Pollack Periodica
Volume/Issue:
Volume 15: Issue 2

Enhance thermal efficiency of parabolic trough collector using Tungsten oxide/Syltherm 800 nanofluid

Authors:
and
Otabeh Al-Oran Department of Energy Engineering, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, 1111 Budapest, Műegyetem rkp.3, Hungary

Search for other papers by Otabeh Al-Oran in
Current site
Google Scholar
PubMed Close
Ferenc Lezsovits Department of Energy Engineering, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, 1111 Budapest, Műegyetem rkp.3, Hungary

Search for other papers by Ferenc Lezsovits in
Current site
Google Scholar
PubMed Close
Pages:
187–198
Online Publication Date:
Aug 2020
Publication Date:
01 Aug 2020
Article Category:
Research Article
DOI:
https://doi.org/10.1556/606.2020.15.2.17
Keywords (English):
Parabolic trough collector; Solar energy; Nanofluid; Thermal efficiency; Enhancement
Open access

Abstract:

Development of thermal efficiency of the concentrated solar energy especially parabolic trough collector using various nanofluids types has a taken high interest in recent years. In this article enhancement thermal performance inside the heating collecting element of trough collector type LS-2 was simulated and improved using nanofluid consist of Tungsten Oxide WO3 inserting in Syltherm 800. Nanofluid effect was examined by solving the energy balance equation using MATLAB Software to cover wide range concentration volume 1-5% and inlet temperatures ranging from 350-650 K for the turbulent flow. The heat transfer performance and thermal efficiency were improved based on the results, and a notable increase was obtained when volume concentration had been increased compared with base fluid.

Abstract:

Development of thermal efficiency of the concentrated solar energy especially parabolic trough collector using various nanofluids types has a taken high interest in recent years. In this article enhancement thermal performance inside the heating collecting element of trough collector type LS-2 was simulated and improved using nanofluid consist of Tungsten Oxide WO3 inserting in Syltherm 800. Nanofluid effect was examined by solving the energy balance equation using MATLAB Software to cover wide range concentration volume 1-5% and inlet temperatures ranging from 350-650 K for the turbulent flow. The heat transfer performance and thermal efficiency were improved based on the results, and a notable increase was obtained when volume concentration had been increased compared with base fluid.

1 Introduction

Growing attention of the pollutant issues, climate change problems in addition to the shortage that occurs on fossil fuel resulted by rising of the consumption contributed to accelerating the needed to replace and search on the clean and alternative types of energy [1]. Solar energy has gained more interest compared to other renewable energy sources like wind energy and biomass energy. Whereas this clean energy plays an essential role in the mitigation of the economic burden particularly for the sunny regions like Jordan [2]. Study solar intensity effect on various collectors’ types in any region demands deep knowledge on how defined various geometrical variables vary with the sun position [3]. Parabolic Trough Collector (PTC) is considered as one of the most typical concentrated solar power devices, which is used widely to produce high and medium temperatures coinciding with high efficiencies [4]. Developing PTC to be more effective and efficient has been reached using various passive and active techniques. These techniques aimed to enhance heat transfer and the ability of fluid to carry focusing rays and decrease heat loss from the heating collecting of the PTC [5]. Recently, inserting metallic and oxides particles that have a small diameter measured in nanoscale in various base fluids took high interest as an enhancement technique called nanofluid [6]-[8]. The effect of using this technique was examined experimentally and numerically using various nanoparticles and base fluids in different scientific researches. The founding results of these researches showed a varying enhancement in the most researches while few pieces of research did not show an increase in the thermal efficiency [9]. Different of the experimental works examined various nanoparticles namely (Al2O3, TiO2, Fe2O3, MWCNT, CuO, SiO2, Nano silica, Ag) in various places of the world and under various conditions. In addition, these researchers examined the mentioned nanoparticles with different base fluids (i.e. water, Ethylene Glycol (EG) and oil). Finally, these examined tests were affected by the different volume or weight concentrations and variable mass or volume flow-rate [10]-[12]. The numerical side took more attention compared to experimental works; for this side defined and solved energy equation of the heat collecting element under various conditions, which was simulated using different programs like using Computational Fluid Dynamics (CFD) [13]. Other simulation programs can be used like engineering equation solver, MATLAB, and Solid Works also were used widely to simulate thermal efficiency and heat transfer performance resulted by inserting different nanofluids. In Table I the researches that used various nanoparticles with oil as a base fluid were presented in addition to the main founding enhancement results and main parameters of PTC and the receiver pipe [14]-[20].

