ANALYSIS OF DIESEL ENGINE COMBUSTION USING IMAGING AND BLIND SOURCE SEPARATION

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6 Νοε 2013 (πριν από 4 χρόνια και 6 μέρες)

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ANALYSIS OF DIESEL ENGINE COMBUSTION

USING IMAGING AND BLIND SOURCE SEPARATION

K. Bizon
1
, S. Lombardi
1
, G. Continillo
1,2
, E. Mancaruso
2
, B. M. Vaglieco
2

1
Università del
Sannio
, Benevento, Italy

2
Istituto Motori
C.N.R
,
Naples
, Italy

OUTLINE



Introduction


Experimental

setup

& procedure


Independent

component

analysis


Analysis

of

crank
-
angle

resolved

measurements


Cycle
-
to
-
cycle

variations

analysis


Comparison

with

other

methods


Summary

&
conclusions

OBJECTIVE

OF

THE WORK



First attempt of application of independent component analysis (ICA)
to
2D images of combustion
-
related luminosity acquired from an optically
accessible Diesel engine



Identification

of

the
leading

independent

structures

(
independent

components
,
ICs
) and:


study of the
transient behavior
of the flame during a single cycle


analysis of the
cycle
-
to
-
cycle variability



Assessment of the alternative decompositions
(e.g. proper orthogonal
decomposition, POD)

INTRODUCTION


The
fast development of optical systems
has made available
measurements of distributed in
-
cylinder variables but the
measurements interpretation is not always easy due to the
huge amount
of data
, and to the
variety of coupled phenomena

taking place in the
combustion chamber


This has lead to the increasing interest in the application of
sophisticated mathematical tools, e.g.
proper orthogonal decomposition
(POD)

has become a popular reduction and analysis tool. It has
contributed to the knowledge of many physical phenomena, but it
cannot separate independent structures
,

i.e.

all POD modes contain
some element of all structures found in all of the fields


Alternative decompositions can be considered, e.g.

independent
component analysis (ICA)

can be expected to provide a more powerful
insight with respect to POD

OUTLINE



Introduction


Experimental

setup

& procedure

´
Independent

component

analysis


Analysis

of

crank
-
angle

resolved

measurements


Cycle
-
to
-
cycle

variations

analysis


Comparison

with

other

methods


Summary

&
conclusions

EXPERIMENTAL

ENGINE


Direct injection four
-
stroke diesel engine with a single cylinder and a multi
-
valve production head


The research engine features only two valves and utilizes a classic extended
piston with a UV grade crown window

Single
cylinder

diesel
engine

Engine

type

4
-
stroke

Bore

8.5 cm

Stroke

9.2 cm

Swept

volume

522 cm
3

CC volume

21 cm
3

Compression ratio

17,7:1

Common
rail

injection

system

Injector

type

Solenoid

driven

Nozzle

Microsac
, single guide

Holes

number

6

Cone angle

148
°

Hole diameter

0.145 mm

Rated flow

400 cm
3
/30 s

OPTICAL
SETUP















High
-
speed digital complementary metal oxide semiconductor (CMOS) camera, controlled
by a trigger signal generated by a delay unit linked to the engine encoder, in combination
with a 45
°

UV/visible mirror located inside the piston


EXPERIMENTAL

PROCEDURE &
RESULTS


Engine
speed of 1000 rpm
, continuous
-
mode
operation, using commercial Diesel fuel


Injection pressure fixed at 600 bar and no EGR


Typical CR injection strategy of
pre, main and post
injections (PMP)
starting at
-
9
°
,
-
4
°

and 11
°

CA
with
duration of 400, 625 and 340
μs


Cylinder pressure recorded at 0.1 CA
°

increments
by
means of a pressure transducer


ROHR calculated
using the first law, perfect gas

approach



CMOS high
-
speed camera:
frame rate of 4 kHz and
exposure
time

of

166
μ
s


888 images of the in
-
cylinder luminosity field,
collected from
-
4
°

to 30.5
°

CA, with CA increment of
1.5
°
, over N= 37

consecutive fired cycles


The original spatial mesh of 529
×
147 is clipped to
120
×
120 pixels
framing the combustion chamber

