Authors:
Humam Kareem JalghafDepartment of Fluid and Heat Engineering, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Miskolc-Egyetemváros, Hungary
Department of Mechanical Engineering, University of Technology, Baghdad, Iraq

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Ali Habeeb AskarDepartment of Fluid and Heat Engineering, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Miskolc-Egyetemváros, Hungary
Department of Mechanical Engineering, University of Technology, Baghdad, Iraq

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Hazim AlbedranDepartment of Mechanical Engineering, Faculty of Engineering, University of Kufa, Najaf, Iraq

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Endre KovácsInstitute of Physics and Electrical Engineering, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Miskolc-Egyetemváros, Hungary
Institute of Energy Engineering and Chemical Machinery, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Miskolc-Egyetemváros, Hungary

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Károly JármaiInstitute of Energy Engineering and Chemical Machinery, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Miskolc-Egyetemváros, Hungary

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Abstract

The paper compares different metaheuristics for using heat exchangers as a benchmark to estimate the best design parameter values using optimization efficient algorithms. Many MATLAB algorithms are used in this study. Also, an engineering equation solver, which is commercial software, is used to solve the issue. The design calculates three variables, which are the length, and inner and outer pipe diameter of the heat exchanger. The results showed that the best algorithms are particle swarm optimization, and when using this algorithm, the optimal design of the double pipe heat exchanger is as follows: the pipe length is 5.6734·10−1 m, the pipe inner diameter is 8.0203·10−3 m, and the pipe outer diameter is 2.2439·10−2 m.

Abstract

The paper compares different metaheuristics for using heat exchangers as a benchmark to estimate the best design parameter values using optimization efficient algorithms. Many MATLAB algorithms are used in this study. Also, an engineering equation solver, which is commercial software, is used to solve the issue. The design calculates three variables, which are the length, and inner and outer pipe diameter of the heat exchanger. The results showed that the best algorithms are particle swarm optimization, and when using this algorithm, the optimal design of the double pipe heat exchanger is as follows: the pipe length is 5.6734·10−1 m, the pipe inner diameter is 8.0203·10−3 m, and the pipe outer diameter is 2.2439·10−2 m.

1 Introduction

The heat exchange between any two types of fluid is done by a device called a heat exchanger, and it is used in different applications. The improvements to the heat transfer processes can be classified into two main categories: active techniques and passive techniques. One of the passive techniques is the use of double pipes with helical coils.

The Double Pipe Heat Exchanger (DPHE) includes a couple of pipes. The two pipes are concentric. The double pipe heat exchanger is followed for low flow rate, excessive temperature, and excessive stress applications. These styles of heat exchangers have discovered their applications in heat recuperation processes, air conditioning and refrigeration structures, foods, and dairy operations. The DPHE is properly used for lots of non-stop structures having small to medium duties, and it is utilized in enterprise along with condensers for chemical techniques and cooling fluid techniques. A lot of studies have been executed on the layout and evaluation of a DPHE.

There are two main types of DPHE: counter-flow and parallel flow. The counter flow is the best design, and it has the optimum heat transfer coefficient (h), and it can heat or cool the systems according to the application. Figure 1 shows the location of the outlet and inlet of the two pipes. As it can be seen in this figure, the fluid flows in opposite directions to each other and achieves the maximum difference in temperature of the two fluids at the two ends of the pipe. The diagram shows the counter flow and will consider the cold fluid as fluid one and the hot fluid as fluid two. The cold temperature at the second outlet is T2out, and it can reach temperatures close to the first inlet time, T1in, and it is clear that this temperature is larger than the first outlet time, T1out. In this counter flow, the temperature of the outlet cold fluid can reach more than the temperature of the outlet hot fluid, but it cannot reach that in parallel.

Fig. 1.
Fig. 1.

Double pipe heat exchangers, counter flow

Citation: Pollack Periodica 2023; 10.1556/606.2022.00543

When the inlets and outlets are on the same side, the heat exchanger is called the parallel flow heat exchanger, as it is shown in Fig. 2. The heat transfers and efficiency of counter flow DPHE are higher than parallel-flow DPHE. However, almost all applications that deal with high temperatures and pressures choose counter flow DPHE.

