Development and characterization of a passive, bio-inspired flow-tracking sensor
Bibliographic record
Abstract
) flow tracking measurements, systems suffer from inertial lag due to the increased size and mass needed for optical visibility, or by carrying a sensor payload, such as an inertial measurement unit (IMU). While IMU-based flow sensing is promising for beyond visual line-of-sight applications, the size and mass of the sensor platform results in reduced flow fidelity and, hence, measurement error. Thus, to extract otherwise inaccessible flow information, a flow-physics-based tracer correction is developed through the application of a low-order unsteady aerodynamic model, inspired by the added-mass concept. The technique is evaluated using a sensor equipped with an IMU and magnetometer. A spherical sensor platform, selected for its symmetric geometry, was subject to two canonical test cases including an axial gust as well as the vortex shedding generated behind a cylinder. Using the measured sensor velocity and acceleration as inputs, an energized-mass-based dynamic model is used to back-calculate the instantaneous flow velocity from the sensor measurements. The sensor is also tracked optically via a high-speed camera while collecting the inertial data onboard. For the 1D test case (axial gust), the true (local) wind speed was estimated from the energized-mass-based model and validated against particle image velocimetry measurements, exhibiting good agreement with a maximum error of 10%. For the cylinder wake (second test case), the model-based correction enabled the extraction of the velocity oscillation amplitude and vortex-shedding frequency, which would have otherwise been inaccessible. The results of this study suggest that inertial (i.e. large and heavy) IMU-based flow sensors are viable for the extraction of Lagrangian tracking at large atmospheric scales and within highly-transient (turbulent) environments when coupled with a robust dynamic model for inertial correction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".