Intake and Exhaust System Optimization of Internal Combustion Engines

8/11/2019 Intake and Exhaust System Optimization of Internal Combustion Engines

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Reporte TcnicoRT-ID- 012/2003

Intake and exhaust system optimization of

internal combustion engines

Juan Pablo Alianak y Norberto Nigro

Departamento de IngenieraEscuela de Ingeniera Mecnica

Facultad de Ciencias Exactas, Ingeniera y AgrimensuraUniversidad Nacional de Rosario

Secretara de Ciencia y Tcnica

Facultad de Ciencias Exactas, Ingeniera y Agrimensura

Universidad Nacional de Rosario

Av. Pellegrini 250 - 2000 Rosario Argentina

http://www.fceia.unr.edu.ar/secyt

Enviado 10 de Octubre de 2003Revisado

Disciplina: Ingeniera Mecnica


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Este documento es publicado por la FCEIA para su consulta externa. El mismo se publica como

Reporte de Investigacin para divulgacin de las tareas cientficas que se desarrollan en la FCEIA,

Universidad Nacional de Rosario. Los autores conservan los derechos de autora y copia de la totalidad

de su trabajo aqu publicado. Luego de su posterior eventual publicacin externa a la FCEIA, los

requerimientos debern dirigirse a los autores respectivos. El contenido de este reporte refleja la visin

de los autores, quienes se responsabilizan por los datos presentados, los cuales no necesariamente

reflejan la visin de la SeCyT-FCEIA. Tanto la SeCyT-FCEIA como los autores del presente reporte

no se responsabilizan por el uso que pudiera hacerse de la informacin y/o metodologas publicadas.

Cualquier sugerencia dirigirla a: [email protected]

Un trabajo basado en este pero resumido ha sido enviado recientemente a la Revista International

Journal of Engine Research y hasta el momento no hemos recibido respuesta de parte de ellos acerca desu recepcin


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Optimizacin del sistema de admisin y escape de un

motor de combustin interna

Juan Pablo Alianak* y Norberto Nigro**

Departamento de Ingeniera

Escuela de Ingeniera Mecnica

Facultad de Ciencias Exactas, Ingeniera y AgrimensuraUniversidad Nacional de Rosario

El diseo de motores es uno de los proyectos mas desafiantes de la Ingeniera Mecnica. Esto es principalmente

debido a la gran cantidad de variables involucradas. Mas an, los motores de alta performance aplicados a

vehculos de competicin necesitan un estudio detallado y una muy profunda tarea de optimizacin con el fin dealcanzar los ms altos ndices de potencia especfica. Entre los diferentes factores que influencia el rendimiento

de un motor la eficiencia volumtrica parece ser uno de los mas importantes. Este ndice mide la capacidad de

aspirar de un motor asociado a las carreras de carga de mezcla fresca y barrido de gases quemados a travs de lossistemas de admisin y escape. Siguiendo los ltimos avances cientficos en cuanto a las capacidades

computacionales, los mtodos numricos basados en la teora de a dinmica de gases aparecen como muy

atractivos para resolver este tipo de problemas. En general estas ecuaciones son acopladas con otras de ndoletermodinmica aplicadas a los principales componentes del motor tal como cilindros, tomas de aire, tanques o

recipientes de volumen fijo, silenciadores, etc. Debido a los simples modelos fenomenolgicos incluidos en el

sistema del motor, un gran nmero de coeficientes empricos necesitan ser estimados. Esta tarea involucramediciones experimentales combinadas con una buena estrategia para vincularlos con los principales parmetros

incgnitas. No obstante, no solo la dinmica de fluidos y la termodinmica juegan un rol crucial sino tambin la

respuesta mecnica del tren de vlvulas completo es muy importante para optimizar la eficiencia volumtrica deun motor. En este sentido la teora de sistemas multi cuerpos puede asistir en el entendimiento de cmo las no

linealidades influencian el comportamiento dinmico del tren de vlvulas completo. Este trabajo presenta una

nueva estrategia para calibrar el software de un motor y una metodologa de diseo ptimo de levas para trenesde vlvulas de motores de combustin interna basada en un anlisis combinado de la termodinmica, la dinmica

de gases y la mecnica.Palabras claves:: Dinmica de gases, motores de combustin interna, admisin y escape, tren de vlvulas

Intake and exhaust system optimization of internal

combustion enginesEngine design is one of the most challenging mechanical engineering projects. This is mainly due to the huge amount

of variables involved. Moreover, high performance engines applied to a racing car need a detailed study and a very

good optimization task in order to achieve the highest indices of specific power. Among the different factors thatinfluence the engine performance, volumetric efficiency seems to be one of the most important ones. This index

measures the breathing capacity of an engine associated with the gas charge process through the intake and exhaustsystems. following

Following the latest advances in computers capability, numerical methods whose principal equations come from the

gas-dynamic theory have been applied to solve this problem. In general, these equations are coupled with

thermodynamic equations applied to the principal devices of an engine, such as cylinders, air intake, tanks, mufflers,

etc. Due to the very simple phenomenological models included in the engine system, a number of empirical

coefficients need to be estimated. This task involves experimental measurements combined with a good strategy to

link them with the main numerical unknown parameters. However, not only fluid dynamics and thermodynamicsplay a crucial role, but also the mechanical response of the whole valve train is very important to optimize the

volumetric efficiency of an engine. In this sense, the multibody system theory can be of assistance in order tounderstand how the nonlinearities influence the dynamic behavior of the whole valve train.

