Authors:
Issa Omle Department of Fluid and Heat Engineering, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Miskolc-Egyetemvaros, Hungary
Institute of Physics and Electrical Engineering, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Miskolc-Egyetemvaros, Hungary
Department of Mechanical Power Engineering, Faculty of Mechanical and Electrical Engineering, Al-Baath University, Homs, Syria

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Ali Habeeb Askar Department of Fluid and Heat Engineering, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Miskolc-Egyetemvaros, Hungary
Institute of Physics and Electrical Engineering, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Miskolc-Egyetemvaros, Hungary
Department of Mechanical Engineering, Faculty of Mechanical Engineering, University of Technology – Iraq, Baghdad, Iraq

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Endre Kovács Institute of Physics and Electrical Engineering, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Miskolc-Egyetemvaros, Hungary

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Abstract

This work investigates the effect of two wall roughness types, triangular and circular, on convection and radiation heat transfer in a small space. The ANSYS Fluent is used to do thermal and dynamic modeling; the left wall is warmer than the right one. The upper and lower walls are adiabatic. The Nusselt numbers are compared in all cases and for two Rayleigh values, which change based on the cavity's characteristic length. The results show temperature contours and Nusselt curves. It was observed that the roughness had a strong effect on the air's thermal behavior inside the cavity, where the Nusselt decreased in both roughness cases, especially at small heights. However, the largest decrease is in the triangular case and for angles less than 90°. For 72°, Nusselt is 13.32 and 6% less than smooth and circular cases respectively.

Abstract

This work investigates the effect of two wall roughness types, triangular and circular, on convection and radiation heat transfer in a small space. The ANSYS Fluent is used to do thermal and dynamic modeling; the left wall is warmer than the right one. The upper and lower walls are adiabatic. The Nusselt numbers are compared in all cases and for two Rayleigh values, which change based on the cavity's characteristic length. The results show temperature contours and Nusselt curves. It was observed that the roughness had a strong effect on the air's thermal behavior inside the cavity, where the Nusselt decreased in both roughness cases, especially at small heights. However, the largest decrease is in the triangular case and for angles less than 90°. For 72°, Nusselt is 13.32 and 6% less than smooth and circular cases respectively.

1 Introduction

Natural convection in square cavities has been extensively studied because of its numerous uses, but the relationship between surface radiation and natural convection is rarely discussed. Moreover, cavities with smoother walls were the subject of earlier experimental [1] and numerical [2, 3] investigations. The convection of air in a square enclosure with partly thermally active side walls was studied using 2D simulations. Some studies [4, 5] considered a cavity with basic geometry and showed the structure of the flow field and energy within this cavity. The radiological models included in the Fluent 6.3 program were examined and study the impact it in Computational Fluid Dynamics (CFD) simulations of convection heat transfer in a side heated square cavity in to identify the model produces results that are most similar to experimental data [6]. Upon seeing that the velocity patterns on the y-axis of the radiation-free and radiation-exposed instances varied significantly, it was concluded that the radiation-free case's physical model was rather non-physical. Additionally, the researcher discovered that the S2S model has the best convergence with the experimental data when compared to the models in Fluent 6.3. The studies [7, 8] investigated the interplay of surface radiation with turbulent convection in a chamber with smooth walls. In the Rayleigh number range taken into consideration, it was demonstrated that radiation alters the overall heat transfer by 20% for emissivity ε = 0.2 and by 60% for ε = 1. Rough surfaces have been frequently used as an effective means of enhancing heat transfer efficiency in turbulent thermal convection. However, roughness is not always associated with improved heat transfer and can, on occasion, result in a decrease in the system's total heat transfer. The simulations are performed in two and three dimensions for Rayleigh numbers (Ra = 107–1011). The Nusselt number decreased at a fixed Raleigh number regarding little roughness height h, whereas the heat transfer rate increased for large h [9].

A study [10] examined the effects of a wall with a sinusoidal wave pattern on convection in 2D within a cavity that held nanofluid. The governing equations for the Finite Volume Method (FVM) discretized velocity field and heat transfer are solved with Semi-Infinite Method for Pursuer Linked Equation (SIMPLE). The data show that Volume Fraction (VF), which doubles the Nusselt number as VF grows from 0 to 0.04, has the most effect on heat transfer. Heat transfer by convection in a porous square cavity with a wavy wall and a partially heated bottom surface was examined using the energy-flux-vector method. Modified Darcy, Rayleigh, Prandtl, and partially heated bottom surface lengths have an effect on the mean Nusselt number and energy-flux-vector distribution. Recirculation zones and a high mean Nusselt number are caused by energy flow vectors when the modified Darcy and Rayleigh numbers are high [11, 12].

