Bennett, Colin (2014) Reconstruction of gasoline engine in-cylinder pressures using recurrent neural networks. Doctoral thesis (PhD), University of Sussex.

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Abstract

Knowledge of the pressure inside the combustion chamber of a gasoline
engine would provide very useful information regarding the quality and
consistency of combustion and allow significant improvements in its control,
leading to improved efficiency and refinement. While measurement using incylinder
pressure transducers is common in laboratory tests, their use in
production engines is very limited due to cost and durability constraints.

This thesis seeks to exploit the time series prediction capabilities of recurrent
neural networks in order to build an inverse model accepting crankshaft
kinematics or cylinder block vibrations as inputs for the reconstruction of
in-cylinder pressures. Success in this endeavour would provide information
to drive a real time combustion control strategy using only sensors already
commonly installed on production engines. A reference data set was
acquired from a prototype Ford in-line 3 cylinder direct injected, spark ignited
gasoline engine of 1.125 litre swept volume. Data acquired concentrated on
low speed (1000-2000 rev/min), low load (10-30 Nm brake torque) test
conditions. The experimental work undertaken is described in detail, along
with the signal processing requirements to treat the data prior to presentation
to a neural network.

The primary problem then addressed is the reliable, efficient training of a
recurrent neural network to result in an inverse model capable of predicting
cylinder pressures from data not seen during the training phase, this unseen
data includes examples from speed and load ranges other than those in the
training case. The specific recurrent network architecture investigated is the
non-linear autoregressive with exogenous inputs (NARX) structure. Teacher
forced training is investigated using the reference engine data set before a
state of the art recurrent training method (Robust Adaptive Gradient Descent
– RAGD) is implemented and the influence of the various parameters
surrounding input vectors, network structure and training algorithm are
investigated. Optimum parameters for data, structure and training algorithm
are identified.

Item Type:	Thesis (Doctoral)
Schools and Departments:	School of Engineering and Informatics > Engineering and Design
Subjects:	T Technology > TJ Mechanical engineering and machinery > TJ0751 Miscellaneous motors and engines Including gas, gasoline, diesel engines
Depositing User:	Library Cataloguing
Date Deposited:	27 May 2014 11:44
Last Modified:	28 Jul 2015 09:26
URI:	http://srodev.sussex.ac.uk/id/eprint/48644

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