Authors:
Ammar Saliby Department of Mathematics Analysis, Institute of Mathematics, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Miskolc, Hungary

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Béla Kovács Department of Mathematics Analysis, Institute of Mathematics, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Miskolc, Hungary

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Abstract

Integrating thermal energy storage with thermal conversion systems is necessary to maximize their use. Phase change materials are the best media for storing and releasing thermal energy from various basic material types. Because the phase change materials have a high latent heat of fusion, it is often viable to use these characteristics and include the phase change materials in building envelopes to store thermal energy. The paper provides a thorough categorization of the phase change materials and thermal energy storage systems, in addition to an evaluation of their modeling using computational fluid dynamics. The purpose was to highlight computational fluid dynamics as a useful technique for advancing the engineering of thermal energy storage devices.

Abstract

Integrating thermal energy storage with thermal conversion systems is necessary to maximize their use. Phase change materials are the best media for storing and releasing thermal energy from various basic material types. Because the phase change materials have a high latent heat of fusion, it is often viable to use these characteristics and include the phase change materials in building envelopes to store thermal energy. The paper provides a thorough categorization of the phase change materials and thermal energy storage systems, in addition to an evaluation of their modeling using computational fluid dynamics. The purpose was to highlight computational fluid dynamics as a useful technique for advancing the engineering of thermal energy storage devices.

1 Introduction

The most challenging issue in most countries is the global energy consumption in buildings. The International Energy Agency (IEA) reports that the overall energy consumption for buildings worldwide has lately climbed to 40% for commercial buildings and 61% for residential buildings. Heating, Ventilation, and Air Conditioning (HVAC) utilize the most energy. The energy used by buildings is predicted to rise by up to 37% in 2050 if this trend continues [1]. Maintaining a comfortable atmosphere is one of the top priorities for commercial and residential buildings, which places particular demands on heating and cooling energy. Between 20% and 40% of the total energy demand is used by buildings [2, 3]. Therefore, improving system efficiency would significantly reduce energy use. Good thermal management is necessary to guarantee system effectiveness. Thermal energy can be stored instead of lost through Thermal Energy Storage (TES) systems, a recent advancement in thermal management technology. There are numerous ways to implement this technology, including storing energy via a thermochemical process and using sensible and latent heat [4]. Phase Change Materials (PCMs) are used in the Latent Heat Storage (LHS) system [5]. Studies on the thermo-physical characteristics of various PCMs and strategies for incorporating them into building envelopes have been published by several researchers. Experimental and numerical studies of these materials' thermal energy storage capabilities have been conducted [5–11]. Utilizing Computational Fluid Dynamics (CFD) is anticipated to be a practical method for providing optimization tools for the optimal efficiency of these systems while also saving money and time.

This study focuses on using CFD to model and simulate thermal energy storage in phase change materials embedded in building envelopes.

2 Classification of PCMs

Phase change materials store thermal energy and release it during the phase change from a liquid state to a solid state at a constant temperature as it can be seen in Fig. 1. This feature of phase-changing materials can be exploited to regulate thermal energy in buildings and reduce its consumption [12, 13].

Fig. 1.
Fig. 1.

Heat stored during phase transition (Source: Author's)

Citation: Pollack Periodica 19, 1; 10.1556/606.2023.00930

In general, phase change substances are divided into three classes based on their chemical composition: organic, inorganic, and eutectic. Each category is characterized by thermal and physical properties that enable researchers to choose the most appropriate option, according to the intended application [14].

Most paraffin is made up of hydrocarbons. The biggest phase transition material category is fatty acids, not paraffin. Metallic and salt hydrate PCMs are the two categories of inorganic PCMs. While undergoing a phase shift, hydrate salts are both hydrated and dehydrated, although not all hydrate salts melt simultaneously. Metallic is eutectic metal with low melting points. Although these metallic is highly light and has a high thermal conductivity similar to metal, it has low latent heat of fusion. Eutectics are an additional option for PCMs. Eutectics are mixtures of two or more substances, including organic and organic, inorganic and inorganic, or organic and inorganic. The solution to the melting of hydrate salts has been found via eutectics. Incongruent melting may be avoided while also lowering the melting point and improving the thermal conductivity by combining two kinds of hydrated salts, such as inorganic and inorganic salts, to create a eutectic.

