Application of machine learning in low-order manifold representation of chemistry in turbulent flames
Bibliographic record
Abstract
The Uniform Conditional State (UCS) and the Multidimensional Flamelet Manifold (MFM) models are methods for the tabulation of chemistry in simulations of turbulent flames. The high-dimensionality of the tables these models generate and many possible combinations of the values for the input variables necessitate the allocation of a considerable size of memory during CFD calculations. This issue becomes even more problematic when adding more conditioning variables to the model. In this study, two Artificial Intelligence (AI)-based approaches referred to as Decision Tree (DT) and Artificial Neural Network (ANN) are developed and tested to provide in situ chemistry representation. The goal is to predict four parameters (outputs) accurately with low memory demand and computational cost. The trained AI models are then employed for simulation of a turbulent premixed flame. Comparison of the results from the AI-based approaches to those from the conventional UCS model shows acceptable agreement. The memory and CPU requirements from the different approaches are compared. It is found that the ANN model reduces the size of the chemistry table by around 92%. Conversely, the DT-based model reduces the size of the chemistry model by only 40%. The CPU time for using the DT model during the CFD calculations was around 10% shorter than the conventional approach while it was 8% higher for the ANN model. It was concluded that, based on the particular applications, different AI-based methods can facilitate an efficient representation of the chemistry manifold.
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 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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".