Authors:
Imane Echjijem Energetic Laboratory, Sciences Faculty, Abdelmalek Essaadi University, 93030 Tetouan, Morocco

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Abdelouahed Djebli Energetic Laboratory, Sciences Faculty, Abdelmalek Essaadi University, 93030 Tetouan, Morocco

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Abstract

The optimized chord and twist angle of the preliminary blade design through Blade Element Momentum theory are non-linear distributions, which adds to the complexity of blade manufacture and does not always guarantee the best aerodynamic performance. In this paper, the effect of the linearization on aerodynamic performance using Prandtl-Glauert correction model was investigated through four cases: case 1 and case 2 and case 3, where the chord and the twist angle are linearized and case 4, where sole chord is linearized. The effect of the linearization using Shen correction model while making a comparison to the linearization using Prandtl-Glauert correction model was also studied. The simulation is conducted for S809 wind turbine blade profile. The results show that case 4 using Shen correction model represents the best technique of linearization in terms of higher aerodynamic performance and easy manufacturing process.

Abstract

The optimized chord and twist angle of the preliminary blade design through Blade Element Momentum theory are non-linear distributions, which adds to the complexity of blade manufacture and does not always guarantee the best aerodynamic performance. In this paper, the effect of the linearization on aerodynamic performance using Prandtl-Glauert correction model was investigated through four cases: case 1 and case 2 and case 3, where the chord and the twist angle are linearized and case 4, where sole chord is linearized. The effect of the linearization using Shen correction model while making a comparison to the linearization using Prandtl-Glauert correction model was also studied. The simulation is conducted for S809 wind turbine blade profile. The results show that case 4 using Shen correction model represents the best technique of linearization in terms of higher aerodynamic performance and easy manufacturing process.

1 Introduction

The energy issue is at the heart of international concerns. The global economy is indeed facing a major challenge: meeting the growing need for energy while reducing greenhouse gas emissions. The use of renewable energies remains the most effective solution to this challenge. Great hope is placed in wind energy, which represents one of the world's most responsive renewable energy resources. Most wind turbines aim to capture the maximum energy from the wind. The blade is the main component that converts the kinetic energy of the wind into mechanical energy, so the design of this component has a big impact on the energy efficiency of a wind turbine. The design of a wind turbine blade is primarily based on aerodynamic modeling. In order to maximize power output and minimize costs, many researchers are trying to find methods to optimize blade design [1].

Benini and Toffolo [2] presented a bi-objective optimization method to maximize the annual energy production and minimize the energy cost for the design of a horizontal axis wind turbine. A multi-objective evolutionary algorithm and an aerodynamic model based on blade element theory were used to achieve this goal. Kale and Varma [3] have identified two main objectives to optimize the design of a wind turbine blade which are the improvement of power performance and system start-up. They showed an increase in power coefficient and a reduction in starting speed. Bottasso et al. [4] have treated an aero structural approach to optimize the design of a wind turbine blade. This approach is based on giving the profiles an appropriate position along the length of the blade according to their structural or aerodynamic role. Close to the hub zone are placed airfoils that respond to structural considerations, while in the tip zone are placed airfoils that respond to aerodynamic considerations. Mohammadi et al. [5] presented an optimization process where the goal of the work is to maximize the output torque. This optimization is firstly performed with two variables: profile type and angle of attack; and secondly with three variables by adding the chord variable to the previous variables. They have shown that the output torque has increased very significantly when the three-variable optimization is used. Wang et al. [6] optimized the blade design of a Fixed Pitch Variable Speed wind turbine while taking into account the Reynolds number effect. The objective of this approach is to improve the energy performance of the wind turbine through optimization of the chord and twist angle for each element of the blade. Thumthae [7] sought to find optimal chord and twist angle distributions and adequate rotor speed variation to maximize the energy efficiency of a variable speed horizontal axis wind turbine.