As a result, these studies illustrate the lack of data and the extreme dispersion of the configurations and methodology used to simulate convection and radiation heat transfer inside square cavities especially if one of the cavity's walls is rough. In the previous work [13], the impact of different angles of surface triangle roughness on the interaction between radiation and convection in a cavity is studied. So, to study the impact of surface roughness on the rate of heat transfer in the cavities, this work aims to enrich the database with natural convection and surface radiation coupling inside square cavities containing rough surfaces arranged a triangular and circular shapes.

Now the effect of the shape of the rough surface, whether triangular or circular, is studied on heat transfer by convection and radiation inside square cavities at different Rayleigh numbers and emissivity values using the ANSYS Fluent 19.2 program. First, the triangular surface with different angles and the circular surface with constant radii of surface are compared. Second, the triangular surface with a constant angle of triangle and a different height h and the circular surface with different radii of surface are compared to study the effect of the height of the rough element on heat transfer. Then, the results are compared with those of surfaces without roughness to see which surface most effectively reduces the amount of heat flowing through it.

2 Problem description

2.1 The geometry

The three-dimensional (3D) cavity is shown in Fig. 1; while the cavity's height H and length W are changed, the aspect ratio AR = H/W stays unchanged. The depth is L, and H1, H2 are the changing dimensions depending on the vertex angle change θ. As the direction of heat transfer is perpendicular to the surface, the problem is regarded as two-dimensional (2D), so the z = 0.5 plane is taken as it is shown in Fig. 2, with changes in the third dimension considered minimal [8]. The 3D effects are disregarded in favor of 2D simulations to reduce the time cost of this study, as evidenced by earlier research [14–18]. Table 1 and Fig. 2 show the boundary conditions of the cavities.

Fig. 1.
Fig. 1.

Diagram in 3D of the geometry for walls with a) triangular roughness and b) circular roughness

Citation: Pollack Periodica 2024; 10.1556/606.2024.00986

Fig. 2.
Fig. 2.

a) The z = 0.5 plane with boundary conditions, b) shows an example of the triangular and circular walls, and it shows the surface remains constant when the angle is changed

Citation: Pollack Periodica 2024; 10.1556/606.2024.00986

Table 1.

The boundary conditions of examined cavities

Wall locationType conditionWall Properties
The surfacesEmissivity ε
RightColdTc = 288.5 KNo slip, u = 0, v = 000.40.81
LeftHotTh = 298.5 KNo slip, u = 0, v = 0
LowerAdiabaticT/y=0No slip, u = 0, v = 0
UpperAdiabaticT/y=0No slip, u = 0, v = 0

The following are the approximations and hypotheses of the study:

  1. Heat transfer by radiation and convection are examined in the cavities;

  2. Air fills the cavity, and the Boussinesq buoyancy hypothesis is adopted;

  3. The four inner walls of the cavity are considered to be diffuse, gray, opaque, and to have identical emissivity ε with values listed in Table 1;

  4. It is a steady-state study, meaning that u/t=0 and v/t=0;

  5. The physical properties of the air-fluid at the average temperature, which are shown in Table 2, are considered to remain constant, while density varies according to the Boussinesq model;

  6. In order to facilitate comparison with the reference study results [7], non-dimensional results are provided. Two cavity W lengths were selected, indicating two distinct Rayleigh numbers. As it is shown in Table 3, the definition of the aspect ratio, or dimensionless square body's adiabatic size, is AR = H/W;

  7. It is a steady-state study, meaning that u/t=0 and v/t=0.

Table 2.

Physical properties of air at average temperature Tm = 293.5 K, based on [8]

NameSymbolValueUnit
Thermal conductivity coefficientk0.0249W/(m∙K)
Densityρ1.232kg/m3
Specific heatc1,008J/(kg K)
Kinematic viscosityυ1.43∙10−5m2/s
Dynamic viscosityμ1.761∙10−5kg/(ms)
Thermal expansion coefficientβ0.0034K1
Thermal diffusivityα2∙10−5m2/s
Prandtl numberPr0.71
Temperature differenceT10K
Average temperatureTm293.5K
Table 3.