Fig. 2.
Fig. 2.

Double pipe heat exchangers, parallel flow

Citation: Pollack Periodica 2023; 10.1556/606.2022.00543

Optimization algorithms [1] have gained increasing importance in engineering design during the last decades because of their simplicity and rapidity in finding solutions. They have been used in robot design [2, 3], drilling performance modeling, and a variety of other scientific fields. Meta-heuristics do not obey any rules; they only obey the inspiration behind the algorithm. Researchers have been inspired by animal behaviors such as grey wolves and orcas to develop grey wolf optimization [4] and whale optimization algorithms [5]. The optimization of Fertilization Optimization (FO) algorithm [6] was inspired by the reproduction operation of mammalian animals. Dynamic Differential Annealed Optimization (DDAO) [7] stimulates the production of dual-phase steel. Impressively, even insects like ant lions have inspired the Ant Lion Optimization (ALO) algorithm [8]. The behavior of the flocks of birds and fishes was the inspiration engine behind the Particle Swarm Optimization (PSO) algorithm, while the bee colony was the inspiration engine behind the Artificial Bee Colony (ABC) [9]. The Flower Pollination Algorithm (FPA) was developed in response to the dominance of flowering plants [10]. All these algorithms have been used to solve different optimization problems in different fields, and one of those is heat exchanger design. The shell and tube optimal design of the heat exchanger was conducted by using particle warm optimization [11]. Also the shell and tube is designed by firefly optimization algorithms considering the economic criteria [12]. The optimal design for a polymer heat exchanger was studied considering the use of a multi-objective genetic algorithm [13]. Another usage for the genetic algorithm was introduced to design earth-to-air heat exchangers [14]. The Harmony search algorithm, which stimulates the jazz troy, has been used to solve the heat exchanger design problem [15]. There are three approaches to multi-objective optimization: I Reinforcement Learner Non-dominated Sorting Genetic Algorithm (NSGA-RL), II Non-dominated Sorting Genetic Algorithm (NSGA-II), and III Chaotic Non-dominated Sorting Genetic Algorithms (CNSGA). The basic aims are to maximize the thermal performance index and the Nusselt number and to reduce the Fanning friction factor to the ideal design of DPHE, with perforated baffles on the annulus side. Also, an analysis of the optimization strategies is done utilizing three performance measures, and the impact of maximizing the thermal performance index is shown [16]. A Tubular Exchanger Manufacturers Association (TEMA)-compliant Shell and Tube Heat Exchanger (STHE) design model with key restrictions was created. Mixed-Integer NonLinear Programing (MINLP) used 10 design variables (continuous, integer, discrete, binary, and type) [17].

In this work, a comparative study is presented to investigate the best optimization algorithm for the sake of solving the heat exchanger optimization problem. Seven meta-heuristics had a fair competition on the problem with the same run conditions and the host machine. The following PSO, ABC, Grey Wolf Optimization (GWO), DDAO, FPA, Whale Optimization Algorithm (WOA), and ALO have been employed in this study, and the comparison was in terms of the best solution (best), the worst solution (worst), the average solution (average) among independent runs, and the STandard Deviation (STD) for the solutions over the independent runs. Particle swarm optimization, whale optimization algorithm, and ant lion optimizer have the best performance among other meta-heuristic algorithms in terms of minimizing the objective function. However, particle swarm optimization had returned the most feasible solution that was used for further calculations.

Engineering Equation Solver (EES) is a strong tool for solving engineering issues and is comparatively easy to train. It is especially good at solving thermodynamic and heat transfer issues since it provides various built-in functions that cover thermodynamic and thermo-physical issues, so there is no need to look at tables. One main feature of EES is that it can deal with a system of synchronic equations, which is not easy to deal with in Excel Code [18].

2 Methodology

Using a numerical approach to investigate and optimize DPHE design by different methods including EES and MATLAB algorithms.

2.1 The test section

The DPHE is schematically represented in Fig. 3, the test section consists of a double pipe; the material of the pipe is copper and it has a length X1, inner diameter X2, and outer diameter X3. The working fluid is water- R134a, and the mass flow rate of the hot fluid was of varied from 0.0330 kg s−1–0.260 kg s−1.