Blade Element Momentum (BEM) theory is one of the most widely used methods in wind turbine aerodynamics as it provides an acceptable and efficient approach to wind turbine blade design and analysis [8]. However, BEM theory has failed to match the experience. To improve this theory, several studies have made corrections to this model which mainly concern the correction of tip loss. Certainly, a wind turbine with a finite number of blades is different from a wind turbine with an infinite number of blades. To account for this difference, Prandtl introduced the phenomenon of tip loss. Based on the simple one-dimensional theory that allows the prediction of the wind turbine performance, Glauert was able to develop the BEM theory. To obtain results consistent with reality, Glauert added Prandtl's tip loss correction to the calculations of the BEM theory. According to Glauert, only the induced velocities were affected by tip loss. According to de Vries, the correction includes the induced velocities as well as the mass flow. Shen et al. showed that existing tip loss corrections are not coherent and do not predict a correct physical behavior near the tip. To give better predictions of the load in the tip region, a new model for tip loss correction has been developed by Shen et al. [9, 10]. Besides, the non-linearization of the chord and twist angle of the BEM theory poses problems in the manufacturing of the wind turbine blade. Several researchers have worked on the linearization of the chord and twist angle to overcome the difficulty of manufacturing and to reduce the cost of manufacturing wind turbine blades. Manwell [11] used two constants to linearize the expression of the chord and one constant to linearize the expression of the twist angle. Maalawi et al. [12] estimated a linear distribution of the chord and an exponential distribution of the twist angle. The linearized chord is presented by a line tangential to the theoretical distribution of the chord at the blade position of 0.75 R . Tony Burton [1] linearized the chord by drawing a straight line through the 0.75 R and 0.9 R positions of the theoretical chord distribution. Liu et al. [13] presented an approach to optimize the design of a fixed-pitch fixed-speed horizontal axis wind turbine blade with the objective of maximizing annual energy production and minimizing manufacturing cost. This approach is based on the linearization of the chord and twist angle distributions by both setting the chord and twist angle values at the tip of the blade and varying the values at the root of the blade. An appropriate solution is one that meets their predefined objective. Tahani et al. [14] proposed a new method of linearizing the chord and the twist angle to obtain a maximum power coefficient. Linearization consists in drawing several tangent lines at different points of the theoretical distributions of the chord and the twist angle and then selecting the line that gives a maximum power coefficient. Yang et al. [15] examined the effects of the design parameters used in linearization on the aerodynamic performance of the wind turbine blade. They also developed an optimization algorithm for linearization and an objective function that applies multiple tip speed ratios for optimizing the aerodynamic efficiency.

The BEM theory gives non-linear distributions of the optimized chord and twist angle. The non-linearization of these distributions poses problems in the manufacturing process of a wind turbine blade and does not always guarantee the best aerodynamic performance. In this paper, the effect of the linearization on aerodynamic performance using Prandtl-Glauert correction model was investigated through four cases. The effect of the linearization on the aerodynamic performance using Shen correction model was also studied. The results have determined the best technique of linearization in terms of higher aerodynamic performance and easy manufacturing process.

2 Materials and methods

2.1 Aerodynamic design

The aerodynamic design of a wind turbine is based on the BEM theory, which is the composition of the momentum theory and the blade element theory.

The blade element theory consists in dividing the blade into a sufficient number of elements and using the geometrical parameters of the section of the blade under study to calculate the forces acting on each of these elements [16, 17] (see Fig. 1). Therefore, the thrust d T and the torque d M given by blade element theory are as follow [18, 19]:
d T = 1 2 ρ N V r e l 2 c ( r ) C n d r
d M = 1 2 ρ N V r e l 2 r c ( r ) C t d r
where ρ is the air density, N is the number of the blade, c ( r ) is the chord length C n = C L cos φ + C D sin φ and C t = C L sin φ C D cos φ are the normal and tangential loads coefficients respectively, C L and C D are the lift and drag coefficients respectively. The relative wind speed V r e l and the inflow angle φ are defined as:
V r e l = V 0 ( 1 a ) 2 + λ r 2 ( 1 + a ) 2
tan φ = ( 1 a ) λ r ( 1 + a )
where V 0 is the wind velocity, λ r = Ω r / V 0 = r λ / R is the local speed ratio, Ω is the angular velocity of rotor, r is the local radius, λ is the tip speed ratio, R is the radius of rotor, a and a are the axial and the tangential induction factors respectively.
Fig. 1.
Fig. 1.

