The optimal approach of various analysis methodologies of a dual-purpose solar collector

Mustafa Moayad Hasan; Krisztián Hriczó

doi:10.1556/606.2024.01244

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Pollack Periodica

Volume/Issue:: Accepted Manuscript / Online First

The optimal approach of various analysis methodologies of a dual-purpose solar collector

Authors:

Mustafa Moayad Hasan

Mustafa Moayad Hasan Institute of Mathematics, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Miskolc, Hungary
Basra Technological Technical Institute, Southern Technical University, Basra, Iraq

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hasan.mustafa.moayad@student.uni-miskolc.hu https://orcid.org/0000-0001-6046-0728

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Krisztián Hriczó

Krisztián Hriczó Institute of Mathematics, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Miskolc, Hungary

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krisztian.hriczo@uni-miskolc.hu https://orcid.org/0000-0003-3298-6495

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Online Publication Date:: 15 Apr 2025

Publication Date:: 15 Apr 2025

Article Category:: Research Article

DOI:: https://doi.org/10.1556/606.2024.01244

Keywords:: mathematical model; dual-purpose solar collector; effectiveness method; numerical simulation; heat removal factor

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Abstract

The present study uses three mathematical approaches to analyze the dual-purpose solar collector, making it a novel contribution. Three MATLAB codes (C1, C2, and C3), each with its mathematical model, are developed. The effectiveness- number of transfer unit method is used in C1. The heat removal term is used in C2. In C3, the effectiveness term of the parallel flow heat exchanger is used. Mathematical modeling, simulation, and experimental validation are conducted for each code. The calculated numerical-experimental errors revealed excellent convergence. The root mean square error and the mean absolute error for C3 are 1.8 and 1.5, respectively, which are less than for C2 and C3. This indicates that C3 is a reliable approach and can assist researchers in future analysis of the dual-purpose solar collector.

Abstract

1 Introduction

Solar collectors are critical for absorbing renewable energy for various applications, including space heating, water heating, and electricity generation [1]. A solar thermal collector is a type of heat exchanger that transforms solar radiation energy into internal energy in the transport medium [2]. Typically, three categories of flat-plate solar heating collectors may be found: water-heating, air-heating, and dual-purpose collectors [3]. The bi-fluid thermal collector, also known as a Dual-Purpose Solar Collector (DPSC), consists of two separate sections: one for air heating and the other for water heating [4]. This hybrid system is a strategic approach that deals with the trade-offs between the lower efficiency of air-based heating collectors and the better performance of water-based ones in the domain. By using this cutting-edge technology, customers may simultaneously save expenses, optimize installation space, improve operational effectiveness, and have access to both heated air and water for a range of household or industrial uses [5], providing a comprehensive and effective solution for exploiting solar energy.

Like other solar collector types, evaluating DPSC is a crucial endeavor that aims to maximize their efficacy and validate their practical applicability. Researchers worldwide widely employ the Effectiveness- Number of Transfer Unit (ε-NTU) method for conducting comprehensive performance assessments of the DPSC. Researchers choose this technique because of its simplicity and versatility, which allows it to reliably forecast collector performance regardless of the system's design. Assari et al. [6] employed the ε-NTU approach to develop a mathematical model that assessed the thermal performance of a DPSC in various air channel designs. Significant factors were thoroughly examined, like how the DPSC's heat exchange efficiency fluctuates by the water inlet temperature, air flow rate, and air duct design. After prolonged testing, the model has demonstrated its suitability for both liquid and air collectors of any shape. Jaffari et al. [7] thoroughly examined the DPSC, considering its energetic and energetic aspects. Their investigation employed the ε-NTU approach, emphasizing triangular-shaped air channels. The results demonstrate that the DPSC has higher energy and exergy efficiency than individual collectors. Zhang et al. [8] constructed a mathematical model to explain the functioning of a DPSC in three distinct modes: air heating, water heating, and air-water combination heating. The mathematical representation of the DPSC has been derived by applying the principles of the effectiveness-NTU method. Various experiments were carried out to assess the collector's performance in real-world situations. The findings suggest that dual-purpose systems can improve efficiency while reducing heat dissipation. More and Pole [9] enhanced the thermal performance of the DPSC by making changes to the design proposed by earlier researchers. Furthermore, they constructed a mathematical model utilizing the ε-NTU technique and validated its correctness through actual observations. The results were confirmed against experimental data, demonstrating the model's ability to make exact predictions.

