Smoothed particle hydrodynamics method applied to pulsatile flow inside a rigid two‐dimensional model of left heart cavity
Bibliographic record
Abstract
This paper aims to extend the application of smoothed particle hydrodynamics (SPH), a meshfree particle method, to simulate flow inside a model of the heart's left ventricle (LV). This work is considered the first attempt to simulate flow inside a heart cavity using a meshfree particle method. Simulating this kind of flow, characterized by high pulsatility and moderate Reynolds number using SPH is challenging. As a consequence, validation of the computational code using benchmark cases is required prior to simulating the flow inside a model of the LV. In this work, this is accomplished by simulating an unsteady oscillating flow (pressure amplitude: A = 2500 N ∕ m(3) and Womersley number: W(o) = 16) and the steady lid-driven cavity flow (Re = 3200, 5000). The results are compared against analytical solutions and reference data to assess convergence. Then, both benchmark cases are combined and a pulsatile jet in a cavity is simulated and the results are compared with the finite volume method. Here, an approach to deal with inflow and outflow boundary conditions is introduced. Finally, pulsatile inlet flow in a rigid model of the LV is simulated. The results demonstrate the ability of SPH to model complex cardiovascular flows and to track the history of fluid properties. Some interesting features of SPH are also demonstrated in this study, including the relation between particle resolution and sound speed to control compressibility effects and also order of convergence in SPH simulations, which is consistently demonstrated to be between first-order and second-order at the moderate Reynolds numbers investigated.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".