Velocities and forces on a blade element

Citation: International Review of Applied Sciences and Engineering 2022; 10.1556/1848.2022.00439

In the momentum theory, the application of axial momentum conservation and angular momentum conservation gives the thrust, torque and power coefficient expressions as follows (see Fig. 2):
d T = 4 a ( 1 a ) ρ V 0 2 π r d r
d M = 4 a ( 1 a ) ρ V 0 Ω π r 3 d r
C P = 8 λ 2 λ h u b λ a ( 1 a ) λ r 3 d λ r
and the axial and tangential induction factors are given by:
a = 1 4 sin φ 2 σ C n + 1
a = 1 4 sin φ cos φ σ C t 1
where σ = B c ( r ) / 2 π r is the solidity of rotor.
Fig. 2.
Fig. 2.

One element of a multiple stream tube

Citation: International Review of Applied Sciences and Engineering 2022; 10.1556/1848.2022.00439

However, the BEM theory is based on a rotor composed of an infinite number of blades. In fact, the rotor has a finite number of blades and the passage of air from the high pressure region to the low pressure region through the blade tips generates losses known as ‘tip losses’. d T and d M are reduced due to this phenomenon which can be quantified by Prandtl's correction factor F [11]:
F = 2 π arccos [ exp ( N ( R r ) 2 r sin φ ) ]
After considering tip loss factor F , the correction of d T and d M , a and a are respectively:
d T = 4 F a ( 1 a ) ρ V 0 2 π r d r
d M = 4 F a ( 1 a ) ρ V 0 Ω π r 3 d r
a = 1 4 F sin φ 2 σ C n + 1
a = 1 4 F sin φ cos φ σ C t 1
Furthermore, the experimental measurements revealed that when the axial induction factor is greater than 0.5 , the BEM theory is no longer valid. Therefore, several empirical models were developed [17, 20, 21] and among them we found the Spera model; For a c = 0.2 , the axial induction factor can be expressed as:
a = { 1 K + 1 f o r a a c 1 2 2 + K 1 2 a c ( K ( 1 2 a c ) + 2 ) 2 + 4 K a c 2 1 f o r a > a c
where K = 4 F sin φ 2 / σ C n .
For a real rotor, the axial velocity at the tip is not zero. Thus the angle of attack is non-zero. Using the 2D profile data gives a non-zero force near the tip, because the angle of attack is finite, this situation presents a contradiction with the correct physical behavior, where the force should tend to the zero at the tip due to pressure equalization. Therefore, it is obvious that a correction of the profile data near the tip is necessary. Shen et al. proposed to correct the lift coefficients by introducing the correction factor, F 1 as follows [9, 22]: C n r = F 1 C n , C t r = F 1 C t
F 1 = 2 π arccos [ exp ( g N ( R r ) 2 r sin φ ) ]
g = exp [ 0.125 ( N λ 21 ) ] + 0.1
from the momentum theory; the thrust and the torque are given by:
d T = 4 a F ( 1 a F ) ρ V 0 2 π r d r
d M = 4 a F ( 1 a F ) ρ V 0 Ω π r 3 d r
and from the blade element theory they are given by:
d T = 1 2 N ρ c ( r ) V 0 2 ( 1 a ) 2 sin 2 φ C n r d r
d M = 1 2 N ρ c ( r ) V 0 ( 1 a ) ( 1 + a ) sin φ cos φ Ω r C t r r d r
then the Shen correction for induction factors:
a = 2 + Y 1 4 Y 1 ( 1 F ) + Y 1 2 2 ( 1 + F Y 1 )
a = 1 ( 1 a F ) Y 2 1 a 1
where Y 1 = 4 F sin φ 2 / σ C n r and Y 2 = 4 F sin φ cos φ / σ C t r .
And when a becomes greater than a critical value a c = 1 / 3 , the axial induction factor can be expressed as:
a = { 1 K + 1 f o r a a c 2 + Y 1 4 Y 1 1 F + Y 1 2 2 1 + F Y 1 f o r a > a c
where K = 4 F sin φ 2 / σ C n .

