Metamodelling of Ignition Delay Time for Natural Gas Blends Under Gas Turbine Operating Conditions
Bibliographic record
Abstract
Abstract Characterisation of autoignition risk is crucial for designing and optimising low-emission combustion systems as there is an increased demand for highly reactive and novel fuel mixtures. Achieving a residence time to prevent autoignition and obtaining an adequate mixing quality is a challenging trade-off for these fuels in lean-premixed combustion systems. The level of complexity increases further due to low-temperature chemical pathways and pressure-dependent reactions that strongly influence ignition delay at engine operating conditions. Detailed chemical kinetic mechanisms with hundreds of species and thousands of reactions are developed and employed to address this complexity and predict ignition delay accurately, especially for heavier hydrocarbons. However, direct implementation of these kinetic mechanisms is computationally prohibitive in high-fidelity CFD approaches such as large eddy simulation (LES) and stochastic simulation tools that require a large number of evaluations. Advanced stochastic methods are essential tools to quantify uncertainties due to the inherent variabilities in ambient, operating conditions and fuel composition on ignition delay time calculation for practical applications. This study introduces and implements a computationally efficient method based on metamodellig to predict ignition delay time over a wide range of operating conditions and fuel compositions for gas turbine combustion systems. A metamodel or surrogate model is an accurate and quick approximation of the original computational model based on a detailed chemical kinetic mechanism. Polynomial chaos expansion (PCE) as an advanced method is employed to build metamodels using a limited set of runs of the original ignition delay time model based on NUIGMech1.0 chemical kinetic mechanism as the most detailed and state-of-the-art chemical kinetic mechanism for natural gas. Developed metamodels for ignition delay time are valid over operating conditions of P = 20–40 bar and T = 700–900 K for natural gas containing C1 to C7 hydrocarbons at stoichiometric condition. These metamodels provide a fast, robust, and considerably accurate framework instead of a detailed chemical kinetic model that facilitates (a) characterising ignition delay time at different operating conditions and fuel compositions, (b) designing and optimising premixers and burners and (c) conducting uncertainty quantification and stochastic modelling studies.
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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) | 0.001 | 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 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".