RoJAE Romanian Journal of Automotive Engineering

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

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


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 universities and research institutes and economic environment is an important part of SIAR’s. In this direction, 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.

Honorary Committee of SIAR

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

The Society of Automotive Engineers of Romania 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 University „Politehnica” of Bucharest, Romania Nicolae BURNETE Technical University of Cluj-Napoca, Romania Anghel CHIRU „Transilvania” University of Brasov, Romania Victor OTAT University of Craiova, Romania Ion TABACU University of Pitesti, Romania General Secretary Minu MITREA Military Technical Academy of Bucharest, Romania

RoJAE


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, GIF - Institute for Information Resources, MIAR - Information Matrix for the Analysis of Journals - Barcelona University, Georgetown University Library, SJIF - Scientific Journal Impact Factor - Innovative Space of Scientific Research, DRJI - Directory of Research Journal Indexing - Solapur University, Platforma Editorială Română SCIPIO – UEFISCU, International Society of Universal Research in Sciences, Pak Academic Search, Index Copernicus International RoJAE 21(3) 75 – 116 (2015) ISSN 2457 – 5275 (Online, English) ISSN 1842 – 4074 (Print, Online, Romanian)

RoJAE

Romanian Journal of Automotive Engineering Editor in Chief Cornel STAN West Saxon University of Zwickau, Germany E-mail: [email protected] Executive Editor Nicolae ISPAS „Transilvania” University of Brasov, Romania E-mail: [email protected] Deputy Executive Editor Radu CHIRIAC University „Politehnica” of Bucharest, Romania E-mail: [email protected] Ion COPAE Military Technical Academy of Bucharest, Romania E-mail: [email protected] Stefan TABACU University of Pitesti, Romania E-mail: [email protected] Editors Ilie DUMITRU University of Craiova, Romania E-mail: [email protected] Marin Stelian MARINESCU Military Technical Academy of Bucharest, Romania E-mail: [email protected] Adrian SACHELARIE „Gheorghe Asachi” Technical University of Iasi, Romania E-mail: [email protected] Marius BATAUS University „Politehnica” of Bucharest, Romania E-mail: [email protected] Cristian COLDEA Technical University of Cluj-Napoca, Romania E-mail: [email protected] George DRAGOMIR University of Oradea, Romania E-mail: [email protected]

Advisory Editorial Board Dennis ASSANIS

University of Michigan, USA Rodica A. BARANESCU

Chicago College of Engineering, USA Nicolae BURNETE

Technical University of Cluj-Napoca, Romania 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

University „Politehnica” of Bucharest, Romania Nicolae V. ORLANDEA

University of Michigan, USA Victor OTAT

University of Craiova, Romania 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

The Journal of the Society of Automotive Engineers of Romania www.ro-jae.ro www.siar.ro Copyright © SIAR Production office: The Society of Automotive Engineers of Romania (Societatea Inginerilor de Automobile din România) Universitatea „Politehnica” din Bucuresti, Facultatea de Transporturi, Splaiul Independentei Nr. 313 060042 Bucharest ROMANIA Tel.: +4.021.316.96.08 Fax: +4.021.316.96.08 E-mail: [email protected] Staff: Prof. Minu MITREA, General Secretary of SIAR Subscriptions: Published quarterly. Individual subscription should be ordered to the Production office. Annual subscription rate can be found at SIAR website http://www.siar.ro. The members of the Society of Automotive Engineers of Romania receive free a printed copy of the journal (in Romanian).

RoJAE vol. 21 no. 3 / September 2015 ISSN 2457 – 5275 (Online, English) Romanian Journal of Automotive Engineering ISSN 1842 – 4074 (Print, Online, Romanian)

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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 [ ]0.6; 0.8ϕ = if the travelling speed is less

than 48 km/h and [ ]0.55; 0.7ϕ = if the automotive speed is bigger than 48 km/h [3][14]. For the first

case, it can be written:

( )0.1 14.29

0.7 0.1 0.7 1 0.7 1 0.1429 0.7 10.7 100

ϕ = ± = ± = ± = ±

(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]


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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 [ ]0.6; 0.8ϕ = . 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


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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]:

[ ] [ ]0.1; 0.3 ; 0.40; 0.55e µ= = (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.