Rayleigh numbers depending on characteristic length W

RaW (m)H (m)AR = H/W
1040.0210.0211
1060.0970.0971

2.2 Solution methods

According to the definition of computational fluid dynamics, this field of study deals with the numerical mathematical techniques used to solve flow and heat transfer equations. As computer technology advanced, it became possible to solve a system of millions of equations with millions of unknowns at a reasonable calculation cost, and this science's general principle is summarized according to Fig. 3 [19].

Fig. 3.
Fig. 3.

Scheme of the modeling

Citation: Pollack Periodica 2024; 10.1556/606.2024.00986

In this branch of science, the studied space, which includes the area occupied by the fluid, is divided into a finite number of small cells in accordance with the so-called mesh. The equations governing the flow are written for each cell (the physical model), and the differential equations become converted into a set of linear algebraic equations in accordance with the so-called discretization before being solved for all cells [19].

The results of the solution are displayed subsequently, either in the form of colored images describing the flow structure or in the form of graphic curves that are analyzed to describe and provide an approximation of what is occurring in the domain. Finally, the model and the solution are validated by comparing these results to experimental data or to the results of earlier studies using a few logical approximations that are related to the numerical solution, for example observing the general convergence criterion and determining the value of a point or surface [20].

2.3 The governing equations

Continuity equation:
ux+vy+wz=0,
where u is the x-component, v is the y-component, and w is the z-component of the velocity (m s−1), respectively.
The momentum equation on the x-axis:
ρ(uux+vvy)=px+μ(2ux2+2vy2),
on the y-axis:
ρ(uux+vvy)=px+ρgβ(TT)+μ(2ux2+2vy2),
where ρgβ(TT) is the buoyancy Boussinesq approximation, g is the gravitational acceleration of the Earth and equal 9.81 (m s−2) [21].
The energy equation
(uTx+vTy)=kρc(2Tx2+2Ty2).

The governing equations for natural convection are non-dimensional, and to reduce the total number of variables, they are combined to produce non-dimensional numbers.

The following equations are used to calculate the results in ANSYS Fluent software:
Nutot=Nuconv+Nurad=1ANulocal·dA,
Nuconv=qconv·H·Lk(ThTc),
Nurad=qrad·H·Lk(ThTc),
qtot=qconv+qrad=1Aqlocal·dA,
where L = 1 m, as previously stated.

2.4 The dimensionless numbers

The physics formula for the Rayleigh number is the ratio of buoyancy and viscosity forces multiplied by the ratio of momentum and heat diffusivities as following:
Ra=gβTW3vα,
where W is the distance between the cold and hot surfaces, i.e., the characteristic length (m). Below the critical threshold of Rayleigh number, most heat transfer is accounted for via conduction. Convection accounts for the majority of heat transfer above this threshold value.

To determine the heat transfer rate across the cavity's walls, a surface Nusselt number along the cold the cavity's wall was used.

It is the conductive to convective heat transfer ratio across the boundary. Due to the problem's non-dimensional description, the local Nusselt number along the cavity's right wall can be defined as
Nu=hA(T1T2)kA(T1T2)/W=QconvQcond=hWk,
where the surface area for heat transfer is A (m2), and h is the flow's convective coefficient (W/(m2K)). Consequently, the strength of convective heat transfer in relation to conduction heat transfer through the liquid is indicated by the Nusselt number. So, when the Nusselt number rises, convective heat transfer becomes dominant [22].
The ratio of momentum diffusivity v to thermal diffusivity α is the definition of the Prandtl number in terms of physics. It provides the measurement of the efficiency of diffusion transport over the thermal and velocity boundary layers as follows:
Pr=vα.

3 The mesh construction

According to [23], the fluid's region is divided into a limited number of smaller portions, each known as a cell. After that, the fundamental flow equations for every cell can be expressed and converted into algebraic form, which can be solved numerically.

As it can be seen in Fig. 4, the investigated domain in the current study was meshed using the ANSYS Meshing Application code. Hexahedral cells were used to create the mesh at the smooth wall, and tetrahedral cells at the rough wall. The refinement feature is used to smooth the mesh near surfaces and correct the gradients associated with the boundary layer for rough walls [24].

Fig. 4.
Fig. 4.

