This document discusses a study using a neural network to model the specific fuel consumption of a turbocharged and intercooled diesel engine. Experimental data was collected from tests of the engine under various operating conditions and used to train the neural network model. The trained model was then able to accurately predict specific fuel consumption for conditions it was not directly trained on. Parametric studies were performed using the neural network model to investigate the effects of variables like crankshaft angle, engine speed, and load on specific fuel consumption. The neural network model provided results consistent with experimental data and was able to be used for analysis when direct experimental testing was not possible or practical.

1. A parametric study for specific fuel consumption of an intercooled diesel engine
using a neural network
Abdullah Uzun ⇑
Sakarya Vocational School, Automotive Programming, Sakarya University, 54187 Sakarya, Turkey
a r t i c l e i n f o
Article history:
Received 23 June 2010
Received in revised form 31 October 2011
Accepted 2 November 2011
Available online 15 November 2011
Keywords:
Neural networks
Intercooling
Specific fuel consumption
Scaled conjugate gradient algorithm
Diesel engine
a b s t r a c t
Turbocharging is a process wherein the amount of oxygen used in a combustion reaction is increased to
raise output and decrease specific fuel consumption. On account of this, fuel economy and thermal effi-
ciency are more important for all engines. The use of an intercooler reduces the temperature of intake air
to the engine, and this cooler and denser air increases thermal and volumetric efficiency. Most research
projects on engineering problems usually take the form of experimental studies. However, experimental
research is relatively expensive and time consuming. In recent years, Neural Networks (NNs) have
increasingly been used in a diverse range of engineering applications.
In this study, various parametric studies are executed to investigate the interrelationship between a
single variable and two steadies and two constant parameters on the brake specific fuel consumption
(BSFC, g/kW h). The variables selected are engine speed, load and Crankshaft Angel (CA). The data used
in the present study were obtained from previous experimental research by the author. These data were
used to enhance, train and test a NN model using a MATLAB-based program. The results of the NN based
model were found to be convincing and were consistent with the experimental results. The trained NN
based model was then used to perform the parametric studies. The performance of the NN based model
and the results of parametric studies are presented in graphical form and evaluated.
Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction
Diesel engines have a number of advantages compared with
other types of internal combustion engines, including greater reli-
ability, longer operating life, lower operating costs and increased en-
gine efficiency. They are able to operate at higher compression ratios
than gasoline engines, because the fuel is mixed with the air at the
start of the combustion process. This higher compression ratio leads
to better fuel efficiency. Since diesel engines are more efficient and
robust than gasoline engines, they are widely used. However, owing
to economicconsiderations,greater fueleconomyisstill pursued [1].
The emphasis on fuel conservation in the 1970s stimulated the
use of turbocharging to improve the fuel economy of those diesel
engines that run at or near full load for long periods of time. This
category includes marine, stationary and heavy-road vehicle en-
gines. By improving structural design, many engines used in these
applications are supercharged to high mean effective pressure, of-
ten accompanied by reduced piston speed, with resulting im-
proved mechanical efficiency [2].
The combustion process in a diesel engine is an extremely
complex process. Within a diesel engine, this process depends
primarily on the fuel–air ratio. Intake air mass flow intake air flow
and its temperature is are important parts of this process in terms
of effective engine efficiency [3].
In turbocharged engines, especially at high engine speeds and
loads at the compressor output, temperatures rise and the density
and, consequently, the amount of air induced into the engine de-
crease. To avoid this decrease in engine induction air and engine
power, the compressor output air temperature must be cooled.
An intercooler cools the intake air charge with air or water
coolant from the turbocharger compressor before it enters the en-
gine. In vehicles, a charge air cooler system is mounted in front of
the radiator. In air-to-water charge cooling, the compressed air is
cooled in a heat exchanger, which is either separate from or built
into the intake manifold. In air-to air charge coolers, the lower-
temperature ambient air is utilized directly for cooling rather than
using engine coolant as the intermediate system. During move for
vehicles, the coolant must be at the atmospheric and space
temperature [4].