Fig. 3.
Fig. 3.

Double pipe heat exchanger schematic

Citation: Pollack Periodica 2023; 10.1556/606.2022.00543

2.2 Theory

For the double pipe heat exchanger design by applying energy balance for each side:
δQ=m·di,Q=m·(i2i1).
Energy balance between hot and cold sides is
Q=m·(ih1ih2)=m·(ic2ic1),
Q=(m·Cp)h(Th2Th1)=(m·Cp)c(Tc2Tc1),
Qc=mchfgcDelx,
Qh=(ρCp)huhAh·(ThoThi),
where u=V˙/Ac is the fluid velocity V˙ is the fluid volume flow rate taken from the flow meter the cross-sectional area of tube is given; Ac=π/4(di)2, and m˙=V˙ρ, Tho, Thi inlet and outlet temperature of the fluid.
Heat exchanger efficiency or effectiveness can be defined by:
ε=qqmax,
and there are two types of flow of DPHE.

2.2.1 Parallel flow DPHE

Heat exchanger efficiency arranged parallel:
ε=1expNTU1+Cr1+Cr,
where NTU=UA/Cmin; Cr=Cmin/Cmax; U is the overall average heat transfer coefficient.

2.2.2 Counter flow DPHE

Heat exchanger efficiency with counter pattern is:
ε=1expNTU1+Cr1+CrexpNTU1+Cr,forCr<1,
ε=NTU1+NTU,forCr=1.

From these equations it can be seen that the heat exchanger efficiency depends on many parameters includes: the temperature of the two fluids Th1,Th2, Tc1,Tc2; the surface area of heat transfers A = π D L; the area parameters are: L and D; fluid flow velocity and properties V, ρ, Cp; and thermal resistance between the two fluids R or U. In the following progresses some parameter and the calculation the others parameters to calculate the optimal performance of heat exchanger are fixed.

2.3 EES and MATLAB algorithms for DPHE design

The design procedure will be as follows:

  • Input parameters:

    • The temperature of the two fluids (Th1, Th2, Tc1, Tc2);

    • Working fluids type and velocity (V, ρ, Cp);

    • Thermal resistance or DPHE material;

  • Solutions algorithms:

    • ESS algorithm;

    • MATLAB algorithm;

  • Output parameters:

    • to calculate the optimal design of DPHE;

    • to calculate (L, D and Nu).

Used these algorithms as a tool to design heat exchanger as follow:

2.3.1 Cold fluid

Refrigerant R134a, by using condensation unit with capacity 1.6 kW and make the shell of the heat exchanger is the evaporator of the condensation unit to make constant wall temperature process. The temperature of evaporator is Tc = 5 °C was designed the capillary tube for that purpose.

The heat transfer and mass flow rate for the refrigerant R134a in the outer tube is:
m˙cold=I·V·cosϕηiso·ηmech(henthalpy2henthalpy1),
where henthalpy1 is the specific enthalpy at the inlet of the compressor, henthalpy2 is the specific enthalpy at the outlet of the compressor for isontropic compression in the refrigeration cycle as it is shown in Table 1. This equation used to calculate the mass transfer in evaporator for small condensation unite, where (I·V·cosϕ) is the electric power of the compressor,
Qcold=m˙c·RE,
where RE is the refrigerant effect, RE=henthalpy1henthalpy4. henthalpy4 is the specific enthalpy at the inlet of the evaporator.
Table 1.

Parameters for calculate mass flow rate of refrigerant

I (A)V (V)cosϕηiso%ηmech%m˙c kg/sTc (oC)
0.502200.8585800.00415
The average heat transfer rate Qav is used in the calculation from predict the hot and cold sides, as it was shown by Baba et al. [19]:
Qav=(Qcold+Qhot)2.
Overall heat transfer coefficient (Uin) for fluid flows in a concentric pipe for heat exchanger, calculated by [20]:
Uin=QavAinTLM,
where Ain = πDinL; and ΔTLM is the logarithmic mean temperature difference,
TLM=TaTbln(Ta/Tb),
where Ta=ToTcold and Tb=TinTcold (see Fig. 4); ho is the outside heat transfer coefficient can be calculated from the following Eq. (15),
ho=NucoldkcoldDhyd,
where Dhyd=DoDin; and kf=x·Ffg/Ltube, x=0.75 is the difference in dryness fraction.
Fig. 4.
Fig. 4.

