Modeling n-dodecane Spray Combustion with a Representative Interactive Linear Eddy Model
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Many new combustion concepts are currently being investigated to further improve engines in terms of both efficiency and emissions. Examples include homogeneous charge compression ignition (HCCI), lean stratified premixed combustion, stratified charge compression ignition (SCCI), and high levels of exhaust gas recirculation (EGR) in diesel engines, known as low temperature combustion (LTC). All of these combustion concepts have in common that the temperatures are lower than in traditional spark ignition or diesel engines.</div><div class="htmlview paragraph">To further improve and develop combustion concepts for clean and highly efficient engines, it is necessary to develop new computational tools that can be used to describe and optimize processes in nonstandard conditions, such as low temperature combustion. Thus, in the presented study a recently developed model (RILEM: Representative Interactive Linear Eddy Model [<span class="xref">1</span>]) for regime-independent modeling of turbulent non-premixed combustion is used to simulate the so called ‘Spray B’ of the Engine Combustion Network (ECN), which is a heavy-duty optical engine experiment. RILEM directly resolves the interaction of turbulent mixing with the chemistry along a one-dimensional representative line of sight through the combustion chamber via stochastic sequences of statistically independent eddy events. RILEM in its present form consists of a single (one-dimensional) linear eddy model (LEM) instantiation that is coupled to an unsteady Reynolds-averaged Navier-Stokes solver within the OpenFOAM framework. The coupling is similar to unsteady flamelet concepts but features distinct and important differences, e.g. an intrinsic representation of the scalar dissipation rate distribution and its fluctuations. Cylinder pressure, heat release rates and ignition delay time from the computation are compared to experiments under parametric variation of temperature.</div></div>
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".