Technical Brief

Multi-Objective Design Optimization of a Variable Geometry Spray Fuel Injector

[+] Author and Article Information
J. R. Archer

Graduate Research Assistant
North Carolina State University,
Raleigh, NC 27695
e-mail: jrarcher@ncsu.edu

Tiegang Fang

Associate Professor
North Carolina State University,
Raleigh, NC 27695
e-mail: tfang2@ncsu.edu

Scott Ferguson

Assistant Professor
North Carolina State University,
Raleigh, NC 27695
e-mail: scott_ferguson@ncsu.edu

Gregory D. Buckner

North Carolina State University,
Raleigh, NC 27695
e-mail: gdbucker@ncsu.edu

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received March 18, 2013; final manuscript received November 1, 2013; published online February 12, 2014. Assoc. Editor: Matthew B. Parkinson.

J. Mech. Des 136(4), 044501 (Feb 12, 2014) (9 pages) Paper No: MD-13-1123; doi: 10.1115/1.4026263 History: Received March 18, 2013; Revised November 01, 2013

This paper explores the simulation-based design optimization of a variable geometry spray (VGS) fuel injector. A multi-objective genetic algorithm (MOGA) is interfaced with commercial computational fluid dynamics (CFD) software and high performance computing capabilities to evaluate the spray characteristics of each VGS candidate design. A three-point full factorial experimental design is conducted to identify significant design variables and to better understand possible variable interactions. The Pareto frontier of optimal designs reveals the inherent tradeoff between two performance objectives—actuator stroke and spray angle sensitivity. Analysis of these solutions provides insight into dependencies between design parameters and the performance objectives and is used to assess possible performance gains with respect to initial prototype configurations. These insights provide valuable design information for the continued development of this VGS technology.

Copyright © 2014 by ASME
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Fig. 5

Experimental validation of CFD model

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Fig. 6

The six VGS design variables for the nozzle (left) and pintle (right)

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Fig. 4

CFD results showing the locations of xp, yp and xs, ys

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Fig. 3

2D axisymmetric model showing boundary conditions and domain initialization

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Fig. 2

Cross section of the distal portion of the third-generation VGS prototype

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Fig. 1

2D simulation results of the VGS fuel injection concept

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Fig. 7

Flow chart for the design evaluation process using the HPC (colors indicate which system performed each task)

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Fig. 8

Main effects plot for objective function F1

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Fig. 9

Main effects plot for objective function F2

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Fig. 11

The design variables (VGS geometries) for (a) the second generation prototype, (b) Design 1, and (c) Design 2

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Fig. 12

Graph of spray angle versus pintle position for Design 1, Design 2, and the second generation prototype (Prototype 2)

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Fig. 13

Scatter plot matrix of design variable values on Pareto frontier

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Fig. 14

Scatter plot matrix of design variable relationships

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Fig. 10

VGS performance space highlighting the final Pareto frontier and the second generation prototype




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