Variation of fluid temperatures in a heat exchange

Citation: Pollack Periodica 2023; 10.1556/606.2022.00543

Therefore, the Nusselt number of the cold side calculated from the equation given by (16),
Nucold=0.0082(Recold2kf)0.5.

2.3.2 Hot fluid

Water, the working fluid in this study, let Thin = 40, mh = 0.033 (kg s−1)–0.26 (kg s−1) the water flow rates. The rate of heat transfer for the hot fluid (water) flowing in the inner tube will be expressed as [21]:
Qhot=m˙hot·Cp_hot·(Thot_inThot_o).
The heat transfer coefficient of the fluid, hin, can be calculated from the following equation [20]:
Uin=11hin+Dinln(Do/Din)2k+DinDo1ho
for the inside pipe area, Uin, ho and hi are the individual heat transfer convection coefficients of the fluids outside and inside the pipes respectively and k is the thermal conductivity of the pipe wall. Equation (19) represents the Nusselt number, which is estimate based on thermal conductivity of hot water, the pipe side heat transfer coefficient, and the pipe diameter, [20].
Nuh=hin·Dinkw.
The Reynold number can be estimated as,
Reh=ρwvhDinμw.

3 Optimum design

In this section, a comparison had been performed among PSO, ABC, GWO, DDAO, FPA, WOA, ALO and EES optimization algorithms as it is shown in flow chart (Fig. 5). The run conditions were: population size 10; maximum number of iterations 100; 30 independent runs. Table 2 reveals that PSO, WOA, and ALO have equal performance on the heat exchanger design optimization problem.

Fig. 5.
Fig. 5.

Flow chart of optimum design

Citation: Pollack Periodica 2023; 10.1556/606.2022.00543

Table 2.

Statistical results of the minimization of the heat exchanger problem

BestWorstAverageSTD
PSO1.6055·10−041.6055·10−041.6055·10−040.0000·10+00
ABC1.6055·10−045.0981·10−038.0255·10−041.0464·10−03
GWO1.6055·10−042.3212·10−037.0471·10−046.0895·10−04
DDAO1.4821·10−022.4427·10+004.6434·10−014.9002·10−01
FPA1.6055·10−043.0137·10−034.3905·10−045.6742·10−04
WOA1.6055·10−041.6055·10−041.6055·10−040.0000·10+00
ALO1.6055·10−041.6055·10−041.6055·10−040.0000·10+00

4 Results and discussion

The heat transfer growth of the water was investigated theoretically at constant wall temperature in the entry region to validate the work with the theoretical equation. Figure 6 shows the Reynolds number versus Nusselt number of turbulent flows inside the pipe by using water working fluid, the experimental correlation of Gnielinski's [22] for fluid in a single phase is presented as [20]:
Nu=(f2)(Re1000)Pr1+12.7(f2)0.5(Pr3/21),
where f=(1.58·ln(Re)3.82)2 for 2300<Re<5.106 and 0.5<Pr<2000.
Fig. 6.
Fig. 6.

Comparison distilled water with Gnielinski's equation for validation

Citation: Pollack Periodica 2023; 10.1556/606.2022.00543

From the Nu-Re number relation in Figs 6 and 7 it is showed that the new algorithms (ESS, PSO) gave good performance in comparison with ideal Gnielinski's theoretical equation.

Fig. 7.
Fig. 7.

Comparison Nuselt number based on Gnielinski's equation

Citation: Pollack Periodica 2023; 10.1556/606.2022.00543

The results show that there is an excellent coincides between the results of theoretical and the results from ESS and other algorithms design, and this indicates that the design is the best for testing liquids under constant wall temperature.

4.1 The optimal solution results

PSO, WOA, and ALO have returned solutions with lower value of the cost function (objective function) with different collections of lengths, inner and outer diameter. The solution of the particle swarm optimization is more feasible than other solutions and it has been chosen because it gave the best results as it is shown in Table 3 and the cost function of PSO is steady performance and fast convergence as it is shown in Fig. 8. The parameters chosen for PSO solution is: Din = 8.0203·10−3 (m); Dout = 2.2439·10−2 (m); Lpipe = 5.6734·10−1 (m).

