Computational Fluid Dynamic Solver Based on Cellular Discrete-Event Simulation for use in Biological Systems
Bibliographic record
Abstract
The use of computer simulations in biology and the medical research field has gained in popularity.These simulations are providing researchers the opportunity to better predict the behavior of biological systems before performing long and expensive physical trials.The modeling of large biological systems would benefit from a method of approximating fluid flow quickly and accurately.Currently, no analytical solution exists; instead, many different numerical methods attempt to provide accurate approximations.They are referred to as Computational Fluid Dynamic solvers (CFD).The Discrete Event System Specification (DEVS) has rarely been used for modeling the physics of fluid flow.In this thesis we show how Cell-DEVS, a derivative of the DEVS formalism that conforms to the Cellular Automata parameters, can be used to provide realistic approximations of fluid flow.The algorithms used in the CFD presented in this thesis are based on the Navier-Stokes equations for non-linear fluid flow, which are an extension to Newton'sSecond Law of motion.The goal of the Cell-DEVS based CFD model will be to accurately approximate the fluid flow with minimal computational effort.Furthermore, the design of the solver should be such that it can be easily adjusted for use in a wide range of biological systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.008 | 0.002 |
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".