This paper presents a new strategy to calibrate an engine system software, plus a methodology to design optimal

cams for valve trains of internal combustion engines based on thermodynamics, gas dynamics and mechanicalanalysis.

Keywords: Gas dynamics, internal combustion engines, optimization, intake and exhaust, valve train.

*[email protected]

**


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Introduction

Today, the fast advances in computer hardware make it possible to reproduce an engine test

virtually and with high accuracy before going to the laboratory for fine-tuning. In this way,

an important amount of development lead-time in trials and errors is saved. The strong

influence of the intake and exhaust system dynamics on the volumetric efficiency and

eventually on the engine performance is well known. A strong effort was developed since

the publication of two of the pioneer papers in this area [1, 2] written with an analytical and

graphical point of view.Earlier contributions were based on characteristic method [3, 4]. In

the last decade a great number of robust and accurate algorithms was developed for the gas

dynamics and wave propagation in multi-dimensional configurations. One of the main

research lines has got robust and fast algorithms for unstructured grids in very complex and

variable geometries. On the other hand, numerous scientific papers have been published with

the aim of attaining high resolution schemes in very simple geometries like a one

dimensional domain. The results obtained by the CFD community along this research line

have been very fruitful and they can be used in the simulation of more complex dynamic

systems in which the assumption of one dimensional flow is valid. This is just the case in an

internal combustion engine where the flow in manifolds can be approximated through thisassumption without losing much accuracy, the main goal of the analysis being the tuning

effects. Joining CFD schemes for the manifolds with some thermodynamic models for

cylinders, tanks, junctions and valves, it is possible to build a computational tool for the

whole engine system that is able to predict its performance before going through more

expensive laboratory experiments. Finally, the actual observation in the laboratory

determines the degree of accuracy in the simulation, which allows us to do finer adjustments

to reach the target. This project began some years ago with the development of a single-

cylinder four stroke spark ignition engine simulator. The mathematical model is based on a

thermodynamic and a one dimensional gas dynamics description of the intake and exhaust

system published earlier [5]. Later, multi cylinder configurations were added to this

development in order to enhance this predictive tool. The Euler equations arising from the

gas dynamics model were solved using two different numerical approximations, a streamlineupwind Petrov-Galerkin finite element method (SUPG-FEM) [6, 7], and also a high

resolution finite volume scheme called total variation diminishing (TVD) [8], with no

significant differences in the results obtained with them. For the coupling of tanks and

cylinders with pipes, a model based on a nozzle analogy is used [9], allowing for the

possibility of subsonic or sonic flow condition through the intake and the exhaust valves.

The coupling of pipes and the junctions is solved using a model based on a pressure

equalizer coupled with mass and energy balance equations, characteristic propagation

equations, and an entropy equalizer for the outgoing branches at the junction [10]. Much

work is currently being done along this research line since the code validation is a very

intricate subject. The inherent difficulties in getting detailed measurements in engine

configurations compel us to take indirect and global measures in order to estimate some

engine operation parameters. The selection of this strategy is crucial for the softwarereliability, and good predictions are necessary for design improvements. Nowadays this

open problem is subject of debate; how to combine in an optimal way the theory and the

available measurements to get good software calibration is the question to be answered. This

is one of the main topics that this papers covers, and a novel strategy is proposed as an

answer to the scientific community.

Besides, there is a real need to combine gas dynamics, thermodynamics and mechanical

analysis in order to optimize the volumetric efficiency of an internal combustion engine.

While thermodynamics is mainly necessary to solve the power cycle of the engine operation,


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the flow pattern is crucial to understand how the pressure waves interact with the valves

timings, drastically influencing the pumping work in the engine cycle. But these two

phenomena are not enough to predict the engine performance. The mechanical behavior of

the involved mechanism is also responsible for the normal operation of the engine. The

valves timings, the mechanical reliability and the durability will be warranted only if the

mechanical response is under control. This kind of analysis is not frequently reported in the

literature, probably because there is a standard division between fluid dynamics and solidmechanics for researchers on computational mechanics. This paper also presents an

optimization task linking these three great areas in order to improve the valve train design.

This methodology can easily supplement our daily work so as to get better control on the

whole engine behavior.

The first section of this paper is a brief description of the mathematical and numerical

modeling of the gas dynamics and thermodynamics equations used for the engine system.

The following section presents the code calibration with a few new available basic

measurements in order to link them with the main unknown parameters in the model in order

to reproduce accurately the engine power recorded in the laboratory. The next sections deal

with the intake and exhaust timings optimization task, the optimal cam profile generation

and finally the mechanical response verification. The final section is for conclusions.

Numerical models for the gas dynamics and

thermodynamics in an internal combustion engine

Historically, researchers and engine designers following Heywood [11] and Ramos [12]

have been using four different categories of internal combustion engine models:

1. air standard cycle simulation,

2. zero and quasi-dimensional thermodynamic models,

3. a combination of zero dimensional and one dimensional models,

4. multidimensional models

The first approach was used in the past when only human work was available and there were

no computer capabilities. The limitation of this kind of model was in the prediction of the

pumping cycle, when the influence of the gas dynamics in the manifolds is crucial.

Following in complexity, the zero dimensional thermodynamic models offer the possibility

of including the unsteady behavior of the system and the variable thermo physical properties

along the whole cycle. Because of its simplicity, the emptying and

filling models [13] may result an attractive technique for the intake and the exhaust system.