Shows the mesh used

Citation: Pollack Periodica 2024; 10.1556/606.2024.00986

More cells lead to a more accurate solution, but this costs a lot of computing power. As the number of cells surpasses a certain threshold, the results stay nearly constant, meaning that so-called mesh independence has been attained [25]. Thus, nine meshes with progressively more elements for the smooth wall are employed to determine whether the results are independent of the mesh density [26]. As it is shown in Table 4, the Nusselt number is computed based on the element numbers on the smooth and rough walls. The Nusselt number does not change when the number of elements is greater than 72,370 and 76,100 for smooth and rough walls cases respectively, as it can be seen in Fig. 5. Thus, 72,370 and 76,100 are the total number of elements selected for both.

Table 4.

Nusselt numbers based on the number of elements

SmoothRough
ElementsNuElementsNu
202.368422.031
5602.3591,0082.034
6,2002.3448,5602.0486
12,6802.31015,8002.075
26,7002.30031,8762.078
48,1602.29955,4202.080
72,3702.29876,1002.082
98,0002.298102,0602.082
123,4002.298130,2402.082
Fig. 5.
Fig. 5.

Total Nusselt number for smooth (left axis) and rough (right axis) walls as a function of number of elements

Citation: Pollack Periodica 2024; 10.1556/606.2024.00986

A collection of differential equations is converted into a set of linear (algebraic) equations as part of the solution process then solved using a method similar to the numerical solution [27]. The SIMPLE method is used to achieve the correlation and coupling between pressure and velocity in the continuity and momentum equations. The momentum and energy equations are solved using second-order interpolation. The body force weighted that is suggested in the ANSYS User Guide is applied to the pressure.

4 Results and analysis

4.1 Comparing the Nutotal in case of no radiation ε = 0

For both Rayleigh numbers 104 and 106 and in the case of ε = 0, the total Nusselt number of circular roughness is smaller than triangular roughness with angles larger than 100, but for angles smaller than 100°, the Nusselt number is large as it is shown in Fig. 6.

Fig. 6.
Fig. 6.

Comparing the Nut according to the roughness type at ε = 0, where the left axis refers to Nut at Rayleigh number Ra = 104, and the right one refers to Nut at Ra = 106

Citation: Pollack Periodica 2024; 10.1556/606.2024.00986

4.2 Comparing the Nutotal in case of radiation with ε = 0.4

The total Nusselt number in the case of the triangular roughness surface at angles greater than 92° and for ε = 0.4 is larger than the circular roughness surface case for Rayleigh number values 104 and 106. However, the total Nusselt number becomes smaller at angles less than 92° for triangular roughness as it is shown in Fig. 7.

Fig. 7.
Fig. 7.

Comparing the Nut according to the roughness type at ε = 0.4, where the left axis refers to Nut at Rayleigh number Ra = 104, and the right one refers to Nut at Ra = 106

Citation: Pollack Periodica 2024; 10.1556/606.2024.00986

4.3 Comparing the Nutotal in case of radiation with ε = 0.8

For stronger radiation ε = 0.8 and for Rayleigh number values 104 and 106, the total Nusselt number in triangular roughness case at angles greater than 90° is larger than the circular roughness surface case, but at angles less than 90°, the total Nusselt number becomes smaller in the case of triangular roughness as it is shown in Fig. 8.

Fig. 8.
Fig. 8.

Comparing the Nut according to the roughness type at ε = 0.8, where the left axis refers to Nut at Rayleigh number Ra = 104, and the right one refers to Nut at Ra = 106

Citation: Pollack Periodica 2024; 10.1556/606.2024.00986

The simulations were repeated at ε = 1, it was found the total Nusselt number in the case of the circular roughness is smaller than the triangular one at angles greater than 88°.

From the temperature distribution contours, because the viscous effects become more significant as Ra decreases, in the case of both types of roughness, the fluid will become trapped and accumulate inside the hollow regions between the rough elements as it is shown in Fig. 9. As a result, a larger thermal boundary layer is formed, which impedes the system's overall heat flux by reducing convective heat transfer.

Fig. 9.
Fig. 9.

Temperature distribution contours in the case of circular and triangular rough walls with an angle of 72° for different emissivity

Citation: Pollack Periodica 2024; 10.1556/606.2024.00986

4.5 Comparing the Nutotal in case of changing the height of roughening elements

Figure 10 shows how the height of circular and triangular roughness elements changes with a constant area.

Fig. 10.
Fig. 10.