Table I.

Thermal performance enhancements of PTC using nanofluid technique

RefPTC Model L,W do,di mmNanofluidMax, Increase%
Nanoparticle-base fluidΦ Maxηeffh
[14](2,0.7) (28,26)MWCNT -Thermal-oil6%.v49.5815%
[15]LS2 PTCTio2,Al2O3/Hybrid-Syltherm 8003%vMono 0.7% Hyb 1.8 %Mono 1.35% Hyb 2.4 %
[16]LS2 PTCCuO, Cu,Fe2O3, TiO2,Al2O3, SiO2 -Syltherm 8006%vCu 2.2%∼24% -Cu
[17]LS2 PTCCuO,Cu,Ag,Al2O3- Syltherm 8005%v-36% Ag
[18]100,5.5 (-,65)Al2O3 -Synthetic-oil5%v∼053.5%
[19]Euro TroughAl2O3,CuO-Syltherm 8004%vMax CuO 1.26%∼45%
[20](5,1.5) (40,65)CuO,TiO2, Al2O3,SiO2 -mineral oil5.5%∼0-

From Table I the commercial PTC type LS2 has a larger number of researches compared to other commercial and domestic types, according to the available experimental results data under wide range conditions can be used to validate simulation results [21]. Various nanoparticle types were simulated with high interest for that using Al2O3 compared to other nanoparticles. Moreover, it showed variable enhance between the same nanoparticles and others referred to different aspects like types of the parabola and receiver, type of the nanoparticles, concentrations. Finally, most mentioned research showed enhancing, except some of them that showed negligibly improve [18], [20].

According to the literature there is no research that examined the effect of using Tungsten oxide WO3/Syltherm 800 nanofluid as a heating fluid flow inside the receiver of PTC. So this article aimed at examining the ability of this modified fluid to enhance the thermal efficiency of the parabola type LS2 by solving the thermal balance energy equation using MATLAB code. Moreover, this article aimed at compared thermal efficiency, convective heat transfer coefficient, and Nusselt number that resulted by various volume concentration ranging 1-5%, and wide inlet temperature ranges from 350-650 K. Finally, the radiation intensity that was used through this research was taken as a constant equal maximum value. This maximum value was produced by presenting the radiation intensity using ASHRAE model for typically sunny day of Jordan under geographical location 31.°57'N / 35°55'E.

2 Model specification

To convert concentrated radiation on the heat collecting element of PTC high reflective mirrors were designed on a parabola shape as shown in Fig. 1 to enhance the temperature of the Thermal Fluid (TF), which leads to enhancement of thermal efficiency. The receiver part nowadays is covered with a glass envelope and coated with high absorptivity material to minimize heat losses and to improve heat transfer to TF flow inside the receiver.

Fig. 1.
Fig. 1.

Conceptual parabolic trough collector

Citation: Pollack Periodica 15, 2; 10.1556/606.2020.15.2.17

Parabolic trough model type LS2 developed and analyzed under maximum level radiation intensity of Jordan as a case study and WO3/ Syltherm 800 nanofluid. The heating element of this model was described as a one-dimensional, where the energy balance equations solved numerically using MATLAB software. Actually, all the dimensions and parameters of this PTC type used in this work were taken as summarized in [15], as presented in Table II.

Table II.