Other scholars have used the energy balance equation as one of their techniques for building the DPSC mathematical model. This approach creates an energy balance equation for each component of the solar collector and solves it numerically. Ma et al. [10] developed a mathematical model specifically for the behavior of DPSCs in air heating mode. This model combines the energy balance equations governing the collector's components: the cover, back plate, and absorber plate. The mathematical framework's ability to appropriately represent the collector's behavior in air heating mode was verified via rigorous development and experimental validation. In addition, a theoretical analysis was carried out to assess how the flow rate and structural characteristics affect the collector's performance. Shemelin and Matuska [11] constructed an intricate theoretical framework for DPSCs, validated by experimental testing. This model calculates the one-dimensional energy balance equations of the solar collector under steady-state circumstances, taking into account two distinct designs. In addition, they utilized the model within the TRNSYS software simulation environment to evaluate the collector's energy efficiency. The results showed that the DPSC produced more solar energy than the control group, which might lead to more efficient solar systems. In their numerical study, Venu and Arun [12] assess the performance of an improved DPSC that uses a porous material under the absorber plate. Their fluid flow model used the incompressible Navier-Stokes equation with ANSYS 13 software simulation assistance. The modified DPSC significantly outperforms the conventional DPSC in temperature rise for both the air and water streams. Another mathematical model of a novel DPSC was developed by Somwanshi and Sarkar [13]. Using the energy balance equation, they created a simple mathematical model for each component. They applied the energy balance equation and validated it against the experiments conducted in Raipur, India. They developed a model in their study that can predict the system's performance under various climatic conditions. A DPSC embeddable in a hybrid solar drying system was suggested and built by Hao et al. [14]. They develop a mathematical model for every component using the energy balance concept. They also numerically solve the energy balance equations using the finite difference method, and simultaneously solve them using an implicit technique with MATLAB 2014a software. The experimental measurements examining its operating strategy, thermal performance, and economic analysis agree with this work's numerical simulation findings. Rao and Somwanshi [15] meticulously crafted a comprehensive mathematical model for the V-corrugated double-glazed dual-purpose air-water heater, taking into account the energy balances of the top and bottom glass covers, air flow, absorber plate, water, and basin. Furthermore, they conducted an experimental validation to confirm the accuracy and reliability of their mathematical framework.

The literature review indicates two primary methods for analyzing the DPSC. The first method involves using the energy balance equation, where a system of equations is derived for each component of the DPSC, and these equations are solved numerically. The second method is the effectiveness-NTU method, which can be applied to any design of DPSC. This method defines the heat removal factor (F_R), a pivotal parameter that computes the beneficial heat acquisition of solar collectors as a function of the effectiveness equation. This study proposes an alternative approach to analyzing the DPSC by defining the effectiveness equation for a parallel flow heat exchanger. This allows us to use the effectiveness equation directly for calculating heat gain instead of relying on the heat removal factor. This study seeks to develop a mathematical framework for the DPSC by employing multiple analytical approaches. Three methodologies are utilized to formulate the heat removal factor equation, which is essential for calculating the beneficial heat acquisition of solar collectors. The first method derives the heat removal factor as a function of the effectiveness found in the literature. The second method employs the heat removal factor specifically for the DPSC when operating as a single-purpose collector. The third novel approach considers the DPSC as a parallel flow heat exchanger, using the effectiveness equation instead of the heat removal factor. To the best of available knowledge, previous research has yet to conduct three distinct analyses of the DPSC simultaneously. These methodologies have been numerically investigated using three MATLAB codes to simulate the thermal performance of the DPSC. The models were validated against experimental research conducted by [16]. Additionally, the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) were calculated to assess the accuracy of the proposed models in relation to one another. Based on the estimated percentage error values, the optimal model is identified. This research is considered novel in the field, as it employs three different analytical procedures that have yet to be previously utilized in this context. The new and optimal model could significantly aid researchers in future studies analyzing the DPSC. This multidimensional analysis aims to enhance understanding of the collector's behavior and identify the most appropriate analytical approach for accurate prediction.