Table 3.

Best solutions founded by the best algorithms

AlgorithmLpipe (m)Din (m)Dout (m)
PSO5.6734·10−18.0203·10−32.2439·10−2
WOA6.0328·10−17.0691·10−31.9840·10−2
ALO4.1279·10−11.5000·10−23.2000·10−2
Fig. 8.
Fig. 8.

PSO performance

Citation: Pollack Periodica 2023; 10.1556/606.2022.00543

5 Conclusion

This research discussed the design of the DPHE to test fluids for heat transfer by using EES program and MATLAB algorithms. Using the property that the evaporation of liquids is under a constant temperature, this is used to obtain a constant temperature procedure using a mini-freezing system and design its parts. And there are excellent coincide between the empirical results and the results from algorithms design, and this indicates that the design is the best for testing liquids under constant wall temperature. The best MATLAB algorithms was PSO algorithm because it was perfect in solution (the value of worst, best and average solution are equal) with STD = 0, for that this algorithm advises to design DPHE. In addition to another good algorithm that maybe benefit for another types of heat exchanger like WOA, and ALO, etc.

Acknowledgements

The research was supported by the Hungarian National Research, Development and Innovation Office under the project number K 134358, and by the NTP-SZKOLL-20-0022 identifier “Focus'21-Focus on community by developing digital competencies” project, supported by the Ministry of Human Resources and Human Resources Support Manager.

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  • [1]

    H. N. Ghafil and K. Jármai, Optimization for Robot Modeling with MATLAB. Springer Nature, 2020.

  • [2]

    H. N. Ghafil and K. Jármai, “Kinematic-based structural optimization of robots,” Pollack Period., vol. 14, no. 3, pp. 213222, 2019.

    • Search Google Scholar
    • Export Citation
  • [3]

    S. Alsamia, D. S. Ibrahim, and H. N. Ghafil, “Optimization of drilling performance using various metaheuristics,” Pollack Period., vol. 16, no. 2, pp. 8085, 2021.

    • Search Google Scholar
    • Export Citation
  • [4]

    S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey wolf optimizer,” Adv. Eng. Softw., vol. 69, pp. 4661, 2014.

  • [5]

    S. Mirjalili and A. Lewis, “The whale optimization algorithm,” Adv. Eng. Softw., vol. 95, pp. 5167, 2016.

  • [6]

    H. N. Ghafil, S. Alsamia, and K. Jármai, “Fertilization optimization algorithm on CEC2015 and large scale problems,” Pollack Period., vol. 17, no. 1, pp. 2429, 2022.

    • Search Google Scholar
    • Export Citation
  • [7]

    H. N. Ghafil and K. Jármai, “Dynamic differential annealed optimization: New metaheuristic optimization algorithm for engineering applications,” Appl. Soft Comput., vol. 93, 2020, Paper no. 106392.

    • Search Google Scholar
    • Export Citation
  • [8]

    S. Mirjalili, “The ant lion optimizer,” Adv. Eng. Softw., vol. 83, pp. 8098, 2015.

  • [9]

    H. N. Ghafil and K. Jármai, “Comparative study of particle swarm optimization and artificial bee colony algorithms,” MicroCAD International Multidisciplinary Scientific Conference, Miskolc-Egyetemváros, Hungary, September 5–6, 2018, pp. 16.

    • Search Google Scholar
    • Export Citation
  • [10]

    X. S. Yang, “Flower pollination algorithm for global optimization,” in Unconventional computation and natural computation, vol. 7445, J. Durand-Lose and N. Jonoska, Eds., Lecture Notes in Computer Science 2012, pp. 240249.

    • Search Google Scholar
    • Export Citation
  • [11]

    V. K. Patel and R. V Rao, “Design optimization of shell-and-tube heat exchanger using particle swarm optimization technique,” Appl. Therm. Eng., vol. 30, nos 11–12, pp. 14171425, 2010.

    • Search Google Scholar
    • Export Citation
  • [12]

    D. K. Mohanty, “Application of firefly algorithm for design optimization of a shell and tube heat exchanger from economic point of view,” Int. J. Therm. Sci., vol. 102, pp. 228238, 2016.

