Pressure potential and stability analysis in an acoustical noncontact transportation
Bibliographic record
Abstract
Near field acoustic traveling wave is one of the most popular principles in noncontact manipulations and transportations. The stability behavior is a key factor in the industrial applications of acoustical noncontact transportation. We present here an in-depth analysis of the transportation stability of a planar object levitated in near field acoustic traveling waves. To more accurately describe the pressure distributions on the radiation surface, a 3D nonlinear traveling wave model is presented. A closed form solution is derived based on the pressure potential to quantitatively calculate the restoring forces and moments under small disturbances. The physical explanations of the effects of fluid inertia and the effects of non-uniform pressure distributions are provided in detail. It is found that a vibration rail with tapered cross section provides more stable transportation than a rail with rectangular cross section. The present study sheds light on the issue of quantitative evaluation of stability in acoustic traveling waves and proposes three main factors that influence the stability: (a) vibration shape, (b) pressure distribution and (c) restoring force/moment. It helps to provide a better understanding of the physics behind the near field acoustic transportation and provide useful design and optimization tools for industrial applications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".