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Control-Oriented Data-Driven and Physics-Based Modeling of Maximum Pressure Rise Rate in Reactivity Controlled Compression Ignition Engines
- Behrouz Khoshbakht Irdmousa - Michigan Technological University, Mechanical Engineering-Engineering Mechanics Department, USA ,
- L. N. Aditya Basina - Michigan Technological University, Mechanical Engineering-Engineering Mechanics Department, USA ,
- Jeffrey Naber - Michigan Technological University, Mechanical Engineering-Engineering Mechanics Department, USA ,
- Javad Mohammadpour Velni - Clemson University, Department of Mechanical Engineering, USA ,
- Hoseinali Borhan - Cummins Inc, USA ,
- Mahdi Shahbakhti - University of Alberta, Department of Mechanical Engineering, Canada
Journal Article
03-16-06-0040
ISSN: 1946-3936, e-ISSN: 1946-3944
Sector:
Topic:
Citation:
Irdmousa, B., Basina, L., Naber, J., Mohammadpour Velni, J. et al., "Control-Oriented Data-Driven and Physics-Based Modeling of Maximum Pressure Rise Rate in Reactivity Controlled Compression Ignition Engines," SAE Int. J. Engines 16(6):711-722, 2023, https://doi.org/10.4271/03-16-06-0040.
Language:
English
Abstract:
Reactivity controlled compression ignition (RCCI) is a viable low-temperature
combustion (LTC) regime that can provide high indicated thermal efficiency and
very low nitrogen oxides (NOx) and particulate matter (PM) emissions compared to
the traditional diesel compression ignition (CI) mode [1]. The burn duration in RCCI engines is generally shorter
compared to the burn duration for CI and spark-ignition (SI) combustion modes
[2, 3]. This leads to a high pressure rise rate (PRR) and limits their
operational range. It is important to predict the maximum pressure rise rate
(MPRR) in RCCI engines and avoid excessive MPRRs to enable safe RCCI operation
over a wide range of engine conditions. In this article, two control-oriented
models are presented to predict the MPRR in an RCCI engine. The first approach
includes a combined physical and empirical model that uses the first principle
of thermodynamics to estimate the PRR inside the cylinder, and the second
approach estimates MPRR through a machine learning method based on kernelized
canonical correlation analysis (KCCA) and linear parameter-varying (LPV)
methods. The KCCA-LPV approach proved to have higher prediction accuracy
compared to physics-based modeling while requiring less amount of calibration.
The KCCA-LPV approach could estimate MPRR with an average error of 47 kPa/CAD
while the physics-based approach’s average estimation error was 87 kPa/CAD.