    • Search Google Scholar
    • Export Citation
  • [13]

    U. Han, H. Kang, H. Lim, J. Han, and H. Lee, “Development and design optimization of novel polymer heat exchanger using the multi-objective genetic algorithm,” Int. J. Heat Mass Transf., vol. 144, 2019, Paper no. 118589.

    • Search Google Scholar
    • Export Citation
  • [14]

    R. Kumar, A. R. Sinha, B. K. Singh, and U. Modhukalya, “A design optimization tool of earth-to-air heat exchanger using a genetic algorithm,” Renew. Energy, vol. 33, no. 10, pp. 22822288, 2008.

    • Search Google Scholar
    • Export Citation
  • [15]

    M. Fesanghary, E. Damangir, and I. Soleimani, “Design optimization of shell and tube heat exchangers using global sensitivity analysis and harmony search algorithm,” Appl. Therm. Eng., vol. 29, nos 5–6, pp. 10261031, 2009.

    • Search Google Scholar
    • Export Citation
  • [16]

    A. B. Colaço, V. C. Mariani, M. R. Salem, and L. dos Santos Coelho, “Maximizing the thermal performance index applying evolutionary multi-objective optimization approaches for double pipe heat exchanger,” Appl. Therm. Eng., vol. 211, 2022, Paper no. 118504.

    • Search Google Scholar
    • Export Citation
  • [17]

    Ö. Aras and M. Bayramoğlu, “A MINLP study on shell and tube heat exchanger: Hybrid branch and bound/meta-heuristics approaches,” Ind. Eng. Chem. Res., vol. 51, no. 43, pp. 1415814170, 2012.

    • Search Google Scholar
    • Export Citation
  • [18]

    L. J. Habeeb, A. A. Mohmmed, A. H. Askar, and H. M. Hussain, How to Use Engineering Equation Solver (EES): Refrigeration and Heat Transfer Applications. Publisher: Independently published, 2019.

    • Search Google Scholar
    • Export Citation
  • [19]

    M. S. Baba, M. B. Rao, and A. V. S. R. Raju, “Experimental study of convective heat transfer in a finned tube counter flow heat exchanger with Fe3O4-water nanofluid,” Int. J. Mech. Eng. Technol., vol. 8, no. 11, pp. 500509, 2017.

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    J. P. Holman, Heat Transfer. McGraw-Hill, 2010.

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    Y. Cengel, J. Cimbala, and R. Turner, Fundamentals of Thermal-Fluid Sciences (SI Units). McGraw Hill, 2012.

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    V. Gnielinski, “New equations for heat and mass transfer in turbulent pipe and channel flow,” Int. Chem. Eng., vol. 16, no. 2, pp. 359368, 1976.

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Senior editors

Editor(s)-in-Chief: Iványi, Amália

Editor(s)-in-Chief: Iványi, Péter

 