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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%).


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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]:

2 2 2; ;

x x y y z zJ k mE J k mA J k mA= = = (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.

[ ] [ ] [ ]0.1475; 0.1725 ; 0.2; 0.227 ; 0.2225; 0.2475x y zk k k= = = (4)


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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 g ha k A h k 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:

[ ] [ ]0.37; 0.48 ; 0.37; 0.39a hk k= = (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 1ϕ and 2ϕ 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.


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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:

[ ] [ ]1 2

40.5; 33.3 km/h; 48.8; 57.3 km/hV V= = (7)

but also yaw angular velocities from end of impact:

[ ] [ ]1 2

3.7; 4.2 rad/s; 1.9; 2.4 rad/sz z

Ω = Ω = − − (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.


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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.


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89

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]


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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].

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.

Fig. 2. Greenhouse gas emissions of transportation fuels [19]

Fig. 1. Ethanol production/consumption balance 2013 [23]


91

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]


92

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

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

Fuel Property

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 ] (determined from stoichiometric mixture at 9 MPa and 626 °C) [32]

2465 2401


93

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:

Table 2 Identified additives for Diesel-ethanol stability

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

Additive Observations

Tetrahydrofuran (THF) Obtained from agricultural waste material Ethyl acetate Can be obtained from ethanol PEC additive Pure Energy Corporation of New York AAE additive AAE Technologies of the United Kingdom GE Betz additive Division of General Electrics Biodiesel Increases the percentage of biofuel in the blend


94

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].


95

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 so-called 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. 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:

Table 3 GHS classification of Diesel and ethanol

Classification Diesel [57] Ethanol [58] GHS Classification

Flammable liquids, Category 3. Aspiration hazard, Category 1. Acute toxicity, Category 4, Inhalation. Skin corrosion/irritation, Category 2. Carcinogenicity, Category 2. 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.

Flammable liquids, Category 2 Serious eye damage/eye irritation, Category 2A.

Hazard Statement

PHYSICAL HAZARDS:

H226: Flammable liquid and vapor.

H225: Highly flammable liquid and vapor.

HEALTH HAZARDS:

H304: May be fatal if swallowed and enters airways. H315: Causes skin irritation. H332: Harmful if inhaled. H351: Suspected of causing cancer. H373: May cause damage to organs or organ systems through prolonged or repeated exposure.

H319: Causes serious eye irritation.

ENVIRONMENTAL HAZARDS:

H411: Toxic to aquatic life with long lasting effects. H401: Toxic to aquatic life.

Not classified as an environmental hazard under GHS criteria.

Fig. 4. CFPP, CP and PP of different Diesel-ethanol blends (after [11])


96

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 property, which shows the evaporation 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,

Fig. 5. Distillation curves of Diesel and different Diesel-ethanol blends (after [11])


97

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 EtOH (%)

Additive (%)

Ref. fuel Load Pollutant emissions Performances

Low ↑: CO; HC ↓: Smoke (E10, E20)

Gnanamoorthi &

Devaradjane (2015)

[61]

10 20 30 40

Ethyl acetate (1)

Diethyl carbonate (1)

Diesel

High ↑: HC ↓: CO (E10, E20); Smoke

↑: BTE (for all CRs except E30, E40) ↓: -

Low ↑: - ↓: -

↑: P (for E5, E10); ↓: BSFC

Murcak et al. (2013)

[62]

5 10 20

n/a (1.5)

Diesel

High ↑: - ↓: -

↑: - ↓: P (for E5; E20)

Low ↑: THC ↓: Soot; NOx; CO

Rakopoulos et al.

