Development of a High-pressure Rotational Rheometer for Investigation of Effects of Dissolved CO2
Bibliographic record
Abstract
Rheological information is often used to determine viscoelastic fluid properties, and to model and predict fluid behavior under influence of external stress or deformation. Many industrial processes involve the dissolution of gas under high pressure, so it is important to evaluate the rheological properties of viscoelastic materials under high pressure. In this research, a high pressure rotational rheometer was developed to measure the rheological parameters of viscoelastic fluids and investigate the influence of the dissolution of gases on rheology. The rheometer utilized a piezoelectric torque transducer, which enabled transient and dynamic rheological measurements under high pressure. First, the rheometer was designed, fabricated, and calibrated using a calibration fluid. Second, the capability of the rheometer was verified using polydimethylsiloxane (PDMS), which is a typical viscoelastic fluid. Viscosity and viscoelastic properties, such as storage/loss modulus, and complex viscosity, were evaluated. Thirdly, the effects of the dissolved CO2 on the rheological properties of PDMS were investigated. The effects of temperature and dissolved CO2 were investigated individually at the temperature of 25, 50, 80°C and CO2 saturation pressures of 1, 2, 3 MPa. Then, the combined effect was correlated using a generalized Arrhenius model. The proposed model expressed viscosity as a function of temperature and pressure without the need for thermodynamic and volumetric information of the fluid. The achievement of this research provides an alternative method to measure rheological properties of viscoelastic materials under high pressure and enables the prediction of the viscosity of a fluid with dissolved gas through modelling.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".