Artificial Neural Networks as a Tool for High-Accuracy Prediction of In-Cylinder Pressure and Equivalent Flame Radius in Hydrogen-Fueled Internal Combustion Engines

Artificial Neural Networks as a Tool for High-Accuracy Prediction of In-Cylinder Pressure and Equivalent Flame Radius in Hydrogen-Fueled Internal Combustion Engines

The automotive industry is under increasing pressure to develop cleaner and more efficient technologies in response to stringent emission regulations. Hydrogen-powered internal combustion engines represent a promising alternative, offering the potential to reduce carbon-based emissions while improvi...

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Bibliographic Details
Main Authors: Federico Ricci, Massimiliano Avana, Francesco Mariani
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Energies
Subjects:
hydrogen fuel
internal combustion engine
SI engine
ultra-lean conditions
GT-POWER
1D software
Online Access:https://www.mdpi.com/1996-1073/18/2/299
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