Direct Simulation of Dense Suspensions of Non-Spherical Particles
Bibliographic record
Abstract
We describe the direct simulation of high-solids-fraction suspensions of non-spherical rigid particles that are slightly denser than the fluid. The lattice-Boltzmann method is used to solve the flow of the interstitial Newtonian fluid, and the immersed boundary method is used to enforce a no-slip boundary condition at the surface of each particle. The surface points for the immersed boundary method are also employed for collision handling by applying repulsive forces between nearby surface points. Due to the finite number of these points, the method simulates rough surface collisions. We also discuss methods for integrating the equations of particle motion at low density ratios and propose a method with improved accuracy. Rigid particles shaped like red blood cells were simulated. Simulations of a single particle showed that the particle settles in its original orientation when the Reynolds number is low (1.2) but flips to a higher drag orientation when the Reynolds number is higher (7.3). A simulation with a 45% solids volume fraction and a low solid over fluid density ratio showed the possibility of simulating blood as it is found in the body. A simulation at a lower solids volume fraction (35%) was used to compare the results with the erythrocyte sedimentation rate (ESR), a common blood test. The sedimentation rate was estimated as 0.2 mm/hr, which is an order of magnitude lower than a typical ESR of about 6 mm/hr for a healthy adult. The most likely reasons for the discrepancy are the omission of agglomeration-inducing inter-cellular forces from the simulations and the treatment of the red blood cells as rigid particles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".