2.2 Preliminary blade design

In this paper, the optimum rotor theory [13], which takes into account the relation (25) and neglects the friction i.e. C D = 0 , is used to determine the chord and twist angle distributions of the preliminary blade design. The BEM theory using Prandtl-Glauert correction model is employed to calculate the aerodynamic performance of the blade. The selected profile is S809, which is considered one of the famous profiles for the design of new wind turbine blades. Figure 3 shows the lift and drag coefficients ( C l , C d ) of the profile as a function of the angle of attack at R e = 10 6 [23]. The design parameters are rotor radius R = 5.5 m , number of blade N = 3 , wind velocity V 0 = 7 m / s , angular velocity of rotor Ω = 71,93 rpm , optimal angle of attack α o p t = 6.16 ° , optimal lift coefficient C L , o p t = 0.851 , chord and twist angle distributions non-linear.
a = 1 3 a 4 a 1
c o p t ( r ) = 8 π r ( 1 cos φ o p t ( r ) ) B C L , o p t
θ o p t ( r ) = φ o p t ( r ) α o p t
φ o p t ( r ) = 2 3 tan 1 ( 1 λ r )
Fig. 3.
Fig. 3.

Lift and drag coefficients of S809 profile

Citation: International Review of Applied Sciences and Engineering 2022; 10.1556/1848.2022.00439

2.3 Design linearization method

The chord and twist angle of the preliminary blade design through optimum rotor theory are non-linear distributions, which adds to the complexity of blade manufacture. The use of linear chord and twist angle distributions remains the most effective solution to avoid this complexity. On the other hand, it can also increase the aerodynamic performance. Figures 4 and 5 present the chord and twist angle distributions of the linearized blades and the preliminary blade design.

Fig. 4.
Fig. 4.

Chord distribution of the linearized blades and the preliminary blade

Citation: International Review of Applied Sciences and Engineering 2022; 10.1556/1848.2022.00439

Fig. 5.
Fig. 5.

Twist angle distribution of the linearized blades and the preliminary blade

Citation: International Review of Applied Sciences and Engineering 2022; 10.1556/1848.2022.00439

In this study, the linearization of the chord and twist angle consists first of drawing two straight lines: one passes through the positions 0.6 R 1 R known as linearization  1 and the other passes through the positions 0.3 R 1 R known as linearization  2 of the preliminary blade. It can be estimated by the following equations:
c linearization  1 = [ c 1 R c 0.6 R r 1 R r 0.6 R ] ( r r 0.1 R ) + c 0.1 R
θ linearization  1 = [ θ 1 R θ 0.6 R r 1 R r 0.6 R ] ( r r 0.1 R ) + θ 0.1 R
c linearization  2 = [ c 1 R c 0.3 R r 1 R r 0.3 R ] ( r r 0.1 R ) + c 0.1 R
θ linearization  2 = [ θ 1 R θ 0.3 R r 1 R r 0.3 R ] ( r r 0.1 R ) + θ 0.1 R
where c linearization  1 and θ linearization  1 are the chord and twist angle of the linearization  1 respectively, c linearization  2 and θ linearization  2 are the chord and twist angle of the linearization  2 respectively, c 1 R , c 0.6 R , c 0.3 R and c 0.1 R are the chords at positions 1 R , 0.6 R , 0.3 R and 0.1 R of the preliminary blade respectively θ 1 R , θ 0.6 R , θ 0.3 R and θ 0.1 R are the twist angles at positions 1 R , 0.6 R , 0.3 R and 0.1 R of the preliminary blade respectively. Subsequently, from linearization  1 and linearization  2 , four cases were extracted to be studied as follows:

Case 1 which combines c linearization  1 and θ linearization  2

Case 2 which combines c linearization  2 and θ linearization  2

Case 3 which combines c linearization  1 and θ linearization  1

Case 4 which combines c linearization  1 and θ preliminary where θ preliminary is twist angle of the preliminary blade.