It consists of assuming a fixed volume for each manifold and follow their time evolution

with a spatial average for the thermodynamic variables. In this sense, this strategy represents

a significant improvement in relation to the earlier models because the gas charging process

can be added to the whole computation and tuning may be predicted.However, the traveling waves in the manifolds are not represented at all because of the

spatial averaging. In order to include this effect that has a significant influence on the

volumetric efficiency, the following model includes a one dimensional representation of the

gas flow inside the manifolds solving the mass, momentum and energy balance in each tube

of the whole engine network [5, 9, 14, 15, 16, 17]. A simple spatial discretization is adopted

using a robust numerical scheme to solve it. It is possible to solve the entire engine

configuration including a number of devices like cylinders, mufflers, manifolds, tanks,


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junctions, carburetors, air cleaners, catalytic converters, and so on, with an accuracy level

that depends strongly on experimental measurements to

calibrate the whole model. The last kind of model deals with multidimensional or CFD

models that includes the whole three dimensional domain with the added complexity of

treating moving boundaries, turbulent flows, and reactive flows. This kind of model requires

powerful computer resources like parallel processing on cluster of personal computers or

workstations and they are only used for special purposes. As mentioned above, the goal ofthe engine simulator is to predict the engine performance allowing for some modifications in

order to improve it. Generally, the engine software of thermodynamic based models solving

the flow in the manifolds by a one dimensional CFD scheme is composed by:

a set of cylinders,

the intake and exhaust ports and valves,

the intake and exhaust manifolds

air intake or tanks

junctions

A brief description the devices mentioned above is included below; readers interested in

these topics can refer to the papers published in the related literature [5, 9, 14, 15, 16, 17, 18,19]

Cylinder model

This model assumes the cylinder to be a variable volume reactor. In general, it is an open

thermodynamic system with the inlet and outlet represented by the intake and exhaust

valves. The model is composed by the mass and energy conservation equations and the ideal

gas law assumption.

TRp

QVphdt

dE

mdt

dm

gas

j

j

j j

=

+=

=

where mis the cylinder trapped mass,Eis the internal energy of the cylinder contents, his

for the entalphy, ,pand T are the density, pressure and temperature inside the cylinder,V

is the cylinder volume, Qthe heat flux through the system andRis the gas constant

employed in the ideal law.

To close this model the following models are added:

the crank rod mechanism model

a combustion heat release model

a heat transfer model

a model for the flow through ports and valves

The first model establishes the piston position at each time step solving the motion of the

crank rod mechanism analitically. Using the expression for the piston position in time,


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the heat transfer surface area, the combustion chamber volume and its time variation are

computed straightforwardly using one of the following expressions:

)(4

)(

)sin()cos(

2

222

salD

VV

salDAAA

alas

cc

cpch

++=

+++=

+=

where is the crank angle, Vcis the clearance volume,Dcis the cylinder bore, lis theconnecting rod length, a is the crankshaft radius,Achis the head cylinder surface area andAp

is the crown piston surface area.

The combustion is solved using a single zone model in which the burned fraction of fuel is

computed by the Wiebe function [11], an empirical based formula that expresses the burning

rate as a function of the combustion duration.

The well known Woschni or Annand heat release models may be used for the heat losses

estimation from the hot gases inside the cylinder through the combustion chamber walls to

the cooling system [20, 21]. Mathematically these models are written as:

)( wallht TThAQ =

This model consists of a simple analogy with flow over a flat plate [22] adjusted by

experimental measurements to estimate the heat losses to the engine cooling system. The

main feature of this model is the correlation between the Nusselt, the Reynolds number and

the Prandtl number, which allows us to compute a convective film coefficient (hin the

above expression).Aand Twallare the heat transfer surface area and the cylinder wall

temperature respectively.

For the flow through poppet valves, the treatment is split according to the flow direction

following a model extracted from the earlier Benson papers[9]. In both cases a simple

analogy with flow through nozzles is done, and sonic condition is only possible for the

exhaust valve. For inflow condition the process is treated as subsonic and isentropic. This

condition is generally found for normal intake or for exhaust with backflow at the end of its

stroke.

For outflow from cylinder to the pipe through the exhaust valve, a simple analogy with a

convergent nozzle that may be chocked or not depending on the pressure jump between the

cylinder and the exhaust manifold is used [9]. The same model is also used for backflow at

the intake valve where normally chocked condition is not established.

Manifolds

Manifolds are three dimensional curved ducts with variable cross section in which the flow

inside has a very complex behavior. Because performance prediction of an internal

combustion engine is the main target and due to the computer resources available a one

dimensional approach is adopted for such a device. This approximation allows us to

represent the pressure waves developed inside the manifolds, which makes it possible to


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2

,( ) ( )

j j in j

dp dpc j p

dx dx

5. equal pressure at all branches

i jp p i j=

6. equal entropy at all the outgoing branches22

2 21 1( ) ( ) , ,( 1) 2 ( 1) 2

jii jcc u u i q i j j q

+ = +

All the models above are solved in a time marching procedure using an explicit scheme; the

nonlinearities are solved by a Newton method that proves to be very robust.

Code calibration

This section starts with a preliminary numerical example in which the results achieved with

the 1D engine simulator serve as rough calibration of the code for future design purposes.

Other numerical results and also experimental measurements are available for this example[19].

This example is a good starting point to detect differences in the code implementation prior

to moving to real engine tests in which the differences in the results come from several

factors very difficult to identify, specially those concerning data uncertainties. Many other

academic examples not included in this paper have been tested showing in general a good

agreement.

