Physics-Based Combustion Simulation
Bibliographic record
Abstract
We propose a physics-based combustion simulation method for computer graphics that extends the mathematical models of previous efforts to automatically capture more realistic flames as well as temperature and soot distributions. Our method includes mathematical models for the thermodynamic properties of real-world fuels which enables, for example, the prediction of adiabatic flame temperatures. We couple this with a model of heat transfer that includes convection, conduction as well as both radiative cooling and heating. This facilitates among other things ignition at a distance without heating up the intermediate air. We model the combustion as infinitely fast chemistry and couple this with the thin flame model, spatially varying laminar burning velocities based on local species and empirical measurements, physically validated soot formation and oxidation as well as water vapor production and condensation. We implement this on adaptive octree-like grids with collocated state variables, a new SBDF2-derived semi-Lagrangian time integrator for velocity, and a multigrid scheme used for multiple solver components. In combination, these models enable us to simulate deflagration phenomena ranging from small scale premixed and diffusion flames to fireballs and subsonic explosions which we demonstrate by several examples. In addition, we validate several of the results based on reference footage and measurements and discuss the relation of prevalent heuristic techniques arising in visual effects production to some of the physics-based models we propose.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".