Nanoakışkanların elektriksel iletkenlik ve stabilitelerinin yapay sinir ağları ile modellenmesi: Kütlesel oran ve sıcaklığa bağlı korelasyon türetilmesi

Yıl 2024, , 985 - 992, 15.07.2024

Ömer Genç

https://doi.org/10.28948/ngumuh.1483840

Öz

Hazırlanma sürelerinin uzun olması ve termofiziksel özelliklerinin belirlenmesinin zahmetli olması nanoakışkan çalışmalarını yapay zekâ destekli modelleme çalışmalarına yönlendirmiştir. Bununla birlikte yapılan modelleme çalışmaları ağırlıklı olarak ısıl iletkenlik ve viskozite üzerine yoğunlaşmıştır. Bu çalışmada, nanoakışkan çalışmalarında genellikle ihmal edilen stabilite ve elektriksel iletkenlik göz önüne alınarak, %0.1-%3 kütlesel oran ve 20°C-70°C sıcaklık aralığındaki MgO/EG nanoakışkanların pH, elektriksel iletkenlik ve zeta potansiyelini tahmin etmek için bir yapay sinir ağları modeli geliştirilmiştir. Geliştirilen modelin MSE ve R2 değeri sırasıyla 0.011118 ve 0.99987 iken, pH, elektriksel iletkenlik ve zeta potansiyeli için ortalama mutlak MoD değerleri ise sırasıyla %0.11, %0.78 ve %0.74 olarak belirlenmiştir. Bahsi geçen bu performans parametreleri geliştirilen ağın yüksek performanslı olduğunu ortaya koymuştur. Ayrıca model verileri kullanılarak literatürde ilk defa bu üç özellik için de geçerli, katsayıları birbirinden farklı ortak bir korelasyon ortaya konulmuştur. Yeni korelasyonun pH, elektriksel iletkenlik ve zeta potansiyeli için ortalama mutlak MoD değerleri sırasıyla %0.35, %2.08 ve %1.54’tür. Deneysel veriler ile mutlak % hata değerlerini ortaya çıkaran bu değerler yeni korelasyonun yüksek doğrulukta tahmin yeteneğini ortaya koymaktadır.

Anahtar Kelimeler

Nanoakışkan, Stabilite, Zeta potansiyeli, Elektriksel iletkenlik, Yapay sinir ağları

Modeling electrical conductivity and stability of nanofluids using artificial neural network: Derivation of correlation dependent on mass ratio and temperature

Öz

The lengthy preparation times and the laborious process of determining their thermophysical properties have directed nanofluid studies towards artificial intelligence-supported modeling efforts. Moreover, these modeling studies have primarily focused on thermal conductivity and viscosity. In this study, considering the often neglected aspects of stability and electrical conductivity in nanofluid research, an artificial neural network model was developed to predict the pH, electrical conductivity, and zeta potential of MgO/EG nanofluids within a mass ratio range of 0.1%-3% and a temperature range of 20°C-70°C. The MSE and R2 values of the developed model are 0.011118 and 0.99987, respectively, while the mean absolute percentage deviations (MAPD) for pH, electrical conductivity, and zeta potential are determined to be 0.11%, 0.78%, and 0.74%, respectively. These performance parameters revealed that the developed network is high-performance. Additionally, for the first time in the literature, a common correlation with different coefficients for these three properties was established using the model data. The new correlation has mean absolute percentage deviations of 0.35%, 2.08%, and 1.54% for pH, electrical conductivity, and zeta potential, respectively. These values, which reveal the absolute % error values with experimental data, reveal the high accuracy prediction ability of the new correlation.

Anahtar Kelimeler

Nanofluid, Stability, Zeta potential, Electrical conductivity, Artificial neural networks

Kaynakça

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