2 Mathematical model

The mathematical model described in this work applies to the DPSC systems, including a straight air channel devoid of fins (see Fig. 1).

Fig. 1.

Schematic layout of the DPSC used in the present study

Citation: Pollack Periodica 2025; 10.1556/606.2024.01244

For modeling any solar thermal collector, the most crucial output parameter is the useful heat gain in the collector

\dot{Q_{u}}

, which is obtained by applying the energy balance equation as follows [17]:

\dot{Q_{u}} = {\dot{Q}}_{A b s o r b e d} - {\dot{Q}}_{L o s t},

(1)

where

{\dot{Q}}_{A b s o r b e d}

denote the amount of the absorbed energy in the absorber plate and

{\dot{Q}}_{L o s t}

denote the lost energy to the surroundings. These amounts can be computed using Eqs (2) and (3), respectively, as [18]:

{\dot{Q}}_{A b s o r b e d} = (τ α) I_{t} A_{c},

(2)

{\dot{Q}}_{L o s t} = U_{L} A_{c} (T_{p l a t e} - T_{a}),

(3)

where

τ α

is transmittance-absorptance product,

I_{t}

is the incident irradiance,

A_{c}

stands to the collector area,

T_{p l a t e}

represents the absorber plate temperature, and

T_{a}

corresponds to the outside temperature. Therefore, Eq. (1) can be rewritten as:

\dot{Q_{u}} = A_{c} [(τ α) I_{t} - U_{L} (T_{p l a t e} - T_{a})],

(4)

where the overall heat loss coefficient of the collector

U_{L}

stands for heat losses from the top

U_{t}

, back

U_{b}

, and sides

U_{s}

surfaces of the solar collector [18]:

U_{L} = U_{t} + U_{b} + U_{s} .

(5)

Equation (4) can be expressed in terms of the inlet fluid temperature

T_{f, i n}

and a parameter called the collector heat removal factor F_R as [18]:

{\dot{Q_{u}} = F}_{R} A_{c} [(τ α) I_{t} - U_{L} (T_{f, i n} - T_{a})],

(6)

here the subscript f stands for fluid, water or air. The heat removal factor in equation form is [18]:

F_{R} = \frac{\dot{m_{f}} C_{p, f} (T_{f, o u t} - T_{f, i n})}{A_{c} [(τ α) I_{t} - U_{L} (T_{f, i n} - T_{a})]},

(7)

where

C_{p, f}

is the fluid's specific heat.

The previously outlined mathematical equations are implemented for a single-purpose solar collector or when the DPSC is operated in air or water mode. Researchers adopting a segmented approach to DPSC analysis, dividing it into distinct air and water sections, often utilize Eqs (1) to (7) outlined above. Conversely, researchers treating the DPSC as a unified entity implement the effectiveness-NTU method into their analytical framework. The concept of effectiveness parameter is defined as the ratio of the actual heat delivery to the maximum heat delivery that can be transferred to fluids, and it is given as [6]:

ε_{f} = 1 - \exp [- \frac{h_{f} A_{f}}{\dot{m_{f}} C_{p, f}}] .

(8)

From Eq. (8), NTU defines as:

N T U = \frac{h_{f} A_{f}}{\dot{m_{f}} C_{p, f}},

(9)

were

h_{f}

is the heat transfer coefficient of the fluid (water or air),

A_{f}

is the fluid's heat exchange surface area.

The effectiveness parameter is also used in calculating the heat removal coefficient for the second fluid

U_{f}

as [6]:

U_{f} = \frac{ε_{f} \dot{m_{f}} C_{p, f} (T_{p l a t e} - T_{f, i n})}{A_{f} (T_{p l a t e} - T_{a})} .

(10)

This new term is incorporated into Eq. (5), resulting in a revised expression for the overall heat loss coefficient:

U_{L} = U_{t} + U_{b} + U_{s} + U_{f} .

(11)

Furthermore, the heat removal factor in Eq. (7) can be modified to be [6]:

F_{R} = \frac{ε_{f} \dot{m_{f}} C_{p, f}}{{U_{L} A_{f} + ε}_{f} \dot{m_{f}} C_{p, f}} .

