Applications of machine learning to the analysis of engine in-cylinder flow and thermal process: A review and outlook

Zhao, Fengnian; Hung, David L.S.

doi:10.1016/j.applthermaleng.2022.119633

Cited by 9 publications

(1 citation statement)

References 143 publications

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“…Machine Learning (ML)-based surrogate modeling of internal combustion engines (ICE) has been widely used for a broad range of applications. 25–29 Data-driven ML approaches, in particular, are popular for building ICE surrogate models; such approaches include neural networks (NN), 30–45 Support Vector Machines (SVM), 46–49 Gaussian Processes (GPs, 50–60 also known as kriging 61 ), and other learning models. 62–68 In surrogate modeling applications with limited training runs from expensive simulators, GPs (and its recent non-stationary extensions) have several key advantages over alternate deep learning models.…”

Section: Introductionmentioning

confidence: 99%

A misfire-integrated Gaussian process (MInt-GP) emulator for energy-assisted compression ignition (EACI) engines with varying cetane number jet fuels

Narayanan,

Ji,

Sapra

et al. 2024

International Journal of Engine Research

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For energy-assisted compression ignition (EACI) engine propulsion at high-altitude operating conditions using sustainable jet fuels with varying cetane numbers, it is essential to develop an efficient engine control system for robust and optimal operation. Control systems are typically trained using experimental data, which can be costly and time consuming to generate due to setup time of experiments, unforeseen delays/issues with manufacturing, mishaps/engine failures and the consequent repairs (which can take weeks), and errors in measurements. Computational fluid dynamics (CFD) simulations can overcome such burdens by complementing experiments with simulated data for control system training. Such simulations, however, can be computationally expensive. Existing data-driven machine learning (ML) models have shown promise for emulating the expensive CFD simulator, but encounter key limitations here due to the expensive nature of the training data and the range of differing combustion behaviors (e.g. misfires and partial/delayed ignition) observed at such broad operating conditions. We thus develop a novel physics-integrated emulator, called the Misfire-Integrated GP (MInt-GP), which integrates important auxiliary information on engine misfires within a Gaussian process surrogate model. With limited CFD training data, we show the MInt-GP model can yield reliable predictions of in-cylinder pressure evolution profiles and subsequent heat release profiles and engine CA50 predictions at a broad range of input conditions. We further demonstrate much better prediction capabilities of the MInt-GP at different combustion behaviors compared to existing data-driven ML models such as kriging and neural networks, while also observing up to 80 times computational speed-up over CFD, thus establishing its effectiveness as a tool to assist CFD for fast data generation in control system training.

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