Data-Driven Computational Methods in Fuel Combustion: A Review of Applications
Bibliographic record
Abstract
This review article provides a comprehensive analysis of the recent advancements in combustion science and engineering, focusing on the application of machine learning and genetic algorithms from 2015 to 2024. The study examines the integration of computational methods, including computational fluid dynamics, neural networks, and genetic algorithms, with various fuel types such as biodiesel, biomass, coal, gasoline, hydrogen, and natural gas. A systematic search in the Scopus database identified relevant articles, which were categorized based on fuel types and computational methodologies. The analysis covers key areas such as combustion modelling and simulation, engine applications, alternative fuels, pollutant control, and industrial combustion systems. This review highlights the growing role of machine learning and genetic algorithms in enhancing combustion efficiency, reducing emissions, and optimizing energy production, providing insights into the current state of the art and future trends in this critical field. The study further examines the geographical distribution of research, noting significant contributions from Canada, China, France, Germany, India, Iran, Japan, Malaysia, Pakistan, Saudi Arabia, the United Kingdom, and the United States, alongside other international contributions. A total of 165 peer-reviewed articles were analyzed, covering a range of combustion scenarios and fuel types. The most frequently applied methods include artificial neural networks (ANNs), support vector machines (SVMs), and random forests (RFs) for predictive modeling, as well as genetic algorithms (GAs) for system optimization. ANN-based models achieved high accuracy in predicting NOx emissions and flame speed, with some studies reporting mean absolute errors below 5%. GA methods demonstrated effectiveness in fuel blend optimization and geometry design, achieving emission reductions of up to 30% in experimental setups. This review also highlights persistent challenges such as data availability, model generalization, and reproducibility, and proposes future directions toward more interpretable and standardized applications of ML/GA in combustion science.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".