Date of Award
2024
Document Type
Open Access Dissertation
Degree Name
Doctor of Philosophy in Mechanical Engineering-Engineering Mechanics (PhD)
Administrative Home Department
Department of Mechanical Engineering-Engineering Mechanics
Advisor 1
Jeffrey Naber
Advisor 2
Mahdi Shahbakhti
Committee Member 1
Jeremy Worm
Committee Member 2
David Wanless
Abstract
Reactivity Controlled Compression Ignition (RCCI) engines operates has capacity to provide higher thermal efficiency, lower particular matter (PM), and lower oxides of nitrogen (NOx) emissions compared to conventional diesel combustion (CDC) operation. Achieving these benefits is difficult since real-time optimal control of RCCI engines is challenging during transient operation. To overcome these challenges, data-driven machine learning based control-oriented models are developed in this study. These models are developed based on Linear Parameter-Varying (LPV) modeling approach and input-output based Kernelized Canonical Correlation Analysis (KCCA) approach. The developed dynamic models are used to predict combustion timing (CA50), indicated mean effective pressure (IMEP), maximum pressure rise rate (MPRR), reactivity and stratification metrics as functions of fuel quantity (FQ), start of injection (SOI) timing and premixed ratio (PR) as the RCCI engine's control variables. The identified model is then used for the design of a model predictive controller (MPC) to control crank angle for 50\% fuel burnt (CA50) for varying engine conditions on the actual RCCI engine. This study also established constrained multi-input multi-output (MIMO) model predictive controller (MPC) to track desired crank angle for 50\% fuel burnt and IMEP at various engine conditions. This research has demonstrated and implemented two fast and reliable method to model highly nonlinear RCCI combustion engine and develop control-oriented models for RCCI combustion
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Irdmousa, Behrouz Khoshbakht, "Data Driven and Machine Learning Based Modeling and Predictive Control of Combustion at Reactivity Controlled Compression Ignition Engines", Open Access Dissertation, Michigan Technological University, 2024.
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