Access Type

Dissertation/Thesis

Date of Award

January 2023

Degree Type

Dissertation

Degree Name

Ph.D.

Department

Mechanical Engineering

First Advisor

Naeim Henein

Abstract

The goal of this investigation is to develop a combustion model for the renewable diesel— hydrotreated vegetable oil (HVO) — from a formulated surrogate fuel that represents the target fuel combustion behavior and then optimizing a combustion kinetic mechanism for Computational Fluid Dynamics (3D-CFD) diesel cycle simulation at normal conditions. Surrogate formulation requires emulating physical and chemical processes of the target fuel. Therefore, a good prediction of the modeled mixture’s physical and chemical properties is accomplished. Thus, the technique used in this investigation utilizes Artificial Neural Network of modeled data to predict key properties of the surrogate mixture, during the optimization process, and match them with that of the target fuel. Experimental validation is carried out in Ignition Quality Tester, and Genset diesel engine is used to acquire experimental data of the target fuel to validate the kinetic mechanism of the developed surrogate against.The experimental results from Ignition Quality Tester (IQT) showed a very good agreement in ignition delay and peak RHR and its location, demonstrating the good match of physical and chemical properties during optimization. The candidate surrogates kinetics representation is performed by the reduction of a detailed kinetic mechanism to a skeletal mechanism of number of species and reactions reasonable for running 3D-CFD simulation for combustion modeling. The experimental data of HVO in a compression ignition engine are matched by the simulation data as the critical combustion parameters are accurately represented. The results would be of need to researchers interested in studying combustion and spray characteristics, as well as to Auto industry for development of engines fueled by renewable diesel fuels.

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