3 Results and discussion

3.1 Linearization using Prandtl-Glauert correction model

The comparison between the thrust curves of the preliminary blade and the linearized blades is given by Fig. 6a. The latter indicates that case 1, which combines c linearization  1 and θ linearization  2 , and case 2, which combines c linearization  2 and θ linearization  2 , have smaller thrust along the blade than that of the preliminary blade design, noting that the thrust from case 1 is smaller than that of case 2. Case 3, which combines c linearization  1 and θ linearization  1 , decreases the thrust in the range of 0.1 R 0.75 R and keeps it almost equal to the thrust of the preliminary design in the range of 0.75 R 1 R .The thrust of case 4, which combines c linearization  1 and θ preliminary , is higher at the positions of 0.6 R to 1 R , and lower at the positions of 0.1 R to 0.6 R than that of the preliminary blade design.

Fig. 6.
Fig. 6.

Thrust and torque of the linearized blades and the preliminary blade using Prandtl-Glauert correction model

Citation: International Review of Applied Sciences and Engineering 2022; 10.1556/1848.2022.00439

The curves of the torque, the power, the power coefficient of the linearized blades, and the preliminary blade are presented in Figs 6b and 7 respectively. Compared to the preliminary blade design, case 1 and case 2 and case 3, where the chord and the twist angle are linearized, decrease the torque, the power and the power coefficient along the blade. We see that the decrease of case 1 is more important than those of case 2 and case 3. Case 4, where sole chord is linearized at 0.6 R and 1 R increases the torque, the power and the power coefficient at positions from 0.6 R to 1 R , and decreases them at positions from 0.1 R to 0.6 R .

Fig. 7.
Fig. 7.

Power output and power coefficient of the linearized blades and the preliminary blade using Prandtl-Glauert correction model

Citation: International Review of Applied Sciences and Engineering 2022; 10.1556/1848.2022.00439

Thus, it appears that case 4 using Prandtl-Glauert correction model has the best aerodynamic performance among all four cases. Table 1 shows the maximum torque, the maximum power, and the maximum power coefficient of the linearized blades, and the preliminary blade using Prandtl-Glauert correction model. The results show that the single-chord linearization has a significant effect on the peak torque, power and power coefficient, which allows case 4 to increase these peaks, while the linearization of the two: chord and twist angle leads to a decrease in these peaks along the blade.

Table 1.

Maximums of torque, power and power coefficient of linearized blades, and preliminary blade for Prandtl-Glauert correction model

Maximum torque Maximum power Maximum power coefficient
Preliminary blade 71.7131 540.1787 0.0298
Linearized blades using Prandtl-Glauert correction model Case 1 44.5459 335.5413 0.0192
Case 2 49.9239 376.0513 0.0202
Case 3 65.8494 496.0101 0.0270
Case 4 74.6161 562.0450 0.0308

3.2 Linearization using Shen correction model

This part is interested in examining the effect of the linearization using Shen correction model on the aerodynamic performance while making a comparison to the linearization using Prandtl-Glauert correction model. There is not much difference in the root and middle of the blade between the linearization using Shen correction model and the linearization using the Prandtl-Glauert correction model in all cases (case 1, case 2, case 3, case 4) as shown in Figs 8 and 9a.

Fig. 8.
Fig. 8.

Thrust and torque of the linearized blades and the preliminary blade using Shen correction model

Citation: International Review of Applied Sciences and Engineering 2022; 10.1556/1848.2022.00439

Fig. 9.
Fig. 9.

Power output and power coefficient of the linearized blades and the preliminary blade using Shen correction model

Citation: International Review of Applied Sciences and Engineering 2022; 10.1556/1848.2022.00439

Close to the tip positions, the thrust and the torque and the power of Shen correction model are higher than using Prandtl-Glauert correction model in all cases, except for the thrust from case 3 and case 4, which is smaller than using Prandtl-Glauert correction model in the range 0.97 R 1 R of blade length. In all cases, Fig. 9b shows that near the root, the power coefficient using Shen correction model is almost equal to that using Prandtl-Glauert correction model and near to the tip positions, it is smaller than that of Prandtl-Glauert correction model. At the middle, it is higher than that of Prandtl-Glauert correction model.