3 PCM numerical solution

Numerous studies have been performed on heat transfer in PCMs incorporated within building envelopes. In this section, numerical and analytical solutions and the use of CFD for these problems are explored.

CFD may be useful for improving the engineering development of thermal energy storage systems. Additionally, it is anticipated that using CFD would provide optimization tools for these systems' maximum efficiency and a cost- and time-saving method.

A partial differential equation (PDE) that can be solved analytically or numerically governs how a phase transient, also known as a phase change, is mathematically formulated. The nonlinear phase front interfaces, complicated geometries, and nonstandard boundary conditions make it challenging to solve PCMs analytically. There are a few studies that have been conducted on 1D examples with regular geometries and a common boundary condition. The nonlinearity of numerical solutions at moving interfaces, where the displacement rate is governed by latent heat lost or absorbed at the boundary, makes it challenging to solve.

However, two primary approaches may be used to analyze the heat transport processes in solid-liquid PCMs:
  • Enthalpy-based: The solid-liquid interface location need not be monitored in this manner. Because of the following benefits an enthalpy formulation is frequently used: No explicit requirements must be met at the solid-liquid interface; the governing equation is comparable to that of a single phase; the enthalpy formulation involves the solution in a mushy zone between the two standard phases that contains both solid and liquid components; It is simpler to handle the phase change issue, as it is shown in Eq. (1),

tρH+·ρvH=·kT+S,
where H [J kg−1] is the enthalpy of material; ρ [kg m−3] is the density of material; T [°C] is the temperature; k [W m−1 K] is the thermal conductivity; v [m s−1] is the velocity of fluid; S is the source term.
  • Temperature-based: The only dependent variable in this approach is temperature. Since the solid-liquid interface location can be precisely recorded, the energy conservation equations for the solid and liquid may be stated independently. This allows for an accurate solution to the issues, as it is shown in Eq. (2),

Tsn·ks=TLn·kL+ρ·H·k·vn,
where TS [°C] is the solidus temperatures of PCM; TL [°C] is the liquids temperature of PCM; ks [W m−1 K] is the thermal conductivity of solid phase; kL [W m−1 K] is the thermal conductivity of liquid phase; n is the unit normal vector to the interface; vn is the normal component of the velocity of the interface; H [J kg−1] is the enthalpy of material.

4 CFD analysis

Researchers utilize self-developed programs using language (C++, Fortran, MATLAB, and several commercial software like COMSOL Multiphysics and Fluent by ANSYS) to simulate melting and solidification processes in engineering difficulties as well as the heat transmission phenomena in PCMs [15–17]. This research uses ANSYS Fluent software to simulate melting and solidification processes in engineering applications and the heat transmission phenomena in PCMs.

Engineering challenges have been successfully simulated using the Fluent tool of the workbench ANSYS 2023 R1 [18]; it features a specialized model that can simulate a variety of various melting and solidification difficulties. The program may resolve phase changes across a wide temperature range or at a single temperature.

First, the physical engineering problem is mesh, particularly geometric modeling utilizing mesh creation tools, such as the workbench ANSYS 2023 R1. The boundary layers and zone types are established when the physical configuration is designed and meshed, and the mesh is sent to the Fluent program. To guarantee that the numerical results are independent of the parameters, several grid sizes, and time steps should be used for the numerical model. Small grid sizes and time increments are preferred for a quick computer simulation time.

ANSYS Fluent uses the enthalpy porosity approach to characterize the solidification and melting processes, as demonstrated in Fig. 2. This method does not specifically follow the melting interface. The liquid fraction, or the proportion of the cell volume that is liquid, is a value that is assigned to each cell in the PCM domain. The enthalpy balance is used to calculate the liquid fraction after each repeat. This method's liquid fraction value ranges from 0 to 1, and the phase transition boundary is represented as a mushy zone. The mushy zone's porosity decreases from 1 to 0, similar to a pseudo-porous zone [19]. The porosity and velocity are zero when the material solidifies.