Shows changing the height of triangular and circular roughening elements with constant area

Citation: Pollack Periodica 2024; 10.1556/606.2024.00986

In the case of no radiation with ε = 0 and radiation with ε = 0.8, and for both Rayleigh numbers 104 and 106, the total Nusselt decreases when the number of circular or triangular roughing elements increases and, equivalently, when the height of the roughness element decreases. The total Nusselt number is smaller in the case of triangular roughness compared to circular, as it is shown in Figs 11 and 12. Increasing emissivity and Rayleigh number both lead to an increase in the total Nusselt value.

Fig. 11.
Fig. 11.

Shows the Nut as a function of the roughness element numbers at ε = 0, where the left axis refers to Nut at Rayleigh number Ra = 104, and the right one refers to Nut at Ra = 106

Citation: Pollack Periodica 2024; 10.1556/606.2024.00986

Fig. 12.
Fig. 12.

Shows the Nut as a function of the roughness element numbers at ε = 0.8, where the left axis refers to Nut at Rayleigh number Ra = 104, and the right one refers to Nut at Ra = 106

Citation: Pollack Periodica 2024; 10.1556/606.2024.00986

The temperature contours for different Rayleigh numbers are shown in Fig. 13.

Fig. 13.
Fig. 13.

Temperature distribution contours in the case of changing the roughening height element h for different emissivity and for both Rayleigh numbers 104 and 106

Citation: Pollack Periodica 2024; 10.1556/606.2024.00986

5 Conclusion

This study focused on examining the impact of surface radiation, how the wall's roughness type affects heat transfer and the thermal and hydrodynamic behavior of a fluid inside the cavity.

Two cavities are investigated for this purpose, implying two distinct Rayleigh numbers. In the case of smooth and rough walls, mesh independence was carried out. As a result, it was found that when the angle of the triangular roughing decreases, the heat transfer rate decreases since there is less buoyancy force at Ra = 104, which could cause the fluid to become trapped in the spaces between the roughness pieces due to the low velocity. A decrease in fluid volume, the formation of vortices near roughness components, and a reduction in the effective distance between two walls could be the main factors influencing fluid flow and overall heat transmission in the cavity.

For the triangular and circular roughness, the total Nusselt decreases when the number of circular or triangular roughing elements increases or, equivalently, when the height of the roughness element decreases. The total Nusselt number is smaller in the case of triangular roughness compared to circular, whereas the rough surface in a circular form is reduced by 8.86% for the Nusselt value from the smooth surface.

Increasing emissivity and Rayleigh number both lead to an increase in the total Nusselt value. The heat transfer rate is low if the surface is roughened in a triangular shape with cross-section angles less than 85° at small heights of the roughness elements.

References

  • [1]

    H. Wang, S. Xin, and P. Le Quéré, “Numerical study of the coupling of natural convection with the radiation of surfaces in square cavities filled with air” (in French), Comptes Rendus - Mec., vol. 334, no. 1, pp. 4857, 2006.

    • Search Google Scholar
    • Export Citation
  • [2]

    E. Abu-Nada and A. J. Chamkha, “Mixed convection flow in a lid-driven inclined square enclosure filled with a nanofluid,” Eur. J. Mech. B/Fluids, vol. 29, no. 6, pp. 472482, 2010.

    • Search Google Scholar
    • Export Citation
  • [3]

    R. Anandalakshmi and T. Basak, “Natural convection in rhombic enclosures with isothermally heated side or bottom wall: Entropy generation analysis,” Eur. J. Mech. B/Fluids, vol. 54, pp. 2744, 2015.

    • Search Google Scholar
    • Export Citation
  • [4]

    M. K. Kane, C. Mbow, M. L. Sow, and J. Sarr, “A study on natural convection of air in a square cavity with partially thermally active side walls,” Open J. Fluid Dyn., vol. 7, no. 4, pp. 623641, 2017.

    • Search Google Scholar
    • Export Citation
  • [5]

    S. M. Aminossadati and B. Ghasemi, “Natural convection cooling of a localised heat source at the bottom of a nanofluid-filled enclosure,” Eur. J. Mech. B/Fluids, vol. 28, no. 5, pp. 630640, 2009.

    • Search Google Scholar
    • Export Citation
  • [6]

    S. Thakur and S. Kumar, “Impact of radiation models in CFD simulations of natural convection heat transfer in a side heated square cavity,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 4, no. 4, pp. 832839, 2016.

