Bibliographic record
Abstract
he largest application of fluidization and multiphase flow is in the refining of oil to produce gasoline using catalytic crackers with 75 micron particles. However, the computational fluid dynamics approach to this problem using the principles of conservation of mass, momentum and energy for each phase did not start in the oil industry but in a department of energy laboratory in Morgantown, West Virginia who has funded a number of universities through the university coal research program since about 1980. CFD simulations by several groups in the USA, Europe and China have shown that the multiphase flow models correctly predict transient behavior of fluidized beds: bubbles, clusters and flow regimes, as reviewed by Gidaspow in “Multiphase Flow and Fluidization”, Academic Press, 1994. The early multiphase flow models developed for nuclear safety required that the flow regime be specified. More recently it was shown that the kinetic theory based models can also compute dispersion and mass transfer coefficients. The mass transfer coefficients for fluidization of fine particles have been known to have Nusselt numbers three to six orders of magnitude below the classical conduction or diffusion limit of two. There was no good explanation for over half a century. Our explanation, in agreement with the Morgantown staff, is that this is due to cluster formation which we compute. A recent review of the subject is in ,” Computational Techniques “, D. Gidaspow and V. Jiradilok , Nova Science Publishers, 2009. The CFD approach to fluidization and particulate two-phase flow, like single phase flow, involves the solution of conservation laws for mass, momentum and energy .But for particulate flow, there is the additional energy dissipation due to inelastic collisions. The early work of Stuart Savage at McGill university in Canada (Lun , et al, J. Fluid. Mech., 1984, 223-256) and Roy Jackson in “The Dynamics of Fluidized Particles”, Cambridge University Press, 2000, has shown that the standard kinetic theory of dense gases can be modified by
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".