My Account Log in

1 option

Optimization of Fuel Injection Timing of a Gasoline Engine Using Artificial Neural Network Anna University

SAE Technical Papers (1906-current) Available online

View online
Format:
Conference/Event
Author/Creator:
Vijayashree, author.
Contributor:
Mahalakshmi PhD, N.V.
Tamil porai PhD, P.
V, Ganesan
Conference Name:
8th SAEINDIA International Mobility Conference & Exposition and Commercial Vehicle Engineering Congress 2013 (SIMCOMVEC) (2013-12-04 : Chennai, India)
Language:
English
Physical Description:
1 online resource
Place of Publication:
Warrendale, PA SAE International 2013
Summary:
The fuel injection timing is one of the most important operating parameters that affect the atomization, mixture formation and combustion which determines the performance and emissions of a gasoline engine. Optimizing the injection timing will improve the performance of the engine to a large extend. Towards this end artificial neural-network (ANN) technique using Levenberg-Marquardt (LM) training algorithm is used to train and optimize the fuel injection timing of a single cylinder, four-stroke gasoline engine. Experimental studies have been carried out to obtain training as well as test data. For various engine speeds between 700 and 5000 rpm and for different manifold absolute pressures, fuel injection timing was measured by conducting experiments. The experimental data set generated is used to train the neural network to arrive at the optimized performance of the engine. The optimized fuel injection timing arrived at from ANN is validated by conducting experiments again on the same single cylinder gasoline injected engine from where the initial set of data were obtained. The ANN predicted results are found to be within good acceptable limits and the results show close agreement between predicted and experimental values. From this study it is concluded that for optimizing engine performance with respect to injection timing ANN with LM algorithm can be advantageously used because it saves time and cost
Notes:
Vendor supplied data
Publisher Number:
2013-01-2866
Access Restriction:
Restricted for use by site license

The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.

Find

Home Release notes

My Account

Shelf Request an item Bookmarks Fines and fees Settings

Guides

Using the Find catalog Using Articles+ Using your account