Scientific Secretary

Miklós M. Iványi

Editorial Board

  • Bálint Bachmann (Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Jeno Balogh (Department of Civil Engineering Technology, Metropolitan State University of Denver, Denver, Colorado, USA)
  • Radu Bancila (Department of Geotechnical Engineering and Terrestrial Communications Ways, Faculty of Civil Engineering and Architecture, “Politehnica” University Timisoara, Romania)
  • Charalambos C. Baniotopolous (Department of Civil Engineering, Chair of Sustainable Energy Systems, Director of Resilience Centre, School of Engineering, University of Birmingham, U.K.)
  • Oszkar Biro (Graz University of Technology, Institute of Fundamentals and Theory in Electrical Engineering, Austria)
  • Ágnes Borsos (Institute of Architecture, Department of Interior, Applied and Creative Design, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Matteo Bruggi (Dipartimento di Ingegneria Civile e Ambientale, Politecnico di Milano, Italy)
  • Petra Bujňáková (Department of Structures and Bridges, Faculty of Civil Engineering, University of Žilina, Slovakia)
  • Anikó Borbála Csébfalvi (Department of Civil Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Mirjana S. Devetaković (Faculty of Architecture, University of Belgrade, Serbia)
  • Szabolcs Fischer (Department of Transport Infrastructure and Water Resources Engineering, Faculty of Architerture, Civil Engineering and Transport Sciences Széchenyi István University, Győr, Hungary)
  • Radomir Folic (Department of Civil Engineering, Faculty of Technical Sciences, University of Novi Sad Serbia)
  • Jana Frankovská (Department of Geotechnics, Faculty of Civil Engineering, Slovak University of Technology in Bratislava, Slovakia)
  • János Gyergyák (Department of Architecture and Urban Planning, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Kay Hameyer (Chair in Electromagnetic Energy Conversion, Institute of Electrical Machines, Faculty of Electrical Engineering and Information Technology, RWTH Aachen University, Germany)
  • Elena Helerea (Dept. of Electrical Engineering and Applied Physics, Faculty of Electrical Engineering and Computer Science, Transilvania University of Brasov, Romania)
  • Ákos Hutter (Department of Architecture and Urban Planning, Institute of Architecture, Faculty of Engineering and Information Technolgy, University of Pécs, Hungary)
  • Károly Jármai (Institute of Energy and Chemical Machinery, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Hungary)
  • Teuta Jashari-Kajtazi (Department of Architecture, Faculty of Civil Engineering and Architecture, University of Prishtina, Kosovo)
  • Róbert Kersner (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Rita Kiss  (Biomechanical Cooperation Center, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Budapest, Hungary)
  • István Kistelegdi  (Department of Building Structures and Energy Design, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Stanislav Kmeť (President of University Science Park TECHNICOM, Technical University of Kosice, Slovakia)
  • Imre Kocsis  (Department of Basic Engineering Research, Faculty of Engineering, University of Debrecen, Hungary)
  • László T. Kóczy (Department of Information Sciences, Faculty of Mechanical Engineering, Informatics and Electrical Engineering, University of Győr, Hungary)
  • Dražan Kozak (Faculty of Mechanical Engineering, Josip Juraj Strossmayer University of Osijek, Croatia)
  • György L. Kovács (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Balázs Géza Kövesdi (Department of Structural Engineering, Faculty of Civil Engineering, Budapest University of Engineering and Economics, Budapest, Hungary)
  • Tomáš Krejčí (Department of Mechanics, Faculty of Civil Engineering, Czech Technical University in Prague, Czech Republic)
  • Jaroslav Kruis (Department of Mechanics, Faculty of Civil Engineering, Czech Technical University in Prague, Czech Republic)
  • Miklós Kuczmann (Department of Automations, Faculty of Mechanical Engineering, Informatics and Electrical Engineering, Széchenyi István University, Győr, Hungary)
  • Tibor Kukai (Department of Engineering Studies, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Maria Jesus Lamela-Rey (Departamento de Construcción e Ingeniería de Fabricación, University of Oviedo, Spain)
  • János Lógó  (Department of Structural Mechanics, Faculty of Civil Engineering, Budapest University of Technology and Economics, Hungary)
  • Carmen Mihaela Lungoci (Faculty of Electrical Engineering and Computer Science, Universitatea Transilvania Brasov, Romania)
  • Frédéric Magoulés (Department of Mathematics and Informatics for Complex Systems, Centrale Supélec, Université Paris Saclay, France)
  • Gabriella Medvegy (Department of Interior, Applied and Creative Design, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Tamás Molnár (Department of Visual Studies, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Ferenc Orbán (Department of Mechanical Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Zoltán Orbán (Department of Civil Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Dmitrii Rachinskii (Department of Mathematical Sciences, The University of Texas at Dallas, Texas, USA)
  • Chro Radha (Chro Ali Hamaradha) (Sulaimani Polytechnic University, Technical College of Engineering, Department of City Planning, Kurdistan Region, Iraq)
  • Maurizio Repetto (Department of Energy “Galileo Ferraris”, Politecnico di Torino, Italy)
  • Zoltán Sári (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Grzegorz Sierpiński (Department of Transport Systems and Traffic Engineering, Faculty of Transport, Silesian University of Technology, Katowice, Poland)
  • Zoltán Siménfalvi (Institute of Energy and Chemical Machinery, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Hungary)
  • Andrej Šoltész (Department of Hydrology, Faculty of Civil Engineering, Slovak University of Technology in Bratislava, Slovakia)
  • Zsolt Szabó (Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Hungary)
  • Mykola Sysyn (Chair of Planning and Design of Railway Infrastructure, Institute of Railway Systems and Public Transport, Technical University of Dresden, Germany)
  • András Timár (Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Barry H. V. Topping (Heriot-Watt University, UK, Faculty of Engineering and Information Technology, University of Pécs, Hungary)

