Thermal conductivity prediction of nanofluids containing SiC particles by using artificial neural network

Rehman Muhammad Shahzad; Habib Forootan Fard; Ibrahim Mahariq; Mamdouh El Haj Assad; Mohammad A. AlShabi

doi:10.1117/12.2632639

30 May 2022 Thermal conductivity prediction of nanofluids containing SiC particles by using artificial neural network

Rehman Muhammad Shahzad, Habib Forootan Fard, Ibrahim Mahariq, Mamdouh El Haj Assad, Mohammad A. AlShabi

Proceedings Volume 12090, Energy Harvesting and Storage: Materials, Devices, and Applications XII; 1209007 (2022) https://doi.org/10.1117/12.2632639
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Heat transfer improvement has gained significant importance in the recent decades. In this regard, it is preferred to enhance the thermophysical properties of the fluids that affecting the heat transfer characteristics. To reach this goal, nanofluids have been introduced to be applied in thermal devices due to their relatively higher thermal conductivity that can cause remarkable augmentation in convective heat transfer. Thermal conductivity of these types of fluids is influenced by some elements including the temperature and volume fraction. Considering this fact, these factors must be considered for modeling this property of nanofluids. In the present article, thermal conductivity of the nanofluids with SiC particles is modeled by using artificial neural network as an intelligent method. It is observed that thermal conductivity of the nanofluids is forecasted with high precision. Mean Squared Error (MSE) of the model in optimal architecture was around 2.65× 10⁻⁵, for this network the R² is 0.9986 revealing significant closeness of the forecasted data and corresponding experimental values.

Citation Download Citation

Rehman Muhammad Shahzad, Habib Forootan Fard, Ibrahim Mahariq, Mamdouh El Haj Assad, and Mohammad A. AlShabi "Thermal conductivity prediction of nanofluids containing SiC particles by using artificial neural network", Proc. SPIE 12090, Energy Harvesting and Storage: Materials, Devices, and Applications XII, 1209007 (30 May 2022); https://doi.org/10.1117/12.2632639

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KEYWORDS

Silicon carbide

Neurons

Artificial neural networks

Data modeling

Particles

Nanoparticles

Intelligence systems

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