RT info:eu-repo/semantics/article
T1 Hydrogen’s role in mitigating emissions variability: A chemical kinetic-thermodynamic digital framework for cleaner combustion technologies
A1 Gabana Molina, Pedro
A1 Cova Bonillo, Alexis José
A1 Herreros, José
A1 Tsolakis, Athanasios
K1 Spark-ignition engine
K1 Hydrogen-gasoline mixture
K1 Emissions
K1 Emissions
K1 Two-zone thermodynamic model
K1 Cycle-to-cycle variations
K1 33 Ciencias Tecnológicas
AB The combustion process in spark-ignition (SI) engines inherently presents cycle-to-cycle variations (CCV),leading to engine instability and variability in emissions formation. This work develops a digital framework thatintegrates chemical kinetics and a two-zone thermodynamic diagnostic model to understand the role of hydrogenin mitigating CCV and its impact on emissions formation. The framework predicts the crank angle degreeresolved evolution of CCV of CO, H2, NO, and N2O in the engine combustion chamber’s burned gas zone. Theframework is calibrated with experimental results from an SI engine working with gasoline-hydrogen fuelmixtures under stoichiometric and lean combustion conditions.This investigation has revealed that the formation of nitrogen-based emissions, particularly NO, exhibitshigher variability than CO and exhaust unburnt H2, with coefficients of variation ranging from 7% to 35%. Thehigh NO variability is attributed to the rapid decrease in NO destruction rates (i.e., kinetic “freezing”) at differentin-cylinder pressure and temperature conditions within each thermodynamic cycle. It is elucidated that N2Oformation occurs predominantly during the expansion and exhaust strokes. New knowledge has been created tounderstand how the thermochemical properties of hydrogen reduce NO cycle-to-cycle variability. A synergisticeffect is unveiled, hydrogen enrichment leads to an engine operational shift towards a more dilute state (i.e.,increased residual gases), where hydrogen’s combustion-enhancing properties (e.g., high flame speed, lowignition energy) are crucial for stabilising combustion and thus reducing NO formation variability. Furthermore,the work proposes a new predictive statistical model capable of describing NO dispersion using only the resi-dual–gas fraction and the mean NO level, offering a practical tool for engine calibration and emissions control.Research findings can guide the development of emissions abatement technologies for combustion-based pow-ertrains operating with hydrogen under lean combustion conditions, where conventional catalysts are lesseffective and understanding gains are highly significant. The proposed digital framework offers an emissionsvariability predictive tool facilitating the stable operation of clean powertrain for future energy systems.
PB Elsevier
SN 0196-8904
YR 2026
FD 2026
LK https://uvadoc.uva.es/handle/10324/83971
UL https://uvadoc.uva.es/handle/10324/83971
LA eng
NO Energy Conversion and Management, 2026, vol. 357, p. 121431
NO Producción Científica
DS UVaDOC
RD 11-abr-2026