An academic example

The table below shows the main parameters of the engine used for this preliminary example.

The rest of the data set is described in the related literature [19].

Engine Model FIAT, 4 cylinder GDI

Displacement volume 1.60E-03 m3

Bore x Stroke 0.08051 x 0.0784 m

Rod connected length 0.1285 m

Compression ratio 11.5

IVO IVC (lift = 0.0001 m) 7 BTDC 47 ABDC

EVO EVC (lift = 0.0001 m) 45 BBDC 5 ATDC

Table 1: Engine data Example 1

The engine configuration is sketched in figure, 1 which shows an EGR valve recycling

burned gases from the exhaust to the admission. The main goal of this example is the tuning

of the EGR and throttle valve openings for a preset value of the residual gases trapped in the

cylinder charge to have an idea about the control of this kind of system.

One of the main changes relative to the original problem definition is the usage of a different

valve lift profile where only the maximum lift and the timings were kept. Another difference


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is the absence of a swirl valve placed at the intake port. In this example, only a straight tube

was placed with only one valve having the same geometric area as the two valves of the

original configuration.

The original work includes simulation with and without EGR system and for three engine

speeds. In this work we only include results with the EGR system.

Figures 2 to 4 show the pressure at the intake plenum for 1000 rpm, 2000 rpm and 4000 rpm

respectively. Increasing the engine speed produces an increment in the amplitude of thepressure waves located at the intake plenum and also a change in the shape of the pressure

wave. Figures 5 to 7 show the intake plenum temperature where the mean value ncreases

drastically with the engine speed, as well as the amplitude of the temperature variation

inside this tank.

IntakePlenum

EGRvalve

Intakemanifolds

Exhaustmanifolds

Cylinders

Catalytic

converterMuffler

Throttlevalve

AirCleaner

Figure 1: Engine configuration Example 1


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Figure 2 Plenum pressure 1000 rpm with EGR


Figures 8 to 10 show the cylinder trapped mass. There is more trapped mass for 2000 rpm

than for 1000 rpm, but increasing to 4000 rpm produces a drop of the mass trapped probably

due to a lack of intake and exhaust tuning.


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Figure 5 Plenum temperature 1000 rpm with EGR


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Figure 8 cylinder trapped mass 1000 rpm with EGR



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As mentioned above, the ultimate goal of this test is the manufacturing of a new and more

promising camshaft that will improve engine power performance.

The numerical strategy adopted for this task is below:

1. Finding a new valve timing capable of enhancing the power curve in a wide range of

engine speeds using the engine simulator. No fuel consumption and emissions were

taken into account.2. An optimal valve profile [23] based on kinematics arguments is obtained with this

new valve timing.

3. With this valve profile the corresponding cam profile is generated by an inverse

method using Mecano software.

4. A computational prediction of the new cam profile dynamic response inserted in the

valve train is done using Mecano software. Geometric interferences, mechanical

stresses and valve floating are the main topics to be analyzed.

The first step is purely a thermodynamics and gas dynamics analysis, the second and the

third are based on geometric arguments and the last is a mechanical verification of the

design.

Six cylinder engine test Stage 1

The first item above consists in optimizing the engine performance mainly by changing the

valve timings. Changes in the maximum valve lift were avoided because the piston and the

valve are closely mounted to allow for changes in this value in the current operation of this

engine.

This first task was performed using the engine simulator presented in this paper.

The engine configuration of the six cylinder Chevrolet engine is shown in figure 11, and

details about it are included in the next paragraphs.

Sensor 1

Intake

manifoldCylinder

Exhaust

manifold

Sensor 2Sensor 3

Sensor 4

Figure 11: six cylinder engine configuration


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Intake manifolds: formed by ten tubes and four junctions connecting them. The carburetor is

considered to be at wide open throttle, and due to this operation condition the pressure drop

through it is neglected. The wall temperature of these tubes is considered uniform at a value

of 293 Kelvin degrees.

Cylinder head: formed by a piece of manifold modeled as a tube and a valve modeled as anozzle for each side, the intake and the exhaust ports.

Cylinder: with 0.097m of bore, 0.0675m of stroke, connecting rod length of 0.163m and a

compression ratio of 9.5:1. The wall temperature was taken in relation to data available from

the experimental data set, mainly the oil and water temperature.

Exhaust manifold: formed by nine tubes and three junctions

Calibration of six cylinder engine

In order to make the predictions more accurate, a calibration of the engine simulator withexperimental measurements obtained from a real engine test is needed. After this process, it

is possible to propose some modifications in order to improve the engine design. The

mathematical model of the engine contains some uncertain parameters that introduce errors

in the computation. Supplementing the model with information from the real test minimized

those errors. The following data coming from the real test were used:

a) Flowmeter: The flow rate of air through the cylinder head as a function of the lift of

the valve is measured. This test is performed in a static way, modifying the position

of the valve step by step and forcing the air through the gap between the valve and

the seat through a vacuum of ten inches water column. Thus it is possible to get an

idea of the influence of the three dimensional effects present in the cylinder head,which was not considered with the engine simulator. On the other hand, the dynamic

behavior coupled with the three dimensional flow pattern is not reproduced by this

type of test. However, this kind of information is useful in order to adjust the flow

rate of fresh mixture through the cylinder head. By comparing these measurements

with those obtained doing the same test in a virtual way it is possible to compute the

discharge coefficient, which is one of the parameters to be adjusted.

b) Air consumption :the information of the air consumed by the engine in the real test

is also available. It is very useful information when it comes to doing extra

adjustment of the discharge coefficient with the engine speed.