Optimum diesel engine performance is related to the engine
design, operating parameters and fuel properties. [5]. Brake
specific fuel consumption (BSFC) is a measure of engine and fuel
efficiency within a shaft reciprocating engine. BSFC is the rate of
fuel consumption divided by the power produced. BSFC allows
the fuel efficiency of different reciprocating engines to be directly
0016-2361/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved.
doi:10.1016/j.fuel.2011.11.004
⇑ Tel.: +90 264 295 74 60; fax: +90 264 278 65 18.
E-mail address: abdullahuzunoglu@gmail.com
Fuel 93 (2012) 189–199
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Fuel
journal homepage: www.elsevier.com/locate/fuel

2. compared. The influence of intercooling on diesel engine perfor-
mance parameters has been examined using numerical and exper-
imental methods [6–9].
In the present study, a four-stroke, direct-injection, six cylinder,
turbocharged, intercooled diesel engine was selected as a test en-
gine. First, the engine was tested equipped with a water-cooler
intercooler, at different speeds and load conditions. Then, this en-
gine was again tested without an intercooler under the same con-
ditions. The fuel consumption was measured by operating the test
engine at different loads and speeds with and without an inter-
cooler. In these tests, it is more economical than trying to find work
ranges which were targeted [10]. In many engineering applica-
tions, it can be difficult to obtain experimental data for various
parameters. Neural Networks (NNs) have increasingly been used
in a variety of engineering applications.
There are many NN studies within the literature on diesel en-
gine performance [11–13]. All diesel engine performance data are
difficult to measure experimentally. Test measuring ranges are
1600–2000–2400 rpm and 18–20–22 CA, 400–450–500 N, for all
number of engine speeds for each crankshafts angle values, test
loads. Similarly, performance data have been developed using
NNs. Previous studies have used empirical data to train NNs. After
the training period, the network is used to estimate the values of
parameters that have not been presented to the system.
Table 1
Specification of the test engine [14].
Engine type FORD 6.0 LT T/C intercooling, DI
Stroke 4
Cylinder 6
Cylinder diameter (mm) 104.77
Piston stroke (mm) 114.9
Compression ratio 16.5/1
Max. engine power (kW) 136 (2400 rpm)
Max speed (rpm) 2750–2780
Idle speed (rpm) 665–685
Engine volume (lt) 5.947
Injection timing (°CA) 20
Ignition order 1–5-3–6-2–4
Engine weight (kg) 500
Fig. 1. Testing scheme.
Fig. 2. Test engine set.
Table 2
Range of parameters in the database and normalization values.
Min Max Normalization values
Input parameters
CA 18 24 24
Load(F) 300 550 550
Speed (rpm) 1000 2400 2400
Output parameters
BSFC w. intercooler 120,58 188,37 250
BSFC w.out intercooler 129,49 207,56 250
190 A. Uzun / Fuel 93 (2012) 189–199

3. The main purpose of the present study is to conduct parametric
studies to investigate the effect of CA, engine speed and loads on
diesel engine performance in terms of brake specific fuel consump-
tion (BSFC, g/kW h), both with and without intercooling. Firstly, a
series of experimental studies were conducted. The results of these
tests were used to train the NN based model. The results of the NN
based model used in the present study are convincing and are con-
sistent with the experimental results. NN based model outputs are
compared with experimental results. These results indicated that
the NN based model was highly successful in determining the BSFC
of the diesel test engine. The well-trained NN based model was
then used to perform parametric studies. The performance of the
NN based model and the results of parametric studies are pre-
sented in graphical form and evaluated.
BSFC1
biasbias
CA
F
rpm
BSFC2
hidden layer
Fig. 3. Architecture of the NN model.
0
0,2
0,4
0,6
0,8
1
0 0,2 0,4 0,6 0,8 1
Experimental
NN
(a) training set
0
0,2
0,4
0,6
0,8
1
0 0,2 0,4 0,6 0,8 1
Experimental
NN
(b) testing set
Fig. 4. The performance of NN based model for diesel engine with intercooling.