(2007) [63]

5 10 15

GE Betz additive (1.5)

Diesel

High ↑: THC ↓: Soot; NOx; CO

↑: BSFC; BTE ↓: -

Low ↑: CO; HC ↓: NOx (except E10); Smoke

↑: BSFC ↓: BTE

Huang et al. (2009)

[64]

10 20 25 30

n-Butanol (5)

Diesel

High ↑: HC; NOx (except E10, E20) ↓: CO; Smoke

↑: BTE (except E10); BSFC ↓: -

Low ↑: NOx (5E15DGE, 10E10DGE) ↓: THC; Soot; CO

Herreros et al. (2015)

[65] 5 10 15

BD (5) + DGE (15) (10) (5)

B5

High ↑: NOx (5E15DGE, 10E10DGE) ↓: THC; Soot; CO

↑: ITE ↓: -

Low Park et al. (2012)

[66]

20 20 20

BD (5)

(10) (15)

Diesel High

↑: HC ↓: NOx

↑: ISFC ↓: IMEP

Low ↑: HC; CO ↓: NOx

Caro et al. (2001)

[67] 10 20

1-octylamino-3-octyloxy-2-propanol (1)

N-octyl nitramine (1)

Diesel

High ↑: HC ↓: NOx; CO; Smoke

↑: - ↓: P

Low Rakopoulos et al.

(2008) [68]

5 10 15


Diesel High

↑: - ↓: NO; Soot

↑: - ↓: -

Low Sayin C. (2010)

[69]

5 10

Dodecanol (1)

Diesel High

↑: NOx ↓: CO; THC; Smoke

↑: BSFC ↓: BTE

Low Ballesteros et al.

(2015) [70]

10 - Diesel High

↑: THC; PM TCC, PAH vary with the operating parameters

↑: - ↓: -

Rakopoulos et 5 GE Betz additive Diesel Low ↑: - No cyclic variations.


98

al. (2008)

[71]

10 15

(1.5) High

↓: -

Low De Menezes

et al. (2006)

[72]

2.5 5 10 2.5 5 10

ETBE (2.5) (5)

(10) TAEE (2.5)

(5) (10)

Diesel

High

↑: - ↓: -

↑: BSFC ↓: Specific work

Low ↑: - ↓: -

Karabektas et al.

(2013) [73]

15 - Diesel

High ↑: HC ↓: CO; NOx

↑: BSFC ↓: P; BTE

Low ↑: HC ↓: Smoke; NOx; CO

Rakopoulos et al.

(2014) [74]

5 10 15


Diesel

High ↑: HC ↓: Smoke; NOx; CO


Low -: NOx

↓: Smoke Ren et al.

(2008) [45]

5 10 15 20

Isoamyl nitrate (0.2)

Diesel

High ↑: - ↓: NOX; Smoke


Low ↑: - ↓: -

↑: Ignition delay ↓: -

Li et al. (2008)

[46]

5 10 15 20 5 10 15 20

Isoamyl nitrate (0.2) (0.2) (0.2) (0.2)

Diesel

High

↑: - ↓: -

↑: - ↓: Ignition delay

B5 BD CR DGE E# ETBE IMEP P PAH ISFC ITE TAEE TCC

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

Reference EtOH (%)

Additive (%)

Ref. fuel Load Pollutant emissions Performances

Low ↑: CO; THC ↓: NOx; Smoke

Lei et al. (2012)

[75]

5 10 15

CLZ (0.8) (0.8) (1) Diesel

High -: NOx ↓: CO; THC; Smoke

↑: BTE ↓: -

Low ↑: CO; THC; NOX ↓: Smoke; PM

Kim & Choi (2015)

[42]

15 15

THF (2)

THF (2) + 2EHN ULSD

High ↑: NOx; THC ↓: Smoke; PM - : CO

↑: BSFC -: BTE


99

Low ↑: THC ↓: NOx; CO; Soot

Rakopoulos et al.

