Modeling acoustic interactions with a dynamic rough sea surface boundary
Bibliographic record
Abstract
The acoustic wave scattering properties of a dynamic pressure-release surface boundary are analyzed using a numeric technique based on the finite-difference time-domain (FDTD) method. Of primary interest is to study the impact of assuming a “frozen” sea-surface on long duration sonar transmissions. Although relatively uncommon in ocean acoustics, the FDTD approach is well suited for modeling boundary roughness and motion. This technique has the additional benefit that the pressure fields are resolved over time, which allows for transient analysis of the observed wave scattering effects. The method is adapted from electromagnetic wave scattering and can properly model the physics of the observed system, which shows a frequency modulated reflection that includes a double-Doppler effect. First, a traditional analytic solution for the static smooth surface boundary ocean half-space model, the Lloyd Mirror, is compared to an equivalent simulation using the FDTD method. Then a dynamic smooth surface boundary is investigated using a modified Lloyd-mirror solution and the FDTD method. Finally, surface roughness for both static and dynamic boundary cases will be considered. Agreement is shown between FDTD simulations and the modified Lloyd Mirror model for both one-dimension and two-dimension cases. [Work supported by the Office of Naval Research.]
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".