0
0,2
0,4
0,6
0,8
1
Experimental
NN
(a) training set
0
0,2
0,4
0,6
0,8
1
Experimental
NN
(b) testing set
0 0,2 0,4 0,6 0,8 1 0 0,2 0,4 0,6 0,8 1
Fig. 5. The performance of NN based model for diesel engine without intercooling.
Table 3
General properties of Model A.
CA F rpm
A1 18 400 Var.
20
22
A2 18 450 Var.
20
22
A3 18 500 Var.
20
22
A4 18 400 Var.
450
500
A5 20 400 Var.
450
500
A6 22 400 Var.
450
500
A. Uzun / Fuel 93 (2012) 189–199 191

4. 2. Details of the experimental setup
The experimental set up is a six-cylinder, four-stroke, diesel en-
gine with an intercooler; and turbocharger, which was used with a
hydraulic dynamometer. The specifications of the diesel engine are
given in Table 1.
All of the experiments [10] were performed for various speeds
and loads, both with and without an intercooler. A heat exchanger
is used to cool the engine water. Tank capacity is 300 L. Intake and
outlet air temperature of the intercooler are measured. Intercooler
cooling water flow rate is measured as 8.5 L/min. During the test,
values were measured and recorded for engine speed, load, intake
manifold temperature, exhaust gas temperature, cooling water in-
let and outlet temperatures, intercooler input and output temper-
atures, oil temperature, test cabin temperature, oil pressure, media
pressure, ambient temperature, intake manifold pressure, air in-
take pressure difference (in oblique manometer), cooling water
pressure difference (U-manometer) and fuel consumption. At the
end of each experimental period, the injection advance of the en-
gine was changed. It seems to repeat information that has already
been given in the earlier text. Intercooler and without intercooler
different loads, inject angle and speed into tables and graphs in
the characteristic values are presented as a comparison. The values
of some test variables were calculated using empirical measure-
ments, namely specific fuel consumption, effective engine
efficiency, excess air coefficient. The engine installation and testing
scheme are given in Fig. 1 and the test engine is shown in Fig. 2.
The accuracy in measurements is about 3% in the experiment.
3. Neural Networks (NN)
Recently, NNs have been successfully employed as a computa-
tional tool to solve complex problems in various engineering fields.
NNs provide an alternative computational model inspired by a neu-
rological model of the human brain. NNs can simulate operational
features of the human brain. The NN architecture is composed of
one input layer, one output layer and one or more hidden layers.
In the NN architecture, the layers are composed of neurons, which
are the fundamental processing element of the NN [15,16].
Neurons in each layer are totally connected to other neurons in
following layers. These NN architectures are commonly referred to
as a fully interconnected feed forward multilayer perception. There
are two biases in the NN. One of them is connected to neurons in
the hidden layer and the other one is connected to neurons in
the output layer. The numbers of neurons in the input and output
layers are designated according to the problem [15].
The back-propagation (BP) algorithm is a widely used training
algorithm for multi-layered feed forward networks. The BP algo-
rithm basically consists of two phases. The first is the forward
phase, where the activations are propagated from the input to the
output layer. In the first phase (the forward phase), the activations
are propagated from the input to the output layer. In the next phase
(the backward phase), the error between the observed actual value
and the desired nominal value in the output layer is propagated
backwards in order to modify the weights and bias values NN.
All the data used in the NN must be initialized before the train-
ing procedure. In the forward phase, the weighted sum of input
components is calculated as
netj ¼
Xn
i¼1
wijxi þ biasj ð1Þ
where netj is the weighted sum of the jth neuron for the input re-
ceived from the preceding layer with n neurons, wij is the weight be-
tween the jth neuron and the ith neuron in the preceding layer, xi is
the output of the ith neuron in the preceding layer. The output of the
jth neuron outj is calculated with a sigmoid function, as follows:
outj ¼ fðnetjÞ ¼
1
1 þ eÀðnetjÞ
ð2Þ
During the training process, the weights are modified to capture
the relationship between the input and output patterns. The training
of the network is carried out by adjusting the weights. The objective
of the training procedure is to find the optimal set of weightings.