    • Search Google Scholar
    • Export Citation
  • [7]

    Z. Guo, J. Wang, A. K. Mozumder, and P. K. Das, “Mixed convection of nanofluids in a lid-driven rough cavity,” in 7th BSME International Conference on Thermal Engineering, Dhaka, Bangladesh, December 22–24, 2016, vol. 1851, 2017, Art no. 020004.

    • Search Google Scholar
    • Export Citation
  • [8]

    N. H. Laaroussi, “Contribution to the numerical simulation of heat transfers by conduction, radiation and thermosolutal convection in cavities” (in French), Doctoral Thesis, University Paris-Est, 2008.

    • Search Google Scholar
    • Export Citation
  • [9]

    Y. Z. Zhang, C. Sun, Y. Bao, and Q. Zhou, “How surface roughness reduces heat transport for small roughness heights in turbulent Rayleigh-Bénard convection,” J. Fluid Mech., vol. 836, 2018, Art no. R2.

    • Search Google Scholar
    • Export Citation
  • [10]

    M. S. Zarei, A. T. K. Abad, M. Hekmatifar, and D. Toghraie, “Heat transfer in a square cavity filled by nanofluid with sinusoidal wavy walls at different wavelengths and amplitudes,” Case Stud. Therm. Eng., vol. 34, 2022, Art no. 101970.

    • Search Google Scholar
    • Export Citation
  • [11]

    W. Al-Kouz, K. B. Saleem, and A. Chamkha, “Numerical investigation of rarefied gaseous flows in an oblique wavy sided walls square cavity,” Int. Commun. Heat Mass Transf., vol. 116, 2020, Art no. 104719.

    • Search Google Scholar
    • Export Citation
  • [12]

    M. Hirpho, “Mixed convection of Casson fluid in a differentially heated bottom wavy wall,” Heliyon, vol. 7, no. 6, 2021, Art no. e07361.

    • Search Google Scholar
    • Export Citation
  • [13]

    I. Omle, E. Kovács, and B. Bolló, “The effect of surface triangular roughness inspired by nature with different angles on the interaction between free convection and surface radiation in a square cavity,” Heat Transf. Res., vol. 55, no. 5, pp. 124, 2024.

    • Search Google Scholar
    • Export Citation
  • [14]

    I. Omle, A. H. Askar, E. Kovács, and B. Bolló, “Comparison of the performance of new and traditional numerical methods for long-term simulations of heat transfer in walls with thermal bridges,” Energies, vol. 16, no. 12, 2023, Art no. 4604.

    • Search Google Scholar
    • Export Citation
  • [15]

    B. Yang and C. Lawn, “Fluid dynamic performance of a vertical axis turbine for tidal currents,” Renew. Energy, vol. 36, no. 12, pp. 33553366, 2011.

    • Search Google Scholar
    • Export Citation
  • [16]

    R. Lanzafame, S. Mauro, and M. Messina, “2D CFD modeling of H-Darrieus wind turbines using a transition turbulence model,” Energy Proced., vol. 45, pp. 131140, 2014.

    • Search Google Scholar
    • Export Citation
  • [17]

    S. McTavish, D. Feszty, and T. Sankar, “Steady and rotating computational fluid dynamics simulations of a novel vertical axis wind turbine for small-scale power generation,” Renew. Energy, vol. 41, pp. 171179, 2012.

    • Search Google Scholar
    • Export Citation
  • [18]

    A. Sayyed, “Air flow optimization through an intake system for a single cylinder formula student (FSAE) race car,” Int. J. Eng. Res., vol. 6, no. 1, pp. 183188, 2017.

    • Search Google Scholar
    • Export Citation
  • [19]

    T. J. Chung, Computational Fluid Dynamics. Cambridge University Press, 2002.

  • [20]

    I. Omle, E. Kovács, and B. Bolló, “Applying recent efficient numerical methods for long-term Simulations of heat transfer in walls to optimize thermal insulation,” Results Eng., vol. 20, 2023, Art no. 101476.

    • Search Google Scholar
    • Export Citation
  • [21]

    Y. Cengel and A. Ghajar, Heat and Mass Transfer: Fundamentals and Applications, McGraw-Hill, 2014.

  • [22]

    J. Holman, Heat Transfer, 10th ed. McGraw-Hill, 2010.

  • [23]

    F. Moukalled, L. Mangani, and M. Darwish, The Finite Volume Method in Computational Fluid Dynamics. Springer, 2016.

  • [24]

    ANSYS Fluent Theory Guide, 2017.

