As part of the dtec.bw MORE project, serial hybrid powertrains featuring
alternative combustion regimes, specifically homogeneous reactivity-controlled
compression ignition (hRCCI), are under investigation to maximize thermal
efficiency while minimizing NOx and soot emissions. The proposed hRCCI concept
utilizes dual-fuel stratification, employing early direct injection (DI) of
octanol (high-reactivity fuel) alongside port fuel injection (PFI) of ethanol
(low-reactivity fuel).
While 0D/1D engine modeling is essential for developing predictive powertrain
simulation frameworks, conventional models often lack the robustness required to
capture the complexities of low-temperature combustion (LTC). This study
addresses this limitation by developing a multi-zone “onion skin”
quasi-dimensional model. Since for LTC, ignition and heat release rates are
highly sensitive to thermal and chemical stratification, capturing these
gradients is critical. 3D computational fluid dynamics (CFD) simulations are
capable of capturing thermal and chemical stratifications to a high degree of
accuracy, whereas 0D simulation models, in general, do not consider them.
The primary contribution of this work is the translation of high-fidelity 3D CFD
data into a computationally efficient quasi-dimensional environment. A
simplified 3D CFD architecture was utilized to map the effects of various
operating parameters on mixture distribution. Using these results, a regression
learning model was trained to predict octanol and temperature stratification
within the combustion chamber. Validation against simulation data demonstrates
that this coupled machine learning and multi-zone approach provides a robust,
predictive tool for evaluating advanced LTC concepts within larger system-level
simulations.