The best-known training algorithm for the multi-layer feed-for-
ward neural network is the back-propagation algorithm. Two back-
propagation training algorithms, called gradient descent and gradi-
ent descent with momentum, run very slowly. Therefore, several
adaptive training algorithms for NN have recently been developed,
such as Conjugate Gradient Algorithm (CG) and Scaled Conjugate
Gradient Algorithm (SCG). The present study is used SCG as the
optimization algorithm. Details of the SCG algorithm can be found
in the literature [17].
The output of the NN model is compared with the experimental
results to obtain error values. The sum of the squares error (SSE) is
used as the performance function for feed forward networks. The
training process continues until the optimal sum of the squares er-
ror is determined. The SSE is defined as;
SSE ¼
Xm
i¼1
ðTi À outiÞ2
ð3Þ
Table 4
General properties of Model B.
CA F rpm
B1 18 Var. 1600
20
22
B2 18 Var. 2000
20
22
B3 18 Var. 2400
20
22
B4 18 Var. 1600
2000
2400
B5 20 Var. 1600
2000
2400
B6 22 Var. 1600
2000
2400
Table 5
General properties of Model C.
CA F rpm
C1 Var. 400 1600
2000
2400
C2 Var. 450 1600
2000
2400
C3 Var. 500 1600
2000
2400
C4 Var. 400 1600
450
500
C5 Var. 400 2000
450
500
C6 Var. 400 2400
450
500
192 A. Uzun / Fuel 93 (2012) 189–199

5. where Ti and outi are the target outputs and the actual output of
neural network values, respectively, for ith output neuron; and m
is the number of neurons in the output layer.
4. Numerical study
A parametric study is carried out to investigate the effect of CA,
engine speed and loads on diesel engine performance in terms of
brake specific fuel consumption (BSFC, g/kW h), both with and
without intercooling. A NN based model was used to perform the
parametric studies. Firstly, experimental studies were conducted.
Then, the data obtained from the experimental results were used
as initial inputs to the NN based model.
The data are divided into two parts, training and testing sets, for
both models. Sixty data-sets were selected as the training set and
used to train the NN based model. A further 34 data sets, which
are not used in the training process, are selected as the testing
set and used to validate the generalization capability of the NN
based model. Inputs and outputs are normalized in the (0–1) range
by using simple normalization methods and values are given in Ta-
ble 2. The maximum and minimum values of inputs and outputs
are also given in Table 2.
In the NN model, the numbers of neurons in the input and
output layers are based on the geometry of the problem. How-
ever, there is no general rule for determining the number of neu-
rons in a hidden layer and the number of hidden layers [15].
130
140
150
160
170
180
190
200
1000 1200 1400 1600 1800 2000 2200 2400
speed (rpm)
BSFC(g/kWh)
ca=18 ca=20 ca=22
F=400N
)(a1
130
140
150
160
170
180
190
200
speed (rpm)
BSFC(g/kWh)
ca=18 ca=20 ca=22
F=400N
)(b1
130
140
150
160
170
180
190
200
speed (rpm)
BSFC(g/kWh)
ca=18 ca=20 ca=22
F=450N
)(a2
130
140
150
160
170
180
190
200
speed (rpm)
BSFC(g/kWh)
ca=18 ca=20 ca=22
F=450N
)(b2
130
140
150
160
170
180
190
200
speed (rpm)
BSFC(g/kWh)
ca=18 ca=20 ca=22
F=500N
)(a3
130
140
150
160
170
180
190
200
speed (rpm)
BSFC(g/kWh)
ca=18 ca=20 ca=22
F=500N
)(b3
1000 1200 1400 1600 1800 2000 2200 2400
1000 1200 1400 1600 1800 2000 2200 2400 1000 1200 1400 1600 1800 2000 2200 2400
1000 1200 1400 1600 1800 2000 2200 2400 1000 1200 1400 1600 1800 2000 2200 2400
Fig. 6. Interrelationship between the BSFC and speed (According to F): (a) with intercooling (b) without intercooling.