POLLACK PERIODICA
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Institute: University of Pécs
Address: Boszorkány utca 2. H–7624 Pécs, Hungary
Phone/Fax: (36 72) 503 650

E-mail: peter.ivanyi@mik.pte.hu 

or amalia.ivanyi@mik.pte.hu

Indexing and Abstracting Services:

  • SCOPUS
  • CABELLS Journalytics

 

2021  
Web of Science  
Total Cites
WoS
not indexed
Journal Impact Factor not indexed
Rank by Impact Factor

not indexed

Impact Factor
without
Journal Self Cites
not indexed
5 Year
Impact Factor
not indexed
Journal Citation Indicator not indexed
Rank by Journal Citation Indicator

not indexed

Scimago  
Scimago
H-index
12
Scimago
Journal Rank
0,26
Scimago Quartile Score Civil and Structural Engineering (Q3)
Materials Science (miscellaneous) (Q3)
Computer Science Applications (Q4)
Modeling and Simulation (Q4)
Software (Q4)
Scopus  
Scopus
Cite Score
1,5
Scopus
CIte Score Rank
Civil and Structural Engineering 232/326 (Q3)
Computer Science Applications 536/747 (Q3)
General Materials Science 329/455 (Q3)
Modeling and Simulation 228/303 (Q4)
Software 326/398 (Q4)
Scopus
SNIP
0,613

2020  
Scimago
H-index
11
Scimago
Journal Rank
0,257
Scimago
Quartile Score
Civil and Structural Engineering Q3
Computer Science Applications Q3
Materials Science (miscellaneous) Q3
Modeling and Simulation Q3
Software Q3
Scopus
Cite Score
340/243=1,4
Scopus
Cite Score Rank
Civil and Structural Engineering 219/318 (Q3)
Computer Science Applications 487/693 (Q3)
General Materials Science 316/455 (Q3)
Modeling and Simulation 217/290 (Q4)
Software 307/389 (Q4)
Scopus
SNIP
1,09
Scopus
Cites
321
Scopus
Documents
67
Days from submission to acceptance 136
Days from acceptance to publication 239
Acceptance
Rate
48%

 

2019  
Scimago
H-index
10
Scimago
Journal Rank
0,262
Scimago
Quartile Score
Civil and Structural Engineering Q3
Computer Science Applications Q3
Materials Science (miscellaneous) Q3
Modeling and Simulation Q3
Software Q3
Scopus
Cite Score
269/220=1,2
Scopus
Cite Score Rank
Civil and Structural Engineering 206/310 (Q3)
Computer Science Applications 445/636 (Q3)
General Materials Science 295/460 (Q3)
Modeling and Simulation 212/274 (Q4)
Software 304/373 (Q4)
Scopus
SNIP
0,933
Scopus
Cites
290
Scopus
Documents
68
Acceptance
Rate
67%

 

Pollack Periodica
Publication Model Hybrid
Submission Fee none
Article Processing Charge 900 EUR/article
Printed Color Illustrations 40 EUR (or 10 000 HUF) + VAT / piece
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Editorial Board / Advisory Board members: 50%
Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%
Subscription fee 2023 Online subsscription: 336 EUR / 411 USD
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Pollack Periodica
Language English
Size A4
Year of
Foundation
2006
Volumes
per Year
1
Issues
per Year
3
Founder Akadémiai Kiadó
Founder's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 1788-1994 (Print)
ISSN 1788-3911 (Online)

Monthly Content Usage

Abstract Views Full Text Views PDF Downloads
Aug 2022 0 0 0
Sep 2022 0 0 0
Oct 2022 0 0 0
Nov 2022 0 0 0
Dec 2022 0 0 0
Jan 2023 0 87 47
Feb 2023 0 0 0