Table 2 presents a comparison of the maximum torque, power and power coefficient values obtained using the Shen correction model and those obtained using the Prandtl-Glauert correction model. The result shows that in all cases, the peaks of torque, power and power coefficient increased significantly when using the Shen correction model, while maintaining case 4 as the best linearization case compared to the other cases studied.

Table 2.

Maximums of torque, power and power coefficient of linearized blades, and preliminary blade for two correction models

Maximum torque Maximum power Maximum power coefficient
Preliminary blade 71.7131 540.1787 0.0298
Linearized blades using Prandtl-Glauert correction model Case 1 44.5459 335.5413 0.0192
Case 2 49.9239 376.0513 0.0202
Case 3 65.8494 496.0101 0.0270
Case 4 74.6161 562.0450 0.0308
Linearized blades using Shen correction model Case 1 47.9381 361.0937 0.0199
Case 2 52.4661 395.2002 0.0222
Case 3 69.8250 525.9560 0.0326
Case 4 78.7702 593.3359 0.0376

4 Conclusion

The Blade Element Momentum theory gives non-linear distributions of the optimized chord and twist angle, which adds to the complexity of blade manufacture and does not always guarantee the best aerodynamic performance. To avoid this complexity and achieve easy manufacturing, we used linear chord and twist angle in the design of wind turbine blades. In this paper, the linearization of the chord and twist angle consists first of drawing two straight lines: one passes through the positions 0.6 R 1 R known as linearization  1 and the other passes through the positions 0.3 R 1 R known as linearization  2 of the preliminary blade. Subsequently, from linearization  1 and linearization  2 , four cases were extracted as follows:

Case 1 which combines c linearization  1 and θ linearization  2

Case 2 which combines c linearization  2 and θ linearization  2

Case 3 which combines c linearization  1 and θ linearization  1

Case 4 which combines c linearization  1 and θ preliminary where θ preliminary is twist angle of the preliminary blade.

The effect of the linearization on aerodynamic performance using Prandtl-Glauert correction model was investigated. The effect of the linearization using Shen correction model while making a comparison to the linearization using Prandtl-Glauert correction model was also studied. The results showed that case 4 using Shen correction model represents the best technique of linearization in terms of higher aerodynamic performance and easy manufacturing process.

Although the presented BEM method has advantages, it also has some limitations. In particular, we find that this method does not take into account structural considerations and cannot increase aerodynamic performance in the inner positions of the blade.

Nomenclature

a

Axial induction factor

a

Tangential induction factor

c

Chord length

N

Number of blades

C L

Lift coefficient

C D

Drag coefficient

V r e l

Relative wind speed

V 0

Wind velocity

φ

Inflow angle

θ

Twist angle

α

Angle of attack

F

Tip loss factor

r

Local radius

Ω

Angular velocity of rotor

R

Radius of rotor

ρ

Air density

λ

Tip speed ratio

λ r

Local speed ratio

σ

Solidity of rotor

d T

Aerodynamic thrust

d M

Aerodynamic torque

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  • [1]

    T. Burton , N. Jenkins , D. Sharpe , and E. Bossayni , Wind Energy Handbook, 2nd ed. Chichester, England: John Wiley & Sons, Ltd, 2011.

    • Search Google Scholar
    • Export Citation
  • [2]

    E. Benini and A. Toffolo , “Optimal design of horizontal-axis wind turbines using blade-element theory and evolutionary computation,” J. Sol. Energy Eng., vol. 124, no. 4, pp. 357363, 2002.

