Romanian Journal of Automotive Engineering

Transcription

ISSN 2457 – 5275 (Online, English)
ISSN 1842 – 4074 (Print, Online, Romanian)
September 2015
Volume 21
4 th Series
Number 3
RoJAE
Romanian
Journal of Automotive Engineering
The Journal of the Society of Automotive Engineers of Romania
www.siar.ro
www.ro-jae.ro
RoJAE
Romanian
Societatea Inginerilor de Automobile din România
Society of Automotive Engineers of Romania
www.siar.ro
SIAR – The Society of Automotive Engineers of Romania is the professional organization of automotive
engineers, an independent legal entity, non-profit, active member of FISITA (Fédération Internationale des Sociétés
d'Ingénieurs des Techniques de l'Automobile - International Federation of Automotive Engineering Societies) and
EAEC (European Cooperation Automotive Engineers).
Founded in January 1990 as a professional association, non-governmental, SIAR’s main objectives are:
development and increase the exchange of professional information, promoting Romanian scientific research
results, new technologies specific to automotive industry, international cooperation.
Shortly after its constitution, SIAR was affiliated to FISITA - International Federation of Automotive Engineers and
EAEC - European Conference of Automotive Engineers, thus ensuring full involvement in specific activities
undertaken globally.
In order to help promoting the science and technology in the automotive industry, SIAR is issuing 4 times a
year RIA - Journal of Automotive Engineers (on paper in Romanian and electronically in Romanian and English).
The organization of national and international scientific meetings with a large participation of experts from
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SIAR holds an annual scientific event with a wide international participation. The SIAR annual congress is hosted
successively by large universities that have ongoing programs of study in automotive engineering.
Developing relationships with the economic environment is a constant concern. The presence in Romania
of OEMs and their suppliers enables continuous communication between industry and academia. Actually, a
constant priority in SIAR’s activity is to ensure optimal framework for collaboration between universities and
research, industry and business specialists.
The Society of Automotive Engineers of Romania
Honorary Committee of SIAR
President
Adrian Constantin CLENCI
University of Pitesti, Romania
E-mail:
Honorary President
Mihai Eugen NEGRUS
University „Politehnica” of Bucharest, Romania
Vice-Presidents
Cristian Nicolae ANDREESCU
Nicolae BURNETE
Technical University of Cluj-Napoca, Romania
Anghel CHIRU
„Transilvania” University of Brasov, Romania
Victor OTAT
University of Craiova, Romania
Ion TABACU
General Secretary
Minu MITREA
Military Technical Academy of Bucharest, Romania
Pascal CANDAU
Renault Technologie Roumanie
www.renault-technologie-roumanie.com
George-Adrian DINCA
Romanian Automotive Register
www.rarom.ro
Florian MIHUT
The National Union of Road Hauliers from Romania
www.untrr.ro
Werner MOSER
AVL Romania
www.avl.com
SIAR – Society of Automotive Engineers of Romania is member of:
FISITA - International Federation of Automotive Engineers Societies
www.fisita.com
EAEC - European Automotive Engineers Cooperation
RoJAE
Romanian
CONTENTS
Volume 21, Issue No. 3
September 2015
Analysis and Reconstruction of Car Crashes in Case of Uncertainties
Ramona-Monica STOICA, Marian-Eduard RĂDULESCU, Irinel DINU, George ENE, Marius
SIMIONESCU and Ion COPAE .....................................................................................................
79
Diesel-Ethanol Blends and their Use in Diesel Engines
Nicolae Vlad BURNETE,Nicolae FILIP and István BARABÁS ......................................................
88
Evaluation the Dissipated Energy by the Automobile Dampers
Veronel-George JACOTA ..............................................................................................................
106
The collections of the journals of the Society of Automotive Engineers of Romania are avaibles at the
Internet website www.ro-jae.ro.
The Romanian Journal of Automotive Engineering is indexed/abstracted in Directory of Science, WebInspect,
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– UEFISCU, International Society of Universal Research in Sciences, Pak Academic Search, Index Copernicus
International
RoJAE 21(3) 75 – 116 (2015)
RoJAE
Romanian
Editor in Chief
Cornel STAN
West Saxon University of Zwickau, Germany
E-mail: [email protected]
Executive Editor
Nicolae ISPAS
„Transilvania” University of Brasov, Romania
Deputy Executive Editor
Radu CHIRIAC
Ion COPAE
Stefan TABACU
Editors
Ilie DUMITRU
Marin Stelian MARINESCU
Adrian SACHELARIE
„Gheorghe Asachi” Technical University of Iasi, Romania
Marius BATAUS
Cristian COLDEA
George DRAGOMIR
University of Oradea, Romania
Advisory Editorial Board
Dennis ASSANIS
University of Michigan, USA
Rodica A. BARANESCU
Chicago College of Engineering, USA
Nicolae BURNETE
Giovanni CIPOLLA
Politecnico di Torino, Italy
Felice E. CORCIONE
Engines Institute of Naples, Italy
Georges DESCOMBES
Conservatoire National des Arts et Metiers de Paris, France
Cedomir DUBOKA
University of Belgrade, Serbia
Pedro ESTEBAN
Institute for Applied Automotive Research Tarragona, Spain
Radu GAIGINSCHI
„Gheorghe Asachi” Technical University of Iasi, Romania
Eduard GOLOVATAI-SCHMIDT
Schaeffler AG & Co. KG Herzogenaurach, Germany
Peter KUCHAR
University for Applied Sciences, Konstanz, Germany
Mircea OPREAN
Nicolae V. ORLANDEA
University of Michigan, USA
Victor OTAT
Andreas SEELINGER
Institute of Mining and Metallurgical Engineering, Aachen,
Germany
Ulrich SPICHER
Kalrsuhe University, Karlsruhe, Germany
Cornel STAN
West Saxon University of Zwickau, Germany
Dinu TARAZA
Wayne State University,USA
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RoJAE vol. 21 no. 3 / September 2015
ANALYSIS AND RECONSTRUCTION OF CAR CRASHES
IN CASE OF UNCERTAINTIES
Ramona-Monica STOICA∗, Marian-Eduard RĂDULESCU, Irinel DINU,
George ENE, Marius SIMIONESCU, Ion COPAE
Military Technical Academy, B-dul George Coşbuc Nr. 39-49, 050141 Bucharest, Romania
(Received 23 June 2015; Revised 15 July 2015; Accepted 31 July 2015)
Abstract: The existence of all sorts of uncertainties leads to the necessity of new approaches in the analysis and
reconstruction of car crashes, which results in using sizes interval values and not their unique values. Likewise, the
relative new developed uncertainty theory studies human uncertainties, that is why it has a remarkable importance
in the analysis and reconstruction of car crashes leading to a rising role for the technical experts. Based on the
specialty literature hypothesis that uncertainties are the subject of normal distribution, we obtain that the best
estimate of the size value is the middle of the interval established by calculus (meaning the value with the highest
probability to advent).
Key-Words: car crash, uncertainty, Gauss distribution, uncertainty theory.
1. INTRODUCTION
In technical field but not only in this one, the uncertainties always exists and the experts have to
frequently deal with it. For example, out of car crashes practice, there are uncertainties regarding the
weight of moving vehicle, the mass moments of inertia, the rolling radius, the road resistance, the
aerodynamic coefficient, the front face, the adhesion coefficient, the position of centre of gravity, the
position of crash centre, the restitution coefficient, the coefficient of tangential friction between vehicles,
reaction time and driver action etc. As we can see, uncertainties are related to those three factors
participant to the car crash: the vehicle, the environment and the driver [1][2][5][11].
In general, quantitatively speaking, uncertainties can be defined as an expected set of values. For
example, there can be estimated values for functional parameters, operating parameters or design
value: automotive weight depending of the number of passengers and the quantity of fuel from the tank;
the adhesion coefficient for a specific category of road etc. Also, it is considered that uncertainties show
the impossibility in practice of making necessitarian foreknowledge, which means using unique values.
This is actually the essence of analysis in presence of uncertainties, which does not use unique values,
but value intervals; obviously, in this case are used mathematical operations with value intervals [8].
Follow-up it is presented an example of uncertainty that appears in the analysis and reconstruction of car
crashes, namely adhesion coefficient ϕ . So, the PC-Crash programme uses an adhesion coefficient for
an used rolling track made of dry asphalt in the value interval ϕ = [0.6; 0.8] if the travelling speed is less
than 48 km/h and ϕ = [ 0.55; 0.7 ] if the automotive speed is bigger than 48 km/h [3][14]. For the first
case, it can be written:


ϕ = 0.7 ± 0.1 = 0.7  1 ±
0.1 
 14.29 
 = 0.7 (1 ± 0.1429 ) = 0.7  1 ±

0.7 
100 

(1)
Therefore, as suppose to the values presented in specialty literature [3][14], it can be considered that for
a drive on a worn rolling track of dry asphalt type, the adhesion coefficient can be considered 0.7 with an
uncertainty of 14.29% if the automotive speed is less than 48 km/h. As we can see from this phrase,
another uncertainty appears regarding whether the automotive speed is less than 48 km/h.
∗
Corresponding author e-mail: [email protected]
79
The values presented in specialty literature are obtained based on previously made measurement. In the
case described above, for experiments conducted with an automotive, the adhesion coefficient real value
is not precisely known, that is way it is considered to be a mean value of those presented in specialty
literature (meaning 0.7); the mean value is also known as nominal value and it represents the centre of
the interval ϕ = [0.6; 0.8] . If there are no other informations, the fair thing to do is to perform calculations
with the entire recommended interval, the adhesion coefficient represents an uncertain variable;
obviously, the result consists of value intervals and not an unique value.
