Affiliation:
1. Indian Institute of Technology Madras, India
2. Indian Institute of Technology Madras, Mechanical
Engineering, India
Abstract
<div>The present study examines the effect of the multiple injection
strategies in a common rail diesel engine using machine
learning, image processing, and object detection techniques.
The study demonstrates a novel approach of utilizing
image-processing tools to gain information from heat release
rates and in-cylinder visualizations from experimental or
computational studies. The 3D CFD combustion and emission
predictions of a commercial code ANSYS FORTE© are validated
with small-bore common rail diesel engine data with known
injection strategies. The validated CFD tool is used as a
virtual plant model to optimize the injection schedule for
reducing oxides of nitrogen (NO<sub>x</sub>) and soot
emissions using an apparent heat release rate image-based
machine learning tool. A methodology of the machine learning
tool is quite helpful in predicting the NO–soot trade-off.
This methodology shows a significant reduction in soot and
NO emissions using a pilot–main–post-injection schedule of
25% pilot, 25% post-, and 50% main injection, compared to a
baseline pilot–main injection schedule. In addition, this
work attempts a robust and high-fidelity optimization of the
fuel injection schedule using the random forest algorithm
for predicting the NO and soot emissions using 73
simulations done with different pilot–main and
pilot–main–post-injection strategies on a small-bore diesel
engine. Further, the object detection algorithm is trained
on simulation data from the small-bore engine for detecting
the interaction between the developed combustion from the
pilot or main with sprays of subsequent injections using
in-cylinder 3D CFD simulation and experimental data. A
small-bore engine dataset shows that the trained object
detection algorithm successfully corroborates the simulation
and experimental data interaction. This investigation,
therefore, presents a novel application of object detection
methodology by automating the process and providing a
general-purpose object detection algorithm. This approach
can be used on any new simulation or experimental data for
automated detection of the spray–thermal zone interaction
without human intervention.</div>
Subject
Fuel Technology,Automotive Engineering,General Earth and Planetary Sciences,General Environmental Science