Impact Localisation in Orthotropic Plates Using Flexural Wave Intensity Measurement
Bibliographic record
Abstract
In this paper, a method for the localisation and quantification of impacts on orthotropic plates, based on the measurement of the resulting vibrational energy flow, or structural intensity, is presented. The outputs of a compact array of bonded piezo-electric sensors are digitally filtered and combined to yield a local estimate of the instantaneous vibrational energy flow. In the case of transient vibration due to an impact, two or more such estimates can be used to localise the point of impact. The use of energy flow vectors for localisation requires a priori knowledge of the material properties, but may provide more direct access to position information than conventional techniques. Furthermore, the magnitude of the measured energy flow, when combined with knowledge of the impact location, can provide a quantitative measure of impact severity. The approach is based on Kirchhoff orthotropic plate theory, and is thus applicable to low frequencies, and utilises instantaneous estimates of the local wave field to allow the estimation of the various forces, moments and velocities that are required to calculate the vibrational energy flow. The theoretical background and principles of implementation of the approach are outlined, and the effects of sensor array design on systematic errors and sensitivity to measurement noise are discussed. Numerical simulations are used to assess the effectiveness of the technique and to determine its sensitivity to noise and other errors. These simulations suggest that the obtained energy flow estimates can be used for impact localisation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".