2. ANALYSIS AND RECONSTRUCTION OF CAR CRASHES
At this moment there are multiple mathematical models and specialised softwares for the analysis and
reconstruction of car crashes [1][2][3][4][12][13][14]. If we do not consider all sorts of uncertainties, then
there are nominal mathematical models, in which case the parameters are chosen from the middle of the
interval value; in this case the result is an unique value. If we consider the uncertainties, then, there are
uncertain mathematical models which implies operating with the entire interval value; in this situation the
result is an interval value, and the most probable value is in the middle of the interval (according to the
hypothesis that uncertainties are the subject of normal distribution, in which case the middle of the
interval represent the arithmetic mean).
All specialised programmes for the analysis and reconstruction of car crashes do not consider
associated uncertainties; even one of the most complex existing programme, PC-Crash, does not
operate with uncertainties, but with an unique value. Therefore, in follow-up we consider the impact
between two automotives, a BMW-1 Coupe 235i automotive (noted A1 in figure 1) and a Daewoo-Matiz
800s automotive (noted A2), follow-up entitled as BMW and Matiz. The main technical caracteristics for
the two automotives from programme’s database, are presented in figure 1: the length L, the width B, the
axle base A, the distance between the centre of gravity and the front axle a, the mass m and the centre
of gravity height hg; the index 1 is for A1 automotive, and the index 2 is for A2 automotive. Also there are
known the mass moments of inertia regarding the three coordinate axis: Jx1=670.66 kgm2, Jx2=259.98
kgm2, Jy1=Jz1=2235.52 kgm2, Jy2=Jz2=866.58 kgm2; it must be mentioned that these values are uncertain
too, which yet again leads to the necessity to operate with interval values for moments of inertia.
The two automotives are located in initial positions at a distance between centres of gravity of 9.029 m,
meaning at a distance between nearest points of 4.692 m, in the scheme being highlighted programme
possibility to measure distances; as it can be seen, the centre of gravity for the BMW automotive is in the
coordinate axis origin (x, y).
In the initial position from figure 1 automotive disposal angles are θ1=0.1 [degrees] and θ2=150.8
[degrees], and their speeds are vi1=90 km/h and vi2=80 km/h .
Likewise, from the crash scene it is known that the drivers guessed the impact, so, in the initial positions,
both vehicles brakes were operated.
Figure 1. Automotives initial positions
80
In order to apply the impact algorithm, PC-Crash has to espouse two parameters: rendering coefficient e
and coefficient of tangential friction between vehicles µ. In specialty literature it is considered that in most
of collisions these two parameters vary in the intervals [1][2][4][5]:
e = [ 0.1; 0.3] ;
µ = [0.40; 0.55]
(2)
Also, in specialty literature, in such cases is frequently adopted the middle of the intervals meaning e=0.2
and µ =0.475. If these two values are adopted, then in figure 2 and figure 3 are presented the
automotives speed and covered distance, but also some impact parameters, values obtained by using
PC-Crash programme.
Figure 2. The automotives speed
From the graphs is determined that from the automotives initial positions up to the impact moment have
passed 0.14 s. So, the BMW automotive speed dropped from vi1=90 km/h to v1=87.02 km/h, the last
value represents the BMW speed from the begining of the collision (figure 2). Similar, the Matiz
automotive speed has dropped from vi2=80 km/h to v2=77.1 km/h, the last value represent the Matiz
speed from the begining of the collision (impact speed).
Figure 3. The automotives covered distance
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At the end of the impact (at the end of the separation phase) the BMW automotive speed has dropped to
V1=64.75 km/h, and the Matiz automotive speed has dropped to V2=20.46 km/h.
After the impact, the BMW automotive speed has finally reached the null value at t =2,79 s, and the
Matiz automotive at t =1.15 s (figure 3); So, after the full stop of the Matiz automotive, the BMW
automotive is still moving.
From figure 3 it can be determined that until the BMW automotive stops it drove a distance of S1=27.03
m, and the Matiz covered a distance of S2=6.05 m.
The two previous graphs also return the main parameters of impact. Therefore, in figure 2 are also
presented the variations of automotive speed during impact: dV1=36.01 km/h and dV2=63,4 km/h; in
addition, it is also shown the relative separation speed Vs=106.6 km/h. Likewise, in figure 3 are also
returned the values of coordinates for centre of impact C.
In figure 4 is presented the plan contact angle value α =21.75 degrees, the percussion value
P=14704.56 Ns and it’s direction ξ =138.32 degrees, kinetic torque arms h1=0.62 m and h2=0.12 m, and
also principal direction of force (PDOF) according to SAE regulation (the angles PDOF1=41.74 degrees
and PDOF2=12.48 degrees). In figure 4 is represented the impact position in phase of body maximum
proximity (deformation). The contact surface contains tangential axis C-t, and normal axis C-n is
perpendicular to it (the program does not indicate O-n axis).
In figure 4 also appears the percussion P, which acts on A2 automotive; as it is known, there is also the
symmetrical percussion -P (unmarked here) which acts on A1 automotive. Also, in figure 4 is marked the
friction cone, which is symmetrical towards normal axis O-n. As it can be observed, the direction of P
percution is at the edge (coating) of friction cone, which means that the movement of the two vehicles
are at dynamic stability limit.
In figure 4 also appear vehicles deformations on range with percusion, noted in program ETD1 and ETD2
(ETD - Equivalent Test Deformation) and whose values are presented in figure 5: ETD1=0.77 m and
ETD2=0.69 m.
Figure 4. The position of both automotives at the end of the phase of impact
In figure 5 are presented the curves of kinetic energy variation for the two automotives. As it can be
observed, from initial moment up to collision moment at t =0.14 s, the energies of the two automotives
fell down which confirms their braking. In addition, at time t =0.14 s when the impact took place, kinetic
energy values of the two automotives suddenly dropped: for BMW from 430.54 kJ to 256.05 kJ, and for
Matiz from 192.01 kJ to15.25 kJ.
In figure 5 are shown the values of deformation energy for the two vehicles (Ed1=184.24 kJ and
Ed2=164.24 kJ), but also the total deformation energy Ed=348.48 kJ.
82
Therefore results the ratio between total deformation energy and kinetic energy at the initial impact
moment: kd=55.98%; so, almost 56% of kinetic energy is in vehicles deformations.
Figure 5. The kinetic energy variation for the two automotives
In figure 5 are presented the values of EES (EES1=57 km/h and EES2=71.4 km/h), but also values of
the two automotives rigidity: k1=618.5 kN/m for BMW and k2=693.8 kN/m for Matiz.
In figure 6 are presented the three positions for both automotives: initial, of impact and final (stopped
vehicles). According to figure 6, there are no repeated collisions between the two automotives and there
are skid marks of the BMW automotive; these skid marks suggests a pronounced convolution of the
automotive, which can be proven with yaw angle values (from automotive dynamics). Likewise, it is
shown that in final positions (stopped positions) the distance between the two automotives centres of
gravity is 24.258 m.
Figure 6. The positions for both automotives: initial, of impact and final
In addition, from figure 6 it can be noticed that the distance between the centre of gravity in initial
position and final position of the BMW automotive is 26.677 m; because the travel space of the
automotive is S1=27.03 m (figure 3), results that by yaw did not lead to moving the centre of gravity (only
0.353 m, meaning 1.306%).
83
Similar, at Matiz automotive the distance was 5.603 m (figure 6) and the traveled space S2=6.05 m
(figure 3), which means that yaw added 0.447 m, meaning 7.388%. In can be concluded that in case of
Matiz automotive, yaw affected travel space more than it did in case of BMW automotive.
PC-Crash programme offers the possibility to study kinematics and automotive dynamics for those
involved in car crash, some of these graphs already been presented by using Matlab software (figure 2,
figure 3 and figure 5). In addition, in figure 7 are presented centres of gravity yaw angles for both
automotives around the three coordinate axis.
From figure 7 it is shown that there were all three types of rotational movement, but roll movement and
pitch motion are minor. On the other hand, yaw movement is more pronounced, especially in case of
BMW automotive.
So, from figure 7a it is shown that initial value of yaw angle for BMW is Φ3i =0.1 degrees=θ1, and final
value Φ3f =328.8 degrees; so, after the impact, BMW automotive rotated with 328.7 degrees (almost a
complete rotation of 360 degrees), which can be seen from skid marks rate of curve from figure 6.
Instead, from figure 7b it can be observed that yaw angle initial value at Matiz is Φ3i =150.8 degrees=θ2,
and final value Φ3f =187 degrees; so, after collision Matiz automotive rotated with only 37.8 degrees,
which can also be noticed from figure 6.
Figure 7. The angles of rotation of both automotives
The results presented above are obtained using fixed value for the impact specific parameters
(restitution coefficient, tangential friction coefficient between automotives, moments of inertia,
automotives mass etc.).
If we consider the uncertainties impact on parameters, is required the use of value intervals of type (2).