Main parameters and dimensions [15]

LS-2 Parameter [Symbols]SpecificationsParameter [Symbols]Specifications
Length of the PTC [L]7.8 mEmittance of glass cover [εc]0.9
Aperture Width of the

PTC [Wa]		5 mIncident Angle [θ]0
Aperture Area [Aa]39 m2Max optical efficiency [ηopt]74.5%
Focal length [F]1.71 mGlass cover absorbance [αc]0.02
Concentration ratio [C]22.74Glass cover transmittance [τc]0.95
Absorber inner diameter [dri]0.066 mAbsorber absorbance [αr]0.96
Absorber outer diameter [dro]0.07 mConcentrator reflectance [ρc]0.83
Glass inner diameter [dci]0.109 mIntercept factor [γ]0.99
Glass outer diameter [dco]0.115 mEmittance of the absorber [εr]0.2

2.1 Radiation intensity

Estimated radiation intensity was defined basing on 15th of July as a typical sunny day of the Jordan, which located under geographical location 31.°57'N/35°55'E using the ASHRAE model. Basically, this model was based on the following equations to define normal solar beam radiation and diffused radiation in the horizontal surface,
DNI=A×e−Bcosθz
Id=C×DNI.

The factors (A, B, and C) are equal to 1085, 0.207, 0.136, while describes zenith angel [22]. All the correlations were inserted as subroutine code to define the variation of the solar radiation on the PTC. The results showed maximum radiation of 998.7 W/m2 at midday as it is illustrated in Fig. 2.

Fig. 2.
Fig. 2.

Total radiation intensity on the PTC and horizontal surface under radiation intensity of Amman

Citation: Pollack Periodica 15, 2; 10.1556/606.2020.15.2.17

2.2 Thermal model

The main goal of this section was to describe the thermal model inside the receiver tube of the PTC by solving the energy balance equation at different nods by dividing the receiver section into different segments. Through this section, the thermal resistance, heat loss and heat transfer directions from thermal heating fluid and outside have been presented. Study the mode of the heat transfer convection, radiation and conduction at a different point of glass through receiver tube until you reach the TF used to describe the heat gained by the system illustrated as it is shown in Fig. 3, which was used to estimate the thermal efficiency as expressed in the following equations (3)-(6) [19].
ηthe=QuQs,
Qu=m⋅Cp(Tf,out−Tf,in).
Fig. 3.
Fig. 3.

Evacuated tube receiver and resistance nods

Citation: Pollack Periodica 15, 2; 10.1556/606.2020.15.2.17

Useful energy (Qu) can be calculated by multiplying the convection heat transfer coefficient (h) by the temperatures difference between inlet receiver temperature (Tri), and fluid mean temperature (Tfm), as expressed in the following equation,
Qu=hAri(Tri−Tfm),
where the sensible heat defined as the amount of intensity beam irradiation Gb captured multiplied by the reflected apertures area Aap, where equation (6) expressed that
Qs=Aap.Gb.

2.3 Thermal fluid specifications

This section summarized the equations and main correlations obtained from literature to describe the thermal properties of the nanofluid. For this research, a modified nanofluid subscribed by (nf) was obtained by mixing Syltherm-800 as a base fluid (bf) and Tungsten oxide WO3 nanoparticle subscribed by (np) to define the density (ρ), specific heat capacity (Cp), thermal conductivity (k) and dynamic viscosity (μ) that lead enhancement in the thermal efficiency. The effect of using Tungsten oxide nanofluid in the thermal efficiency of PTC was analyzed under variable inlet temperature 350 K - 650 K, volume fraction φ (1%-5%), and maximum obtained radiation attends to the parabola surface, which is equal to 998.78 W/m2. The following equations from (7) to (10) were used to define various thermal nanofluid properties [23]-[26],
ρnf=(1−φ)ρbf+φρnp,
Cpnf=1ρnf[(1−φ)]ρbfCpbf+φρnpCpnp,
knf=kbf[knp+2kbf+2(knp−kbf)(1−β)3φknp+2kbf−(knp−kbf)(1−β)3φ],
μnf=μbf1(1−φ)2.5.

Mainly, the correlations that used to cover the thermal properties of Syltherm-800 as a base fluid were selected from literature, as mentioned by Mwesigye and Huan [27]. While the thermal properties of the nanoparticle presented in Table III were picked, as indicated by Sharafeldin and Gróf, in their research [28].

