CFD-CRN Study of NOx Formation in a High-Pressure Combustor Fired With Lean Premixed CH4 / H2 - Air Mixtures
Bibliographic record
Abstract
Abstract The main motivation of this study is to investigate detailed NOx and CO formation in high-pressure dump combustor fired with lean premixed methane-air mixture using CFD-CRN hybrid approach. Further, this study is extended to investigate the effect of H2 enrichment on emission formation in the same combustor. Three-dimensional steady RANS CFD simulations have been performed using a Flamelet Generated Manifold (FGM) model in Simcenter STAR-CCM+ 2019.2 with the DRM22 reduced mechanism. The CFD simulations have been modelled along with all three heat transfers modes: conduction, convection and radiation. The conjugate heat transfer (CHT) approach and participating media radiation modelling have been used here. Initially, CFD simulations are performed for five lean equivalence ratios (ϕ = 0.43–0.55, Tinlet = 673 K, Vinlet = 40 m/s) of pure methane-air mixture operating at 5 bar. The exit temperature and flame-length are compared with available experimental data. The automatic chemical reactor network has been constructed from CFD data and solved using the recently developed reactor network module of Simcenter STAR-CCM+ 2019.2 in a single framework for each cases. It is found out that the CRNs up to 10,000 PSRs can provide adequate accuracy in exit NOx predictions compared to experiments for pure methane cases. The contribution of NOx formation pathway, changes from N2O intermediate to thermal NO as equivalence ratio increases. Further studies are performed for two equivalence ratios (ϕ = 0.43 and 0.50 to simulate the impact of H2 addition (up to 40% by volume) on NOx formation pathways and CO emission. It is found out here that the contribution from NNH pathway increases for leaner equivalence ratio cases (ϕ = 0.43), while thermal pathway slightly increases for ϕ = 0.50 with increase in H2 content from 0% to 40%.
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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) | 0.000 | 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".