To this end are taken into account the specialty literature, and also the uncertainties resulting from crash
scene (on vehicles orientation angles). For example, it is still used the Brach planar impact model [1] and
the uncertainties mentioned below. Thus, the uncertainties on restitution coefficient e and on tangential
friction coefficient between automotives µ is taken according to intervals (2), but other values can be
taken too. Uncertainties regarding mass moments of inertia are highlighted in multiple papers. Therefore,
NHTSA (National Highway Traffic Safety Administration), based on statistics made due to experimental
establishing of moments of inertia for 496 automotives of different types, determined ratios such as [6]:
J x = k x mE 2 ;
J y = k y mA2 ;
J z = k z mA2
(3)
where m represents mass, E gauge and A automotive axle base, and the coefficients kx, ky and kz are
placed in interval values depending on automotive type; for example, for passenger vehicles.
k x = [ 0.1475; 0.1725] ;
k y = [0.2; 0.227 ] ;
k z = [ 0.2225; 0.2475]
84
(4)
Uncertainties on coordinates/the centre of gravity disposal: distance a from front axle (distance b up to
rear axle: b=A-a) and height hg, according to the same previous papers [6], are established with the
ratios:
a = ka A; hg = kh H
(5)
where H represents the body ceiling height, and the coefficients ka and kh are based on vehicles type; for
example, for passenger vehicles:
ka = [0.37; 0.48] ; kh = [0.37; 0.39]
(6)
In car crash analysis the Brach model establishes impact final parameters, in the hypothesis of knowing
initial parameters, but it can also be backward applied for the reconstruction of car crash, in which case
impact final parameters are known.
The scheme of impact simulation is presented in figure 8 [1]. Two vehicles are considered A1 and A2 of
m1 and m2 mass, in which we know the moments of inertia around vertical axis Jz1 and Jz2, but also the
impact centre C. Likewise, it is adopted a fixed local coordinate system (x, y), attached to the ground. In
the moment of impact, the direction of the two automotives is given by θ1 and θ2 angles relative to the
fixed system (x, y). The impact centre C is relatively located to the centres of gravity CG1 and CG2 of the
two vehicles by d1 and d2 distances, and ϕ1 and ϕ 2 angles. Also, a normal and tangential coordinate
system is adopted (n, t) related to the centre of impact or deformed surface, which is γ angle oriented
towards the fixed system (x, y); in the scheme γ =0 degrees.
In figure 8 are presented the impact parameters nominal values. From the scheme it is also observed the
adopted convention that impact parameters are marked with small letter (v1 and v2 speeds, ωz1 and ωz2
yaw angular velocities); on the other hand, parameters from end of the collision (at end of separation
phase) are marked with capital letter (V1 and V2 speeds, Ωz1 and Ωz2 yaw angular velocities).
In addition, from figure 8 we can note that impact initial speeds are known (v1, v2 , ωz1, ωz2), that is why
speeds of end of the impact are unknown (V1, V2, Ωz1, Ωz2).
Figure 8. Brach model scheme
In case of uncertainties, Brach model uses vectors and arrays with interval values. That is why there is
un uncertain mathematical model, which uses interval analysis and mathematical operations with
intervals.
85
So, the solution of the mathematical model represents a value intervals vector for each parameter.
For example, it is considered that uncertainties exists only regarding restitution coefficient, which is taken
from the interval e=[0,1; 0,3], and is obtained the graph from figure 9.
From the graph results the speeds from end of impact:
V1 = [ 40.5; 33.3]
km/h; V2 = [ 48.8; 57.3]
km/h
(7)
but also yaw angular velocities from end of impact:
Ω z1 = [ 3.7; 4.2]
rad/s;
Ω z 2 = [ −1.9; − 2.4 ]
rad/s
(8)
As it can be observed from figure 9, for V1 and Ωz2 have been obtained improper intervals, meaning that
when e increases the two parameters decrease.
Figure 9. The separation speeds and the yaw angular velocities
If is adopted the hypothesis from specialty literature (uncertainties are the subject of normal distribution),
then the value with the highest probability of advent is interval arithmetic mean, so it’s middle. The
centres of speeds intervals from formula (7) are the most expected values of speeds: V1m=36.9 km/h and
V2m=53 km/h (figure 9a). Similar, from expression (8) results the most probable values for yaw angular
velocities: Ωz1m=3.9 rad/s and Ωz2m=-2.1 rad/s (figure 9b); as it can be observed, at the end of separation
phase the A1 automotive rotated counterclockwise, and A2 rotated clockwise.
The calculus may continue by establishing other parameters specific to impact, but also establishing
automotives kinetical energies, deformation energies etc.
Also, the calculus can carry on by considering other uncertainties, and the most probable values are
associated with centres of intervals.
3. CONCLUSIONS
Considering all uncertainties assures a major warranty regarding obtained results, because in real
situation we do not exactly know the parameters. That is why in specialty literature is considered that, in
case of uncertainties, the middle of the obtained interval represents a more certain value than
disregarding it and using the middle of the interval for each parameter.
If the concepts of uncertainty theory are applied, which is a branch of mathematics complementary to
probability theory, then there can be established parameters values which are not measurable, adopted
from specialty literature in form of values intervals.
86
REFERENCES
[1] Brach, Raymond and Brach, Matthew, Vehicle accident analysis and reconstruction methods. SAE.
Varrendale. USA. 2011.
[2] Burg, Heinz and Moser, Andreas, Handbook of accident reconstruction. Viewg&Teubner.
Kippenheim. Germany. 2013.
[3] Datentechnik, Steffan, PC-Crash. Operating and technical manual. Mea forensic. USA. 2013.
[4] Franck, Harold and Franck, Darren, Mathematical methods for accident reconstruction. CRC Press.
Boca Raton. 2013.
[5] Gaiginschi, Radu, Reconstrucția și expertiza accidentelor rutiere. Ed. Tehnică. Bucureşti. 2009.
[6] Heydinger G., a. o. Measured Vehicle Inertial Parameters–NHTSA’s data. SAE. 1999.
[7] Huang, Matthew, Vehicle crash mechanics. CRC Press. Boca Raton. 2002.
[8] Jaulin, Luc, Applied interval analysis. Springer-Verlag. London. 2001.
[9] Liu, Baoding, Uncertainty Theory. Springer-Verlag. Berlin. 2007.
[10] Mastinu, Giampiero and Ploechl, Manfred, Road and off-road vehicle system dynamics handbook.
CRC Press. Boca Raton. 2014.
[11] Struble, Donald, Automotive accident reconstruction. CRC Press. London. 2014.
[12] Tsongos, Nicholas, Crash 3. Technical manual. NHTSA. USA. 1986.
[13] Varat, Michael, Crash reconstruction research. SAE. Varrendale. 2008.
[14] Wach, Wojciech, Simulation of vehicle accidents using PC-Crash. Institute of forensic Research
Publishers. Cracow. Poland. 2011.
87
88
DIESEL-ETHANOL BLENDS AND THEIR USE IN DIESEL ENGINES
Nicolae Vlad BURNETE*, Nicolae FILIP, István BARABÁS
Technical University of Cluj-Napoca, B-dul Muncii Nr. 103-105, 400641 Cluj-Napoca, România
(Received 11 June 2015; Revised 07 July 2015; Accepted 27 July 2015)
Abstract: In order to reach the pollution and renewable energy targets for 2020, considerable research and
development efforts are required. Because new technologies require a significant amount of time to cover a large
enough market share, fuel improvements are a viable solution that has the capability of affecting the whole vehicle
park. One possible option is the addition of ethanol to Diesel to form blends that can be used in Diesel engines.
This will not only increase the amount of biofuel used but it will also have environmental benefits. The resulting
benefits are dependent not only on the production technology of ethanol but also on the operating conditions of the
engine. To understand the advantages and challenges of using ethanol in Diesel engines a study of Diesel-ethanol
blends properties has been covered. A review of the available literature on the use of Diesel-ethanol blends
revealed a reduction in NOx emissions, smoke and PM but an increase in HC emissions. The evolution of CO
emissions, when compared to those of pure Diesel, is load dependent. Engine power, fuel consumption and
thermal efficiency are also affected when using Diesel-ethanol blends.
Key-Words: biofuels, ethanol, diesel, blends, pollution
1. INTRODUCTION
The last century has brought unprecedented advances in all of life domains and, along with it, a
proportional growth in energy consumption [1]. In the last 40 years, the world energy consumption has
doubled, reaching 8978.86 Mtonne (in 2012) of which a share of 27.9% is attributed to the transport
sector. In the same year, fossil fuels accounted for 81.7% of the world’s primary energy supply (31.4% oil,
29% coal and 21.3% natural gas) [2]. Because of the continuous increase in energy consumption,
environmental impact awareness, high fluctuations of oil market prices and the search for a sustainable
fuel supply, biofuels are attracting more and more interest. Furthermore, a renewable energy directive [3]
was approved, which sets the 2020 goals for renewable energy use in electricity production (in heating
and cooling) and in road transport (mandatory value of 10%). The directive also provides a detailed set
of sustainability standards.
In order to reduce the impact of road transport pollution on the environment, the emission regulations
continue to impose more and more stricter pollution limits [4]. Vehicle manufacturers are investing many
research and development resources in improving the efficiency and pollutant emissions of the internal
combustion engines. With every new generation of vehicles, new and/or improved technologies are
introduced in order to meet these limits: electrification, hybridization, exhaust gas recirculation (EGR),
variable valve timing (VVT), exhaust gas aftertreatment etc. [5][6][7][8][9]. However, these technologies
require a significant amount of time to cover a large enough market share and there is also the problem
of the population income which, directly influences the adoption capability. One way to overcome these
challenges is the improvement of the in use fuels. However, this method requires comprehensive studies
regarding the pollution benefits, costs and compatibility with in use engine technologies [10][11][12][13]
[14][15][16]. Before the market introduction of a fuel, several factors need to be evaluated [10]:
• amount of hardware modifications required for the in use technologies;
• infrastructure and processing costs for the new fuel;
• environmental impact compared to existing fuels;
• additional maintenance, repair and operating costs for the end user.
