Modeling and prediction of thermal conductivity ratio of metal-oxide based nano-fluids using artificial neural network and power law

• Autorzy: Hanief Mohammad , Irfan Quresh , Parvez Malik

Identyfikatory

DOI

10.24425/cpe.2022.140818

• Konferencja: The International Chemical Engineering Conference 2021 (ICHEEC): 100 Glorious Years of Chemical Engineering and Technology, September 16–19, 2021
• Języki publikacji: EN

Abstrakty

EN

In this study, the thermal conductivity ratio model for metallic oxide based nano-fluids is proposed. The model was developed by considering the thermal conductivity as a function of particle concentration (percentage volume), temperature, particle size and thermal conductivity of the base fluid and nano-particles. The experimental results for Al2O3, CuO, ZnO, and TiO2 particles dispersed in ethylene glycol, water and a combination of both were adopted from the literature. Artificial neural network (ANN) and power law models were developed and compared with the experimental data based on statistical methods. ANOVA was used to determine the relative importance of contributing factors, which revealed that the concentration of nano-particles in a fluid is the single most important contributing factor of the conductivity ratio.

Słowa kluczowe

EN

nano-fluids; thermal conductivity ratio; artificial neural network; regression; ANOVA

PL

nanociecze; współczynnik przewodności cieplnej; sztuczna sieć neuronowa; regresja; ANOVA

• Wydawca: Komitet Inżynierii Chemicznej i Procesowej Polskiej Akademii Nauk
• Czasopismo: Chemical and Process Engineering
• Rocznik: 2022
• Tom: Vol. 43, nr 2
• Strony: 159–--163
• Opis fizyczny: Bibliogr. 13 poz.,

Twórcy

autor

Hanief Mohammad

• Mechanical Engineering Department, National Institute of Technology Srinagar, India

autor

Irfan Quresh

• Mechanical Engineering Department, National Institute of Technology Srinagar, India

autor

Parvez Malik

• Chemical Engineering Department, National Institute of Technology Srinagar, India

Bibliografia

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