Table III:

Nanoparticles specifications on the basis of [28]

Property/NanoparticlesTungsten oxide nanoparticle WO3
Specific heat Cpnp (J/kg K)315.4
Density ρnp (kg/m3)7160
Thermal conductivity knp (W/m K)16

Thermal efficiency improvement using modified nanofluid of WO3/Syltherm 800 was simulated by solving thermal energy balance using MATLAB code, where the flow chart in Fig. 4 used to illustrate the procedure, input, and output that were used in this research.

Fig. 4.
Fig. 4.

Simulation model flow chart

Citation: Pollack Periodica 15, 2; 10.1556/606.2020.15.2.17

3 Results and discussions

A validation of a thermal model using Syltherm-800 as a base fluid showed high accuracy behavior with the experimental results of the same type of PTC; that was conducted by Dudley [21]. Various thermal efficiency results were illustrated for different conditions as it is shown in Fig. 5 under a mean deviation equal to 1.15%.

Fig. 5.
Fig. 5.

Comparison between present model with previous experimental results that conducted by Dudley [21]

Citation: Pollack Periodica 15, 2; 10.1556/606.2020.15.2.17

As it has been mentioned before, various inputs are taken as a constant parameter regarding the main aim of this type of research, which aimed to improve the thermal efficiency of PTC using Tungsten oxide nanofluid under variable volume concentration. Regarding the radiation intensity, which was taken as a maximum value of 998.7 W/m2, while the optimum volumetric flow-rate was taken 150 L/min after presenting the relationship between the efficiency and volumetric flow-rate at different temperatures as it is shown in Fig. 6.

Fig. 6.
Fig. 6.

Thermal efficiency attitude versus variable volume flow-rate for different inlet temperature

Citation: Pollack Periodica 15, 2; 10.1556/606.2020.15.2.17

Fig. 7a - Fig. 7d present the description of the thermal conductivity, density, specific heat capacity and dynamic viscosity with variable inlet temperature (350-concentrations compared with Syltherm-800. While on another hand, a specific heat capacity of the nanofluid concentrations presented opposite effect compared with base fluid. Moreover, the nanofluid thermal properties behavior varied between different concentrations and showed high variation at high volume concentration.

Fig. 7.
Fig. 7.

Thermal properties trends with variable inlet temperatures and volume concentrations

Citation: Pollack Periodica 15, 2; 10.1556/606.2020.15.2.17

The change in the thermal properties of the nanofluid has a positive attitude reflected in the thermal performance of the PTC. Fig. 8 and Fig. 9 illustrate the effect of the thermal properties in the dimensionless Nusselt number and convective heat transfer coefficient, respectively.

Fig. 8.
Fig. 8.

Nusselt number versus inlet temperatures for different volume concentrations of WO3/ Syltherm 800 nanofluid

Citation: Pollack Periodica 15, 2; 10.1556/606.2020.15.2.17

Fig. 9.
Fig. 9.

Convective heat transfer coefficient versus inlet temperatures for different volume concentrations of WO3/Syltherm 800 nanofluid

Citation: Pollack Periodica 15, 2; 10.1556/606.2020.15.2.17

Fig. 8 presents the Tungsten oxide nanofluid impact in the dimensionless Nusselt number compared with the base fluid. The results showed an increase in Nusselt number with increasing inlet temperature and nanofluid volume concentrations. For more details, Nusselt number of the base fluid increased from 178.9 up to 620.7 for the temperatures range from 350 - 650 K while increased from 187.23 up to 667.798 for the WO3 nanofluid at volume concentration equal 5%. This leaded to maximum enhancement ratio equal 7.6%. This enhancement can be justified according the thermal properties effect in the Nusselt number definition.