A. Uzun / Fuel 93 (2012) 189–199 193

6. Therefore, the number of hidden layers and the number of neu-
rons in each layer are selected by trial and error. In order to
determine the most appropriate NN model, many different NN
models with neurons in hidden layers were trained and tested
for 2500 epochs. The sum of the squares errors (SSE) is used to
establish the most appropriate NN model. The required sum of
the squares error (SSE) was selected as 10À5
. The most appropri-
ate NN models corresponding to the performance of both training
and testing sets in terms of SSE are chosen. Consequently, the NN
model selected to define specific fuel consumption of an inter-
cooled diesel engine had three neurons in the input layer, five
neurons in the hidden layer and two neurons in the output layer
(Fig. 3).
A MATLAB program with a graphical user interface (GUI) was
developed to train and test the NN model [10]. In the NN model,
the type of back-propagation used was the scaled conjugate gradi-
ent algorithm (SCGA). The activation function was a sigmoidal
function, whose epochs (learning cycle) are 40,000.
The performance of the NN based model of the intercooled die-
sel engine shows that the correlations between experimental re-
sults and NN based model results are consistent for training and
testing sets, as shown in Fig. 4.
The performance of the NN based model for the diesel engine
without intercooling are shown in Fig. 5. Fig. 5 shows that the cor-
relations between targets and outputs are reliable for training and
testing sets.
130
140
150
160
170
180
190
200
speed (rpm)
BSFC(g/kWh)
F=400 F=450 F=500
CA=18
130
140
150
160
170
180
190
200
speed (rpm)
BSFC(g/kWh)
F=400 F=450 F=500
CA=18
130
140
150
160
170
180
190
200
speed (rpm)
BSFC(g/kWh)
F=400 F=450 F=500
CA=20
130
140
150
160
170
180
190
200
speed (rpm)
BSFC(g/kWh)
F=400 F=450 F=500
CA=20
130
140
150
160
170
180
190
200
speed (rpm)
BSFC(g/kWh)
F=400 F=450 F=500
CA=22
130
140
150
160
170
180
190
200
speed (rpm)
BSFC(g/kWh)
F=400 F=450 F=500
CA=22
1000 1200 1400 1600 1800 2000 2200 2400 1000 1200 1400 1600 1800 2000 2200 2400
1000 1200 1400 1600 1800 2000 2200 2400 1000 1200 1400 1600 1800 2000 2200 2400
1000 1200 1400 1600 1800 2000 2200 2400 1000 1200 1400 1600 1800 2000 2200 2400
)(a1 )(b1
)(a2 )(b2
)(a3 )(b3
Fig. 7. Interrelationship between the BSFC and speed (According to CA): (a) with intercooling (b) without intercooling.
194 A. Uzun / Fuel 93 (2012) 189–199

7. The results of Fig. 4 indicate that the NN based models are suc-
cessful in learning the relationship between the input parameters
and outputs. The results of Fig. 5 show that the NN based models
are capable of generalizing between input and output variables.
As can be seen from the Figs. 4 and 5, NN based model results agree
well with the experimental results.
5. Parametric study
By using a well-trained NN based model, various parametric
studies are conducted to investigate the BSFC at different positions.
The three main models are formed and the interrelationship be-
tween the one variable and two steadies on the BSFC are studied
for each model. The selected variables are engine speed, load and
CA in Models A, B and C, respectively (Table 3–5).
5.1. Model A
In Model A, a parametric study is carried out to reveal the
behavior of BSFC with respect to engine speed for various load
(F) and CA values in a diesel engine, both with and without interco-
oling. The interrelationship between the one variable and two
steadies for Model A are shown six positions in Table 3. The results
are presented graphically in Fig. 6 and 7, indicating the relationship
between BSFC and engine speed.
