Numerical investigation of synergistic effects of nanopulsed plasma and water addition on hydrogen combustion and NO emissions
Bibliographic record
Abstract
This study presents a novel integrated approach using nanopulsed repetitively pulsed discharge (NRPD) plasma with H 2 /H 2 O/Air mixtures to overcome high NO emissions and ignition delay timing (IDT) in hydrogen combustion, utilizing zero-dimensional ChemPlasKin simulations and Design of Experiments (DoE). A key finding is that while water vapor inhibits conventional autoignition, plasma-assisted ignition (PAI) is insensitive to water, as plasma dissociates H 2 O into reactive radicals (H, O, OH), effectively turning water into an ignition promoter and enhancing early radical formation. PAI achieves significantly lower flame temperatures (12.3 % reduction) and approximately 58 % lower NO emissions compared to conventional autoignition through non-thermal chemical pathways. Comparative analysis quantified that plasma kinetic effects play a dominant role in reducing IDT, contributing substantially more to enhancement than plasma thermal effects alone. Furthermore, the sensitivity analysis of NO formation revealed that water vapor significantly alters the kinetics of NO-related reactions, changing the sensitivity and role of key pathways like H + NO 2 ⇌ NO + OH, suggesting water can reverse certain reactions from producing NO to consuming it. This plasma-water synergy successfully broadens and stabilizes low-NO combustion regimes. • Plasma–water synergy cuts NO x emissions by up to 58 % vs autoignition. • NRPD plasma offsets water's inhibition, cutting ignition delay. • Plasma lowers flame temperature ∼12.3 %, limiting thermal NO x . • Water generates radicals (H, O, OH) in plasma, boosting ignition. • DoE optimization conditions for stable, low-NO x hydrogen combustion.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".