(2008) [76]

5 10 15


LSD

High ↑: THC ↓: NOx; CO; Soot

↑: BSFC, BTE ↓: -

Low ↑: CO; THC ↓: NOx

Park et al. (2011)

[77]

10 20

BD (10)

ULSD

High ↑: CO ↓: NOx; TUHC

↑: - ↓: -

Low Song et al. (2007)

[78]

5 10 15 20

-

Diesel High

↑: THC; CO ↓: Smoke; PAH (only E5) -: NOx; PM

↑: - ↓: -

Low ↑: HC ↓: Smoke; PM -: NOx

Lapuerta et al. (2008)

[32]

10 -

LSD

High ↑: NOx; PM ↓: Smoke


Low ↑: - ↓: Smoke (except E6); PM

Di et al. (2009)

[79]

6.1 12.2 18.2 24.2

1-dodecanol (1) (1) (1)

(1.5)

ULSD

High ↑: - ↓: Smoke; PM

↑: BTE (except E6) ↓: -

Low ↑: - ↓: Smoke; PM

Di et al. (2009)

[80]

6.1 12.2 18.2 24.2

1-dodecanol (1) (1) (1)

(1.5)

ULSD

High ↑: - ↓: Smoke; PM


Low Hamdan & Khalil (2010)

[81]

5 10 15

-

Diesel High

↑: - ↓: -

↑: BSFC; BTE ↓: Torque, P

Low ↑: CO ↓: Smoke; NO

Hulwan & Joshi (2011)

[82]

20 30 40

BD (10)

Diesel

High

↑: NO (for retarded injection timing); CO; CO2

↓: NO (for advanced injection timing and E30, E40); CO -: CO2

↑: BSFC ↓: BTE

Low ↑: HC; CO ↓: NOx; Smoke

Guido et al. (2013)

[83]

20 RME (10)

B10

High ↑: HC ↓: NOx; Smoke; CO

↑: BSFC ↓: FCE (values obtained for a specified IMEP)

Cold start

↑: NOx; THC; CO; Smoke ↓: -

Armas et al. (2012)

[84]

10 -

LSD Warm start

↑: - ↓: NOx; THC; CO; Smoke

↑: - ↓: -

ECE R49

↑: NOx; CO ↓: Smoke; PN

Kim et al. (2010)

[43]

15 15

-

2EHN (7500 ppm)

ULSD

ESC ↑: NOx

↓: CO; PN

↑: BSFC ↓: -

Low ↑: CO; HC ↓: NOx; Soot (for E20)

Park et al. (2010)

[85]

10 20

BD (10)

ULSD

High ↑: - ↓: -

↑: - ↓: -

Putrasaria et 2.5 Sorbitan methyl Diesel Low

↑: - ↑: P; BSFC; BTE


100

↓: CO, HC (except E10) al. (2013)

[86]

5 7.5 10

ester (1) (SOLAR)

High ↑: - ↓: Smoke (except E2.5)

↓: -

Low

↑: CO; Acetaldehyde ↓: NOx; CO2; Smoke; THC (except E10, E30 no ignition improver)

He et al. (2003)

[44]

10 30 10 30

Additive (2)

Additive (2) + Isooctyl nitrate

(0.1) (1)

Diesel

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 and E15, EB5) ↓: Smoke

Labeckas et al.

(2014) [87]

5 10 15 15

-

BD (5) LSD

High ↑: HC (except E15; EB5) ↓: NOx; CO; Smoke


Low ↑: CO; HC ↓: NOx; Smoke

Xing-Cai et al. (2004)

[88]

15 (0) CN improver

(0.2) CN improver

(0.4)

Diesel

High ↑: - ↓: NOx; CO; HC (for 0.4 CN improver); Smoke


Low ↑: CO; HC ↓: NOx; PM

Beatrice et al. (2014)

[89]

