Quantification of a Single Gas Bubble Growth in Solvent(s)–CO2–Heavy Oil Systems With Consideration of Multicomponent Diffusion Under Nonequilibrium Conditions
Bibliographic record
Abstract
A mechanistic model has been developed and validated to quantify a single gas bubble growth with considering multicomponent gas diffusion in solvent(s)–CO2–heavy oil systems under nonequilibrium conditions. Experimentally, constant-composition expansion (CCE) experiments are conducted for C3H8–CO2–heavy oil systems under equilibrium and nonequilibrium conditions, respectively. Theoretically, the classic continuity equation, motion equation, diffusion–convection equation, real gas equation, and Peng–Robinson equation of state (PR EOS) are integrated into an equation matrix to dynamically predict gas bubble growth. Also, the viscous term of motion equation on the gas phase pressure is included due mainly to the viscous nature of heavy oil. The newly proposed model has been validated by using the experimentally measured gas bubble radius as a function of time with good accuracy. Combining with the experimental measurements, the critical nucleus radius and gas bubble growth are quantitatively predicted with the newly proposed model. Effects of mass transfer, supersaturation pressure, mole concentration of each component, liquid cell radius, and pressure decline rate on the gas bubble growth are examined and analyzed. In general, gas bubble growth rate is found to increase with an increase of each of the aforementioned five parameters though the contribution of individual component in a gas mixture to the bubble growth rate is different. A one-step pressure drop and the unlimited liquid volume surrounding a gas bubble are considered to be the necessary conditions to generate the linear relationship between gas bubble radius and the square root of time.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".