Machine learning benchmark for flow reconstruction in the TCC–III optical engine
Bibliographic record
Abstract
We present EngineBench, the first machine learning (ML) benchmark designed for engine in-cylinder flow research. The benchmark data consist of curated particle image velocimetry (PIV) measurements previously gathered from the Transparent Combustion Chamber (TCC-III) by the General Motors University of Michigan Automotive Cooperative Research Laboratory. 1 The dataset is then leveraged in order to benchmark the performance of four ML methods for a flow reconstruction (inpainting) task. We propose large gaps at the edges of the field of view as the benchmark task in order to reflect realistic scenarios in which data are harder to obtain closer to walls, and to challenge the ability of the models to predict the turbulent flow motion with limited access to surrounding data points. Pixel-wise, vector-based and multi-scale performance metrics are used to provide broad evaluations of the models. We find that models which utilise skip connections show significantly improved performances at this task on both small and large gap sizes, due to their enhanced ability to leverage contextual information. The benchmark proposed in this paper supports the development of ML models for engine design problems, as well as PIV data enhancement more generally. In addition, the ML model comparisons allow for more informed selection of models for problems in experimental flow diagnostics. All data and code are publicly available at https://eng.ox.ac.uk/tpsrg/research/enginebench /.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".