Fig. 2.
Fig. 2.

Enthalpy porosity approach (Source: Author's)

Citation: Pollack Periodica 19, 1; 10.1556/606.2023.00930

The energy equation is written as it is shown in Eq. (3) [19],

The sum of the sensible heat and the latent heat is employed to calculate the material's enthalpy,
H=h+ΔH,
where h [J kg−1] is the sensible heat; ΔH [J kg−1] is the latent heat content, where
h=href+TrefTCp·dT,
where href [J kg−1] is the reference enthalpy; Tref [°C] is the reference temperature; Cp [J kg−1 K] is the specific heat at a constant pressure of PCM.
The liquid-fraction (f) can be defined as:
f={0,ifT<TS(Solidification),TTSTLTS,ifTS<T<TL(Mushyzone),1,ifT>TL(Melting),
where TS [°C] is the solidus temperatures of PCM; TL [°C] is the liquids temperatures of PCM.
The latent heat content can be written in terms of the latent heat of material as
ΔH=f·L.

The latent heat content can vary between zero (for a solid) and L (for a liquid) state.

The Fluent software has two main solvers: the pressure-based solver and the coupled density-based solver. The melting and solidification issues can only be simulated using the first technique. Two pressure-based solver algorithms, a segregated algorithm, and a coupled algorithm, are shown in Fig. 3. Both are included in Fluent.

Fig. 3.
Fig. 3.

Pressure-based flow chart (Source: Authors')

Citation: Pollack Periodica 19, 1; 10.1556/606.2023.00930

For the convection terms in Fluent, many discretization strategies are available. Most often utilized with solidification and melting issues are the first order upwind, power law, and second-order upwind schemes. The solution technique overview is depicted in Fig. 4. More details about the solution, initialization, and discretization methods can be found [18].

Fig. 4.
Fig. 4.

Fluent solution flow chart (Source: Authors')

Citation: Pollack Periodica 19, 1; 10.1556/606.2023.00930

Materials' physical characteristics, including density, thermal conductivity, heat capacity, and viscosity, may vary with temperature or rely on their chemical composition. A polynomial, piecewise linear or piecewise-polynomial function is the foundation for temperature dependence. Either the user defines or computes each component's attributes using kinetic theory. However, to specify the temperature dependence of the thermophysical characteristics, these physical qualities can be expressed as a constant value, a temperature-dependent function, or a User-Defined Function (UDF) that can be written in a particular computer language. Density and viscosity, two thermo-physical characteristics of PCMs, can occasionally be considered temperature-dependent and governed by certain correlations,
ρ=ρ1β(TT1)+1,
μ=exp(A+BT),
where ρ1 [kg m−3] is the density of PCM at the melting temperature T1; β is the thermal expansion coefficient; A, B are constant coefficients.

5 Numerical results

The main goal of this paragraph is a summary of the numeric study of the melting process of phase change materials for the implementation of thermal storage systems in building envelopes. The numerical study of paraffin wax melting in a 10 × 200 mm rectangular zone is heated by convection from the left and right and adiabatically from the other two sides. The data were collected at 30 min intervals over the whole simulation's 360 min melting cycle. The change in a liquid fraction over time is seen in Fig. 5. It is clear how the melting interface moves and changes shape as time goes on. When a substance is entirely liquid (f = 1), and when it is entirely solid (f = 0). The melting front, which separates the liquid from the solid region, is represented by the mushy zone. For the first 30 min of the melting process, the melting interface is virtually parallel to the left wall, demonstrating that conduction is the main means of heat transfer at this time. The liquid PCM rises and falls repeatedly after 60 min has passed due to its lower density and higher temperature. Another thing to note is that the PCM chamber's top layer takes around 60 min to melt. In the following 90 min, around half of the PCM in the PCM chamber melts.

Fig. 5.
Fig. 5.