*
Corresponding author e-mail: [email protected], [email protected]
89
Biofuels are renewable energy sources produced from agricultural residues, forest biomass, energy
crops, algae/aquatic biomass and other sources of organic matter [17] that can substitute fossil derived
fuels. These types of fuels have several advantages over the conventional fossil fuel like: higher
combustion efficiency, sustainability and improved fuel security, stimulation of rural development,
reduced environmental impact, reduced dependency on petroleum imports, conversion of wastes and
residues [13][17]. There are several factors that need consideration in order to ensure a sustainable,
clean and conflict free energy supply [2] [10][12][18][19]:
• competition between food and biomass production for land use and its influences on food prices;
• land degradability;
• overall environmental/economical impact – the life cycle assessment and the renewable fuel/fossil
fuel ratio (output/input);
• social impact.
Currently, the most common biofuels are ethanol (produced from crops such as corn, wheat, sugar cane
and sugar beet) and biodiesel (produced from oil seeds, animal fats and algae) [17][20]. As this paper
focuses on Diesel-ethanol (DE) blends, details about biodiesel use as replacement for Diesel fuel or their
blends will not be pursued any further. The main purpose of this study is the identification (from the
available literature of the past 15 years) of the current state and challenges of bioethanol production and
of the most important properties and influences of DE blends with respect to their use in Diesel engines.
2. ETHANOL AS BIOFUEL
Ethanol (CH3CH2OH) is a clear colorless liquid also known as ethyl alcohol, grain alcohol and EtOH. It is
obtained through fermentation of biomass like corn, sugar beet, sugar cane and wheat (also called first
generation ethanol). In order to obtain the desired purity, distillation is followed by a dehydration process
[21] [22] [19]. Currently, the largest ethanol producers in the world are Brazil and the USA (see Fig. 1
[23]).
Ethanol can be used as fuel for internal combustion engines either directly or in blends [24]. Making
ethanol available as a vehicle fuel involves several steps:
• growth, collection and transportation of feedstock;
• production of first/second generation ethanol;
• preparation of E10, E15 or E85 and their distribution to the gas stations [21].
Fig. 1. Ethanol production/consumption balance
2013 [23]
Fig. 2. Greenhouse gas emissions of
transportation fuels [19]
Materials for production of second generation bioethanol come from the non-edible parts of crops, waste
products or energy crops grown on lands not suitable for other crops.
90
Its production is not limited by the feedstock supply (according to Lin and Tanaka [25], 7-18 billion tons of
lignocellulosic biomass are available for use every year) but by technical and economical challenges:
• due to the recalcitrance of biomass, a relatively harsh pretreatment process of the feedstock is
required, which causes fermentation problems;
• production of efficient enzymes to hydrolyze the cellulose at a cost competitive to first generation
enzymes hydrolyzing starch [26];
• cost of feedstock [13].
In order to assess the environmental performance of all life stages of a product (material extraction,
processing, manufacturing, distribution, use and disposal/recycling) a so-called life cycle analysis is
performed. According to U.S. Department of Energy [19], a study performed by the Argonne National
Laboratory found that, a wheel to wheel analysis for corn-ethanol use instead of gasoline, would lead to
a reduction of GHG emissions by more than 70%, regardless of the ethanol production pathway (Fig. 2).
This is mainly due to the recapture of the CO2 (released by burning ethanol) when crops for ethanol
production are grown. Regarding the energy balance analysis of ethanol production, the majority of
studies presented a positive value but, there are also some that state the contrary [19]. A well-to-wheel
analysis (WTW) performed by the Joint Research Centre-EUCAR-CONCAWE Collaboration in 2014
revealed that, although a considerable reduction in GHG emissions is possible when using alternative
fuels, the total energy consumption increases (Fig. 3) [27].
Some advantages of the biofuel industry would include added value to the feedstock, jobs in rural areas,
increased income taxes, reduced GHG emissions, investments in plants and technology and reduced
dependency on oil imports. When studying the economic feasibility of ethanol production the co-products
of ethanol production process must also be considered: high protein animal feed, CO2 used in fizzy
drinks or in greenhouses (in order to improve fruit and vegetable production) as well as renewable and
low carbon fuels [21]. Current and future policy support focus on creating favorable economic and legal
frameworks to accelerate biofuel market penetration in order to achieve the set pollution limits and to
decrease the fossil fuel dependency [3][13]. Rigorous sustainability criteria for biofuels have been set in
order to guaranty a sustainable fuel supply [28].
Fig. 3. WTW analysis for alternative fuel resources [27]
91
The identified disadvantages of ethanol production and its use in internal combustion engines are:
• higher energy input requirements for production as compared to other energy crops [10];
• it can lead to the destruction of soil when it is not produced in a sustainable way [12];
• corrosiveness;
• increased aldehyde, formaldehyde and hydrocarbon emissions;
• in order to provide satisfactory drivability for older technologies, adjustments of the injection
parameters are necessary.
3. DIESEL-ETHANOL BLENDS
The idea of Diesel-ethanol blends is not new. Research studies dating since the 1980s have shown that
these blends are suitable for use in compression ignition (CI) engines [29], [30], [31]. Pollutant emissions
improvements associated with the use of DE blends in CI engines (without any modifications) are
strongly dependent on the operating conditions of the respective engine. By adjusting the injection
parameters, this dependence could controlled and the benefits enhanced [15].
Diesel and ethanol have considerable different physical and chemical properties, which affect the
properties of the resulting blend. A comparison between the main characteristics of ethanol and Diesel
can be seen in Table 1.
Table 1 Main properties of Diesel and ethanol [11] [19] [32], EN 590
Property
Fuel
Diesel
Ethanol (anhydrous)
Density at 15°C [kg/m3]
820 - 845
792
Cetane number (CFR)
min. 51
~8
Lower Heating Value [MJ/kg]
43.700
26.900
Kinematic viscosity at 40°C [mm2/s]
2 – 4.5
1.13
Flash point [°C]
min. 55
12.8
Autoignition temperature [°C]
~ 315
~ 423
C [wt. %]
~ 85.24
~ 52.17
H [wt. %]
~ 13.92
~ 13.04
O [wt. %]
~ 0.74
~ 34.78
S [wt. %]
max. 0.01
0.0
C/H mass ratio [-]
6.12
3.97
Stoichiometric Air/Fuel ratio [-]
14.60
9.01
Adiabatic flame temperature [°C ]
2465
2401
(determined from stoichiometric mixture at 9 MPa
and 626 °C) [32]
3.1 Blend stability
The solubility of ethanol in Diesel fuel is affected mainly by two factors: environmental temperature and
water content of the blend [15], [16]. At temperatures above 30°C, percentages of up to 15% v/v
anhydrous ethanol can be mixed with Diesel without phase separation. However, at temperatures below
10°C and without the use of additives a phase separation can be observed [11]. Another factor that can
affect blend stability is the aromatic content of Diesel fuel, which acts, to some degree, as a bridging
agent and co-solvent [15], [33]. The water content of the blend affects not only the blend stability but also
the combustion characteristics and the durability of the fuel injection system components [34]. Moreover,
special measures must be taken when storing DE blends for longer periods because of an increased
hygroscopic characteristic of ethanol. Due to variations in homogeneity of DE blends, a precise control of
the injected ethanol quantity can be difficult and injection and combustion problems may arise. In order
to stabilize the blend two methods are proposed:
• addition of an emulsifier – it suspends small droplets of ethanol within Diesel fuel;
• addition of a co-solvent – acts as a bridging agent between molecules [35].
Emulsification usually involves a series of heating and blending steps, whereas the use of co-solvents
92
simplifies the process by allowing “splash-blending” [15]. Some of the additives found by researchers
([35] [36] [37] [38] [39]) to inhibit phase separation are presented in Table 2:
Additive
Tetrahydrofuran (THF)
Ethyl acetate
PEC additive
AAE additive
GE Betz additive
Biodiesel
Table 2 Identified additives for Diesel-ethanol stability
Observations
Obtained from agricultural waste material
Can be obtained from ethanol
Pure Energy Corporation of New York
AAE Technologies of the United Kingdom
Division of General Electrics
Increases the percentage of biofuel in the blend
The required additive percentage is dictated by the lower temperature at which the blend stability must
be guaranteed [36].
3.2 Energy content
Ethanol has lower energy density than Diesel and therefore, a blend resulting by mixing the two fuels will
have a reduced lower heating value (LHV). The energy content reduction is proportional to the ethanol
content (approximately 4% for each 10% v/v of ethanol added). As a result, the brake specific fuel
consumption (BSFC) will increase while the engine power output, which is also directly influenced by the
energy content of the fuel, will slightly decrease (see Table 4 and Table 5).
3.3 Cetane number
The cetane number of ethanol is estimated to have a value of 8, which is significantly lower than the
minimum value of 51 imposed by EN 590 for Diesel. The cetane number is a measure of the fuels
autoignition quality and dictates the ignition delay. This has a considerable influence on the fuel
conversion efficiency, smoke emissions, noise, smoothness of operation and starting ease. Low values
of the cetane number result in longer ignition delay, violent/incomplete combustion, reduced power
output and a poor fuel conversion efficiency [40]. Adding ethanol to Diesel increases ignition delay and
subsequently the rate of heat release (ROHR) but it leads to an improved brake thermal efficiency (BTE)
of the engine. However, some adjustments to the injection strategy and timing could further improve the
emission performances of the engine. Test performed by Moses et al. [41] showed some differences in
the cetane number between DE emulsions and stable blends of aqueous ethanol and Diesel (without
additive). They concluded that the ethanol emulsion had a lower influence on the cetane number of the
blend. In order to improve the ignition qualities of DE blends, researchers have used cetane improvers
like: 2EHN ([42] [43]), isooctyl nitrate ([44]), isoamyl nitrate ([45] [46]) etc.
