The primary aim of this study is to develop and validate a novel multi-gene genetic programming approach for accurately predicting Laminar Flame Speed (LFS) in ammonia (NH3)/hydrogen (H2)/air mixtures, a key aspect in the advancement of carbon-free fuel technologies. Ammonia, particularly when blended with hydrogen, presents significant potential as a carbon-free fuel due to its enhanced reactivity. This research not only investigates the effects of hydrogen concentration, initial temperature, and pressure on LFS and Ignition Delay Time (IDT) but also explores the impact of oxidizing agents like ozone (O3) in augmenting NH3 combustion. A modified reaction mechanism was implemented and validated through parametric analysis. Main findings demonstrate that IDT decreases with higher hydrogen concentrations, increased initial temperature, and initial pressure, although the influence of pressure decreases above 10 atm. Conversely, at lower temperatures (below 1200 K) and higher hydrogen concentrations (30 % and 50 %), the dominance of H2 chemistry can negatively impact initial pressure. LFS increases with higher temperature and hydrogen concentration, but decreases under elevated pressure, with its effect becoming negligible above 5 atm. An optimized equivalence ratio (Φ) range of 1.10 – 1.15 is identified for efficient combustion. Introducing ozone into the oxidizer notably improves LFS in NH3/H2/air mixtures, with the addition of 0.01 ozone mirroring the effect of a 10 % hydrogen addition under normal conditions. The study's fundamental contribution is the development of a multi-gene genetic algorithm, showcasing the correlation between predicted LFS values and actual values derived from chemkin simulations. The successful validation of this methodology across various case studies underscores its potential as a robust tool in zero-carbon combustion applications, marking a significant stride in the field.
Predictive models of laminar flame speed in NH3/H2/O3/air mixtures using multi-gene genetic programming under varied fuelling conditions
Ali Shah, Zubair;Marseglia, G.;De Giorgi, M. G.
2024-01-01
Abstract
The primary aim of this study is to develop and validate a novel multi-gene genetic programming approach for accurately predicting Laminar Flame Speed (LFS) in ammonia (NH3)/hydrogen (H2)/air mixtures, a key aspect in the advancement of carbon-free fuel technologies. Ammonia, particularly when blended with hydrogen, presents significant potential as a carbon-free fuel due to its enhanced reactivity. This research not only investigates the effects of hydrogen concentration, initial temperature, and pressure on LFS and Ignition Delay Time (IDT) but also explores the impact of oxidizing agents like ozone (O3) in augmenting NH3 combustion. A modified reaction mechanism was implemented and validated through parametric analysis. Main findings demonstrate that IDT decreases with higher hydrogen concentrations, increased initial temperature, and initial pressure, although the influence of pressure decreases above 10 atm. Conversely, at lower temperatures (below 1200 K) and higher hydrogen concentrations (30 % and 50 %), the dominance of H2 chemistry can negatively impact initial pressure. LFS increases with higher temperature and hydrogen concentration, but decreases under elevated pressure, with its effect becoming negligible above 5 atm. An optimized equivalence ratio (Φ) range of 1.10 – 1.15 is identified for efficient combustion. Introducing ozone into the oxidizer notably improves LFS in NH3/H2/air mixtures, with the addition of 0.01 ozone mirroring the effect of a 10 % hydrogen addition under normal conditions. The study's fundamental contribution is the development of a multi-gene genetic algorithm, showcasing the correlation between predicted LFS values and actual values derived from chemkin simulations. The successful validation of this methodology across various case studies underscores its potential as a robust tool in zero-carbon combustion applications, marking a significant stride in the field.File | Dimensione | Formato | |
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