It can be seen in Figs. 6 and 7 that, with variable engine speed
(rpm), BSFC decreases in the case of CA = 20. Lower fuel
130
140
150
160
170
180
190
200
210
300 325 350 375 400 425 450 475 500 525 550
Load (N)
BSFC(g/kWh)
ca=18 ca=20 ca=22
speed=1600 rpm
130
140
150
160
170
180
190
200
210
Load (N)
BSFC(g/kWh)
ca=18 ca=20 ca=22
speed=1600 rpm
130
140
150
160
170
180
190
200
210
Load (N)
BSFC(g/kWh)
ca=18 ca=20 ca=22
speed=2000 rpm
130
140
150
160
170
180
190
200
210
Load (N)
BSFC(g/kWh)
ca=18 ca=20 ca=22
speed=2000 rpm
130
140
150
160
170
180
190
200
210
Load (N)
BSFC(g/kWh)
ca=18 ca=20 ca=22
speed=2400 rpm
130
140
150
160
170
180
190
200
210
Load (N)
BSFC(g/kWh)
ca=18 ca=20 ca=22
speed=2400 rpm
300 325 350 375 400 425 450 475 500 525 550
300 325 350 375 400 425 450 475 500 525 550 300 325 350 375 400 425 450 475 500 525 550
300 325 350 375 400 425 450 475 500 525 550 300 325 350 375 400 425 450 475 500 525 550
)(a1 )(b1
)(a2 )(b2
)(a3 )(b3
Fig. 8. Interrelationship between the BSFC and load (According to speed): (a) with intercooling (b) without intercooling.
A. Uzun / Fuel 93 (2012) 189–199 195

8. consumption occurs with low load (400 N) and 18 CA; and with
middle and high loads (450 and 500 N) and 20 CA at approximately
2000 rpm. The results of this section, shown in Figs. 6a and b, also
suggest that the trend of increased fuel consumption with higher
engine speed is less pronounced in used intercooled engine than
without used intercooling. According to Fig. 6, the best operating
ranges are in Fig. 6 a3 and b3. In case of F = 500 N, conversely the
poor working conditions seen in Fig. 6a1 and b2 in case of
F = 400 N. As can be seen Fig. 6, the best work range conditions
clearly correspond to the case of CA = 20.
In the case of variable CA, the ideal working range can be
observed as 18 CA–400 N, 20 CA–450 N and 22 CA–500 N. Also,
the intercooled diesel engine achieved lower fuel consumption
than the non-intercooled engine, as might be expected (Fig. 7a
and b). As shown in Fig. 7a1 in the case of CA = 18, BSFC decreases
slightly until nearly 5–8%. Since the starter value of BSFC is very
high, this case does not represent an ideal working range. An ideal
working range can be seen in Fig. 8a2 in the case of CA = 22, in
which the starter value of BSFC and the ratio of increase in BSFC
are very low.
5.2. Model B
In Model B, the parametric study is carried out to reveal the
behavior of BSFC with respect to loads (F) for various engine speeds
(rpm) and CA values, both with and without intercooling. The
130
140
150
160
170
180
190
200
210
Load (N)
BSFC(g/kWh)
speed=1600 speed=2000 speed=2400
CA=18
130
140
150
160
170
180
190
200
210
Load (N)
BSFC(g/kWh)
speed=1600 speed=2000 speed=2400
CA=18
130
140
150
160
170
180
190
200
210
Load (N)
BSFC(g/kWh)
speed=1600 speed=2000 speed=2400
CA=20
130
140
150
160
170
180
190
200
210
Load (N)
BSFC(g/kWh)
speed=1600 speed=2000 speed=2400
CA=20
130
140
150
160
170
180
190
200
210
Load (N)
BSFC(g/kWh)
speed=1600 speed=2000 speed=2400
CA=22
130
140
150
160
170
180
190
200
210
Load (N)
BSFC(g/kWh)
speed=1600 speed=2000 speed=2400
CA=22
300 325 350 375 400 425 450 475 500 525 550 300 325 350 375 400 425 450 475 500 525 550
300 325 350 375 400 425 450 475 500 525 550 300 325 350 375 400 425 450 475 500 525 550
300 325 350 375 400 425 450 475 500 525 550 300 325 350 375 400 425 450 475 500 525 550
)(a1
)(b1
)(a2 )(b2
)(a3 )(b3
Fig. 9. Interrelationship between the BSFC and load (According to CA): (a) with intercooling (b) without intercooling.
196 A. Uzun / Fuel 93 (2012) 189–199

9. relationship between the one variable and two steadies for Model
B are shown six positions in Table 4. The results are presented
graphically in Figs. 8 and 9, indicating the relationship between
BSFC and engine loads.