3.4 Density
The density of a DE blend decreases proportionally with the ethanol content and/or temperature. Tests
conducted by Torres-Jimenez et al. [11] showed that the density value of a mixture containing 15% v/v
ethanol remains within the standard reference limits. A decrease in density leads to a retarded start of
injection, which can deteriorate the engines emission performances [11] but this problem can be solved
by adjusting the injection timing [47]. Furthermore, for high pressure differences (injection pressure –
cylinder pressure > 55 MPa), density is the only fuel property influencing the injector mass flow rate [48].
Therefore, a reduction of the injection density can lead to loss in engine power.
3.5 Viscosity
At 40°C, ethanol has a viscosity of about 1.1 mm2/s, a value much lower than that of Diesel (2-4.5 mm2/s)
and therefore, it will lead to lower viscosity values of the blend.
Still, according to the tests performed by Torres-Jimenez et al. [11] the viscosity value of a blend
93
containing 15% v/v ethanol remained above the minimum value (2 mm2/s) specified by the EN 590
standard [11]. A low viscosity value has negative influences on lubricity and on the maximum fuel delivery
rate of the pump (due to the increased leakage). Ultimately, this results in a reduced power output of the
engine. According to the study performed by Dernotte et al. [48], viscosity is also the main influencing
parameter of the injector discharge coefficient when the difference between the injection pressure and
the cylinder pressure is smaller than 55 MPa. The author noted an increase of the discharge coefficient
when using a fuel with lower viscosity. However, a lower viscosity value has a positive influence on spray
atomization: the mean Sauter diameter of the droplets is smaller and, as a consequence, the total
surface area of the droplets increases; this facilitates the evaporation process [40].
3.6 Lubricity, corrosiveness and engine wear
In order to protect the moving parts with which it comes in contact (by reducing the friction between solid
surfaces in relative motion) the fuel must have a minimum level of lubricity. There are three ways used to
evaluate fuel lubricity: vehicle testing (high fuel, time and effort demand), fuel injection bench test (is the
most accurate) and laboratory lubricity tests (HFRR – High-Frequency Reciprocating Rig, SLBOCLE –
Scuffing Load Ball-on-Cylinder Lubricity Evaluator) [49].
Diesel injection systems rely solely on the lubricating qualities of Diesel fuel. When mixing ethanol with
Diesel, the resulting blend will have a lower lubricity than that of pure Diesel [11] [50]. In spite of this
reduction, a study performed by Lapuerta et al. [50] on a high frequency reciprocating rig at different
temperatures, revealed that ethanol addition can in fact improve lubricity at high temperatures (60°C).
This is considered an effect of ethanol evaporation, which would compensate for its poorer tribological
properties. Corrosiveness to copper tests performed by several authors [11] [51] [52] revealed that
ethanol addition does not lead to a higher corrosiveness than that of pure Diesel. Tested samples
containing 5, 10 and 15% v/v of ethanol were classified as 1a [11]. EN 590 specifies for Diesel a class 1
corrosiveness to copper.
Some early studies ([29] [53] [31] [30]) regarding engine wear when using DE blends (containing 10, 15
or 30% v/v anhydrous ethanol) have indicated no abnormal wear in the tested engines (the injection
timing of the engines was adapted to the new fuel). A 500h lab test with DE15 stabilized with PEC
additive (2.35% v/v) on a Cummins ISB 235 engine reported no abnormal deterioration in engine
condition [54].
In 2001, a test conducted on farm tractors using DE10 stabilized with GE Betz additive also reported,
based on oil analysis, no abnormal wear of the engines [39]. Tests performed by Armas et al. [34] on two
identical common rail injection systems revealed similar wear patterns of the fuel injection pump parts
both for Diesel and the DE blend (7.7% v/v).
However, an analysis of the injector nozzle showed a reduction of the nozzle section effective area,
which lead to a decrease of the total fuel delivery by approximately 30%. This was believed to be a result
of sedimentation/oxidation due to the increase in water content of the DE blend from 243 ppm (at the
beginning of the test) to 640 ppm at the end of the 600 hours of testing. One other test, that studied the
effect of ethanol addition to a diesel-biodiesel blend (7.7% v/v) on a common rail injection system,
revealed similar wear patterns for both blends [55].
3.7 Low temperature operability
Several factors describe a fuels low temperature operability (or low temperature filterability), which is
dependent not only on the presence of wax crystals but also on their shape and size [31]. The standard
test in Europe is the Cold Filter Plugging Point (CFPP) test, which requires the cooling of the fuel sample
by immersion in a constant temperature bath (40°C/hour cooling rate). The CFPP is the temperature at
which 20 mL of fuel fail to pass through a wire mesh (45 µm cell size and at 20 kPa vacuum pressure) in
less than 60 s. Adding ethanol to Diesel does not significantly influence the value of the CFPP (Fig. 4).
However, other properties like the cloud point, the plugging point and the filter plugging tendency show
significant differences between DE blends and pure Diesel [11].
94
The cloud point (CP) is the temperature at which a cloud
of was crystals first appears in a liquid when cooled
under controlled conditions described in a standardized
test. As can be seen in Fig. 4, the addition of ethanol to
Diesel fuel results in CP values much higher than that of
pure Diesel and DE5 thus, highlighting the temperature
dependence of DE blends. The plugging point (PP) is
defined as the temperature at which fuel can no longer
flow due to gel formation [56]. The PP is improved but, for
a content of 15% v/v ethanol the apparatus used by
Torres-Jimenez et al. [11] displayed (due to phase
separation) the ethanol PP and not the PP of the blend.
For a precise determination of the PP, the blends need to
be stable, that is, no phase separation can occur.
In order to assess the tendency of particulates to plug or
block the filter (which the CFPP cannot detect) the socalled Filter Plugging Tendency (FPT) value is calculated.
Values determined by Torres-Jimenez et al. [11] showed
a reduction in FTP and pumping pressure values
proportional to the content of ethanol in the blend.
Fig. 4. CFPP, CP and PP of different Diesel-ethanol
blends (after [11])
3.8 Safety, distillation curve and environmental impact
The flash point is the lowest temperature at which the vapors formed above the liquid fuel surface ignite
when an ignition source is applied. It determines the required shipping, storage and handling safety
measures. The flash point is the most important property when assessing the physical hazard
classification of a fuel. According to the Globally Harmonized System of Classification and Labelling of
Chemicals (GHS) Diesel and ethanol are classified as follows:
Classification
GHS
Classification
PHYSICAL
HAZARDS:
HEALTH
HAZARDS:
ENVIRONMENT
AL HAZARDS:
Table 3 GHS classification of Diesel and ethanol
Diesel [57]
Ethanol [58]
Flammable liquids, Category 3.
Flammable
liquids,
Aspiration hazard, Category 1.
Category 2
Acute toxicity, Category 4, Inhalation.
Serious
eye
Skin corrosion/irritation, Category 2.
damage/eye irritation,
Carcinogenicity, Category 2.
Category 2A.
Specific target organ toxicity - repeated exposure,
Category 2, Blood, Thymus, Liver.
Hazardous to the aquatic environment - Long-term
Hazard, Category 2.
Acute hazards to the aquatic environment, Category 2.
Hazard Statement
H226: Flammable liquid and vapor.
H225:
Highly
flammable liquid and
vapor.
H304: May be fatal if swallowed and enters airways.
H319: Causes serious
H315: Causes skin irritation.
eye irritation.
H332: Harmful if inhaled.
H351: Suspected of causing cancer.
H373: May cause damage to organs or organ systems
through prolonged or repeated exposure.
H411: Toxic to aquatic life with long lasting effects.
Not classified as an
H401: Toxic to aquatic life.
environmental hazard
under GHS criteria.
95
The flash point of DE blends is similar to that of
pure ethanol [10] [11] and therefore, the mixture
will take the H225 classification: highly flammable
liquid and vapor. This requires additional safety
measures to be taken when storing and
transporting the resultant fuel. From a health and
environmental point of view, the blend will be as
toxic as Diesel fuel. Another safety property of the
fuel is conductivity, which is defined as a measure
of its ability to dissipate static electric charge [49].
There appears to be a lack of research papers
covering this property of DE blends.
The distillation curve is a fundamental fuel
Fig. 5. Distillation curves of Diesel and different
property,
which
shows
the
evaporation
Diesel-ethanol blends (after [11])
characteristics of a fuel [49]. It is used to calculate
the cetane index and to evaluate the percentage
of light, medium and heavy fractions, which are needed to characterize the fuels behavior during storage,
at cold start, consumption characteristics and volatility. The initial boiling point of DE blends has a similar
value as the boiling point of pure ethanol and, as a result, in the first part of the distillation curve, there is
a considerable difference between the curve of the tested blends and that of pure Diesel (see Fig. 5).
After the evaporation of the ethanol fraction, the distillation curves follow an almost identical trend [11].
When assessing the environmental impact of a fuel, another important issue is biodegradability. Tests
performed by Speidel et al. [59], [60] showed that fuels containing a higher degree of components
derived from renewable sources are more degradable than conventional fossil fuels. The authors
reported a 70% increase in biodegradability for DE blends as compared to pure Diesel.
