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Analysis of thermal performance and ultrasonic wave power variation on heat transfer of heat exchanger in the presence of nanofluid using the artificial neural network: experimental study and model fitting

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Abstract

The present study has experimentally and numerically investigated the influence of ultrasonic waves and temperature variations on the heat transfer coefficient (HTC) by using CNT-water nanofluid. According to the test results, applying the ultrasonic waves in two powers of 35–50 W improves the heat transfer coefficient. The heat transfer coefficient is also enhanced with increasing the concentration of the nanofluid and fluid temperature. The Nusselt number also improved by 540.23% with increasing fluid temperature from 25 to 35 °C, the mass fraction from 0.12 to 0.25, the flow rate, and the application of ultrasonic waves. Furthermore, the Artificial Neural Network, ANN, is used to evaluate the heat transfer coefficient and Nusselt number. The employed ANNs have the feed-forward architecture with hidden “tansig” and output “purelin” neurons. The training method for this network is the backpropagation. Different number of hidden layer neurons as well as dissimilar training method are investigated to find the best ANN performance. Examining the results, it can be seen that the best combination for the present problem is an ANN with 15 hidden neurons and a “trainbr” training algorithm. Comparing the experimental and ANN results reveals an excellent consistency.

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Abbreviations

m m :

Mass flow rate (kg sec−1)

Q:

Heat transfer (J)

m v :

Volume flow rate (m3 min−1)

T f :

Fluid temperature (K)

Cp:

Specific heat capacity (J kg K−1)

T s :

Surface temperature (K)

LMTD:

Mean logarithmic temperature difference (K)

L H :

Length of the heat exchanger (m)

D h :

Hydraulic diameter (m)

A H :

Internal surface area of the heat exchanger (m2)

b:

Inner height of heat exchanger cross section (m)

a:

Inner width of heat exchanger cross section (m)

k:

Thermal conductivity coefficient (W m−1 K)

h:

Convection heat transfer coefficient (W m−2 K)

Re:

Reynolds number

p:

Power (W)

Nu:

Nusselt number

φ :

Mass fraction (%)

ρ :

Density (kg m−3)

i:

Inlet

avg:

Average

o:

Outlet

nf:

Nanofluid

U:

Applying ultrasonic waves

w:

Water

s:

Sonication

H:

Heater

I:

Without ultrasonic waves

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Authors and Affiliations

  1. Department of Mechanical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

    Hamidreza Azimy, Amir Homayoon Meghdadi Isfahani, Seyed Amin Bagherzadeh & Masoud Farahnakian

  2. Department of Mechanical Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

    Noushin Azimy

  3. Aerospace and Energy Conversion Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran

    Amir Homayoon Meghdadi Isfahani

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  1. Hamidreza Azimy
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  2. Noushin Azimy
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  4. Seyed Amin Bagherzadeh
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Contributions

HA: Data curation, Visualization, Writing – original draft. NA: Writing – original draft, Data curation. AHMI: Methodology, Investigation, Conceptualization, Supervision. SAB: Software. Masoud Farahnakian: Formal analysis.

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Correspondence to Amir Homayoon Meghdadi Isfahani.

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Azimy, H., Azimy, N., Meghdadi Isfahani, A.H. et al. Analysis of thermal performance and ultrasonic wave power variation on heat transfer of heat exchanger in the presence of nanofluid using the artificial neural network: experimental study and model fitting. J Therm Anal Calorim 148, 8009–8023 (2023). https://doi.org/10.1007/s10973-022-11827-1

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