It can be seen in Fig. 9 that, with variable engine load, all of the
BSFC values are close to the case of low load. BSFC generally
decreased with increased engine load. However, there are sudden
changes in all BSFC values at loads of around 400–450 N. According
to Fig. 9, the best operating range occurs in Fig. 9a1 at an engine
speed of 1600 rpm. As can be seen from Fig. 8, BSFC with interco-
oling diesel engine is less than without intercooling.
As shown in Fig. 9, there is no significant difference between
BSFC at all engine loads, engine speeds; or with and without
intercooling. Fig. 9a1–3 indicates that all BSFC values are approxi-
mately equal. However, it can be seen that when BSFC decrease
in starter values, at around F = 400–450 N, BSFC increases until
around F = 450–500 N. As explained above, the best work range
in this model is found in Fig. 10a2. In Fig. 9, at middle load range
(400–450 N), the fuel consumption appears to be lowest at CA
20, which is clearly much lower than the result sin both CA 18
and CA 22 (Fig. 9a and b).
5.3. Model C
In Model C, the parametric study examines the relationship
between BSFC and CA. The trend of BSFC with respect to CA for
130
140
150
160
170
180
190
200
210
18 19 20 21 22 23 24
CA
BSFC(g/kWh)
speed=1600 speed=2000 speed=2400
F=400 N
130
140
150
160
170
180
190
200
210
CA
BSFC(g/kWh)
speed=1600 speed=2000 speed=2400
F=400 N
130
140
150
160
170
180
190
200
210
CA
BSFC(g/kWh)
speed=1600 speed=2000 speed=2400
F=450 N
130
140
150
160
170
180
190
200
210
CA
BSFC(g/kWh)
speed=1600 speed=2000 speed=2400
F=450 N
130
140
150
160
170
180
190
200
210
CA
BSFC(g/kWh)
speed=1600 speed=2000 speed=2400
F=500 N
130
140
150
160
170
180
190
200
210
CA
BSFC(g/kWh)
speed=1600 speed=2000 speed=2400
F=500 N
18 19 20 21 22 23 24
18 19 20 21 22 23 24 18 19 20 21 22 23 24
18 19 20 21 22 23 24 18 19 20 21 22 23 24
)(a1
)(b1
)(a2 )(b2
)(a3 )(b3
Fig. 10. Interrelationship between the BSFC and CA (According to F): (a) with intercooling (b) without intercooling.
A. Uzun / Fuel 93 (2012) 189–199 197

10. variable F and engine speed values is investigated for a diesel en-
gine both with and without intercooling; the results are shown
in Figs. 10 and 11. As clearly indicated in Fig. 10a and b, The fuel
consumption made at CA 20 and 450–500 N for all three load lev-
els, while the CA is kept variable. The interrelationship between
the one variable and two steadies for Model C are shown in six
positions in Table 5. The results are presented graphically in Figs.
10 and 11, to indicate the relationship between BSFC and the CA.
As shown Fig. 10, BSFC decreases slightly in starter values until
around 19 CA. BSFC tends to increase for all engine speeds after 19
CA. As shown in Fig. 10, there is no significant difference in BSFC at
all CA values and engine speeds. As can be seen from Fig. 10, in all
cases, the engine with intercooling consumes less fuel than the en-
gine without intercooling. The results shown in Fig. 10 suggest the
ideal BSFC level is around 18–20 CA and 450 N for the lower engine
speeds. Additionally, it is observed that increasing loads and en-
gine speeds lead to increasing BSFC. The results shown in
Fig. 10a and b suggest the same indications made before for the ef-
fect of intercooling on engine performance.
Similar trends for all BSFC values are observed in Fig. 11 for die-
sel engines with intercooling and without intercooling. As shown
in Fig. 11, there is no considerable statistically significant differ-
ence between BSFC trends in all CA and engine loads for cases with
intercooling and without intercooling. As explained above, the best
work range is found in Fig. 11a1. In Fig. 11, at middle load range
(450–500 N), fuel consumption is lowest at F = 450 N, which is
clearly much lower than the results for both F = 400 N and
F = 500 N (Fig. 11a2–1a3).
