Dynamics of Fluid Particles in Turbulent Flows; CFD simulations, Model Development and Phenomenological Studies
Bibliographic record
Abstract
The thesis investigates the dynamics of fluid particles in turbulent flows, which plays an important role in the chemical, pharmaceutical, food and petroleum industries. Phenomenological studies, new mathematical models for the breakup phenomena, and development of a simulation method for chemical reactors constitute the main parts of this thesis. \nThe simulation method, based on CFD population balance modeling, was successfully used in the development process of a new reactor designed by Alfa Laval. Validation with experimental measurements, for a wide range of hydrodynamic conditions and fluid properties relevant to technical applications, showed that predictions with high accuracy can be obtained. \nDetailed studies on the breakup mechanisms of fluid particles were made with a high-speed imaging technique devised for this work. It was shown that although the initial stage of the breakup process is similar for bubbles and drops, the outcomes differ significantly. An internal flow mechanism was identified as responsible for the difference in the resulting daughter size distributions. While bubbles generate unequal-sized fragments, drops often form equal-sized fragments. The number of fragments formed by breakup is also different for bubbles and drops. \nA new model for the breakup rate of fluid particles was developed in this work. In the model two criteria must be fulfilled for breakup to occur. A new model for the interaction frequency between fluid particles and turbulent eddies was also developed. Validation with experimental measurements of the breakup rate showed that the new model gives excellent predictions. Furthermore, the model reveals that eddies close in size to, and up to three times larger than, the fluid particles contribute to the breakup. This prediction agrees with the studies of the breakup mechanisms, which show that fluid particles often deform significantly before breakup occurs. \n\n\n\n
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".