(12)

Equations (11) and (12) are substituted into Eq. (6) to obtain the useful heat gain of the DPSC. The present study treats the DPSC as a parallel flow heat exchanger. The effectiveness equation for this exchanger type is utilized in the current mathematical analysis, as referenced in standard heat transfer handbooks. It can be calculated as [19]:

ε_{f} = \frac{1 - \exp [(- h_{f} A_{f} / C_{\min}) (1 + C_{\min} / C_{\max})]}{(1 + C_{\min} / C_{\max})},

(13)

here (

C = \dot{m_{f}} C_{p, f}

) is defined as the capacity rate, which relies on the fluid's mass-flow rates and specific heat. The minimum and/or maximum capacity rate may pertain to either air or water [19].

The novelty of the current study lies in developing the effectiveness-NTU method, a widely employed approach found in existing literature and research papers. In this research, the effectiveness term given in Eq. (13) is directly implemented into Eq. (6) to figure out the useful heat gain of DPSC instead of using the term F_R. To the best of the authors' knowledge and thorough examination of the literature in this domain, no other researchers have employed this particular method. The heat removal factor $F_{R}$ is a function of effectiveness ε, while in the adopted model; it is replaced with Eq. (13). This decision is motivated by the alignment of the heat removal factor's definition with that of the effectiveness parameter [18, 19]. Furthermore, Eq. (8) is combined with Eq. (12) as a second analytical method of the DPSC. Additionally, as a third analytical technique, the performance of the DPSC is analyzed in a way analogous to that conducted by other researchers by considering it as a single-purpose collector (i.e., a DPSC but working in a single mode either water or air heating mode) and employed Eq. (7) for this analysis.

3 Numerical simulation and error metrics

In this work, a computer program was developed to simulate the operation of the DPSC. Three distinct MATLAB codes have been created for the present simulation, each corresponding to a unique mathematical model technique. The three codes have design and operational properties closely connected to the experimental study of Saleh and Jasim [16]. The selected analytical approach for each code varies from one another. In the first code C1, the effectiveness ε given by Eq. (8) is substituted into Eq. (12), which is used to calculate the heat removal factor

F_{R}

, which in turn utilized to calculate the useful heat gain as depicted in Eq. (6). As stated previously in the introduction section, this approach is widely employed by researchers. In the second code C2, Eq. (7) is used to calculate the heat removal factor

F_{R}

, which is used for the DPSC when it is operated as a single-purpose collector. Meanwhile, in the third code C3, Eq. (13) is implemented, which stands for the effectiveness of the paralleled flow heat exchanger instead of using Eq. (12) as a novel analysis for the DPSC. Thus, Eq. (8) will be calculated as a function of ε rather than

F_{R}

. The latter code manifests the novelty in the current work. The simulation software is utilized to forecast the properties of a system, including the temperature of hot water and air, and the amount of usable energy absorbed by the fluid. The computational algorithm is depicted in the flow chart displayed in Fig. 2. Two indicators have been employed among the many tools that have been used to analyze the DPSC performance prediction accuracy. The first is the MAE, which captures the average absolute deviation between two datasets. It is expressed mathematically as [20]:

M A E = \frac{1}{n} \sum_{i = 1}^{n} | (T_{o u t, (S)} - T_{o u t, (E)}) | .

(14)

Fig. 2.

Simulation flow chart

Citation: Pollack Periodica 2025; 10.1556/606.2024.01244

The second criterion is the RMSE, a defined measure that combines the spread of individual errors. The largest values strongly dominate it due to the squaring operation. Especially in cases where prominent outliers occur, the usefulness of RMSE is questionable, and the interpretation becomes more complex [21]:

R M S E = \sqrt{\frac{1}{n} \sum_{i = 1}^{n} {(T_{o u t, (S)} - T_{o u t, (E)})}^{2}} .

(15)

In this context, n, $T_{o u t, (S)}$ , and $T_{o u t, (E)}$ are the total number of values that have been analyzed, the outlet fluid (water and air) temperature received from the simulation, and the temperature obtained from experiments.

