LES Analysis of Cyclic Variability in a GDI Engine
Bibliographic record
Abstract
The paper critically discusses Large-Eddy Simulation (LES) potential to investigate cycle-to-cycle variability (CCV) in internal combustion engines. Particularly, the full load/peak power engine speed operation of a high-performance turbocharged GDI unit, for which ample cycle-to-cycle fluctuations were observed during experimental investigations at the engine test bed, is analyzed through a multi-cycle approach covering 25 subsequent engine cycles. In order to assess the applicability of LES within the research and development industrial practice, a modeling framework with a limited impact on the computational cost of the simulations is set up, with particular reference to the extent of the computational domain, the computational grid size, the choice of boundary conditions and numerical sub-models [1, 2, 3]. In order to evaluate the applicability of the adopted approach to the resolution of an adequate portion of the overall turbulent energy spectrum, different grid metrics are at first introduced, based on criteria available in literature [4, 5]. A qualitative comparison between CFD results and experimental evidence is then carried out in terms of both in-cylinder pressure envelope and coefficients of variation for any of indicated mean effective pressure, 10%, 50% and 90% of fuel burnt distributions among the investigated cycles. Particularly, a detailed analysis of the physical factors influencing the exhibited cycle-to-cycle variability is performed through the use of correlation coefficients, aiming at highlighting possible hierarchies between the many involved phenomena and the observed engine behavior. Finally, a phase-dependent Proper Orthogonal Decomposition (POD). Particularly, while POD applications available in literature mainly cover vector fields and flow structures [6, 7], in the present paper the analysis is extended to scalar fields describing the combustion process evolution and its cyclic variability, and results are critically analyzed and commented.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".