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DISCHARGE COEFFICIENT

0,00

0,05

0,10

0,15

0,20

0,25

0,30

0,35

0 5 10 15 20

Valve lift [mm]

CD

Figure 12: Discharge coefficient from flowmeter measurments

Figure 12 shows a typical discharge flow coefficient curve obtained through a port

flowmeter test

c) Exhaust temperature: One of the main drawbacks of most engine simulators oriented

to race applications is the need to introduce estimations of the combustion duration.

As the combustion is modeled with a single zone model, it is one of the main

unknown parameters that should be input in the computation. As far as the authors

know, the novel methodology proposed in this paper, which is able to estimate the

combustion duration through measurements of the exhaust temperature, has never

been published before in the related bibliography. In the real test some thermocouples

were placed at a distance of 0.300 m from the cylinder head. By working out

averages with these temperature values over the six cylinders and repeating thesemeasurements for the whole range of engine speeds used in its normal operation, it is

possible to get an idea of how long the combustion lasts as a function of the engine

speed. When plotting the temperature curve versus. rpm and approximating this

behavior with a curve, the following two features should be remarked:

1- The slope of the approximate curve represents the sensitivity of the

combustion duration with the engine speed, i.e. the ratio between the time

consumed for the combustion of the whole fresh mixture at high engine speed in

relation to that at low engine speed. This argument is based on the assumption

that the increasing in the exhaust temperature may be caused by a delayed

quenching of the flame front.

2- The value of the exhaust temperature represents how long the combustion

takes for an specific engine speed.

This assumption was checked on the engine simulator using different combustion durations

and keeping the ignition angle fixed until the exhaust temperature away from the cylinder

agrees with the experimental measurement. These computational results allow us to quantify

this lack of information about the combustion duration. On the other hand, looking at the


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torque and fuel consumption plots of the real engine it is possible to enforce the combustion

duration results obtained with the above mentioned procedure.

d) Torque and power curve: This information is used to scale the convective heat

transfer film coefficient in the Woschni or Anand model. This parameter mainly

change the level of engine power keeping the curve shape unaltered.

150

170

190

210

230

250

270

5500 6500 7500 8500 9500 10500

Curve 1 Curve 2

Figure 13: Power for different convective film coefficient

Curve number two in figure 13 was obtained using a convective film coefficient smaller than

that of curve number one producing a larger power.

e) Fuel and air consumption. Fuel and air flow rate allows us to adjust the

equivalence ratio of the fresh mixture going into the engine. This information is

necessary due to the fact that the engine tested uses carburetor for fuel metering.

The table below summarizes the strategy adopted :

Real Data Adjusted variable

Head cylinder flowmeter Discharge coefficient

Exhaust temperature Combustion duration approximated

by a straight line

Torque and power Convective heat transfer coefficient

Fuel and air consumption Equivalence ratio of fresh mixture

Table 3: Real data and adjusted variables used for calibration

Results after calibration

In the pictures below both the simulation results and the experimental ones obtained in the

laboratory can be plotted.

Figure 14 shows the torque and power curve, figures 15,16 and 17 show the temperature of

the exhaust gases, the air flow rate and the fuel consumption respectively.


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The difference between the simulated and the real fuel consumption is mainly related to the

fuel metering of the engine; the carburetor in the real engine does not keep the equivalence

ratio fixed with the engine speed (rpm), and the engine simulator was used with a fixed

value obtained by an average of the real ones.

After the calibration the curves achieved by the engine simulator agree quite well with the

real ones.

Remarks

1. the simulator has the necessary calibration to be used as a good diagnostic tool for

this engine,

2. after the calibration, improvement of the engine is feasible. In order to achieve this,

one of the main variables to optimize are the valve timings.

3. Regarding the assumption of the variable combustion duration in relation to the

engine speed, the fuel consumption (figure 17) does not show a significant variation

at different engine speeds while the torque (figure 14) drops drastically at high rpms.

Assuming that the fuel is completely burned, the fact that the combustion duration at

high rpm is longer than that at low rpm can be inferred.

150

170

190

210

230

250

270

290

5500 6500 7500 8500 9500

rpm

[HP][Nm]

Torque_Sim Power_Sim Torque_exp Power_exp

Figure 14: Experimental versus simulation torque and power


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800

900

1000

1100

1200

1300

1400

1500

5500 6500 7500 8500 9500 10500

rpm

[F]

Temp_Sim Temp_exp

Figure 15: Exhaust temperature

800

900

1000

1100

1200

1300

1400

1500

5500 6500 7500 8500 9500 10500

rpm

[Lbs/Hr]

Mass air sim Mass air exp

Figure 16: Air mass flow rate


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70

75

80

8590

95

100

105

110

115

5500 6500 7500 8500 9500

rpm

[Lbs/hr]

Mass fuel sim Mass fuel exp

Figure 17: Fuel mass flow rate

Intake and exhaust optimization

The optimization of the engine is defined in terms of the description of the objective

function, the constraints and the variables to be modified.

With regard to the objective function, the main goal is the improvement of the power curve

at a specified engine speed range. At first sight there is no constraint in this problem; only

lower and upper bound for the variables are desirable.

The variables chosen for the optimization task are the angles where the intake and the

exhaust valves open and close.