4. EMISSIONS AND PERFORMANCE
A study of the available literature on the topic of DE blends and their use in Diesel has led to the
formulation of several conclusions, based on the majority of the reported findings:
I. Comparing the results of the reviewed literature revealed conflicting conclusions regarding the use
of DE blends in Diesel engines. This is attributed to the considerable differences in the equipment
and methodology used for testing;
II. Significant variations can be observed in the quantity of blended ethanol (with values ranging from
2% to 50% v/v) and in the fuel used as reference (Diesel, Diesel No. 2, Ultra-Low Sulfur Diesel and
Low Sulfur Diesel);
III. There is a lack of papers covering the computer simulation of Diesel engines running on DE blends.
When coupled with experiments, this is believed to be a more reliable way of investigation;
IV. Using DE blends leads to an increased ignition delay, which, in spite of promoting mixture formation,
can have negative effects on pollutant emissions. The retarded ignition timing is a result of the
higher heat of vaporization value of ethanol and of its lower CN. As can be seen from the distillation
curves (Fig. 5) the ethanol fraction evaporates before the Diesel fraction. This reduces the
temperature in the combustion chamber and consequently increases the evaporation duration of
Diesel. The extent of this influence is dependent on several engine construction factors and load.
Adjustments of the injection timing or the use of cetane improvers can eliminate the possible
inconveniences.
V. Due to the lower energy content of the resulting blend (proportional to the added ethanol
percentage), a decrease in engine brake power is to be expected.
VI. When maintaining the same engine performances the brake specific fuel consumption (BSFC)
increases along with the brake thermal efficiency.
VII. Low load tests revealed a decrease of smoke, PM and NOx emissions but an increase in CO and
HC emissions. Due to the lower temperatures in the combustion chamber NOx formation is
inhibited but, this also affects the oxidation of CO to CO2. The reduced smoke and PM emissions
can be attributed to the reduced carbon content and, in the same time, to the increase the oxygen
fraction contained in the fuel. During the increased ignition delay a higher amount of fuel is injected,
96
which affects the mixture formation thus leading to increased HC emissions [40].
VIII. High load tests however, revealed a reduction of not only smoke, PM and NOx but also of CO. The
increased temperature promotes the oxidation of CO but it is still lower than that of Diesel and
thereby, it leads to lower NOx emissions. Although having considerable lower values, HC emissions
values remained above those of pure Diesel.
Tables 4 and 5 represent a summary of the obtained results with regard to pollutant emissions,
performances and the used blends for both single cylinder and multi cylinder experiments.
Table 4 Summary of single cylinder investigations
Reference
Gnanamoorthi
&
Devaradjane
(2015)
[61]
Murcak et al.
(2013)
[62]
EtOH
(%)
10
20
30
40
Ethyl acetate
(1)
Diethyl
carbonate (1)
5
10
20
n/a
(1.5)
Rakopoulos et
5
al.
10
(2007)
15
[63]
Huang et al.
(2009)
[64]
10
20
25
30
Herreros et al.
(2015)
[65]
5
10
15
Park et al.
(2012)
[66]
Caro et al.
(2001)
[67]
20
20
20
10
20
Rakopoulos et 5
al.
10
(2008)
15
[68]
Sayin C.
(2010)
[69]
Additive
(%)
5
10
Ballesteros et
al.
10
(2015)
[70]
Rakopoulos et 5
Ref. fuel
BD (5) + DGE
(15)
(10)
(5)
BD
(5)
(10)
(15)
Pollutant emissions
↑: CO; HC
↓: Smoke (E10, E20)
High
↑: HC
↓: CO (E10, E20); Smoke
Low
↑: ↓: -
↑: P (for E5, E10);
↓: BSFC
High
↑: ↓: -
↑: ↓: P (for E5; E20)
Low
↑: THC
↓: Soot; NOx; CO
High
↑: THC
↓: Soot; NOx; CO
Low
↑: CO; HC
↓: NOx (except E10); Smoke
↑: BSFC
↓: BTE
High
↑: HC; NOx (except E10, E20)
↓: CO; Smoke
↑: BTE (except E10); BSFC
↓: -
Diesel
Diesel
Low
B5
High
Low
Diesel
↑: BTE (for all CRs except
E30, E40)
↓: -
↑: BSFC; BTE
↓: -
↑:
NOx
(5E15DGE,
10E10DGE)
↓: THC; Soot; CO
↑: ITE
↓: ↑:
NOx
(5E15DGE,
10E10DGE)
↓: THC; Soot; CO
↑: HC
↓: NOx
High
1-octylamino-3octyloxy-2Diesel
propanol (1)
N-octyl nitramine
(1)
Performances
Low
Diesel
GE Betz additive
Diesel
(1.5)
n-Butanol
(5)
Load
Low
↑: HC; CO
↓: NOx
High
↑: HC
↓: NOx; CO; Smoke
↑: ISFC
↓: IMEP
↑: ↓: P
Low
GE Betz additive
Diesel
(1.5)
Dodecanol
(1)
↑: ↓: NO; Soot
↑: ↓: -
↑: NOx
↓: CO; THC; Smoke
↑: BSFC
↓: BTE
High
↑: THC; PM
TCC, PAH vary with the
operating parameters
↑: ↓: -
Low
↑: -
No cyclic variations.
High
Low
Diesel
High
Low
-
Diesel
GE Betz additive Diesel
97
al.
(2008)
[71]
10
15
(1.5)
De Menezes
et al.
(2006)
[72]
2.5
5
10
2.5
5
10
ETBE (2.5)
(5)
(10)
TAEE (2.5)
(5)
(10)
Karabektas et
al.
15
(2013)
[73]
Rakopoulos et 5
al.
10
(2014)
15
[74]
Ren et al.
(2008)
[45]
Li et al.
(2008)
[46]
B5
BD
CR
DGE
E#
ETBE
IMEP
P
PAH
ISFC
ITE
TAEE
TCC
Reference
Lei et al.
(2012)
[75]
Kim & Choi
(2015)
[42]
5
10
15
20
5
10
15
20
5
10
15
20
↓: High
-
Low
Low
↑: ↓: -
High
↑: HC
↓: CO; NOx
Low
↑: HC
↓: Smoke; NOx; CO
High
↑: HC
↓: Smoke; NOx; CO
Low
-: NOx
↓: Smoke
High
↑: ↓: NOX; Smoke
Diesel
Diesel
Low
Isoamyl nitrate
(0.2)
(0.2)
(0.2)
(0.2)
↑: BSFC
↓: Specific work
High
GE Betz additive
Diesel
(1.5)
Isoamyl nitrate
(0.2)
↑: ↓: -
Diesel
Diesel
High
↑: BSFC
↓: P; BTE
↑: BSFC; BTE
↓: -
↑: BSFC; BTE
↓: -
↑: ↓: -
↑: Ignition delay
↓: -
↑: ↓: -
↑: ↓: Ignition delay
Blend of Diesel (95% v/v) and biodiesel (5% v/v)
Biodiesel
Compression ratio
Diethylene glycol diethyl ether
Represents the percentage (#) of ethanol in the blend
Ethyl tert-butyl ether
Indicated mean effective pressure
Power
Polycyclic aromatic hydrocarbon
Indicate specific fuel consumption
Indicated thermal efficiency
Tert-amyl ethyl ether
Total carbonyl compounds
Table 5 Summary of multi-cylinder investigations
EtOH
(%)
5
10
15
15
15
Additive
(%)
CLZ (0.8)
(0.8)
(1)
Ref. fuel
Load
Low
↑: CO; THC
↓: NOx; Smoke
High
-: NOx
↓: CO; THC; Smoke
Low
↑: CO; THC; NOX
↓: Smoke; PM
High
↑: NOx; THC
↓: Smoke; PM
- : CO
Diesel
THF (2)
Pollutant emissions
THF (2) + 2EHN ULSD
98
Performances
↑: BTE
↓: -
↑: BSFC
-: BTE
Rakopoulos et 5
al.
10
(2008)
15
[76]
Park et al.
(2011)
[77]
Song et al.
(2007)
[78]
10
20
GE Betz additive
(1.5)
BD
(10)
5
10
15
20
-
Lapuerta et al. 10
(2008)
[32]
-
Di et al.
(2009)
[79]
Di et al.
(2009)
[80]
High
↑: THC
↓: NOx; CO; Soot
Low
↑: CO; THC
↓: NOx
High
↑: CO
↓: NOx; TUHC
LSD
ULSD
Low
6.1
12.2
18.2
24.2
1-dodecanol (1)
(1)
(1)
(1.5)
6.1
12.2
18.2
24.2
1-dodecanol (1)
(1)
(1)
(1.5)
Hulwan &
Joshi
(2011)
[82]
20
30
40
BD
(10)
↑: HC
↓: Smoke; PM
-: NOx
High
↑: NOx; PM
↓: Smoke
Low
↑: ↓: Smoke (except E6); PM
High
↑: ↓: Smoke; PM
Low
↑: ↓: Smoke; PM
High
↑: ↓: Smoke; PM
ULSD
ULSD
Low
Diesel
↑: ↓: -
High
RME
(10)
Armas et al.
(2012)
[84]
10
Kim et al.
(2010)
[43]
15
-
15
2EHN
(7500 ppm)
LSD
10
20
2.5
BD
(10)
Sorbitan methyl
↑: BSFC; BTE
↓: -
↑: BTE (except E6)
↓: -
↑: BSFC; BTE
↓: -
↑: BSFC; BTE
↓: Torque, P
High
Low
↑: HC; CO
↓: NOx; Smoke
High
↑: HC
↓: NOx; Smoke; CO
Cold
start
↑: NOx; THC; CO; Smoke
↓: -
ECE
R49
↑: NOx; CO
↓: Smoke; PN
ESC
↑: NOx
↓: CO; PN
Low
↑: CO; HC
↓: NOx; Soot (for E20)
High
↑: ↓: -
ULSD
Diesel
↑: ↓: -
↑: NO (for retarded injection ↑: BSFC
timing); CO; CO2
↓: BTE
↓: NO (for advanced injection
timing and E30, E40); CO
-: CO2
Warm ↑: start ↓: NOx; THC; CO; Smoke
ULSD
↑: ↓: -
↑: CO
↓: Smoke; NO
B10
-
↑: BSFC, BTE
↓: -
Low
Diesel
20
↑: THC; CO
↓: Smoke; PAH (only E5)
-: NOx; PM
Low
LSD
-
Putrasaria et
↑: THC
↓: NOx; CO; Soot
High
5
10
15
Park et al.
