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Research on the influence of convector factors on a panel radiator’s heat output and total weight with a machine learning algorithm

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Abstract

In the current work, the impacts of convector factors of a panel radiator regarding heat output and total weight have been investigated using a machine learning algorithm. An artificial neural network model, widely evaluated by machine learning algorithms, has been created to determine the heat output and total weight values of panel radiators. There are 10 neurons in the hidden layer of the machine learning model, which was trained using 111 numerically obtained data sets. A comprehensive numerical investigation has been done for dissimilar geometrical dimensions of convectors evaluated in panel radiators and validated with experimental results. Afterward, the Levenberg–Marquardt structure has been employed as the training one in the multilayer perceptron network structure. The heat output and total weight outcomes acquired from the artificial neural network have been contrasted with the computational data and the compatibility of the data has been examined comprehensively. Furthermore, various performance parameters have also been determined and the estimation performance of the neural network has been examined thoroughly. The mean deviation values for the thermal power and weight values gained from the network structure have been determined as 0.04 and 0.004%, in turn, and the R-value has been obtained as 0.99999. The investigation outcomes indicated that the proposed neural network can forecast the heat output and total weight values of the panel radiator with very high accuracy.

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Data Availability Statement

This manuscript has associated data in a data repository. [Authors’ comment: Data sharing not applicable to this article as no datasets were generated or analysed during the current study.]

Abbreviations

AI:

Artificial intelligence

ANN:

Artificial neural network

BP:

Back propagation

CFD:

Computational fluid dynamics

c p :

Specific heat (J/kg K)

d :

Distance of opposing convectors (mm)

D i :

Inlet diameter of radiator (mm)

FF:

Feed forward

h :

Enthalpy (J/kg K)

H :

Convector height (mm)

k :

Conductivity (W/m K)

L :

Trapezoidal height of convector (mm)

m :

Panel radiator weight (kg)

\(\dot{m}\) :

Mass flow rate (kg/s)

MLP:

Multilayer perceptron

MSE:

Mean squared error

PCCP:

Panel-convector-convector-panel

PN:

Pitch number

Q :

Heat output (W)

Ra:

Rayleigh number

Re:

Reynolds number

T :

Temperature (°C)

W :

Panel width (mm)

X :

Variable

x, y, z :

Coordinates

e :

Excess

f :

Film

i :

Inlet

o :

Outlet

s :

Surface

β :

Coefficient of thermal expansion (1/K)

\(\mu\) :

Dynamic viscosity (kg/m s)

\(\nu\) :

Kinematic viscosity (m2/s)

ρ :

Density (kg/m3)

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Acknowledgements

Ministry of Science, Industry, and Technology of Turkey (Grant No. 0641.STZ.2014) for financial support and Demir Döküm A.Ş. are thankfully acknowledged.

Funding

Bilim, Sanayi ve Teknoloji Bakanliği, 0641.STZ.2014, Senol Baskaya.

Author information

Authors and Affiliations

  1. Mechanical Engineering Department, Engineering Faculty, Gazi University, 06570, Ankara, Turkey

    Tamer Calisir & Senol Baskaya

  2. Information Technologies Application and Research Center, Istanbul Commerce University, 34445, Istanbul, Turkey

    Andaç Batur Çolak

  3. Department of Mechanical Engineering, Eastern Mediterranean University, G. Magosa, TRNC, Mersin 10, Turkey

    Devrim Aydin

  4. Department of Mechanical Engineering, Mechanical Engineering Faculty, Yildiz Technical University, 34349, Istanbul, Turkey

    Ahmet Selim Dalkilic

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  1. Tamer Calisir
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  2. Andaç Batur Çolak
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Correspondence to Andaç Batur Çolak.

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Calisir, T., Çolak, A.B., Aydin, D. et al. Research on the influence of convector factors on a panel radiator’s heat output and total weight with a machine learning algorithm. Eur. Phys. J. Plus 138, 43 (2023). https://doi.org/10.1140/epjp/s13360-022-03622-6

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