Changes in liquid-fraction contours, a) Change in liquid-fraction (0–120 min), b) Change in liquid-fraction (150–240 min), c) Change in liquid-fraction (270–360 min) (Source: Authors')

Citation: Pollack Periodica 19, 1; 10.1556/606.2023.00930

6 Conclusion

The paper comprehensively categorizes PCMs utilized in thermal energy storage technologies. The purpose was to highlight CFD as a useful technique for advancing the engineering of thermal energy storage systems. Because of the extremely exact findings, applying CFD to constructing PCM thermal storages is a method that may be used. In order to assist customers, save time and money and work as efficiently as possible, CFD also offers optimization tools.

According to numerical modeling and simulation of the PCM melting process under convection heat settings, heat transport happens mostly through conduction during the first 0–30 min of melting before switching to natural convection with more heating. The melting rate increases with time. As the melting process progresses, it grows in the middle and at the conclusion, although initially the same. In order to prevent convergence errors during the solution, the melting issue must be modeled using ANSYS (Fluent) with the appropriate meshing and time step selection.

References

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  • [1]

    The future of cooling: Opportunities for energy-efficient air conditioning. Int. Energy Agency, [Online]. Available: https://www.oecd-ilibrary.org/energy/the-future-of-cooling_9789264301993-en. Accessed: May 15, 2023.

    • Search Google Scholar
    • Export Citation
  • [2]

    S. Ferrari and V. Zanotto, “Adaptive comfort: Analysis and application of the main indices,” Build Environ., vol. 49, pp. 2532, 2012.

    • Search Google Scholar
    • Export Citation
  • [3]

    S. Kumar, M. K. Singh, R. Kukreja, S. K. Chaurasiya, and V. K. Gupta, “Comparative study of thermal comfort and adaptive actions for modern and traditional multi-storey naturally ventilated hostel buildings during monsoon season in India,” J. Build. Eng., vol. 23, pp. 90106, 2019.

    • Search Google Scholar
    • Export Citation
  • [4]

    B. Naili, I. Háber, and I. Kistelegdi, “Façade typology development in high-rise office building envelope,” Pollack Period., vol. 18, no. 2, pp. 151156, 2023.

    • Search Google Scholar
    • Export Citation
  • [5]

    A. Sharma, V. V. Tyagi, C. R. Chen, and D. Buddhi, “Review on thermal energy storage with phase change: materials and applications,” Renew. Sustain. Energy Rev., vol. 13, no. 2, pp. 318345, 2009.

    • Search Google Scholar
    • Export Citation
  • [6]

    B. Zalba, J. M. Marín, L. F. Cabeza, and H. Mehling, “Review on thermal energy storage with phase change: materials, heat transfer analysis and applications,” Appl. Therm. Eng., vol. 23, no. 3, pp. 251283, 2003.

    • Search Google Scholar
    • Export Citation
  • [7]

    B. P. Jelle and S. E. Kalnæs, “Phase change materials for application in energy-efficient buildings,” in Cost-Effective Energy Efficient Building Retrofitting: Materials, Technologies, Optimization and Case Studies, Ch. 3, Elsevier, 2017, pp. 57118.

    • Search Google Scholar
    • Export Citation
  • [8]

    A. Bontemps, M. Ahmad, K. Johanns, and H. Sallée, “Experimental and modeling study of twin cells with latent heat storage walls,” Energy and Build, vol. 43, no. 9, pp. 24562461, 2011.

    • Search Google Scholar
    • Export Citation
  • [9]

    T. Silva, R. Vicente, C. Amaral, and A. Figueiredo, “Thermal performance of a window shutter containing PCM: Numerical validation and experimental analysis,” Appl. Energy, vol. 179, pp. 6484, 2016.

    • Search Google Scholar
    • Export Citation
  • [10]

    R. Saxena, D. Rakshit, and S. C. Kaushik, “Experimental assessment of Phase Change Material (PCM) embedded bricks for passive conditioning in buildings,” Renew. Energy, vol. 149, pp. 587599, 2020.

    • Search Google Scholar
    • Export Citation
  • [11]

    P. K. S. Rathore and S. K. Shukla, “An experimental evaluation of thermal behavior of the building envelope using macroencapsulated PCM for energy savings,” Renew. Energy, vol. 149, pp. 13001313, 2020.