(2010)
[85]
Low
Diesel
Hamdan &
Khalil
(2010)
[81]
Guido et al.
(2013)
[83]
Low
↑: 99
↑: BSFC
↓: FCE
(values obtained for a
specified IMEP)
↑: ↓: -
↑: BSFC
↓: -
↑: ↓: -
↑: P; BSFC; BTE
al.
(2013)
[86]
5
7.5
10
ester
(1)
He et al.
(2003)
[44]
10
30
Additive
(2)
10
Additive (2) +
Isooctyl nitrate
(0.1)
(1)
30
Labeckas et
al.
(2014)
[87]
5
10
15
15
Xing-Cai et al. 15
(2004)
[88]
Beatrice et al. 20
(2014)
[89]
Cheung et al. 6.1
(2008)
12.2
[90]
18.2
24.2
Di et al.
(2009)
[91]
6.1
12.2
18.2
24.2
↓: CO, HC (except E10)
(SOLAR)
↑: ↓: Smoke (except E2.5)
Low
↑: CO; Acetaldehyde
↓: NOx; CO2; Smoke; THC
(except E10, E30 no ignition
improver)
High
↑: ↑: CO (except E10, E30
↓: ignition
improver);
Acetaldehyde
↓: NOx; CO2; Smoke; THC
(except E30 no ignition
improver)
Low
↑: NOx (except 2000 rpm); CO
(except 1400 rpm and EB5);
HC (except 1400, 1800 rpm
↑: BSFC; BTE
and E15, EB5)
↓: ↓: Smoke
High
↑: HC (except E15; EB5)
↓: NOx; CO; Smoke
Low
↑: CO; HC
↓: NOx; Smoke
Diesel
-
BD (5)
(0)
CN improver
(0.2)
CN improver
(0.4)
RME
(10)
1-dodecanol (1)
(1)
(1)
(1.5)
1-dodecanol (1)
(1)
(1)
(1.5)
LSD
Diesel
High
Chen et al.
(2009)
[93]
B10
C4H6
CLZ
CN
10
30
10
20
30
↑: CO; HC
↓: NOx; PM
High
↑: ↓: NOx; PM; CO; HC
Low
↑: CO; NO2; THC
↓: NOx; PM
High
↑: NO2; NOx
↓: CO; THC; PM
B10
ULSD
Low
ULSD
Ester (5)
(10)
(10)
10
15
Solvent
(1)
↑: BSFC; BTE
↑: ↓: ↓: NOx; CO; HC (for 0.4 CN
improver); Smoke
Low
High
Chen et al.
(2007)
[92]
↓: -
High
Low
Diesel
High
↑: ↓: -
↑:
CO;
NO2;
THC;
Formaldehyde (for E6, E12);
Acetaldehyde
↓: NOx; NO
↑: BTE
↓: ↑: NOx; NO2; NO; CO; HC;
Acetaldehyde; C4H6 (for E6,
E24)
↓: Formaldehyde; C4H6
↑: Sulfate
↓: HC; PM; Smoke; SOF; Soot ↑: ↓: ↑: Sulfate; SOF
↓: HC; PM; Smoke; Soot
Low
↑: CO; HC; SOF
↓: NOx; Soot
High
↑: NOx
↓: CO; Soot
-: HC
Diesel
↑: BSFC
↓: -
↑: BSFC
↓: P
Blend of Diesel (90% v/v) and biodiesel (10% v/v)
1,3-butadiene
Emulsifier containing biofuel, castor oil and other single emulsifiers [75]
Cetane number
100
ECE
R49
2EHN
ESC
FCE
LSD
PN
RME
SOF
THF
TUHC
ULSD
Economic Commission Europe R49 test
2-ethylhexyl nitrate
European stationary cycle
Fuel conversion efficiency
Low sulfur Diesel
Particle number
Rapeseed methyl ester
Soluble organic fraction
Tetrahydrofuran
Total unburned hydrocarbons
Ultra low sulfur Diesel
5. CONCLUSIONS
The study of the most important characteristics of the ethanol life cycle, of Diesel-ethanol blends and
their influences on emissions and performances of internal combustion engines has led to the following
conclusions:
I.
Ethanol has proven to be one viable solution to replace fossil fuels. Overcoming the economical
production challenges of second generation bioethanol could help create a secure and
sustainable fuel supply and also reduce the fossil fuel dependency.
II.
DE blends have a stability problem, which depends greatly on temperature and water content.
The low temperature operability of these blends requires more attention due the problems caused
by phase separation. Additives can be used to minimize this inconvenience.
III.
The values of properties like density, viscosity, lubricity and corrosiveness (for tested blends
containing up to 15% v/v ethanol) remained within the standard reference limits for Diesel fuels.
IV.
The thermal efficiency of the engine increases but a reduced power output and an increased fuel
consumption are to be expected due to the lower energy content of these blends.
V.
The injection parameters require some adjusting in order to eliminate the disadvantages caused
by the increased ignition delay, which is a result of the poorer cetane number of the blends and
the higher heat of vaporization of ethanol.
VI.
Adding ethanol to Diesel fuel can significantly reduce smoke and PM emissions but it has a
negative influence on HC emissions. Due to a lower combustion temperature, NOx emissions are
also reduced. The changes in CO emissions are load dependent: at low loads, the CO emissions
of DE are higher than those of pure Diesel, while at high loads, they are smaller.
ACKNOWLEDGEMENT
This work was partially supported by the strategic grant POSDRU/159/1.5/S/137070 (2014) of the
Ministry of National Education, Romania, co-financed by the European Social Fund – Investing in People,
within the Sectorial Operational Programme Human Resources Development 2007-2013.
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106
EVALUATION THE DISSIPATED ENERGY BY THE AUTOMOBILE DAMPERS
Veronel-George JACOTA*
Politehnica University of Bucharest, Splaiul Independentei Nr. 313, 060042 Bucuresti, Romania
(Received 15 May 2015; Revised 23 June 2015; Accepted 28 July 2015)
Abstract: Simulation of suspension system and evaluation of dissipated energy by the system highlights the
potential of the car operation mode, where the suspension can provide a significant amount of power. A roughness
road profile and a car with elastic suspension springs and stiff dampers can provide significant energy. This energy
varies between 4% and 8% of the energy consumed by the engine vehicle, considering the road speed profiles
below 60 km/h and a vehicle with reduced rolling resistance and drag coefficient.
Key-Words: Simulation, suspension, stiffness, damping, road profiles, dissipated energy
1. INTRODUCTION
Characterization of automotive suspensions, in terms of energy dissipated by the suspension dampers
while running, is a complex process that takes into account a number of factors, such as road profile,
vehicle characteristics, running speed. All these factors contribute to determining the conditions under
which the dampers dissipate a large amount of possible energy. In order to simulate the systems
suspension operation and to evaluate the dissipated energy by the system, there were considered the
following parameters:
• road profile;
• mass parameters and general organization of the car;
• operating parameters of the suspension;
• simulation conditions.
2. ROAD PROFILE
The road profile is comprised of two components:
• the road microstructure;
• the road macrostructure.
The road microstructure road represents the uneven humps of tread, felt by the vehicle driver as
vibrations or small oscillations.
This is divided into four classes, depending on the variation of high road irregularities (∆h) in relation with
theoretical nominal profile, measured in mm [1]:
• ISO A-B, ∆h = ± 15 mm;
• ISO B-C, ∆h = ± 25 mm;
• ISO C-D, ∆h = ± 50 mm;
• ISO D-E, ∆h = ± 100 mm.
*
Corresponding author e-mail: [email protected]
107
Figure 1. Microstructure of road profile
The road macrostructure is the longitudinal profile of the road, being characterized by the following
parameters [2]:
- the maximum longitudinal gradients, α ;
- the minimum radius of the convex road connection, Rconvex;
- the minimum radius of the concave road connection, Rconcav.
Figure 2. Macrostructure of road profile sequence
Depending on the mentioned macrostructures parameters, there were defined eight road profiles, whose
design speeds are in the range 25 km/h - 120 km/h, with the following characteristics:
Table 1. Macrostructure of road profile
Road profile speed
[km/h]
25
30
40
50
60
80
100
120
α[°]
Rconvex [m]
Rconcav [m]
8
7,5
7
7
6,5
6
5
5
500
800
1000
1300
1600
4500
10000
18000
300
500
1000
1000
1500
2200
3000
6500
108
Following the conditions from the table 1, it results a sequence of road characteristics used in simulation:
Figure 3. Characteristics of macrostructure road profile sequence
Table 2. Characteristics of macrostructure road profile
Road profile
speed [km/h]
25
30
40
50
60
80
100
120
H [m]
h [m]
D [m]
d [m]
1.6
2.2
2.4
3.1
3.3
8.1
7.1
12.2
0.9
1.4
2.4
2.4
3.2
3.9
2.1
4.5
80
120
140
181
207
538
748
1330
48
75
140
140
196
224
263
480
The road profile sequences with a concave and convex radius, will be repeated until the length of road,
in horizontally profile, will have the value of 1 km (distance used in simulation).