-40
-30
-20
-10
0
10
20
30
40
C
r
a
n
k

a
n
g
l
e

[
d
e
g
r
e
e
s
]
0
10
20
30
40
50
60
C
o
m
b
u
s
t
i
o
n

p
r
e
s
s
u
r
e

[
b
a
r
]
0
10
20
30
D
r
i
v
e

c
u
r
r
e
n
t

[
A
m
p
e
r
e
]
0
40
80
120
160
R
a
t
e

O
f

H
e
a
t

R
e
l
e
a
s
e

[
k
J
/
k
g
/
°
]
OUTLINE



Introduction


Experimental

setup

& procedure


Independent

component

analysis

´
Analysis

of

crank
-
angle

resolved

measurements


Cycle
-
to
-
cycle

variations

analysis


Comparison

with

other

methods


Summary

&
conclusions

POD VS. ICA

Proper

orthogonal

decompositon


Extracts dominant structures
-

orthonormal

and
optimal in the
L
2

sense


Relatively simple
eigenvalue

problem

to solve


Fields of application
: turbulent flows, model
reduction, image processing,
PIV data & flame
luminosity from SI & Diesel engines



Independent

component

analysis


Extracts a set of
mutually
independent signals

from the
mixture of signals, i.e. permits to
separate the data into
underlying
informational components


Optimization problem
maximizing
some measure of the
independence


Fields of application
:
neuroimaging
, spectroscopy,
combustion engines (separation of
vibration sources)

POD VS. ICA

Proper

orthogonal

decompositon


Extracts dominant structures
-

orthonormal

and
optimal in the
L
2

sense


Relatively simple
eigenvalue

problem

to solve


Fields of application
: turbulent flows, model
reduction, image processing,
PIV data & flame
luminosity from SI & Diesel engines



Independent

component

analysis


Extracts a set of
mutually
independent signals

from the
mixture of signals, i.e. permits to
separate the data into
underlying
informational components


Optimization problem
maximizing
some measure of the
independence


Fields of application
:
neuroimaging
, spectroscopy,
combustion engines (separation of
vibration sources)

Given
:


:
random

vector

of

temporal

mixtures


:
temporal

(
mutually

independent
)
source
signals



The
mixing
model

can
be

written

as
:




If

then

matrix

is

invertible

and the
model

can
be

rewritten

as
:




The ICA
problem

consist

of

calculating


such

that

is

an

optimal

estimation

of


ICA problem can be solved by
maximization of the statistical independence

of the estimates




ICA:
DEFINITION







1
,,
m
t x t x t

 
 
x






1
,,
n
t s t s t

 
 
s
x = As
n m

A
s = Wx
1

W= A
y = Wx
s
y
ICA:
APPROACHES



Maximization

of

nongaussianity

(“
nongaussian

is

independent
”)


Maximization

of

kurtosis

(e.g. a
fast
-
point

algorithm

using

kurtosis

called

FastICA
)


Maximization

of

negentropy

(
normalized

version

of

differential

information
entropy
)



Minimization

of

mutual

information


Maximum

likelihood

estimation


Tensorial

methods


Nonlinear

decorrelation

and
nonlinear

PCA



ICA:
FASTICA

ALGORITHM


FastICA

algorithm maximizes non
-
gaussianity

by means of a gradient
method. The (non
-
)
gaussianity

is estimated by the absolute value of
kurtosis defined as:




The algorithm is employed on
centered

(having zero mean) and
whitened
data
(uncorrelated and have unit variances), i.e.:





-

raw data


-

POD eigenvectors


-

POD
eigenvalues

(on the diagonal)


If the number of ICs is smaller than the number mixtures,
the data
can be reduced during the whitening
using
leading POD modes





1 2
T
E

 

x =D E x x








2
4 2
kurt 3
y E y E y
 

x
E
D
n
m
ICA:
SEPARATION

OF

IMAGE

MIXTURE

sources

independent

components

POD
modes

mixtures

m
i
x
i
n
g

s
e
p
a
r
a
t
i
o
n

OUTLINE



Introduction


Experimental

setup

& procedure


Independent

component

analysis


Analysis

of

crank
-
angle

resolved

measurements

´
Cycle
-
to
-
cycle

variations

analysis


Comparison

with

other

methods


Summary

&
conclusions

CRANK

ANGLE
RESOLVED

MEASUREMENTS


PMP at
-
9
°
,
-
4
°

and 11
°

CA

first luminous spots
due to ignition of the
preinjected

fuel

main injection
combustion

combustion present
on all jets and in the
vicinity of the chamber
wall

combustion
zone moves
towards the
bowl wall

simultaneous
ignition of
postinjection

jets

maximum of post
combustion
luminosity

Images of combustion luminosity for multiple injections
in a cycle, at several crank angles