Fig. 9 illustrates the convective heat transfer coefficient of the Tungsten oxide nanofluid as well as Syltherm 800 versus variable inlet temperature and volume concentrations. The results showed an increase in the convective coefficient with increasing volume concentration and the temperature. The maximum results were obtained at temperature approximately equal 575 K. This referred to a decrease of the thermal conductivity enhancement rate after this temperature. So the maximum convective heat transfer coefficient reached 833 W/m2.K for WO3 nanofluid at 5% volume concentration while reached 650W/m2.K for the base fluid under enhancement ratio equal 28.15%. Regarding to this value, the enhancement in the convection coefficient is greater than the enhancement in the dimensionless Nusselt number referred to the mean definition of this coefficient, which is equal Nu*k/dr,in.

Finally, the enhancement results of the dimensionless Nusselt number and convective coefficient lead increase the thermal efficiency and the outlet temperature of the nanofluids. So, Fig. 10 illustrates the thermal efficiency results for different volume concentration of the WO3 nanoflid with variable inlet temperature. The results showed decrease in the thermal efficiency with increasing temperature, while showed increasing in the thermal efficiency with the volume concentration. The major results showed slightly enhancement at high-level temperature and high volume concentration reached 0.66%.

Fig. 10.
Fig. 10.

Thermal efficiency versus inlet temperatures for different volume concentrations of WO3/Syltherm 800 nanofluid

Citation: Pollack Periodica 15, 2; 10.1556/606.2020.15.2.17

4 Conclusion

In this paper, inserting WO3/Syltherm-800 as a new modified nanofluid was used to improve the thermal performance of commercial PTC type LS2 under variable volume concentrations and temperatures. Moreover, this improvement, analyzed under the maximum obtained radiation intensity of Amman, was the maximum value 998.7 W/m2. Finally, the enhancement in the thermal performance of this PTC was presented and compared with base fluid, where the obtained results showed high enhancement at high volume concentration reached 7.6% and 28.15% for the dimensionless Nusselt number and convective heat transfer coefficient, respectively, while the thermal efficiency showed slightly enhancement reached 0.66%.

References

  • [1]↑

    Caló A., Pongrácz E. The role of smart energy networks to support the application of waste-to-energy technologies, Pollack Periodica , Vol. 9, Supplement 1, 2014, pp. 61‒73.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [2]↑

    Jaber J. O., Elkarmi F., Alasis E., Kostas A. Employment of renewable energy in Jordan: Current status, SWOT and problem analysis, Renewable and Sustainable Energy Reviews, Vol. 49, 2015, pp. 490‒499.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [3]↑

    Duffie J. A., Beckman W. A. Solar engineering of thermal processes, John Wiley, 2013.

  • [4]↑

    Fernández-García A., Zarza E., Valenzuela L., Pérez M. Parabolic-trough solar collectors and their applications, Renewable and Sustainable Energy Reviews, Vol. 14, No. 7, 2010, pp. 1695‒1721.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [5]↑

    Akbarzadeh S., Valipour M. S. Heat transfer enhancement in parabolic trough collectors: A comprehensive review, Renewable and Sustainable Energy Reviews, Vol. 92, 2018, pp. 198‒218.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [6]↑

    Sajid M. U., Ali H. M. Recent advances in application of nanofluids in heat transfer devices: A critical review, Renewable and Sustainable Energy Reviews, Vol. 103, 2019, pp. 556‒592.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [7]

    Bellos E., Tzivanidis C., Tsimpoukis D. Enhancing the performance of parabolic trough collectors using nanofluids and turbulators, Renewable and Sustainable Energy Reviews, Vol. 91, 2018, pp. 358‒375.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [8]↑

    Kamel M. S., Lezsovits F. Simulation of nanofluids laminar flow in a vertical channel, Pollack Periodica, Vol. 13, No. 2, 2018,147‒158.

