Development of a Zero-Dimensional Thermodynamic Model for Internal Combustion Engines: Application to Diesel and Hydrogen Fuels
S. Jain (TU Delft - Mechanical Engineering)
BJ Boersma – Mentor (TU Delft - Marine and Transport Technology)
R. Pecnik – Mentor (TU Delft - Energy Technology)
Sikke Klein – Graduation committee member (TU Delft - Energy Technology)
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Abstract
To improve the understanding and analysis of internal combustion engines, this thesis develops a thermodynamic model that simplifies the complex processes within an engine cylinder into a single, uniform system. The core energy conversion phases of compression and expansion were first simulated using a computationally effective ”zero-dimensional” method, which was first applied to a diesel engine using MATLAB. A key investigation involved comparing four established empirical models used to estimate in-cylinder heat loss, a critical factor for engine efficiency. This comparison revealed that there are significant variations in the predicted engine performance depending on the model choice. The diesel simulation was then validated against experimental data and refined using more realistic gas properties and a dual-zone combustion model to better represent the diesel combustion.
A major contribution of this work was adapting the validated framework to a hydrogen-fuelled spark-ignition engine, testing the model’s flexibility for alternative fuels. This involved recalibrating fuel properties and the combustion model to match hydrogen’s unique burning characteristics. When compared against experimental results, the simulation accurately replicated in-cylinder pressure and energy release patterns. The Woschni heat loss model was identified as most representative for the hydrogen experiments; however, the simulation consistently overestimated the engine’s work output and thermal efficiency.
The study concludes that these simplified models are highly effective for analysing internal thermodynamic trends and comparing the behaviour of different fuels. However, their ability to predict absolute performance is fundamentally limited by the accuracy of the empirical sub-models used for heat transfer and, most significantly, combustion. This work underscores that achieving high-accuracy performance predictions from such models requires robust experimental calibration to account for real-world combustion inefficiencies not captured by idealised functions.