S. Hales Swift | Searcharxiv

arXiv2026

Piezoelectric tiles for passive flow rate monitoring across a surface

We introduce a method for measuring the velocity of turbulent fluid flow passing through a pipe using piezoelectric tiles without penetrating the pipe, and without having previously designed the pipe to easily allow monitoring. To measure the flow, the vibrations induced on the pipe by the fluctuating pressure loading induced by the turbulent flow are measured and compared across flow speeds to establish effective invertible relationships from vibration to velocity. Measurements are reported for instrumented pipes transporting, in separate experiments, water and air. The water experiment was able to resolve linear velocity differences on the order of 1~cm/second, while the air experiment was able to resolve on the order of 15~cm/sec. Turned inside out, a similar system might be used to assess external flow velocity, determine differential velocities on opposite sides of a body traveling through air and water, and thus provide navigational data in the form of speed and attitude/angle of attack information. Although this approach is prototyped for a single sensor, it is likely to benefit substantially from the noise suppression possible when employing an array of sensors.

physics.flu-dyn↗

arXiv2026

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

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.

cs.LG↗