Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Estimating Flow Velocity and Vehicle Angle-of-Attack from Non-invasive Piezoelectric Structural Measurements Using Deep Learning

Chandler B. Smith, S. Hales Swift, Andrew Steyer, Ihab El-Kady

Abstract

Accurate estimation of aerodynamic state variables such as freestream velocity and angle of attack (AoA) is important for aerodynamic load prediction, flight control, and model validation. This work presents a non-intrusive method for estimating vehicle velocity and AoA from structural vibration measurements rather than direct flow instrumentation such as pitot tubes. A dense array of piezoelectric sensors mounted on the interior skin of an aeroshell capture vibrations induced by turbulent boundary layer pressure fluctuations, and a convolutional neural network (CNN) is trained to invert these structural responses to recover velocity and AoA. Proof-of-concept is demonstrated through controlled experiments in Sandia's hypersonic wind tunnel spanning zero and nonzero AoA configurations, Mach~5 and Mach~8 conditions, and both constant and continuously varying tunnel operations. The CNN is trained and evaluated using data from 16 wind tunnel runs, with a temporally centered held-out interval within each run used to form training, validation, and test datasets and assess intra-run temporal generalization. Raw CNN predictions exhibit increased variance during continuously varying conditions; a short-window moving-median post-processing step suppresses this variance and improves robustness. After post-processing, the method achieves a mean velocity error relative to the low-pass filtered reference velocity below 2.27~m/s (0.21\%) and a mean AoA error of $0.44^{\circ} (8.25\%)$ on held-out test data from the same experimental campaign, demonstrating feasibility of vibration-based velocity and AoA estimation in a controlled laboratory environment.

Estimating Flow Velocity and Vehicle Angle-of-Attack from Non-invasive Piezoelectric Structural Measurements Using Deep Learning

Abstract

Accurate estimation of aerodynamic state variables such as freestream velocity and angle of attack (AoA) is important for aerodynamic load prediction, flight control, and model validation. This work presents a non-intrusive method for estimating vehicle velocity and AoA from structural vibration measurements rather than direct flow instrumentation such as pitot tubes. A dense array of piezoelectric sensors mounted on the interior skin of an aeroshell capture vibrations induced by turbulent boundary layer pressure fluctuations, and a convolutional neural network (CNN) is trained to invert these structural responses to recover velocity and AoA. Proof-of-concept is demonstrated through controlled experiments in Sandia's hypersonic wind tunnel spanning zero and nonzero AoA configurations, Mach~5 and Mach~8 conditions, and both constant and continuously varying tunnel operations. The CNN is trained and evaluated using data from 16 wind tunnel runs, with a temporally centered held-out interval within each run used to form training, validation, and test datasets and assess intra-run temporal generalization. Raw CNN predictions exhibit increased variance during continuously varying conditions; a short-window moving-median post-processing step suppresses this variance and improves robustness. After post-processing, the method achieves a mean velocity error relative to the low-pass filtered reference velocity below 2.27~m/s (0.21\%) and a mean AoA error of on held-out test data from the same experimental campaign, demonstrating feasibility of vibration-based velocity and AoA estimation in a controlled laboratory environment.
Paper Structure (13 sections, 2 equations, 18 figures, 3 tables)

This paper contains 13 sections, 2 equations, 18 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Method Overview
  3. Problem formulation and target quantities
  4. CNN architecture
  5. Experimental Setup
  6. Test-training data partitioning methodology
  7. Velocity and AoA Estimation Experimental Validation
  8. Reference freestream velocity estimation under zero AoA conditions
  9. Differential data requirements for steady vs transient conditions for zero AoA flow
  10. Velocity component and AoA estimation under nonzero AoA conditions
  11. Differential data requirements for steady vs transient conditions for nonzero AoA flow
  12. Results Summary
  13. Conclusion

Figures (18)

  • Figure 1: Body-referenced coordinate frame $(x_b,y_b)$ and estimated in-plane body-referenced velocity components $(v_x,v_y)$. The vector $v_{hwt}$ indicates the wind tunnel flow direction and magnitude used to define the freestream reference.
  • Figure 2: The sharp cone as installed in the Sandia HWT and the cabling to the piezoelectric network. The nose tip has been temporarily removed and replaced with a calibrator.
  • Figure 3: Diagram of the internal piezoelectric sensor layout where a single array of 48 piezoelectric sensors is highlighted.
  • Figure 4: The raw reference wind tunnel velocity estimate (blue) and the low-pass filtered reference velocity (dashed-red) for Mach 8 runs.
  • Figure 6: Mach 8 velocity high-frequency fluctuations (blue), computed as the facility velocity estimate minus the low-pass filtered reference. The mean (black dashed) and 90th percentile bounds (red) are indicated.
  • ...and 13 more figures