A Representative Interactive Linear Eddy Model (RILEM) for Non-Premixed Combustion
Bibliographic record
Abstract
To further improve the efficiency and emissions profiles of internal combustion engines, many new combustion concepts are currently being investigated. Examples include homogeneous charge compression ignition (HCCI), stratified charge compression ignition (SCCI), lean stratified premixed combustion, and the use of high levels of exhaust gas recirculation (EGR) in diesel engines. The typical combustion temperatures in all of these concepts are lower than those in traditional spark ignition or diesel engines.Most of the combustion models that are currently used in computational fluid dynamics (CFD) simulations were developed to describe either premixed or non-premixed combustion under the assumption of fast chemistry. The refinement of existing combustion concepts for highly efficient clean engines and the development of new ones would be greatly facilitated by the introduction of new computational tools and combustion models that are mode- and regime-independent, i.e. capable of modeling both premixed and non-premixed and also fast and non-fast chemistry. Such tools should enable more accurate simulation of combustion under non-standard conditions such as those established during low temperature combustion.This paper presents a new regime-independent combustion modeling strategy for non-premixed combustion in which the linear eddy model (LEM) is used as a representative interactive regime-independent turbulent combustion model and coupled to a 3D CFD solver. Parameters and boundary conditions that determine the evolution of the LEM are supplied by the 3D CFD calculation and updated at each time step. The LEM is then solved for the corresponding time step, providing the 3D CFD code with an updated composition state.This new representative interactive linear eddy model (RILEM) is used to simulate an n-heptane spray, demonstrating some potential to describe spray combustion processes.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".