Figure 4. Road profiling of macrostructures sequences
The road profiles used in the simulation consists of overlapping macrostructures and microstructures.
Thus, a combination of 27 profiles road results. Due to passenger’s discomfort caused by strong
vibrations, the ISO profile B-C, C-D and D-E will not be subject of the simulation in high speeds area.
109
Table 3.The road profiles used in simulation
120 km/h
100 km/h
80 km/h
60 km/h
50 km/h
40 km/h
30 km/h
25 km/h
ISO A-B
ISO B-C
ISO C-D
ISO D-E
3. THE CAR PARAMETERS
Parameters used in the car simulation have been chosen as the average values of middle-class cars:
- unladed weight:
m0 = 1100 kg;
- total weight:
ma = 1600 kg;
- wheelbase:
L = 2600 mm;
- the distance
a0 = 1170mm;
- the distance
b0 = 1430mm;
- the distance
a1 = 1430mm;
- the distance
b1 = 1170mm;
- the ratio
a0 / L = 0.45;
- the ratio
b0 / L = 0.55;
- the ratio
a1 / L = 0.55;
- the ratio
b1 / L = 0.45;
where:
- a0 is the distance between the center of the front axle and the mass center of the vehicle,
horizontally measured, considering only the car's unladed weight;
- b0 is the distance between the center of the rear axle and the mass center of the vehicle,
horizontally measured, considering only the car's unladed weight;
- a1 is the distance between the center of the front axle and the mass center of the vehicle,
horizontally measured, considering the total weight of car;
- b1 is the distance between the center of the rear axle and the mass center of the vehicle,
horizontally measured, considering the total weight of car;
4. THE SUSPENSION PARAMETERS
Vehicle suspensions used in the simulation have the following characteristics:
- unsprung mass, corresponding to the front axle, ms1 = 46 kg, [3][4];
- unsprung mass, corresponding to the rear axle, ms2 = 46 kg, [3][4];
- sprung mass, corresponding to the front axle (for unladed car weight), m1 = 605 kg;
- sprung mass, corresponding to the rear axle (for unladed car weight), m2 = 495 kg;
- sprung mass, corresponding to the front axle (for total car mass), ma1 = 720 kg;
- sprung mass, corresponding to the rear axle (for total car mass), ma2 = 880 kg;
- front suspension spring rate (for one spring): ks1 = 23929 N/m, [5];
- rear suspension spring rate (for one spring): ks2 = 28500 N/m, [5];
- front suspension damping (for one damper): cs1 = 1712 Ns/m, [6];
- rear suspension damping (for one damper): cs2 = 1725 Ns/m, [6];
- tire stiffness front axle (for one tire): kt1 = 165000 N/m, [7];
110
-
tire stiffness rear axle (for one tire): kt2 = 165000 N/m, [7];
tire damping front axle (for one tire): ct1 = 3430 Ns/m, [8];
tire damping rear axle (for one tire): ct2 = 3430 Ns/m, [8];
front suspension excitation: Xr1 - depending on road profile;
rear suspension excitation: Xr2 - depending on road profile.
5. CONDITIONS OF SIMULATION
The conditions required for vehicle during the simulation are:
- simulation performed in two conditions, the car's unladed weight and with total weight;
- straight displacement at a constant speed;
- all the profiles road used in simulation have a length of 1 km;
- the cross profile of the road is symmetrical.
6. SUSPENSION MATHEMATICAL MODEL
Each suspension vehicle consists of:
- the suspension itself;
- the tyres.
The suspension itself includes the springs, the dampers and the arms of the car body. Here it was
defined the suspension mass (ms), vehicle sprung mass (m1), the suspension spring rate (ks) and the
suspension damping (cs).The tire was defined as an independent suspension with the same elements,
spring and damper. It was considered the tire stiffness (kt) and tire damping (ct).The suspension
excitation is characteristic for every road profile (Xr) and is identical between the front and rear axle, but
out of phase with the length of the wheelbase.
Figure 5. The suspension model
The mathematical model includes the entire vehicle, the suspension of front and rear axle [9].
m1 &x&1 − c S1 ( x&1 − x& S1 ) − k S 1 ( x1 − x S1 ) = 0
(1.a)
111
m S1 &x&S1 + c S1 ( x&1 − x& S 1 ) + k S1 ( x1 − x S 1 ) − ct1 ( x& S 1 − x& r1 ) − k t1 ( x S 1 − x r1 ) = 0
(1.b)
m2 &x&2 − c S 2 ( x& 2 − x& S 2 ) − k S 2 ( x 2 − x S 2 ) = 0
(2.a)
m S 2 &x&S 2 + c S 2 ( x& 2 − x& S 2 ) + k S 2 ( x 2 − x S 2 ) − ct 2 ( x& S 2 − x& r 2 ) − k t 2 ( x S 2 − x r 2 ) = 0
(2.b)
The (1.a) and (1.b) formulas are applied to the front axle and the (2.a) and (2.b) formulas are applied to
the rear axle. The figure 4 presents the MatLab Simulink model achieved for a single axle. The input
data are: the sprung mass, corresponding to the front/rear axle, the suspension weight and the road
profile. Using these data, as well as operating parameters and the suspension of the car, it was
determined the total energy dissipated by the respective axle shock absorbers.
Figure 6. The suspension model used in MatLab Simulink
7. RESULTS
For each road profile, the energies dissipated by the car suspensions were calculated. The values
obtained are represented in Tables no.4 and no.5, expressed in Joules.
Table 4. The dissipated energy by all the dampers, corresponding to unladed car weight
ISO A-B
ISO B-C ISO C-D ISO D-E
25 km/h
30 km/h
40 km/h
50 km/h
60 km/h
80 km/h
100 km/h
120 km/h
8877
8610
6567
6525
5914
5351
4222
3062
8011
8491
6151
6322
5954
5341
3792
112
8444
9920
8198
7956
6755
6290
-
8852
9905
8519
7905
8125
6771
-
Table 5. The dissipated energy by all the dampers, corresponding to total car weight
ISO A-B
ISO B-C ISO C-D ISO D-E
25 km/h
13930
12380
14760
13650
30 km/h
13000
12730
15000
15490
40 km/h
9328
9577
12240
12880
50 km/h
9363
9482
11960
11670
60 km/h
8502
8339
9830
11300
80 km/h
7592
7323
8855
9553
100 km/h
5735
5449
120 km/h
4297
For a qualitative representation of dissipated energy by the dampers, in relation to the energy consumed
by the car in order to cover the distance of 1 km, it is considered the car has tires rolling resistance
coefficient f = 0.008, the drag coefficient cx = 0.28 and the frontal area Ax = 2 m2. The resistances who
acts on the car are: rolling resistance and aerodynamic drag. The results are presented in the figure 7
and figure 8.
Figure 7. Percentage of energy dissipated by the dampers, in relation to the energy consumed
by the engine car with unladed weight, to cover the distance of 1 km
Figure 8. Percentage of energy dissipated by the dampers, in relation to the energy consumed
by the engine car with total weight, to cover the distance of 1 km
113
8. CONCLUSIONS
The simulation of system suspension shows a relation between the energy dissipated by the damping
car and vehicle and road profile properties. Among the properties of the car, it results that the mass of
the car (m), the suspension spring (ks) and the suspension damping (cs) are the elements that influence
the dissipated energy. An increase of mass vehicle and damping coefficient, corroborated with a
decrease of spring rate, will produce a higher energy dissipation for the dampers. The road profile
subcomponent who have the biggest influence on the suspension excitation is the microstructure. The
macrostructure has an important role only if the road profile speeds is below 60 km/h. Thus, a car
loaded, with elastic suspension and stiff dampers, will require to dissipate more energy through the
dampers. However, macrostructure profiles of road categories with maximum speeds between 25 km/h 60 km/h and microstructures profiles of road categories ISO C-D and ISO D-E contributes to increased
suspension load.
ACKNOWLEDGEMENT
This work was partially supported by the strategic grant POSDRU/187/1.5/S/155420 of the Ministry of
National Education, Romania, co-financed by the European Social Fund – Investing in People, within the
Sectorial Operational Programme Human Resources Development 2007-2013.
REFERENCES
[1] M. Agostinacchio & D. Ciampa & S. Olita, The vibrations induced by surface irregularities
in road pavements – a Matlab® approach, Eur. Transp. Res. Rev. (2014), pg. 271: 267 – 275
[2] Technical Specification 27/01/1998 for the design, construction and modernization of roads.
Published in 06/04/1998, no. 138bis. Entered into force on 06.04.1998. pg. 3: 1 – 7
[3] http://www.miata.net/faq/tire_weights.html
[4] http://www.miata.net/faq/wheel_weights.html#spec
[5] http://www.ultimatesubaru.org/forum/topic/106807-improved-shock-absorbers-and-spring-coils-onloyales
[6] Untaru, Marin; Fratila, Gheorghe; Potincu, Gheorghe; Seitz, Nicolae; Peres, Gheorghe; Tabacu, Ion,
Macarie, Tiberiu, Calculul si constructia automobilelor. Editura Didactica si Pedagogiga. Bucuresti. 1982.
pg. 579: 1 – 625.
[7] Lotus Talk, Theory of Pneumatic Tires, part 5, pg. 78: 75 – 90
[8] Lotus Talk, Theory of Pneumatic Tires, part 5, pg. 80: 75 – 90
[9] D. R. Unaune, M. J. Pawar, Dr. S. S. Mohite, Ride Analysis of Quarter Vehicle Model,
International Conference on Modern Trends in Industrial Engineering, November 17-19, 2011
114
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Transcription

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