The modification of the valve timings achieved during the engine optimization may require

changes in the camshaft design depending on how this changes are selected.

Before making the decision of building a new camshaft, some simple modifications wereexplored. The computational analysis was divided in three stages:

1. keeping the original camshaft, only the position of both cams in relation to the

crankshaft were modified.

2. modifying the position of the two cams independently

3. building a new cam profile

Stage 1:

Figure 18 shows the original configuration and two modifications, one with an advance of

three degrees and the other with a retard of three degrees in relation to the original one.

These curves show that while the engine power is improved at high engine speeds, with a

retarded timing at low engine speeds the timings needs to be advanced.


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Stage 2:This stage was divided in two parts, the first with the exhaust valve timing being fixed in the

original position and moving the intake valve timing, and the second with the intake valve

fixed and changing the exhaust valve timing forward and backward from the original

position. Both results can be seen in figures 19 and 20. Gains and losses should be seen as a

percentage relative to the original configuration.

Stage 3:Finally, a completely new profile was adopted in which the timings were moved separately.

Figure 21shows the behavior when modifying the intake valve and keeping the exhaust

valve fixed to the original timings and figure 22shows the behavior when the exhaust valve

is modified keeping the intake timings fixed to the original values. For example, AAA+5

means an advance of 5 degrees in the intake valve opening in relation to the original value,

the intake valve close and the two exhaust timing being fixed to the original values.

98,5

99,0

99,5

100,0

100,5

101,0

101,5

102,0

102,5

5800 6800 7800 8800

RPM

[%]

3 delay Original 3 advance

Figure 18: Stage 1- comparison of curves with different camshaft positions


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25

98,5

99,0

99,5

100,0

100,5

101,0

101,5

5800 6800 7800 8800

RPM

Adv 6 Adv 3

%

Delay 3 Delay 6

Figure 19: Stage 2- comparison of curves with different intake camshaft positions

97,0

98,0

99,0

100,0

101,0

102,0

103,0

5800 6300 6800 7300 7800 8300 8800 9300

RPM

[%

]

Adv 6 Adv 3 OrigDelay 3 Delay 6 Delay 9Delay 12 Delay 15

Figure 20: Stage 2- comparison of curves with different exhaust camshaft positions.


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26

98,5

99,0

99,5

100,0

100,5

101,0

101,5

102,0

102,5

5500 6000 6500 7000 7500 8000 8500 9000 9500 10000

RPM

[%

]

IVC+5 IVC+10 IVO-5 IVO-10

Figure 21: Stage 3- comparison of curves with different intake camshaft positions

99,0

100,0

101,0

102,0

103,0

104,0

105,0

106,0

107,0

5500 6000 6500 7000 7500 8000 8500 9000 9500 10000

R P M

[%]

EVO-5 EVO-10 EVC+5 EVC+10 Serie3 IVO-10 and EVC+10

Figure 22: Stage 3- comparison of curves with different exhaust camshaft positions


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27

The most relevant conclusion about this optimization task is shown in figure 22,where the

power improvement at almost all engine speeds is obtained modifying only the exhaust

valve close timing. Figures 23, 24 and 25 help clarify the reason of this behavior. The

higher the gas flow rate at the overlapping angles, the lower the pressure inside the cylinder,

which helps get more fresh mixture during the intake stroke. These figures were obtained at

8500 rpm close to the rated speed.

-300 -200 -100 0 100 200 300

0

50

100

150

200

250

VELOCITIES THROUGH VALVES

[m/s]

Figure 23: original configuration (blue) and EVC+10 (red)

-300 -200 -100 0 100 200 300

-0.1

-0.05

0

0.05

0.1

0.15

0.2

MASS FLOW RATE THROUGH VALVES

[Kg/s]

Figure 24: Original configuration (blue) and EVC+10 (red)


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28

-300 -200 -100 0 100 200 300

0.6

0.8

1

1.2

1.4

1.6

1.8

2

CYLINDER PRESSURE

[atm]

Figure: 25: Original configuration (blue) EVC+10 (red)

Further remarks

From the optimization point of view, it can be concluded that the power improvement of this

engine is exhaust valve timings- sensitive, specially at the closing phase, while the intake

timings do not produce significant changes.

An introduction to mechanical analysis

A number of factors should be considered in the design of motor engine valve trains and

cams, which may be briefly classified into thermodynamics, gas dynamics and mechanical

ones.

The maximum valve lift and the valve timings are determined according to thermodynamics

and gas dynamics considerations as presented in the above sections. After finding the best

valve timings from the engine power optimization point of view, the mechanical analysis is

used to build a feasible camshaft whose computed timings and optimal valve lift profile

warrant a safe mechanical behavior. The main goal is to find a way to reproduce the above

defined timings in an optimal way from the mechanical point of view. This task is split in

the following three parts:

1. an ad-hoc novel software is employed to produce an optimal valve lift profile with

several constraints. [23]

2. the next step transforms the valve profile in a cam profile through an inverse

kinematics synthesis.

3. Finally this cam profile is placed inside the whole valve train mechanisms and it is

verified through a dynamical analysis.


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29

For the last two steps Mecano multibody software was used [24].

Structural considerations are taken into account both to satisfy thermodynamics, gas

dynamics and mechanical factors, and to keep the structural integrity of the mechanism and

optimize its performance.

To this aim, efforts should be minimized to work within the allowable stress

levels, and jumping between cam and follower should be avoided.

Further complexity appears because of nonlinearities introduced by the kinematical chainusually interposed between cam and valve. Last but not least, the feasible solution space is

restricted to avoid mechanical interferences.