  • [25]

    O. Al-Oran and F. Lezsovits, “Thermal performance of inserting hybrid nanofluid in parabolic trough collector,” Pollack Period., vol. 16, no. 3, pp. 8893, 2021.

    • Search Google Scholar
    • Export Citation
  • [26]

    M. S. Kamel and F. Lezsovits, “Simulation of nanofluids laminar flow in a vertical channel,” Pollack Period., vol. 13, no. 2, pp. 147158, 2018.

    • Search Google Scholar
    • Export Citation
  • [27]

    J. Strzałkowski, P. Sikora, S. Y. Chung, and M. A. Elrahman, “Thermal performance of building envelopes with structural layers of the same density: Lightweight aggregate concrete versus foamed concrete,” Building and Environment, vol. 196, 2021, Art no. 107799.

    • Search Google Scholar
    • Export Citation
  • [1]

    H. Wang, S. Xin, and P. Le Quéré, “Numerical study of the coupling of natural convection with the radiation of surfaces in square cavities filled with air” (in French), Comptes Rendus - Mec., vol. 334, no. 1, pp. 4857, 2006.

    • Search Google Scholar
    • Export Citation
  • [2]

    E. Abu-Nada and A. J. Chamkha, “Mixed convection flow in a lid-driven inclined square enclosure filled with a nanofluid,” Eur. J. Mech. B/Fluids, vol. 29, no. 6, pp. 472482, 2010.

    • Search Google Scholar
    • Export Citation
  • [3]

    R. Anandalakshmi and T. Basak, “Natural convection in rhombic enclosures with isothermally heated side or bottom wall: Entropy generation analysis,” Eur. J. Mech. B/Fluids, vol. 54, pp. 2744, 2015.

    • Search Google Scholar
    • Export Citation
  • [4]

    M. K. Kane, C. Mbow, M. L. Sow, and J. Sarr, “A study on natural convection of air in a square cavity with partially thermally active side walls,” Open J. Fluid Dyn., vol. 7, no. 4, pp. 623641, 2017.

    • Search Google Scholar
    • Export Citation
  • [5]

    S. M. Aminossadati and B. Ghasemi, “Natural convection cooling of a localised heat source at the bottom of a nanofluid-filled enclosure,” Eur. J. Mech. B/Fluids, vol. 28, no. 5, pp. 630640, 2009.

    • Search Google Scholar
    • Export Citation
  • [6]

    S. Thakur and S. Kumar, “Impact of radiation models in CFD simulations of natural convection heat transfer in a side heated square cavity,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 4, no. 4, pp. 832839, 2016.

    • Search Google Scholar
    • Export Citation
  • [7]

    Z. Guo, J. Wang, A. K. Mozumder, and P. K. Das, “Mixed convection of nanofluids in a lid-driven rough cavity,” in 7th BSME International Conference on Thermal Engineering, Dhaka, Bangladesh, December 22–24, 2016, vol. 1851, 2017, Art no. 020004.

    • Search Google Scholar
    • Export Citation
  • [8]

    N. H. Laaroussi, “Contribution to the numerical simulation of heat transfers by conduction, radiation and thermosolutal convection in cavities” (in French), Doctoral Thesis, University Paris-Est, 2008.

    • Search Google Scholar
    • Export Citation
  • [9]

    Y. Z. Zhang, C. Sun, Y. Bao, and Q. Zhou, “How surface roughness reduces heat transport for small roughness heights in turbulent Rayleigh-Bénard convection,” J. Fluid Mech., vol. 836, 2018, Art no. R2.

    • Search Google Scholar
    • Export Citation
  • [10]

    M. S. Zarei, A. T. K. Abad, M. Hekmatifar, and D. Toghraie, “Heat transfer in a square cavity filled by nanofluid with sinusoidal wavy walls at different wavelengths and amplitudes,” Case Stud. Therm. Eng., vol. 34, 2022, Art no. 101970.

    • Search Google Scholar
    • Export Citation
  • [11]

    W. Al-Kouz, K. B. Saleem, and A. Chamkha, “Numerical investigation of rarefied gaseous flows in an oblique wavy sided walls square cavity,” Int. Commun. Heat Mass Transf., vol. 116, 2020, Art no. 104719.