20 RME (10)

B10

High ↑: - ↓: NOx; PM; CO; HC

↑: BSFC ↓: -

Low ↑: CO; NO2; THC ↓: NOx; PM

Cheung et al. (2008)

[90]

6.1 12.2 18.2 24.2

1-dodecanol (1) (1) (1)

(1.5)

ULSD

High ↑: NO2; NOx ↓: CO; THC; PM

↑: - ↓: -

Low

↑: CO; NO2; THC; Formaldehyde (for E6, E12); Acetaldehyde ↓: NOx; NO

Di et al. (2009)

[91]

6.1 12.2 18.2 24.2

1-dodecanol (1) (1) (1)

(1.5) ULSD

High

↑: NOx; NO2; NO; CO; HC; Acetaldehyde; C4H6 (for E6, E24) ↓: Formaldehyde; C4H6

↑: BTE ↓: -

Low ↑: Sulfate ↓: HC; PM; Smoke; SOF; Soot

Chen et al. (2007)

[92]

10 30 10 20 30

-

Ester (5) (10) (10)

Diesel

High ↑: Sulfate; SOF ↓: HC; PM; Smoke; Soot

↑: - ↓: -

Low ↑: CO; HC; SOF ↓: NOx; Soot

Chen et al. (2009)

[93]

10 15

Solvent (1)

Diesel

High ↑: NOx

↓: CO; Soot -: HC

↑: BSFC ↓: P

B10 C4H6

CLZ CN

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


101

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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107

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]


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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]

α [ ° ] Rconvex [m] Rconcav [m]

25 8 500 300

30 7,5 800 500

40 7 1000 1000

50 7 1300 1000

60 6,5 1600 1500

80 6 4500 2200

100 5 10000 3000

120 5 18000 6500


109

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]

H [m] h [m] D [m] d [m]

25 1.6 0.9 80 48

30 2.2 1.4 120 75

40 2.4 2.4 140 140

50 3.1 2.4 181 140

60 3.3 3.2 207 196

80 8.1 3.9 538 224

100 7.1 2.1 748 263

120 12.2 4.5 1330 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.


110

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];


111

- 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].

0)()( 11111111 =−−−−SSSSxxkxxcxm &&&&

(1.a)


112

0)()()()( 11111111111111 =−−−−−+−+rStrStSSSSSSxxkxxcxxkxxcxm &&&&&&

(1.b)

0)()( 22222222 =−−−−SSSSxxkxxcxm &&&&

(2.a)

0)()()()( 22222222222222 =−−−−−+−+rStrStSSSSSSxxkxxcxxkxxcxm &&&&&&

(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


25 km/h 8877 8011 8444 8852

30 km/h 8610 8491 9920 9905

40 km/h 6567 6151 8198 8519

50 km/h 6525 6322 7956 7905

60 km/h 5914 5954 6755 8125

80 km/h 5351 5341 6290 6771

100 km/h 4222 3792 - -

120 km/h 3062 - - -


113

Table 5. The dissipated energy by all the dampers, corresponding to total car weight


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


114

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-on-loyales [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

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AIMS AND SCOPE The Romanian Journal of Automotive Engineering has as its main objective the publication and dissemination of original research in all fields of „Automotive Technology, Science and Engineering”. It fosters thus the exchange of ideas among researchers in different parts of the world and also among researchers who emphasize different aspects regarding the basis and applications of the field. Standing as it does at the cross-roads of Physics, Chemistry, Mechanics, Engineering Design and Materials Sciences, automotive engineering is experiencing considerable growth as a result of recent technological advances. The Romanian Journal of Automotive Engineering, by providing an international medium of communication, is encouraging this growth and is encompassing all aspects of the field from thermal engineering, flow analysis, structural analysis, modal analysis, control, vehicular electronics, mechatronics, electro-mechanical engineering, optimum design methods, ITS, and recycling. Interest extends from the basic science to technology applications with analytical, experimental and numerical studies. The emphasis is placed on contribution that appears to be of permanent interest to research workers and engineers in the field. If furthering knowledge in the area of principal concern of the Journal, papers of primary interest to the innovative disciplines of „Automotive Technology, Science and Engineering” may be published. No length limitations for contributions are set, but only concisely written papers are published. Brief articles are considered on the basis of technical merit. Discussions of previously published papers are welcome. Notes for contributors Authors should submit an electronic file of their contribution to the Production office: www. siar.ro. All the papers will be

