Date of Award

2019

Document Type

Open Access Master's Thesis

Degree Name

Master of Science in Mechanical Engineering (MS)

Administrative Home Department

Department of Mechanical Engineering-Engineering Mechanics

Advisor 1

Mahdi Shahbakhti

Committee Member 1

Jeffrey Naber

Committee Member 2

Jeremy Worm

Abstract

Low Temperature Combustion (LTC) is a combustion strategy that burns fuel at lower temperatures and leaner mixtures in order to achieve high efficiency and near zero NOx emissions. Since the combustion happens at lower temperatures it inhibits the formation of NOx and soot emissions. One such strategy is Reactivity Controlled Compression Ignition (RCCI). One characteristic of RCCI combustion and LTC com- bustion in general is short burn durations which leads to high Pressure Rise Rates (PRR). This limits the operation of these engines to lower loads as at high loads, the Maximum Pressure Rise Rate (MPRR) hinders the use of this combustion strategy.

This thesis focuses on the development of a model based controller that can control the Crank Angle for 50% mass fraction burn (CA50) and Indicated Mean Effective Pressure (IMEP) of an RCCI engine while limiting the MPRR to a pre determined limit. A Control Oriented Model (COM) is developed to predict the MPRR in an RCCI engine. This COM is then validated against experimental data. A statistical analysis of the experimental data is conducted to understand the accuracy of the COM. The results show that the COM is able to predict the MPRR with reasonable accuracy in steady state and transient conditions. Also, the COM is able to capture the trends during transient operation. This COM is then included in an existing cycle by cycle dynamic RCCI engine model and used to develop a Linear Parameter

Varying (LPV) representation of an RCCI engine using Data Driven Modeling (DDM) approach with Support Vector Machines (SVM). This LPV representation is then used along with a Model Predictive Controller (MPC) to control the CA50 and IMEP of the RCCI engine model while limiting the MPRR. The controller was able to track the desired CA50 and IMEP with a mean error of 0.9 CAD and 4.7 KPa respectively while maintaining the MPRR below 5.8 bar/CAD.

Available for download on Wednesday, August 12, 2020

Share

COinS