Conditional source-term estimation with laminar flamelet decomposition in large eddy simulation of a turbulent nonpremixed flame
Bibliographic record
Abstract
Conditional source-term estimation (CSE) is a method to close the mean chemical reaction source term based on the conditional moment closure hypothesis. It has been shown previously to be successful in a priori tests against direct numerical simulation data and in the large eddy simulation (LES) of a nonpremixed flame using reduced chemistry. Laminar flamelet decomposition (LFD) is a method to incorporate more complex chemistry into CSE by using laminar flamelets as basis functions and inverting an integral equation for a basis function coefficient vector which describes the linear combination of flamelets that best approximates the conditional average of certain scalar fields within an ensemble of points in a flow field. This coefficient vector is used to obtain the conditional average of the chemical source term for this ensemble of points which can then be transformed into the unconditional average chemical source term in the transport equations of reactive scalars in the flow. This study focuses on the application of CSE with LFD in LES of the Sandia D-flame. The simulation results show that LFD is able to predict temperature and major species well with both steady and unsteady flamelet libraries. However, in order to predict NO well, it is necessary to use a mixed library that includes both steady and unsteady flamelets. The computational cost of this method is low because very few transport equations need to be solved (specifically, equations for the filtered continuity, momentum, mixture fraction and its variance, temperature and the mass fraction of CO are solved) while other species mass fractions can be obtained directly from the flamelet library.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".