Exposure of fractal aggregates to accelerating flows at finite Reynolds numbers
Bibliographic record
Abstract
Breakup of small aggregates is governed by the imbalance of imposed hydrodynamic forces and cohesive forces between constituent particles. Aggregate restructuring in ramped shear flows at infinitely low Reynolds number are known to reinforce aggregates, increasing effective cohesive strength. However, non-negligible flow inertia is known to increase breakage rates, and is expected to affect breakage kinetics under finite Reynolds number conditions in accelerated flows. A numerical investigation was conducted to establish the effect of flow acceleration on aggregate evolution. Aggregates were characterized by their size, structure and interparticle forces. Individual aggregates were subjected to accelerating flows imposed through shear stresses at the boundaries, and their structural evolution along with breakage events were recorded. Particles were tracked with Discrete Element Method. The flow was solved using a Lattice Boltzmann method, and two-way coupling between the solid and liquid phase was achieved through an Immersed Boundary Method. The findings show that although aggregates restructure due to the shear flow, their structure at breakage does not depend on shear stress. Increasing flow acceleration is found to slow down aggregate breakage and rotation, despite higher imposed shear stresses at the boundaries of the domain. The observed delays is found to be a transient effect of flow inertia around the aggregates. The reported findings establish a novel addition to the criteria for aggregate breakage, where, along with shear strength of the aggregates, flow accelerations and Reynolds number at the scale of the aggregates must also be considered. • Fully resolved hydrodynamics and cohesive forces acting on aggregates in shear flow. • Aggregate response to shear ramps: restructuring and breakage. • Shear rate and radius of gyration of aggregates at breakage. • Impact of finite Reynolds effects on breakage rate.
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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 | 0.000 | 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 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".