130
140
150
160
170
180
190
200
210
CA
BSFC(g/kWh)
F=400 N F=450 N F=500 N
speed=1600 rpm
130
140
150
160
170
180
190
200
210
CA
BSFC(g/kWh)
F=400 N F=450 N F=500 N
speed=1600 rpm
130
140
150
160
170
180
190
200
210
CA
BSFC(g/kWh)
F=400 N F=450 N F=500 N
speed=2000 rpm
130
140
150
160
170
180
190
200
210
CA
BSFC(g/kWh)
F=400 N F=450 N F=500 N
speed=2000 rpm
130
140
150
160
170
180
190
200
210
CA
BSFC(g/kWh)
F=400 N F=450 N F=500 N
speed=2400 rpm
130
140
150
160
170
180
190
200
210
CA
BSFC(g/kWh)
F=400 N F=450 N F=500 N
speed=2400 rpm
18 19 20 21 22 23 24 18 19 20 21 22 23 24
18 19 20 21 22 23 24 18 19 20 21 22 23 24
18 19 20 21 22 23 24 18 19 20 21 22 23 24
)(a1
)(b1
)(a2 )(b2
)(a3 )(b3
Fig. 11. Interrelationship between the BSFC and CA (According to speed): (a) with intercooling (b) without intercooling.
198 A. Uzun / Fuel 93 (2012) 189–199

11. 6. Conclusion
The present study used parametric analysis to examine the ef-
fect of CA, engine speed and engine loads on the brake specific fuel
consumption (BSFC) of a diesel engine, both with and without
intercooling. The parametric analysis used a novel NN approach.
Firstly, the NN based model was trained and tested with data
obtained experimentally. Following training, the outputs produced
by the NN based model were compared with the experimental re-
sults and found to be similar. Then, the well-trained NN based
model was used to perform the parametric studies. In the paramet-
ric studies, three models were used to show the relationship be-
tween the one variable and two steadies on BSFC. The selected
variables were engine speed, engine load and CA in Models A, B
and C, respectively.
In Model A, the parametric study examined BSFC with respect
to engine speed for various load (F) and CA values, in cases with
and without intercooling. The most appropriate operating ranges
correspond to F = 500 N and the worst operating conditions corre-
spond to F = 400 N.
Model B examined BSFC with respect to loads (F) for various en-
gine speed (rpm) and CA values in cases with and without interco-
oling. There was no statistically significant difference between
BSFC at all engine loads, engine speeds and in cases with intercool-
ing or without intercooling. The results show that, with variable
engine load, all BSFC values are close to the case of low load. BSFC
generally decreased with increased engine load. However, there
are sudden changes in all BSFC values at loads of around
400–450 N.
Model C examined BSFC with respect to CA for various engine
speeds (rpm) and load (F) values, for cases with and without int-
ercooling. BSFC decreased slightly in starter values until around
19 CA. BSFC tends to increase for all engine speeds after 19 CA.
The results suggest the ideal BSFC level at around 18–20 CA and
450 N for the lower engine speeds. It was observed that increasing
loads and engine speeds lead to increasing BSFC. There is no statis-
tically significant difference between BSFC trends in all CA and en-
gine loads for cases with used intercooling and without used
intercooling.
It can be concluded that 20 CA, 400–450 N and medium engine
speed (2000 rpm) produce an optimal operating range for higher or
lower specific fuel consumption in the tested diesel engine with
and without used intercooling. All of the results suggest that the
diesel engine without intercooling consumes more fuel compared
to the same engine with intercooling. The results are reasonable
and found to be consistent with the literature.
The study findings may make an important contribution for
researchers in similar fields, since the use of NN models is quicker,
more convenient and cost-effective than fully experimental stud-
ies. The reasons of BSFC increasing at intercooling diesel engine
can be explained as follows: The result indicating that BSFC in-
creased in a diesel engine with intercooling can be explained as fol-
lows: CA, engine speed and load must be optimized, and the best
combustion process like that, air intake mass flow, should be
cooled by the intercooler.
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