Rapid flow characterization measurements using a modified CPMG measurement with incremented echo times, phase cycling and filtering
Bibliographic record
Abstract
We recently demonstrated a magnetic resonance methodology for measuring and characterizing various pipe flows, using a series of individually-acquired spin echoes at different τ . The key advantage of our approach lies in the simplicity of the experiment, MR hardware, and data processing. However, acquiring each spin echo separately results in prolonged measurement times. To address this, we employ an echo-train approach to acquire the series of variable τ spin echoes. By incrementing CPMG echo pulse spacings within the echo train and implementing a four-step phase cycling scheme to suppress coherence pathway effects, we obtain the same echo phase and magnitude response to flow as a function of τ 2 as in our original method, without requiring individual echo acquisitions. This new approach significantly reduces the number of required experiments, shortening measurement time by a factor of 1 / N , where N is the number of utilized echoes per echo train. Our phase cycling strategy, combined with incremented pulse spacings, enables N = 3 in our benchtop flow measurement. Validation experiments with Newtonian and shear-thinning fluids confirm that the new echo-train technique yields results consistent with the original approach of acquiring each spin echo separately. • A CPMG echo train in the presence of magnetic field gradient encodes velocity. • Variable τ along the train imparts different flow sensitivity to different echoes. • Phase-cycling and filtering select for the direct echo. • Data are fit with an analytical model of the magnetic resonance signal. • Apparatus is low-field and portable for magnetic resonance rheometry.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".