Determination of Thermophysical Properties of Nanofluids containing CuO and ZnO and Modeling with Artificial Neural Network

Abstract

While the demand for energy, which is a necessary factor for the daily vital cycle, is constantly increasing, energy resources are rapidly exhausted. In this respect, it is of great importance to revise existing energy conversion systems and to develop new methods in order to benefit from the limited energy resources. Nowadays it is important to use the energy more efficiently by increasing the heat transfer in the in-pipe flows. In this study, heat transfer coefficients (k) and heat transfer coefficients (h) were determined by passing nanofluids produced using pure water, ethanol and ethylene glycol materials together with CuO and ZnO nanoparticles. In the experimental measurements with the Reynolds number around 1600, the average heat transfer was 16.5% in ZnO and 13.3% in CuO compared to pure water. The relationship between different pH values and Reynolds number values of heat transfer coefficients of nanofluids were shown. For h values of CuO and ZnO based nanofluids, predictive models were created by using artificial neural network. The accuracy rate of the obtained models was compared. The predictive model of CuO-based nanofluids was shown to be 40% more successful than ZnO.

Keywords

• Nanoparticle,nanofluids,thermophysical properties,artificial neural network

Cuo ve Zno İçeren Nanoakışkanların Termofiziksel Özelliklerinin Belirlenmesi ve Yapay Sinir Ağı İle Modellenmesi

Öz

Günlük yaşamsal döngü için gerekli bir faktör olan enerjiye talep sürekli olarak artarken enerji kaynakları da hızlı bir şekilde tükenmektedir. Bu doğrultuda mevcut enerji dönüşüm sistemlerinin yeniden gözden geçirilip var olan sınırlı enerji kaynaklarından daha çok yararlanabilmek için yeni yöntemler geliştirilmesi büyük önem taşımaktadır. Boru içi akışlarda ısı transferini artırarak enerjiyi daha faydalı bir şekilde kullanabilmek günümüzde önem arz etmektedir. Çalışmamızda, CuO ve ZnO nanopartiküllerle beraber saf su, etanol ve etilen glikol malzemeleri kullanılarak üretilen nanoakışkanlar deney düzeneğinden geçirilerek ısı iletim katsayıları (k) ve ısı taşınım katsayıları (h) belirlenmiştir. Reynolds sayısı 1600 civarında olan deneysel ölçümlerde saf suya göre ısı transferinde ZnO’ da %16.5 ve CuO’ da %13.3 değerinde ortalama iyileşme sağlanmıştır. Nanoakışkanların ısı transfer katsayılarının farklı pH değerleri ve Reynolds sayısı değerleri arasındaki ilişki gösterilmiştir. CuO ve ZnO bazlı nanoakışkanların h değerleri için yapay sinir ağı kullanılarak tahminsel modeller oluşturulmuştur. Elde edilen modellerin doğruluk oranı karşılaştırılmıştır. ZnO bazlı nanoakışkanın tahminsel modeli CuO ya göre %40 daha başarılı olduğu gösterilmiştir.

Anahtar Kelimeler

• Nanopartikül,nanoakışkan,termofiziksel özellikler,yapay sinir ağları

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