In [23] a systematic procedure for optimal cam design was presented and this code is applied

in the first step of this work.

After properly defining an optimization problem and solving it, a valve lift profile is

computed as input data to a mechanical synthesis phase of analysis in which the cam profile

required to reach the desired valve motion is the output result, i.e. the second step.

Finally, in the third step, the whole mechanical system is dynamically analyzed in order to

validate the operation conditions.

Optimal Cam profileAmong the various mechanical design factors that influence the design of cams for motor

engine valve trains, we take into consideration geometrical interferences and dynamic

forces. Even though the considerations below were used for overhead cam end pivot rocker

arm valve train configuration, their applications to pushroad type are similar.

Interferences

The intake valve opening and exhaust valve closing are carried out in the area of the piston

top dead centre (TDC). Since the distances between valves-piston and also between valves

themselves are very small, it is necessary to detect and avoid any possible geometrical

interference during the design of valve motion. This factor is very critical especially inengines with large valves overlapping.

Dynamical forces

In order to reach the maximum valve liftL in the time interval when the valve remains open,

it is necessary to specify a motion profile that satisfies not only the above mentioned

interference constraints but also the following dynamic restrictions [25]:

no jumping between cam and follower,

no impact in the valve seating,

maximum stresses bounded for reliability and minimal wear.

Spring dynamics also plays a fundamental role in high speed cam follower systems. At high-

speeds, springs may lose force due to an internal resonance. This resonance may be excited

by high-order harmonics of the cam lift at any speed. Distributed-parameter models of the

spring have been proposed to simulate the spring dynamics [26, 27, 28, 29, 30, 31].

Modelling of coil clash phenomena has been taken into account using a moving boundary

technique [32]. Furthermore, spring designs with variable cross section have also been

proposed to minimize amplitude of spring resonance [33]. Nested springs are used to

introduce dissipation by Coulomb friction between inner and outer spring coils and damp


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internal resonance. The estimation of friction values is a difficult task, so various forms of

predicting them

have been proposed [27, 34].

Constrained Optimization Strategy

In this section we present some brief details about the optimization problem

fully explained in the original paper [23] Motion in time of motor valves may follow the

general description in figure 26.

rise

dwell

return

u

a

alow

ahigh

Figure 26: Valve acceleration profile shape function

Five zones can be distinguished: initial ramp, acceleration, spring-control, deceleration, and

final ramp. Ramps are designed to strike the cam-follower at a given velocity and to allow

some amount of clearance between cam and follower at the closed position. At the end of

the ramp, the valve is accelerated by a curve of increasing slope. The curve should be such

that transmitted load does not suffer sudden changes and the valve effectively follows the

cam. While moving under spring control, the valve decelerates up to reaching zero velocity

and then accelerates downward until closing, passing through a deceleration zone and final

ramp [25].

The maximum values of positive acceleration are limited by the maximum efforts the system

can sustain. On the other hand, in the spring-controlled zone, the negative acceleration

imposed by the cam profile should be lower than a given limit in order to make the inertia

load a fraction of the available spring force and avoid jumping. As mentioned before,


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thermodynamics and gas dynamics factors are used to select both the maximum valve liftL

and the valve timings VO (valve opening angle) and Vc(valve closing angle) are assumedas input data for the mechanical analysis.represents the crank angle. Valve motion isinduced through an imposed smooth profile of acceleration as follows:

( ) ( )==

N

jjaa 1

where

( )( )[ ]( )[ ]

>

< exhuu

where int is the crank angular displacement from which ( ) intuu > at the intake valve.


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33

Positive interval lengths: the interval lengths should be greater than or equal to zero

11,1,0 = jj

Positive valve displacement: the computed valve displacement should be greater than

zero, i.e.

( ) 0u

The objective function and restrictions are scaled so that the optimization problem is well

defined. An optimization problem is therefore defined, whose solution

( )( )** maxarg Aopt=

is computed using standard routines for constrained optimization.

Remarks:

As mentioned before, the valve train configuration used for this development was an

overhead cam end pivot rocker arm. As in this work the engine has a pushroad type

configuration, two minor modifications are implemented: there is no interference between

valves and the acceleration shape functions normally have not a dwell zone. For the latteran overlapping between points 5 and 6 in figure 26 is imposed with a negative acceleration,

a zero velocity condition and maximum lift.

Mechanisms analysis

The valve train configuration is shown in figure 27. It is composed by several mechanical

elements modeled by a multibody system approach and solved by finite elements. The main

elements are described in the following paragraphs. This model is fully parametric and the

configuration is mounted from a set of geometric data.

Figure 27: Valve train configuration


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34

Cam and follower : In the kinematics synthesis or inverse problem we input the valve

movement and we replace the unknown cam profile and its follower by a distance captor

that measures the distance between the end of the hydraulic valve lifter and the center of the

camshaft. The result is the temporal variation of this distance that allows us to draw the cam

profile. In the dynamic or direct analysis, the cam and follower pair are linked by contact

forces, the cam profile input from the inverse synthesis being the medium by which thewhole valve train movement is entered. The follower may be plane or curved with a user

specified radius. In order to avoid numerical drawbacks associated with the contact between

cam and follower, the cam profile is smoothed through a spline curve approximation and its

profile is composed by approximately 180 points written in polar coordinates.

Hydraulic valve lifter: in the multibody system this mechanical element is modeled as a

rigid body and its mass is the main parameter to be input.

