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Development of a High-Fidelity Engin...

Thompson, Bradley Adam.

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Development of a High-Fidelity Engine Modeling Framework in Simulink with Automated Combustion Parameter Tuning.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Development of a High-Fidelity Engine Modeling Framework in Simulink with Automated Combustion Parameter Tuning./
Author:	Thompson, Bradley Adam.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	208 p.
Notes:	Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
Contained By:	Dissertation Abstracts International78-11B(E).
Subject:	Mechanical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10262636
ISBN:	9781369872248

Development of a High-Fidelity Engine Modeling Framework in Simulink with Automated Combustion Parameter Tuning.
Thompson, Bradley Adam.

Development of a High-Fidelity Engine Modeling Framework in Simulink with Automated Combustion Parameter Tuning. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 208 p.

Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.

Thesis (Ph.D.)--The University of Alabama, 2017.

The automotive industry continually seeks to improve performance and fuel efficiency due to increasing fuel costs, consumer demands, and greenhouse gas regulations. With advancements in computer-aided design, engine simulation has become a vital tool for product development and design innovation, and as computation power improves, the ability to optimize designs improves as well. Among the simulation software packages currently available, Matlab/Simulink is widely used for automotive system simulations but does not contain a detailed engine modeling toolbox. To leverage Matlab/Simulink's capabilities, a Simulink-based 1D flow engine modeling architecture is proposed. The architecture allows engine component blocks to be connected in a physically representative manner in the Simulink environment, therefore reducing model build time. Each component model, derived from physical laws, interacts with other models according to block connection.

ISBN: 9781369872248Subjects--Topical Terms:

649730
Mechanical engineering.

Development of a High-Fidelity Engine Modeling Framework in Simulink with Automated Combustion Parameter Tuning.
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245 1 0 $a Development of a High-Fidelity Engine Modeling Framework in Simulink with Automated Combustion Parameter Tuning.
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300 $a 208 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
500 $a Adviser: Hwan-Sik Yoon.
502 $a Thesis (Ph.D.)--The University of Alabama, 2017.
520 $a The automotive industry continually seeks to improve performance and fuel efficiency due to increasing fuel costs, consumer demands, and greenhouse gas regulations. With advancements in computer-aided design, engine simulation has become a vital tool for product development and design innovation, and as computation power improves, the ability to optimize designs improves as well. Among the simulation software packages currently available, Matlab/Simulink is widely used for automotive system simulations but does not contain a detailed engine modeling toolbox. To leverage Matlab/Simulink's capabilities, a Simulink-based 1D flow engine modeling architecture is proposed. The architecture allows engine component blocks to be connected in a physically representative manner in the Simulink environment, therefore reducing model build time. Each component model, derived from physical laws, interacts with other models according to block connection.
520 $a The presented engine simulation platform includes a semi-predictive spark ignition combustion model that correlates the burn rate to combustion chamber geometry, laminar flame speed, and turbulence. Combustion is represented by a spherical flame propagating from the spark plug. To accurately predict the burn rate, the quasi-dimensional model requires tuning. A method is proposed for fitting turbulence and burn rate parameters across an engine's operating space. The method reduces optimization time by eliminating the intake and exhaust flow models when evaluating the fitness function. Using the proposed method, 12 combustion model parameters were optimized to match cylinder pressure. Optimization and validation results are given for a 2.0 L Mazda Skyactiv-G engine.
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