    • Search Google Scholar
    • Export Citation
  • [12]

    B. Lamrani, K. Johannes, and F. Kuznik, “Phase change materials integrated into building walls: An updated review,” Renew. Sustain. Energy Rev., vol. 140, 2021, Art no. 110751.

    • Search Google Scholar
    • Export Citation
  • [13]

    V. V. Tyagi, A. K. Pandey, D. Buddhi, and R. Kothari, “Thermal performance assessment of encapsulated PCM based thermal management system to reduce peak energy demand in buildings,” Energy Build., vol. 117, pp. 4452, 2016.

    • Search Google Scholar
    • Export Citation
  • [14]

    A. Váz Sá, R. M. S. F. Almeida, H. Sousa, and J. M. P. Q. Delgado, “Numerical analysis of the energy improvement of plastering mortars with phase change materials,” Adv. Mater. Sci. Eng., vol. 2014, 2014, Art no. 582536.

    • Search Google Scholar
    • Export Citation
  • [15]

    A. S. Dashtaki, A. A. Nadooshan, A. Abedi, S. Dashtaki, and A. Nadooshan, “Effect of type and location of a Phase Change Material (PCM) layer in a building wall on energy consumption using numerical nimulation,” Int. J. Adv. Des. Manuf. Technol., vol. 12, no. 4, pp. 33–46, 2019.

    • Search Google Scholar
    • Export Citation
  • [16]

    W. Sun, R. Huang, Z. Ling, X. Fang, and Z. Zhang, “Numerical simulation on the thermal performance of a PCM-containing ventilation system with a continuous change in inlet air temperature,” Renew. Energy, vol. 145, pp. 16081619, 2020.

    • Search Google Scholar
    • Export Citation
  • [17]

    M. S. Albdour, B. Baranyai, and M. M. Shalby, “Overview of whole-building energy engines for investigating energy-related systems,” Pollack Period., vol. 18, no. 1. pp. 3641, 2022.

    • Search Google Scholar
    • Export Citation
  • [18]

    ANSYS help, Chapter 23: Modeling Solidification and Melting. [Online]. Available: https://ansyshelp.ansys.com/account/secured?returnurl=/Views/Secured/corp/v221/en/flu_ug/flu_ug_chp_melt_freeze.html. Accessed: Feb. 10, 2023.

    • Search Google Scholar
    • Export Citation
  • [19]

    ANSYS help, Chapter 16: Solidification and Melting. [Online]. Available: https://ansyshelp.ansys.com/account/secured?returnurl=/Views/Secured/corp/v221/en/flu_th/flu_th_chp_melt_freeze.html. Accessed: Feb. 10, 2023.

    • Search Google Scholar
    • Export Citation
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  • 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
Pollack Mihály Faculty of Engineering
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

 

2022  
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
14
Scimago
Journal Rank
0.298
Scimago Quartile Score

Civil and Structural Engineering (Q3)
Computer Science Applications (Q3)
Materials Science (miscellaneous) (Q3)
Modeling and Simulation (Q3)
Software (Q3)

Scopus  
Scopus
Cite Score
1.4
Scopus
CIte Score Rank
Civil and Structural Engineering 256/350 (27th PCTL)
Modeling and Simulation 244/316 (22nd PCTL)
General Materials Science 351/453 (22nd PCTL)
Computer Science Applications 616/792 (22nd PCTL)
Software 344/404 (14th PCTL)
Scopus
SNIP
0.861

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
Print + online subscription: 405 EUR / 492 USD
Subscription Information Online subscribers are entitled access to all back issues published by Akadémiai Kiadó for each title for the duration of the subscription, as well as Online First content for the subscribed content.
Purchase per Title Individual articles are sold on the displayed price.

 

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
Nov 2023 0 0 0
Dec 2023 0 0 0
Jan 2024 0 0 0
Feb 2024 0 93 57
Mar 2024 0 142 42
Apr 2024 0 126 44
May 2024 0 0 0