity between the experiment and the prediction using direct numerical simulations (DNS), as well as our relative confidence in the measurements and the estimated state. We demonstrate the robustness of the predicted flow by direct propagation of posterior statistics. The approach provides a unique first look at the flow beyond the sensor data, and rigorously accounts for the role of nonlinearity unlike previous efforts that adopted ad-hoc inflow syntheses. Away from the wall, two- and three-dimensional assimilated states both show rope-like structures, qualitatively similar to independent schlieren visualizations. Despite this resemblance, and even though the planar second modes are the most unstable upstream, three-dimensional (3D) waves must be included in the assimilation in order to accurately reproduce the wall-pressure measurements recorded in the Ludwieg-Tube facility. The results highlight the importance of three-dimensionality of the field and of the base-state distortion on the instability waves in this experiment, and motivate future measurements that probe the 3D nature of the flow field.