A multi-domain lattice Boltzmann mesh refinement method for non-Newtonian blood flow modeling
Bibliographic record
Abstract
Multi-domain grid refinement is a well-established technique in lattice Boltzmann methods. However, the method is currently limited to the Newtonian flow and no established method exists for lattice Boltzmann mesh refinement in non-Newtonian fluids. This study introduces a new method for lattice Boltzmann multi-domain mesh refinement in non-Newtonian fluids, by employing rescaling, transition, and interpolation of the relaxation frequencies across the domains interface. The method also involves a correction scheme to resolve shear rate inequality across the interface, particularly in low shear rate regions of a shear-thinning flow. To adapt the method for blood flow simulations in vascular systems, it was further extended to address three dimensional (3D) cases with curved boundary interfaces, using a ghost node technique. The method was verified in two dimensions, through Hagen–Poiseuille and lid-driven cavity flows, as well as in 3D, with steady flow in an idealized stenosis, and pulsatile flow in a patient-specific aneurysm. Results were compared with fine single-resolution simulations and existing literature, showing strong agreement. The aneurysm simulation showed good agreement with wall shear stress predictions from the fine single-resolution simulation. The relative L2 norm of wall shear stress difference between the multi-domain and fine-grid simulation were 0.006 and 0.009 at end-diastole and peak-systole, respectively. Overall, the proposed method facilitates the efficient use of computational resources through mesh refinement. Combined with the high scalability of the lattice Boltzmann method for parallel simulations—attributable to the locality of computations, including shear rate calculations—this approach is well-suited for high-fidelity investigations of blood flow in arteries.
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 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".