  • [9]↑

    Coccia G., Di Nicola G., Colla L., Fedele L., Scattolini M. Adoption of nanofluids in lowenthalpy parabolic trough solar collectors: numerical simulation of the yearly yield, Energy Conversion Management , Vol. 118, 2016, pp. 306‒319.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [10]↑

    Rehan M. A., Ali M., Sheikh N. A., Khalil M. S., Chaudhary G. Q., ur Rashid T., Shehryar M. Experimental performance analysis of low concentration ratio solar parabolic trough collectors with nanofluids in winter conditions, Renewable Energy, Vol. 118, 2018, pp. 742‒751.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [11]

    Subramani J., Nagarajan P. K., Wongwises S., El‐Agouz S. A., Sathyamurthy R. Experimental study on the thermal performance and heat transfer characteristics of solar parabolic trough collector using Al2O3 nanofluids, Environmental Progress and Sustainable Energy, Vol. 37, No. 3, 2018, pp. 1149‒1159.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [12]↑

    Alsaady M., Fu R., Yan Y., Liu Z., Wu S., Boukhanouf R. An experimental investigation on the effect of ferrofluids on the efficiency of novel parabolic trough solar collector under laminar flow conditions, Heat Transfer Engineering, Vol. 40, No. 9-10, 2019, pp. 753‒761.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [13]↑

    Ghasemi S. E., Ranjbar A. A. Thermal performance analysis of solar parabolic trough collector using nanofluid as working fluid: A CFD modelling study, Journal of Molecular Liquids, Vol. 222, 2016, pp. 159‒166.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [14]↑

    Kasaiean A., Sameti M., Daneshazarian R., Noori Z., Adamian A., Ming T. Heat transfer network for a parabolic trough collector as a heat collecting element using nanofluid, Renewable Energy , Vol. 123, 2018, pp. 439‒449.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [15]↑

    Bellos E., Tzivanidis C. Thermal analysis of parabolic trough collector operating with mono and hybrid nanofluids, Sustainable Energy Technologies and Assessments, Vol. 26, 2018, pp. 105‒115.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [16]↑

    Bellos E., Tzivanidis C. Thermal efficiency enhancement of nanofluid-based parabolic trough collectors, Journal of Thermal Analysis Calorimetry, Vol. 135, No. 1, 2019, pp. 597‒608.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [17]↑

    Basbous N., Taqi M., Janan M. A. Thermal performances analysis of a parabolic trough solar collector using different nanofluids, International Renewable and Sustainable Energy Conference, Marrakech, Morocco, 14-17 November 2016, pp. 322‒326.

    • Search Google Scholar
    • Export Citation
  • [18]↑

    Ferraro V., Settino J., Cucumo M. A., Kaliakatsos D. Parabolic trough system operating with nanofluids: comparison with the conventional working fluids and influence on the system performance, Energy Procedia, Vol. 101, 2016, pp. 782‒789.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [19]↑

    Bellos E., Tzivanidis C. Parametric investigation of nanofluids utilization in parabolic trough collectors, Thermal Science and Engineering Progress, Vol. 2, 2017, pp. 71‒79.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [20]↑

    Toghyani S., Baniasadi E., Afshari E. Thermodynamic analysis and optimization of an integrated Rankine power cycle and nanofluid based parabolic trough solar collector, Energy Conversion and Management, Vol. 121, 2016, pp. 93‒104.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [21]↑

    Dudley V. E., Evans L. R., Matthews C. W. Test results, industrial solar technology parabolic trough solar collector, Technical Report ,Sandia National Labs, Albuquerque, NM, United States, 1995.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [22]↑

    Maleki S. A. M., Hizam H., Gomes C. Estimation of hourly, daily and monthly global solar radiation on inclined surfaces: Models re-visited, Energies, Vol. 10, No. 1, 2017, pp. 134‒162.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [23]↑

    Li P., Zhang D., Xie Y. Heat transfer and flow analysis of Al2O3–water nanofluids in microchannel with dimple and protrusion, International Journal of Heat and Mass Transfer, Vol. 73, 2014, pp. 456‒467.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [24]

    Zhou S. Q, Ni R. Measurement of the specific heat capacity of water-based Al 2 O 3 nanofluid, Applied Physics Letters, Vol. 92, No. 9, 2008, paper No. 093123.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [25]

    Yu W., Choi S. U. S. The role of interfacial layers in the enhanced thermal conductivity of nanofluids: A renovated Maxwell model, Journal of Nanoparticle Research, Vol. 5, No. 1-2, 2003, pp. 167‒171.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [26]↑

    Brinkman H. C. The viscosity of concentrated suspensions and solutions, The Journal of Chemical Physics, Vol. 20, No. 4, 1952, page 571.

