Modal analysis of a multilayered gradient coil insert in a 4‐T MRI scanner
Bibliographic record
Abstract
Abstract In ultrahigh field strength Magnetic Resonance Imaging (MRI), the pulsed electrical currents passing through gradient coil windings cause powerful alternating forces that act on the gradient coil structure and create structural vibration and consequential acoustic noise (as high as 130 dB). The dynamic response of the gradient coil cylinder largely determines the acoustic noise generated in terms of frequency components, sound pressure levels (SPL), and the spatial distribution within the bore of the scanner. The objective of this article is to characterize the dynamic behavior of a real (multilayered) gradient coil insert in a 4‐T MRI scanner (from 200 to 3,000 Hz). The simply supported (assembly condition) and free–free (modal testing) condition are investigated in the dynamic analysis. In‐situ vibration testing, modal testing, and numerical analyses are applied to uncover the dynamic features. The results indicate that bending modes in combination with different specific free–free and simply supported radial mode shapes provide the fundamental characteristics of the dynamic behavior of the gradient coil cylinder in the frequency range tested. The change in dominant bending mode as a function of scanning input frequency was also studied. © 2007 Wiley Periodicals, Inc. Concepts Magn Reson Part B (Magn Reson Engineering) 31B: 237–254, 2007
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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 | 0.000 |
| 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".