Analysis of Guided Waves Dispersion and Acoustoelastic Effect in Stressed Waveguides by Eigenfrequency Method and Experimental Study
Bibliographic record
Abstract
Abstract On the basis of the effect of initial stress and acoustoelastic coefficients on the dispersion behavior of guided waves in stressed waveguides. a finite element method is presented to analyze the wave propagation in prestressed waveguides. The approach was based on the acoustoelastic theory to solve the eigenfrequency of prestressed waveguides, where wavenumbers and modes are distinguished by modal shape, and the solutions of the phase velocity and the group velocity were determined. The algorithm was applied to analyze the dispersion and acoustoelastic coefficients of a prestressed plate and an axisymmetric bar model. Obtained results were consistent with previous research, proving that the approach is useful for the analysis of dispersion and acoustoelastic effect in prestressed waveguides. The acoustoelastic effect of the longitudinal mode guided wave in the rod was experimentally studied according to on the method of eigenfrequency analysis. The results show that the trend of the experimental results is in good agreement with that of the eigenfrequency method. The detection frequency of L(0.1) mode is around 72 kHz, the error of L(0.2) mode is small in the range of 240–280 kHz, which is more suitable for acoustoelastic stress detection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".