    • Search Google Scholar
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Senior editors

Editor-in-Chief: Ákos, Lakatos University of Debrecen (Hungary)

Founder, former Editor-in-Chief (2011-2020): Ferenc Kalmár University of Debrecen (Hungary)

Founding Editor: György Csomós University of Debrecen (Hungary)

Associate Editor: Derek Clements Croome University of Reading (UK)

Associate Editor: Dezső Beke University of Debrecen (Hungary)

Editorial Board

  • Mohammad Nazir AHMAD Institute of Visual Informatics, Universiti Kebangsaan Malaysia, Malaysia

    Murat BAKIROV Center for Materials and Lifetime Management Ltd. Moscow Russia

    Nicolae BALC Technical University of Cluj-Napoca Cluj-Napoca Romania

    Umberto BERARDI Ryerson University Toronto Canada

    Ildikó BODNÁR University of Debrecen Debrecen Hungary

    Sándor BODZÁS University of Debrecen Debrecen Hungary

    Fatih Mehmet BOTSALI Selçuk University Konya Turkey

    Samuel BRUNNER Empa Swiss Federal Laboratories for Materials Science and Technology

    István BUDAI University of Debrecen Debrecen Hungary

    Constantin BUNGAU University of Oradea Oradea Romania

    Michele De CARLI University of Padua Padua Italy

    Robert CERNY Czech Technical University in Prague Czech Republic

    György CSOMÓS University of Debrecen Debrecen Hungary

    Tamás CSOKNYAI Budapest University of Technology and Economics Budapest Hungary

    Eugen Ioan GERGELY University of Oradea Oradea Romania

    József FINTA University of Pécs Pécs Hungary

    Anna FORMICA IASI National Research Council Rome

    Alexandru GACSADI University of Oradea Oradea Romania

    Eric A. GRULKE University of Kentucky Lexington United States

    Janez GRUM University of Ljubljana Ljubljana Slovenia

    Géza HUSI University of Debrecen Debrecen Hungary

    Ghaleb A. HUSSEINI American University of Sharjah Sharjah United Arab Emirates

    Nikolay IVANOV Peter the Great St.Petersburg Polytechnic University St. Petersburg Russia

    Antal JÁRAI Eötvös Loránd University Budapest Hungary

    Gudni JÓHANNESSON The National Energy Authority of Iceland Reykjavik Iceland

    László KAJTÁR Budapest University of Technology and Economics Budapest Hungary

    Ferenc KALMÁR University of Debrecen Debrecen Hungary

    Tünde KALMÁR University of Debrecen Debrecen Hungary

    Milos KALOUSEK Brno University of Technology Brno Czech Republik

    Jan KOCI Czech Technical University in Prague Prague Czech Republic

    Vaclav KOCI Czech Technical University in Prague Prague Czech Republic

    Imra KOCSIS University of Debrecen Debrecen Hungary

    Imre KOVÁCS University of Debrecen Debrecen Hungary

    Éva LOVRA Univesity of Debrecen Debrecen Hungary

    Tamás MANKOVITS University of Debrecen Debrecen Hungary

    Igor MEDVED Slovak Technical University in Bratislava Bratislava Slovakia

    Ligia MOGA Technical University of Cluj-Napoca Cluj-Napoca Romania

    Marco MOLINARI Royal Institute of Technology Stockholm Sweden

    Henrieta MORAVCIKOVA Slovak Academy of Sciences Bratislava Slovakia

    Phalguni MUKHOPHADYAYA University of Victoria Victoria Canada

    Balázs NAGY Budapest University of Technology and Economics Budapest Hungary

    Husam S. NAJM Rutgers University New Brunswick United States

    Jozsef NYERS Subotica Tech College of Applied Sciences Subotica Serbia

    Bjarne W. OLESEN Technical University of Denmark Lyngby Denmark

    Stefan ONIGA North University of Baia Mare Baia Mare Romania

    Joaquim Norberto PIRES Universidade de Coimbra Coimbra Portugal

    László POKORÁDI Óbuda University Budapest Hungary

    Antal PUHL University of Debrecen Debrecen Hungary

    Roman RABENSEIFER Slovak University of Technology in Bratislava Bratislava Slovak Republik

    Mohammad H. A. SALAH Hashemite University Zarqua Jordan

    Dietrich SCHMIDT Fraunhofer Institute for Wind Energy and Energy System Technology IWES Kassel Germany

