This dataset contains raw and analyzed numerical simulation data, tables, and figures focused on the synergistic effects of nanosecond repetitively pulsed discharge (NRPD) plasma and water addition in hydrogen combustion. The data were generated using zero-dimensional (0D) plasma-assisted combustion simulations for H₂/H₂O/air mixtures. The simulation framework utilized a Design of Experiments (DoE) approach to systematically explore parameter interactions. This comprehensive numerical dataset offers insight into how plasma effects and water vapor influence hydrogen combustion and NOx formation. The data are valuable for researchers and engineers seeking to design and optimize plasma-assisted hydrogen engines and low-emission combustion systems by providing information on ignition delay, radical formation, and emission behavior under various plasma–water conditions. It also supports the development and validation of chemical kinetic models and machine-learning-based combustion optimization frameworks using well-defined simulation parameters and responses.
Dataset of numerical assessment on the combined effects of non-thermal plasma and water addition in hydrogen combustion
Mehdi G.
;Chandio M. B.;De Giorgi M. G.
2026-01-01
Abstract
This dataset contains raw and analyzed numerical simulation data, tables, and figures focused on the synergistic effects of nanosecond repetitively pulsed discharge (NRPD) plasma and water addition in hydrogen combustion. The data were generated using zero-dimensional (0D) plasma-assisted combustion simulations for H₂/H₂O/air mixtures. The simulation framework utilized a Design of Experiments (DoE) approach to systematically explore parameter interactions. This comprehensive numerical dataset offers insight into how plasma effects and water vapor influence hydrogen combustion and NOx formation. The data are valuable for researchers and engineers seeking to design and optimize plasma-assisted hydrogen engines and low-emission combustion systems by providing information on ignition delay, radical formation, and emission behavior under various plasma–water conditions. It also supports the development and validation of chemical kinetic models and machine-learning-based combustion optimization frameworks using well-defined simulation parameters and responses.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