  • [27]↑

    Mwesigye A., Huan Z. Thermal and thermodynamic performance of a parabolic trough receiver with Syltherm800-Al2O3 nanofluid as the heat transfer fluid, Energy Procedia, Vol. 75, 2015, pp. 394‒402.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [28]↑

    Sharafeldin M. A., Gróf G. Efficiency of evacuated tube solar collector using WO3/Water nanofluid, Renewable Energy, Vol. 134, 2019, pp. 453‒460.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [1]

    Caló A., Pongrácz E. The role of smart energy networks to support the application of waste-to-energy technologies, Pollack Periodica , Vol. 9, Supplement 1, 2014, pp. 61‒73.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [2]

    Jaber J. O., Elkarmi F., Alasis E., Kostas A. Employment of renewable energy in Jordan: Current status, SWOT and problem analysis, Renewable and Sustainable Energy Reviews, Vol. 49, 2015, pp. 490‒499.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [3]

    Duffie J. A., Beckman W. A. Solar engineering of thermal processes, John Wiley, 2013.

  • [4]

    Fernández-García A., Zarza E., Valenzuela L., Pérez M. Parabolic-trough solar collectors and their applications, Renewable and Sustainable Energy Reviews, Vol. 14, No. 7, 2010, pp. 1695‒1721.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [5]

    Akbarzadeh S., Valipour M. S. Heat transfer enhancement in parabolic trough collectors: A comprehensive review, Renewable and Sustainable Energy Reviews, Vol. 92, 2018, pp. 198‒218.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [6]

    Sajid M. U., Ali H. M. Recent advances in application of nanofluids in heat transfer devices: A critical review, Renewable and Sustainable Energy Reviews, Vol. 103, 2019, pp. 556‒592.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [7]

    Bellos E., Tzivanidis C., Tsimpoukis D. Enhancing the performance of parabolic trough collectors using nanofluids and turbulators, Renewable and Sustainable Energy Reviews, Vol. 91, 2018, pp. 358‒375.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [8]

    Kamel M. S., Lezsovits F. Simulation of nanofluids laminar flow in a vertical channel, Pollack Periodica, Vol. 13, No. 2, 2018,147‒158.

  • [9]

    Coccia G., Di Nicola G., Colla L., Fedele L., Scattolini M. Adoption of nanofluids in lowenthalpy parabolic trough solar collectors: numerical simulation of the yearly yield, Energy Conversion Management , Vol. 118, 2016, pp. 306‒319.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [10]

    Rehan M. A., Ali M., Sheikh N. A., Khalil M. S., Chaudhary G. Q., ur Rashid T., Shehryar M. Experimental performance analysis of low concentration ratio solar parabolic trough collectors with nanofluids in winter conditions, Renewable Energy, Vol. 118, 2018, pp. 742‒751.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [11]

    Subramani J., Nagarajan P. K., Wongwises S., El‐Agouz S. A., Sathyamurthy R. Experimental study on the thermal performance and heat transfer characteristics of solar parabolic trough collector using Al2O3 nanofluids, Environmental Progress and Sustainable Energy, Vol. 37, No. 3, 2018, pp. 1149‒1159.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [12]

    Alsaady M., Fu R., Yan Y., Liu Z., Wu S., Boukhanouf R. An experimental investigation on the effect of ferrofluids on the efficiency of novel parabolic trough solar collector under laminar flow conditions, Heat Transfer Engineering, Vol. 40, No. 9-10, 2019, pp. 753‒761.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [13]

    Ghasemi S. E., Ranjbar A. A. Thermal performance analysis of solar parabolic trough collector using nanofluid as working fluid: A CFD modelling study, Journal of Molecular Liquids, Vol. 222, 2016, pp. 159‒166.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [14]

