A novel particle swarm and genetic algorithm hybrid method for improved heuristic optimization of diesel engine performance

Thumbnail Image
Date
2014-01-01
Authors
Bertram, Aaron
Major Professor
Advisor
Song-Charng Kong
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
Authors
Research Projects
Organizational Units
Organizational Unit
Mechanical Engineering
The Department of Mechanical Engineering at Iowa State University is where innovation thrives and the impossible is made possible. This is where your passion for problem-solving and hands-on learning can make a real difference in our world. Whether you’re helping improve the environment, creating safer automobiles, or advancing medical technologies, and athletic performance, the Department of Mechanical Engineering gives you the tools and talent to blaze your own trail to an amazing career.
Journal Issue
Is Version Of
Versions
Series
Abstract

This study explores a novel application of the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) heuristic methods in a hybrid construction on a 4 cylinder medium-duty diesel engine at part-load conditions. The application of the hybrid PSO-GA approach is compared with a basic PSO in the optimization of the control parameters of a diesel engine utilizing high EGR capability, modestly high fuel pressure capability, and a two-injection fuel strategy.

The results indicate that the application of the GA to the basic PSO method improved the search breadth and convergence rate when compared to the basic PSO method alone. The novel approach of applying the GA to the fuel schedule is found to be worthy of further investigation. Applying the GA to specific parameters as way to improve optimizations on was effective in reducing the iterations and time taken to achieve satisfactory objective values. The hybrid method showed up to a 49% improvement in objective value over the basic PSO with less operational time in testing.

Comments
Description
Keywords
Citation
DOI
Source
Subject Categories
Copyright
Wed Jan 01 00:00:00 UTC 2014