    Lorand SZABÓ Technical University of Cluj-Napoca Cluj-Napoca Romania

    Csaba SZÁSZ Technical University of Cluj-Napoca Cluj-Napoca Romania

    Ioan SZÁVA Transylvania University of Brasov Brasov Romania

    Péter SZEMES University of Debrecen Debrecen Hungary

    Edit SZŰCS University of Debrecen Debrecen Hungary

    Radu TARCA University of Oradea Oradea Romania

    Zsolt TIBA University of Debrecen Debrecen Hungary

    László TÓTH University of Debrecen Debrecen Hungary

    Anton TRNIK Constantine the Philosopher University in Nitra Nitra Slovakia

    Ibrahim UZMAY Erciyes University Kayseri Turkey

    Tibor VESSELÉNYI University of Oradea Oradea Romania

    Nalinaksh S. VYAS Indian Institute of Technology Kanpur India

    Deborah WHITE The University of Adelaide Adelaide Australia

    Sahin YILDIRIM Erciyes University Kayseri Turkey

International Review of Applied Sciences and Engineering
Address of the institute: Faculty of Engineering, University of Debrecen
H-4028 Debrecen, Ótemető u. 2-4. Hungary
Email: irase@eng.unideb.hu

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2021  
Scimago  
Scimago
H-index
7
Scimago
Journal Rank
0,199
Scimago Quartile Score Engineering (miscellaneous) (Q3)
Environmental Engineering (Q4)
Information Systems (Q4)
Management Science and Operations Research (Q4)
Materials Science (miscellaneous) (Q4)
Scopus  
Scopus
Cite Score
1,2
Scopus
CIte Score Rank
Architecture 48/149 (Q2)
General Engineering 186/300 (Q3)
Materials Science (miscellaneous) 79/124 (Q3)
Environmental Engineering 118/173 (Q3)
Management Science and Operations Research 141/184 (Q4)
Information Systems 274/353 (Q4)
Scopus
SNIP
0,457

2020  
Scimago
H-index
5
Scimago
Journal Rank
0,165
Scimago
Quartile Score
Engineering (miscellaneous) Q3
Environmental Engineering Q4
Information Systems Q4
Management Science and Operations Research Q4
Materials Science (miscellaneous) Q4
Scopus
Cite Score
102/116=0,9
Scopus
Cite Score Rank
General Engineering 205/297 (Q3)
Environmental Engineering 107/146 (Q3)
Information Systems 269/329 (Q4)
Management Science and Operations Research 139/166 (Q4)
Materials Science (miscellaneous) 64/98 (Q3)
Scopus
SNIP
0,26
Scopus
Cites
57
Scopus
Documents
36
Days from submission to acceptance 84
Days from acceptance to publication 348
Acceptance
Rate

23%

 

2019  
Scimago
H-index
4
Scimago
Journal Rank
0,229
Scimago
Quartile Score
Engineering (miscellaneous) Q2
Environmental Engineering Q3
Information Systems Q3
Management Science and Operations Research Q4
Materials Science (miscellaneous) Q3
Scopus
Cite Score
46/81=0,6
Scopus
Cite Score Rank
General Engineering 227/299 (Q4)
Environmental Engineering 107/132 (Q4)
Information Systems 259/300 (Q4)
Management Science and Operations Research 136/161 (Q4)
Materials Science (miscellaneous) 60/86 (Q3)
Scopus
SNIP
0,866
Scopus
Cites
35
Scopus
Documents
47
Acceptance
Rate
21%

 

International Review of Applied Sciences and Engineering
Publication Model Gold Open Access
Submission Fee none
Article Processing Charge 1100 EUR/article
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Limited number of full waiver available. Editorial Board / Advisory Board members: 50%
Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%
Subscription Information Gold Open Access

International Review of Applied Sciences and Engineering
Language English
Size A4
Year of
Foundation
2010
Volumes
per Year
1
Issues
per Year
3
Founder Debreceni Egyetem
Founder's
Address
H-4032 Debrecen, Hungary Egyetem tér 1
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 2062-0810 (Print)
ISSN 2063-4269 (Online)

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