    • Search Google Scholar
    • Export Citation
  • [12]

    M. Hirpho, “Mixed convection of Casson fluid in a differentially heated bottom wavy wall,” Heliyon, vol. 7, no. 6, 2021, Art no. e07361.

    • Search Google Scholar
    • Export Citation
  • [13]

    I. Omle, E. Kovács, and B. Bolló, “The effect of surface triangular roughness inspired by nature with different angles on the interaction between free convection and surface radiation in a square cavity,” Heat Transf. Res., vol. 55, no. 5, pp. 124, 2024.

    • Search Google Scholar
    • Export Citation
  • [14]

    I. Omle, A. H. Askar, E. Kovács, and B. Bolló, “Comparison of the performance of new and traditional numerical methods for long-term simulations of heat transfer in walls with thermal bridges,” Energies, vol. 16, no. 12, 2023, Art no. 4604.

    • Search Google Scholar
    • Export Citation
  • [15]

    B. Yang and C. Lawn, “Fluid dynamic performance of a vertical axis turbine for tidal currents,” Renew. Energy, vol. 36, no. 12, pp. 33553366, 2011.

    • Search Google Scholar
    • Export Citation
  • [16]

    R. Lanzafame, S. Mauro, and M. Messina, “2D CFD modeling of H-Darrieus wind turbines using a transition turbulence model,” Energy Proced., vol. 45, pp. 131140, 2014.

    • Search Google Scholar
    • Export Citation
  • [17]

    S. McTavish, D. Feszty, and T. Sankar, “Steady and rotating computational fluid dynamics simulations of a novel vertical axis wind turbine for small-scale power generation,” Renew. Energy, vol. 41, pp. 171179, 2012.

    • Search Google Scholar
    • Export Citation
  • [18]

    A. Sayyed, “Air flow optimization through an intake system for a single cylinder formula student (FSAE) race car,” Int. J. Eng. Res., vol. 6, no. 1, pp. 183188, 2017.

    • Search Google Scholar
    • Export Citation
  • [19]

    T. J. Chung, Computational Fluid Dynamics. Cambridge University Press, 2002.

  • [20]

    I. Omle, E. Kovács, and B. Bolló, “Applying recent efficient numerical methods for long-term Simulations of heat transfer in walls to optimize thermal insulation,” Results Eng., vol. 20, 2023, Art no. 101476.

    • Search Google Scholar
    • Export Citation
  • [21]

    Y. Cengel and A. Ghajar, Heat and Mass Transfer: Fundamentals and Applications, McGraw-Hill, 2014.

  • [22]

    J. Holman, Heat Transfer, 10th ed. McGraw-Hill, 2010.

  • [23]

    F. Moukalled, L. Mangani, and M. Darwish, The Finite Volume Method in Computational Fluid Dynamics. Springer, 2016.

  • [24]

    ANSYS Fluent Theory Guide, 2017.

  • [25]

    O. Al-Oran and F. Lezsovits, “Thermal performance of inserting hybrid nanofluid in parabolic trough collector,” Pollack Period., vol. 16, no. 3, pp. 8893, 2021.

    • Search Google Scholar
    • Export Citation
  • [26]

    M. S. Kamel and F. Lezsovits, “Simulation of nanofluids laminar flow in a vertical channel,” Pollack Period., vol. 13, no. 2, pp. 147158, 2018.

    • Search Google Scholar
    • Export Citation
  • [27]

    J. Strzałkowski, P. Sikora, S. Y. Chung, and M. A. Elrahman, “Thermal performance of building envelopes with structural layers of the same density: Lightweight aggregate concrete versus foamed concrete,” Building and Environment, vol. 196, 2021, Art no. 107799.

    • Search Google Scholar
    • Export Citation
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Senior editors

Editor(s)-in-Chief: Iványi, Amália

Editor(s)-in-Chief: Iványi, Péter

 

Scientific Secretary

Miklós M. Iványi

Editorial Board

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

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

E-mail: peter.ivanyi@mik.pte.hu 

or amalia.ivanyi@mik.pte.hu

Indexing and Abstracting Services:

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  • 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
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Pollack Periodica
Language English
Size A4
Year of
Foundation
2006
Volumes
per Year
1
Issues
per Year
3
Founder Akadémiai Kiadó
Founder's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 1788-1994 (Print)
ISSN 1788-3911 (Online)

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