Using transient energy release measurements for the in‐line characterization of<scp>non‐Newtonian</scp>fluids and fluid state in pipe flow
Bibliographic record
Abstract
Abstract Alberini et al. have developed a new technology based on a passive acoustic emission (AE) sensing system that uses only a single sensor, with the goal of providing live and in‐situ measurement of rheology. For this study, three different types of fluids were selected to represent common rheological behaviours: Newtonian behaviour, non‐Newtonian behaviour with power law, and non‐Newtonian behaviour with Herschel–Bulkley relationship. By analyzing the transient energy released during the interaction between the probe and the fluid, distinct acoustic fingerprints were identified in the frequency domain. These acoustic fingerprints were found to be characteristic of the different fluids and their rheology, and were validated in triplicate. Furthermore, the results showed that the intensity of the acoustic emissions increased with higher flow rates (30 to 50 L/min). To test the correlation between flow rate and acoustic response, a neural network regression test was conducted, which demonstrated a direct correlation between AE peaks and flow rate. The neural network used was nonlinear autoregressive network with exogenous inputs (NARX), and the test involved a stepwise regression with 70% training and 30% network validation. The study also introduced the Rheology‐AE quotient, which maps fluid constituents against the acoustic signal. Results showed that this was a reliable means of deriving live rheology from a fluid's frequency domain. Finally, the results obtained from this study were validated using an offline rotational rheometer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".