    Kasaiean A., Sameti M., Daneshazarian R., Noori Z., Adamian A., Ming T. Heat transfer network for a parabolic trough collector as a heat collecting element using nanofluid, Renewable Energy , Vol. 123, 2018, pp. 439‒449.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [15]

    Bellos E., Tzivanidis C. Thermal analysis of parabolic trough collector operating with mono and hybrid nanofluids, Sustainable Energy Technologies and Assessments, Vol. 26, 2018, pp. 105‒115.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [16]

    Bellos E., Tzivanidis C. Thermal efficiency enhancement of nanofluid-based parabolic trough collectors, Journal of Thermal Analysis Calorimetry, Vol. 135, No. 1, 2019, pp. 597‒608.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [17]

    Basbous N., Taqi M., Janan M. A. Thermal performances analysis of a parabolic trough solar collector using different nanofluids, International Renewable and Sustainable Energy Conference, Marrakech, Morocco, 14-17 November 2016, pp. 322‒326.

    • Search Google Scholar
    • Export Citation
  • [18]

    Ferraro V., Settino J., Cucumo M. A., Kaliakatsos D. Parabolic trough system operating with nanofluids: comparison with the conventional working fluids and influence on the system performance, Energy Procedia, Vol. 101, 2016, pp. 782‒789.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [19]

    Bellos E., Tzivanidis C. Parametric investigation of nanofluids utilization in parabolic trough collectors, Thermal Science and Engineering Progress, Vol. 2, 2017, pp. 71‒79.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [20]

    Toghyani S., Baniasadi E., Afshari E. Thermodynamic analysis and optimization of an integrated Rankine power cycle and nanofluid based parabolic trough solar collector, Energy Conversion and Management, Vol. 121, 2016, pp. 93‒104.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [21]

    Dudley V. E., Evans L. R., Matthews C. W. Test results, industrial solar technology parabolic trough solar collector, Technical Report ,Sandia National Labs, Albuquerque, NM, United States, 1995.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [22]

    Maleki S. A. M., Hizam H., Gomes C. Estimation of hourly, daily and monthly global solar radiation on inclined surfaces: Models re-visited, Energies, Vol. 10, No. 1, 2017, pp. 134‒162.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [23]

    Li P., Zhang D., Xie Y. Heat transfer and flow analysis of Al2O3–water nanofluids in microchannel with dimple and protrusion, International Journal of Heat and Mass Transfer, Vol. 73, 2014, pp. 456‒467.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [24]

    Zhou S. Q, Ni R. Measurement of the specific heat capacity of water-based Al 2 O 3 nanofluid, Applied Physics Letters, Vol. 92, No. 9, 2008, paper No. 093123.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [25]

    Yu W., Choi S. U. S. The role of interfacial layers in the enhanced thermal conductivity of nanofluids: A renovated Maxwell model, Journal of Nanoparticle Research, Vol. 5, No. 1-2, 2003, pp. 167‒171.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [26]

    Brinkman H. C. The viscosity of concentrated suspensions and solutions, The Journal of Chemical Physics, Vol. 20, No. 4, 1952, page 571.

  • [27]

    Mwesigye A., Huan Z. Thermal and thermodynamic performance of a parabolic trough receiver with Syltherm800-Al2O3 nanofluid as the heat transfer fluid, Energy Procedia, Vol. 75, 2015, pp. 394‒402.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [28]

    Sharafeldin M. A., Gróf G. Efficiency of evacuated tube solar collector using WO3/Water nanofluid, Renewable Energy, Vol. 134, 2019, pp. 453‒460.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Collapse
  • Expand
Cover Pollack Periodica
Pollack Periodica
Print ISSN:
1788-1994
Online ISSN:
1788-3911
Submit Your Manuscript
 
The author instructions template is available in MS Word.
Please, download the file from HERE.

 

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
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
Apr 2023 0 6 4
May 2023 0 9 14
Jun 2023 0 9 13
Jul 2023 0 23 15
Aug 2023 0 38 18
Sep 2023 0 23 26
Oct 2023 0 1 2