ICA:
CYCLE

8


y
1

y
2

ICA:
CYCLE

9


y
1

y
2

ANALYSIS

OF

IC
S

AND
THEIR

COEFFICIENTS


2
°

CA

9.5
°

CA

5
°

CA

2
°

CA

9.5
°

CA

5
°

CA

y
1
: combustion along the fuel
jets; swirl motion

y
2
: combustion
near

the
chamber

walls


y
1

y
2

y
1

y
2

ICS VS.
ENGINE

PARAMETERS


SOC of PMP:


4
°
, 1
°

& 14
°

CA

main inj.

post inj.

maximum luminosity of the
regular combustion process
near the fuel jets of the
main and post injection

3.5
°

CA

17
°

CA

8
°

CA

OUTLINE



Introduction


Experimental

setup

& procedure


Independent

component

analysis


Analysis

of

crank
-
angle

resolved

measurements


Cycle
-
to
-
cycle

variations

analysis

´
Comparison

with

other

methods


Summary

&
conclusions

CYCLE
-
TO
-
CYCLE

VARIATIONS














Not all jets burn with the same flame behavior; during combustion development
flames are unevenly distributed along the jets’ axes


Post injection starts in a partly burning environment, where the irregular peripheral
combustion influences post
-
injection ignition

2
°

CA

3.5
°

CA

14
°

CA

18.5
°

CA

main injection
combustion

end of main
combustion;

post injection

post injection
combustion

3.5
°
CA
















ICA separates the mean combustion luminosity at each CA from the
irregular flame structure related to cycle variability

14
°
CA
















Separation is worse when the variability is higher, i.e. at the end of main
combustion when the flames move randomly near the bowl wall

18.5
°
CA
















Again, the separation is better when the cyclic variability is lower, i.e. for the
CA characterized by regular combustion typical of
jet
burning

ICS VS.
ENGINE

PARAMETERS



















a
1

peaks

where an irregular combustion process takes place (less effective separation) and is
low when the burning along the jets dominates


CV of
a
2

is at least one order of magnitude higher than the CV of
a
1
, confirming that strong
deviations from the ideal combustion process are located near the bowl wall

pilot injection
fuel burning in
the centre of
the bowl

regular burning of
the main & post
injection fuel
along the jet
directions

random
flames near
the bowl

irregular end of
combustion

OUTLINE



Introduction


Experimental

setup

& procedure


Independent

component

analysis


Analysis

of

crank
-
angle

resolved

measurements


Cycle
-
to
-
cycle

variations

analysis


Comparison

with

other

methods

´
Summary

&
conclusions

ICA VS. POD


Independent

components

POD
modes

Negentropy
,

i.e.
normalized

differential

information
entropy
,
measures

the
amount

of

information

and
is

always

higher

for

ICA
than

for

POD
;
it

is

estimated

as
:















1 2 1 2
2
2
3
;
1 1
kurt
12 48
ICA POD
J J y J y J J J
J y E y y
 
   
 
ICA VS. 1
ST

AND 2
ND

MOMENT













Analysis of cycle variations
(but not crank angle resolved measurements!)
similar conclusions for the
first two statistical moments
(mean & standard
deviation)


Here the "signals" were, in most cases, already spatially separated


Independent

components

1
st

and 2
nd

moment

Crank

angle
resolved

measurments

Cycyle
-
to
-
cycle

variations

OUTLINE



Introduction


Experimental

setup

& procedure


Independent

component

analysis


Analysis

of

crank
-
angle

resolved

measurements


Cycle
-
to
-
cycle

variations

analysis


Comparison

with

other

methods


Summary

&
conclusions

SUMMARY

&
CONCLUSIONS


A
first attempt of the application of ICA
to luminosity image data
collected in an
optical engine
was done


Two independent components
were found related to:


combustion along the fuel jets
presenting low variability over the
cycles


near the bowl walls


highly variable; this confirms quantitatively that
strong deviations from the ideal combustion process are located
near the bowl walls


The
analysis is fast and reliable
-

a single computation takes
less than
0.1 s
on a standard sequential single processor


Benefits of ICA can be much higher than this simple application example
shows. Based on the demonstration case,
more complex data can be
analyzed
, and what was presented here is a
first and convincing
example of how ICA works in an engine context

From

the movie L’
Atalante

by

Jean Vigo (1932)

Dita Parlo (
born

as

Gerda

Olga
Justine

Kornstädt

on
4th
Sept

1908 in
Szczecin
,
Poland