Hydraulic valve lifter and pushrod coupling: this is solved using a spring which works only

in compression with a stiffness coefficient similar to that of steel.

Pushrod: the flexibility was considered only for this mechanical element, and the pushrodwas split in N parts. In this work N=4 was used, but this value can be modified by the user.

Rocker arm: this was modeled as a rigid body with a specified mass and mass moment of

inertia about its pivot. The coupling between the rocker arm and the pushrod is solved by

another spring that works only in compression load with no reaction in traction load. This

gross representation of the real linkage is enough for our goals because the latter is evidence

of a mechanical failure in the valve train in which case the simulation should not continue.

On the other hand, the coupling between the rocker arm and the valve stem is modeled by

another cam and follower element solving the contact forces in detail for the verification of a

valve floating condition.

Valve spring: The force exerted by the valve spring in a running engine deviates

substantially from its static values. The reason for these deviations is that the spring has

internal oscillation modes, the so called spring surge modes. The lowest surge

eigenfrequency lies below the first valve train eigenfrequency, and both are strongly excited

at high engine speeds. The valve spring is a very elastic medium compared to the other valve

train components in which disturbances are transmitted longitudinally and relatively slowly

in the form of waves. The motion of individual elements is governed by the wave equation,

and one way of obtaining the spring force under dynamic conditions is to discretize the

spring into a large number of small spring elements. The external spring and internal springs

are split in N parts choosing N as the number of spring coils, eight in this work.

Valve: This element is considered as a rigid body The interaction between the valve and theport seat has not been taken into account.

Mechanical simulation results

Taking into account the dynamical behavior at different engine speeds and considering that

the more interesting regime is above 8000 rpm, the engine speed has been swept between

8000 to 10000 each 150 rpm. Five cycles are done at each speed in order to stabilize the


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35

engine operation which leads to a clear understanding of how the cam profile and the spring

dynamics interact with the rest of the valve train.

A comparison between the operation of the original configuration in relation to the modified

one allows us to check if the behavior of the new valve train configuration satisfies

mechanical criteria.

Some interesting results with this model are shown in the figures below. They are obtained

for one of the most representative engine speeds.Figure 28shows the contact force acting at the linkage between the main cam and its

follower Here the force never crosses through the horizontal axis and it is always of the

same sign. Therefore, it is never at a critical situation of valve floating. Next, figure 29

shows the external spring force evidencing the dynamic behavior of the spring. Even though

the valve is closed and remains at rest, the spring continues moving, and when the valve

starts to open at the next cycle the spring is not generally at rest. In this way, it is possible to

include the residual vibration caused by the spring internal modes in the analysis. Figure 30

plots the lateral displacement of the pushrod, which shows the flexural behavior of this

element and its own dynamic response caused by the inclusion of the flexibility component.

Finally, figure 31shows the contact force acting at the linkage between the rocker arm and

the valve stem where once more the curve does not cross over the horizontal axis,

evidencing that contact is always warranted.

SAMCEF APR 2 2003 08:52:00

Stress (ORD.)

Time (ABS.)

0.6500 0.6550 0.6600 0.6650 0.6700 0.6750 0.6800 0.6850 0.6900

Time

100.

Str.(EL=401 C=1)

-100.

-200.

-300.

-400.

-500.

-600.

-700.

-800.

-900.

-1000.

Figure 28: Cam follower contact force


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SAMCEF APR 2 2003 08:56:16

Stress (ORD.)

Time (ABS.)

0.7300 0.7350 0.7400 0.7450 0.7500 0.7550 0.7600 0.7650 0.7700 0.7750 0.7800 0.7850

Time

-60.

Str.(EL=1005 C=1)

-80.

-100.

-120.

-140.

-160.

-180.

-200.

-220.

-240.

-260.

Figure 29: external spring forces

SAMCEF APR 2 2003 08:54:14

Displacement (ORD.)

Time (ABS.)

0.6850 0.6900 0.6950 0.7000 0.7050 0.7100 0.7150 0.7200 0.7250 0.7300 0.7350 0.7400

Time

0.2000

Displ.(N=605 C=1)

-0.2000

-0.4000

-0.6000

-0.8000

-1.

-1.2000

Figure 30: Lateral displacement of the pushrod


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SAMCEF APR 2 2003 08:52:58

Stress (ORD.)

Time (ABS.)

0.7600 0.7650 0.7700 0.7750 0.7800 0.7850 0.7900 0.7950 0.8000 0.8050 0.8100 0.8150 0.8200

Time

-50.

Str.(EL=301 C=1)

-100.

-150.

-200.

-250.

-300.

-350.

Figure31: contact forces at the rocker arm and valve stem coupling

Conclusions

A valve train optimization procedure was presented in which gas dynamics, thermodynamics

and mechanical effects are included. This strategy allows us to have better control of some

design variables that prove to have a great influence over the volumetric efficiency of an

internal combustion engine. Besides, a new calibration methodology is proposed, which is

based on the linking of some uncertain critical parameters in the engine simulator data set

and some basic laboratory measurements. This procedure proves to be very efficient,

showing high agreement between numerical predictions and real observations. The whole

procedure finishes with the design description of the cam profile to be manufactured.

Acknowledgements

To Juan Tofoni for his work in part of this project, to Professor Alberto Cardona for his

guidance in topics related with multibody systems, to CONICET and UNR for their

financial support.

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Intake and Exhaust System Optimization of Internal Combustion Engines

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