4 Results and discussion

The DPSC used in the current simulation is identical to the one employed by Saleh and Jasim [16]. The system is comprised of two distinct sections: water and air section. The air portion is a rectangular duct that lacks fins, whereas the water section consists of risers and two primary headers (see Fig. 1). The current numerical simulation utilized identical input values, including climatic and operational parameters, as those employed in [16]. In accordance with the previous debate in Section 3, the analysis includes three numerical simulation models, each employing a distinct analytical approach. The numerical outlet fluid temperatures yielded from these programs contrasted with the experimental work of Saleh and Jasim [16]. The air outlet temperature obtained from the three MATLAB codes C1, C2, and C3 along with the experimental results is illustrated in Fig. 3. The predicted results and experimental data coincide extremely well during the first three hours. The variations begin around eleven o'clock and persist until five o'clock in the afternoon.

Fig. 3.

The variation of air outlet temperature with time for the three codes

Citation: Pollack Periodica 2025; 10.1556/606.2024.01244

It is worth mentioning that the gap between our ingenious code C3 and experimental findings is only one and a half to three degrees Celsius, while it exceeds five degrees Celsius for both C1 and C2. However, the error values between the output numerical results and the measured data are then calculated. The RMSE values for C1, C2, and C3 are 3.4%, 3.8%, and 1.8%, respectively. The MAE values for C1, C2, and C3 were 3%, 3.3%, and 1.5%, respectively. It is crucial to emphasize that the RMSE and MAE values of our mathematical model C3 reveal superior temperature prediction competencies compared to mathematical models C1 and C2. Figure 4 plainly shows the tremendous capability of our innovative and proposed mathematical model C3 for forecasting the water outlet temperature. It is obvious that the predicted values of the outlet water temperature obtained from the simulation codes highly reflect the experimental results. While the predicted temperatures are relatively consistent with the actual temperature, the temperature yielded from code C3 remains superior due to the relatively lower error values compared to the remaining codes C1 and C2. Nevertheless, the RMSE values for C1, C2, and C3 are 0.8%, 1.7%, and 0.3%, respectively. The MAE values for C1, C2, and C3 were 0.7%, 1.4%, and 0.3%, respectively.

Fig. 4.

The variation of water outlet temperature with time for the three codes

Citation: Pollack Periodica 2025; 10.1556/606.2024.01244

5 Conclusion

The development of highly accurate prediction models has been investigated extensively in the present work to enhance the performance of DPSC. To develop the theoretical analysis of the DPSC, the mathematical equation of the heat removal factor $F_{R}$ was improved. Three mathematical codes, C1, C2, and C3, each with its theoretical technique, were numerically tested using the MATLAB simulation tool. In the current study, code C3 comprises a novel mathematical technique among the remaining written codes. However, the current investigation is a new trend in the field of analysis of DPSC since it includes three groundbreaking analytical approaches. The modeling outcomes' preciseness was verified by contrasting them with experimental outcomes. The simulation results show good consistency with the experimental results. Two important metrics, RMSE and MAE, were used to indicate the divergence between the simulation and actual results. It was determined that code C3, which is novel, outperformed the other models based on the computed erroneous because it has the lowest error values compared to the other models. Thus, code C3 can precisely predict the water and air outlet temperature and the thermal performance of the DPSC as well. This can be a significant contribution that can assist future researchers in their studies for DPSC. This study contributes to the ongoing discourse on solar collector analysis by offering insights into the benefits and limitations of employing multiple methods in performance evaluation.

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Pollack Periodica

Print ISSN:: 1788-1994

Online ISSN:: 1788-3911

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
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

2024
Scopus
CiteScore
CiteScore rank
SNIP
Scimago
SJR index	0.385
SJR Q rank	Q3

2023
Scopus
CiteScore	1.5
CiteScore rank	Q3 (Civil and Structural Engineering)
SNIP	0.849
Scimago
SJR index	0.288
SJR Q rank	Q3

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 2025	Online subsscription: 381 EUR / 420 USD Print + online subscription: 456 EUR / 520 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	Faculty of Engineering and Information Technology, University of Pécs
Founder's Address	H–7624 Pécs, Hungary, Boszorkány utca 2.
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)