Nonlinear acoustic beam propagation modeling in dissipative media
Bibliographic record
Abstract
Accurate simulation of an intensive ultrasound beam requires taking nonlinear propagation effects into account.A notable example in the field of biomedical ultrasound where the effect of nonlinearity may play a significant role is the high intensity focused ultrasound (HIFU) as a non-invasive energy-based treatment modality.In this work, a 3D numerical model to simulate nonlinear propagation of continuous wave ultrasound beams in dissipative homogeneous tissue-like media is presented.The model implements a second-order operator splitting method in which the effects of diffraction, nonlinearity and attenuation are propagated over incremental steps.The model makes use of an arbitrary 3D source geometry definition method and a non axi-symmetric propagation scheme, which leads to a 3D solution to the resulting nonlinear ultrasound field.This work builds on methods developed by Tavakkoli et al. (1998) and Zemp et al. (2003) and offers an efficient way to calculate nonlinear field of continuous wave ultrasound sources.The proposed model is a particularly useful computational tool in carrying out simulations of high intensity focused ultrasound beams in soft tissue where the effects of nonlinearity, diffraction, and attenuation are important.The model was validated through comparisons with other established linear and nonlinear numerical models as well as published experimental data.
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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 | 0.001 |
| 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 | 0.001 |
| 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".