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Societatea Inginerilor de Automobile din România Society of Automotive Engineers of Romania

www.siar.ro www.ro-jae.ro

ISSN 2457 – 5275 (Online, English) ISSN 1842 – 4074 (Print, Online, Romanian)

The Scientific Journal of SIAR A Short History

The engineering of vehicles represents the engine of the global development of the economy. SIAR tracks the progress of the automotive engineering in Romania by: the development of automotive engineering, the development of technologies, and road transport services; supporting the work of the haulers, supporting the technical inspection and of the garage; encouraging young people to have a career in the automotive engineering and road haulage; stimulation and coordination of activities that promote an environment that is suitable for continuous education and improving of knowledge of the engineers; active exchange of ideas and experience, in particular for students, master students, PhD students, and young engineers, and dissemination of knowledge in the field of automotive engineering; cooperation with other technical and scientific organizations, employers’ and socio-professional associations through organization of joint actions, of mutual interest. By the accession to FISITA (International Federation of Automotive Engineering Societies) since its establishment, SIAR has been involved in achieving an overall professional community that is homogeneous in competence and performance, interactive, dynamic, and competitive at the same time, oriented towards a balanced and friendly relationship between people and the environment; this action will be constituted as a challenge worthy of effort and recognition. The insurance of a favorable framework for the initiation and the development of cooperation of the specialists in this field of activity allows for an efficient and easy exchange of information, specific knowledge and experience; it supports the cooperation between universities and between research centers and industry; it speeds up the process of implementing the new technologies, it simplifies the identification of training and specialization needs of the personnel involved in the engineering of motor vehicles, transport, and road safety. In order to succeed, ever since its founding, SIAR has considered that the stress should be put on the production and distribution, at national and international level, of a publication of scientific quality. Under these circumstances, the development of the scientific magazine of SIAR had the following evolution: 1. RIA – Revista inginerilor de automobile (in English: Journal of Automotive Engineers) ISSN 1222 – 5142 Period of publication: 1990 – 2000 Format: print, Romanian

Frequency: Quarterly Electronic publication on: www.ro-jae.ro

Total number of issues: 30 Type: Open Access

The above constitutes series nr. 1 of SIAR scientific magazine.

2. Ingineria automobilului (in English: Automotive Engineering) ISSN 1842 – 4074

Period of publication: as of 2006 Format: print and online, Romanian

Frequency: Quarterly Electronic publication on: www.ingineria-automobilului.ro

Total number of issues: 37

(including the December 2014 issue)

Type: Open Access

The above constitutes series nr. 2 of SIAR (Romanian version).

3. Ingineria automobilului (in English: Automotive Engineering) ISSN 2284 – 5690

Period of publication: 2011 – 2014 Format: online, English

Frequency: Quarterly Electronic publication on: www.ingineria-automobilului.ro

Total number of issues: 16

(including the December 2014 issue)

Type: Open Access

The above constitutes series nr. 3 of SIAR (English version).

4. Romanian Journal of Automotive Engineering ISSN 2457 – 5275

Period of publication: from 2015 Format: online, English

Frequency: Quarterly Electronic publication on: www.ro-jae.ro

Total number of issues: 3 (September 2015) Type: Open Access

The above constitutes series nr. 4 of SIAR (English version).

Summary – on September 30, 2015 Total of series: 4 Total years of publication: 21 (11=1990 – 2000; 10=2006-2015) Publication frequency: Quarterly Total issues published: 66 (Romanian), out of which, the last 19 were also published in English

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