Completely noninvasive viscosity characterization using a portable magnetic resonance sensor
Bibliographic record
Abstract
This repository contains all raw data files and processing scripts associated with the paper published in Physics of Fluids entitled "Completely noninvasive viscosity characterization using a portable magnetic resonance sensor" Abstract: Fluid viscosity is typically measured by extracting a sample into a separate container and analyzing the response of a moving probe. However, some fluids are hazardous or sensitive to shear, preventing vial opening or probe insertion. Therefore, completely noninvasive, non-contact measurement is desirable. The natural motion sensitivity of magnetic resonance imaging (MRI) has proven to be an effective method for characterizing fluid rheology, but industrial applications of these techniques are often constrained by the size and cost of conventional magnetic resonance scanners. In recent decades, there has been a shift toward compact MRI instruments designed to complement traditional scanners. Among these, constant-gradient portable magnet arrays represent a subset that sacrifices high-resolution imaging in favor of bulk measurements from a localized "sensitive region." The constant magnetic field gradient enhances magnetic resonance sensitivity to motion. Variations in velocity within the sensitive region lead to phase interference, modulating the signal magnitude when integrated across the dimensions of the sensitive region. In this work, we investigate the effects of the spin-up of a rotating fluid cylinder on magnetic resonance signal and its dependence on viscosity. Although the flow becomes inviscid once solid-body rotation is established, viscosity can be inferred from the rate at which the signal approaches an equilibrium following an impulsive change in fluid rotation. We demonstrate this technique by measuring the signal response of glycerol/water solutions at a range of concentrations, speeds, and heights. This technique is entirely noninvasive and does not require opening the sample vessel, which is advantageous for industrial applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.027 |
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; both teacher heads agree on what is shown here.
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".