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Entropy generation of graphene–platinum hybrid nanofluid flow through a wavy cylindrical microchannel solar receiver by using neural networks

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Abstract

Analyzing microchannel heat sinks (MCHS) in terms of the second thermodynamic law is useful, and it is necessary to examine MCHSs in terms of irreversible factors. In this research, the second thermodynamic law analysis is conducted for graphene–platinum/water hybrid nanofluid flow to assess how a new cylindrical microchannel heat sink has wavy-shaped fins performs. A variety of Reynolds numbers, nanoparticle concentrations as well as wave amplitudes are used to simulate the problem, while the heat flux is constant. Fluent software is employed to solve the governing equations employing the control-volume method. The distributions of velocity and temperature are derived, and the entropy generation rate (including the generation of thermal as well as frictional entropy), along with the Bejan number, is obtained. The minimum values corresponding to the pointed parameters are, respectively, obtained as 7.63 × 10−2, 1.24 × 10−4, and 7.78 × 10−2, while the maximum magnitudes are 1.09 × 10−1, 1.49 × 10−3, and 1.09 × 10−1, respectively. Increasing each factor, including wave amplitude, particle fraction, and Reynolds number, causes a decline in the thermal entropy generation rate, while frictional entropy rises significantly. The Bejan number was obtained greater than 0.98 in all cases, which means that irreversibility mainly results from the thermal entropy generation. This could be a desirable finding, noting that increasing input variables reduced the thermal entropy generation rate. Finally, by employing an artificial neural network, a model is obtained for the entropy generation of entropy based on distinct factors of wave amplitude, nanofluid concentration, and Reynolds number.

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Abbreviations

A :

Wave amplitude (m)

ANN:

Artificial neural network

Be:

Bejan number

C p :

Specific heat capacity (J kg−1 K−1)

d h :

Hydraulic diameter (m)

EG:

Entropy generation

f :

Friction factor

H :

Height (m)

HNF:

Hybrid nanofluid

k :

Thermal conductivity (W m−1 K−1)

L :

Length (m)

N :

Number of fins

Nu:

Nusselt number

P :

Pressure (Pa)

q″ :

Heat flux (W m−2)

R :

Radius (m)

Re:

Reynolds number

R Th :

Thermal resistance (K W−1)

\(\dot{S}\) :

Entropy generation (EG) rate (W K−1)

T :

Temperature (K)

V :

Velocity (m s−1)

W :

Width (m)

z :

Axial distance from inlet (m)

ave:

Average

c:

Channel

in:

Inlet

out:

Outlet

s:

Solid

w:

Wall

λ :

Wave length (m)

μ :

Dynamic viscosity (Pa s)

ρ :

Density (kg m−3)

φ:

Concentration (mass%)

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Authors and Affiliations

  1. Department of Mechanical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

    Raouf Khosravi, Saeed Rabiei & Mohammad Khaki

  2. Department of Civil and Environmental Engineering, Florida International University, Miami, FL 33174, USA

    Mohammad Reza Safaei

  3. Department of Mathematics, Faculty of Science, King Abdulaziz University, P.O. Box 80259, Jeddah, Saudi Arabia

    Marjan Goodarzi

  4. Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan

    Marjan Goodarzi

Authors
  1. Raouf Khosravi
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  2. Saeed Rabiei
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  3. Mohammad Khaki
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  4. Mohammad Reza Safaei
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  5. Marjan Goodarzi
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Correspondence to Marjan Goodarzi.

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Khosravi, R., Rabiei, S., Khaki, M. et al. Entropy generation of graphene–platinum hybrid nanofluid flow through a wavy cylindrical microchannel solar receiver by using neural networks. J Therm Anal Calorim 145, 1949–1967 (2021). https://doi.org/10.1007/s10973-021-10828-w

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