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Experimental study and neural network-based prediction of thermal performance of applying baffles and nanofluid in the double-pipe heat exchangers

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Abstract

The present study’s goal was to investigate the efficiency of applying carbon/water Nanofluid and triangular baffles in the double-tube heat exchangers. The Reynolds number was in the range of 4000 to 14,000, and the carbon/water Nanofluid, which was with various volume fractions ranged between 0.1 and 0.3 volume percentages, was applied. Moreover, triangular baffles were implemented as turbulators with the numbers of 4, 8, and 12, as well as angles of 70\(^\circ\) and 40\(^\circ\). Using the response surface method, the experiments design was carried out. The prediction process was eventually conducted using Perceptron artificial neural network that included the input data of Reynolds number, Nanofluids volume fraction, baffles number and angle, as well as Nusselt number target. Results showed that the simultaneous use of 12 triangular baffles at the 40\(^\circ\) angle and a Nanofluid with a 0.3 volume fraction led to the 35% increase in Nusselt number compared to applying smooth tubes and water-based fluid. The coefficient of friction with a 0.3% volume fraction of Nanofluid associated with 12 triangular baffles at a 40\(^\circ\) angle was, respectively, 5–10% and 1.5 times higher than that of the water flow within a smooth tube without the presence of baffles and Nanofluids. Moreover, results of Perceptron artificial neural network showed that applying a 1-32-4 topology and Levenberg–Marquardt algorithm would derive 0.98828 and 3.6275 values for the mean squared error (MSE) and coefficient of correlation, respectively.

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Abbreviations

A :

Heat transfer area (m2)

C p :

Specific heat (kJ kg−1 °C−1)

d :

Diameter (m)

h :

Convective heat transfer coefficient (W m−2ok−1)

k :

Thermal conductivity (W m−1 K−1)

L :

Tube length (m)

m o :

Mass flow rate (kg s−1)

Nu:

Nusselt number (Dimensionless)

TPF:

Thermal performance factor

MSE:

Mean squared error

Q :

Heat transfer rate (W)

Re:

Reynolds number (Dimensionless)

T :

Temperature (oC)

V :

Velocity (ms−1)

f :

Friction factor

pr:

Prandtl number (Dimensionless)

\({\Delta T}_{{\text{lm}}}\) :

Logarithmic mean temperature difference (oC)

\({\Delta P}_{{\text{nf}}}\) :

Pressure drop

\(\rho\) :

Density (kg m−3)

\(\varphi\) :

Nanoparticle volume concentration (Dimensionless)

\(\mu\) :

Viscosity

h:

Hot

c:

Cold

f:

Fluid

i:

Inlet

nf:

Nanofluid

bf:

Base fluid

o:

Outlet

p:

Particles

w:

Wall

b:

Bulk

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Authors and Affiliations

  1. Department of Chemical Industry, Shahid Rajaei Lahijan Technical College, Technical and Vocational University of Gilan Province, Lahijan, Iran

    Amirhossein Khadang

  2. Department of Chemical Engineering, Imam Hossein University, Tehran, Iran

    Mousa Nazari

  3. Department of Process, Energy and Environmental Technology, University of South-Eastern Norway, Notodden, Norway

    Heydar Maddah

  4. Department of Chemistry, Science Faculty, Arak Branch, Islamic Azad University, Arak, Iran

    Heydar Maddah

  5. Faculty of Mechanical Engineering, Shahrood University of Technology, Shahrood, Iran

    Mohammad Hossein Ahmadi

  6. Department of Mechanical and Aeronautical Engineering, University of Pretoria, Pretoria, 0002, South Africa

    Mohsen Sharifpur

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

    Mohsen Sharifpur

  8. School of Mechanical, Industrial and Aeronautical Engineering, University of the Witwatersrand, Private Bag 3, Wits 2050, South Africa

    Mohsen Sharifpur

Authors
  1. Amirhossein Khadang
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  2. Mousa Nazari
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  3. Heydar Maddah
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  4. Mohammad Hossein Ahmadi
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  5. Mohsen Sharifpur
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Correspondence to Heydar Maddah, Mohammad Hossein Ahmadi or Mohsen Sharifpur.

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Khadang, A., Nazari, M., Maddah, H. et al. Experimental study and neural network-based prediction of thermal performance of applying baffles and nanofluid in the double-pipe heat exchangers. J Therm Anal Calorim 149, 4239–4259 (2024). https://doi.org/10.1007/s10973-024-12969-0

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