This study proposes a machine learning tabulation (MLT) method that employs deep
neural networks (DNNs) to predict ignition delay and knock propensity in spark
ignition (SI) engines. The commonly used Arrhenius model and Livengood–Wu
integral for fast knock prediction are not accurate enough to account for
residual gas species and may require adjustments or modifications to account for
specific engine characteristics. Detailed kinetics modeling is computationally
expensive, so the MLT approach is introduced to solve these issues. The MLT
method uses precalculated thermochemical states of the mixture that are
clustered based on a combustion progress variable. Hundreds of DNNs are trained
with the stochastic Levenberg–Marquardt (SLM) optimization algorithm, reducing
training time and memory requirements for large-scale problems. MLT has high
interpolation accuracy, eliminates the need for table storage, and reduces
memory requirements by three orders of magnitude. The proposed MLT approach can
operate across a wider range of conditions and handle a variety of fuels,
including those with complex reaction mechanisms. MLT computational time is
independent of the reaction mechanism’s size. It demonstrates a remarkable
capability to reduce computation time by a factor of approximately 300 when
dealing with complex reaction mechanisms comprising 621 species. MLT has the
potential to significantly advance our understanding of complex combustion
processes and aid in the design of more efficient and environmentally friendly
combustion engines. In summary, the MLT approach has acceptable accuracy with
less computation cost than detailed kinetics, making it ideal for fast
model-based knock detection. This article presents a detailed description of the
MLT method, including its workflow, challenges involved in data generation,
pre-processing, data classification and regression, and integration into the
engine cycle simulation. The results of the study are summarized, which includes
validation against kinetics for ignition delay and engine simulation for knock
angle prediction. The conclusions are presented along with future work.
