On the accurate analysis of vibroacoustics in head insert gradient coils
Bibliographic record
Abstract
PURPOSE: To accurately analyze vibroacoustics in MR head gradient coils. THEORY AND METHODS: A detailed theoretical model for gradient coil vibroacoustics, including the first description and modeling of Lorentz damping, is introduced and implemented in a multiphysics software package. Numerical finite-element method simulations were used to establish a highly accurate vibroacoustic model in head gradient coils in detail, including the newly introduced Lorentz damping effect. Vibroacoustic coupling was examined through an additional modal analysis. Thorough experimental studies were used to validate simulations. RESULTS: Average experimental sound pressure levels (SPLs) and accelerations over the 0-3000 Hz frequency range were 97.6 dB, 98.7 dB, and 95.4 dB, as well as 20.6 g, 8.7 g, and 15.6 g for the X-, Y-, and Z-gradients, respectively. A reasonable agreement between simulations and measurements was achieved. Vibroacoustic coupling showed a coupled resonance at 2300 Hz for the Z-gradient that is responsible for a sharp peak and the highest SPL value in the acoustic spectrum. CONCLUSION: We have developed and used more realistic multiphysics simulation methods to gain novel insights into the underlying concepts for vibroacoustics in head gradient coils, which will permit improved analyses of existing gradient coils and novel SPL reduction strategies for future gradient coil designs. Magn Reson Med 78:1635-1645, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".