Determination of Heat Transfer Coefficients at Different pH Values of a Nanofluids and Modeling with Decision Tree Algorithm

Öz

It is important to be able to use the energy in a more beneficial way by increasing the heat transfer in the in-pipe flows. Because, with the technological developments, there is an increasing energy demand in the industry sector. For this reason, researchers have been working on new generation heat transfer fluids in recent years. In our study, nanoparticle production of CuO (copper oxide) was performed. Scanner electron microscope (SEM) image analysis and X-ray diffraction method analysis (XRD) analysis were performed to show that the material produced has the properties of nano material. A nanofluid was obtained using pure water, ethanol and ethylene glycol materials with CuO nanoparticles. Heat transfer coefficients were determined at different pH values of the obtained nanofluid. In the experimental studies, the Re value was 887 and 2290, whereas the heat transfer coefficient value was 349,821 (W/m² ° C) and 374,253 (W/m²°C), respectively. The pH value was 7.84 and 9.95, while the heat transfer coefficient was 349,821 (W/m²°C) and 374,253 (W/m²°C), respectively. Predictive models were obtained by using decision tress (DT) algorithms for heat transfer coefficients calculated by experimental studies. In order to determine the validity of the obtained models, mean square error (MSE), root mean square error (RMSE) and mean absolute percentage error (MAPE) analysis were performed. As a result, it was observed that the heat transfer coefficient value of the nanofluid decreased as the pH values increased.  It was calculated that the heat transfer coefficient of the nanofluid obtained at different Reynolds values was 13.3% higher than the heat transfer coefficient of pure water.It has been shown that the KA algorithm, which is a computational intelligence method, was successful in estimating the thermophysical properties of nanofluids according to the value of 0.891 MAPE.

Anahtar Kelimeler

• Nano fluid,Nano material,Heat transfer coefficient,Decision tree

Bir Nanoakışkanın Farklı pH Değerlerindeki Isı Transfer Katsayılarının Belirlenmesi ve Karar Ağacı Algoritması ile Modellenmesi

Öz

Boru içi akışlarda ısı transferini artırarak enerjiyi daha faydalı bir şekilde kullanabilmek önemlidir. Çünkü teknolojik gelişmelerle birlikte sanayi sektöründe artan bir enerji talebi mevcuttur. Bu nedenle araştırmacılar son yıllarda yeni nesil ısı transfer akışkanları üzerinde çalışmaktadırlar. Çalışmamızda, CuO (bakır oksit) nanopartikül üretimi yapıldı. Üretilen malzemenin nano malzeme özelliğine sahip olduğunu gösteren taramalı elektron mikroskopu (TEM) görüntü analizi ve X ışını kırınım yöntemi analizi  (XRD) analizleri yapılmıştır. CuO  nanopartiküllerle beraber saf su, etanol ve etilen glikol malzemeleri kullanılarak bir nanoakışkan elde edilmiştir. Elde edilen nanoakışkanın farklı pH değerlerinde ısı transfer katsayıları belirlenmiştir. Ayrıca farklı pH değerlerinde ısı transfer katsayıları ile Reynolds sayısı arasındaki ilişkiyi incelenmiştir. Yapılan deneysel çalışmalarda Re değeri 887 ve 2290 iken ısı transfer katsayısı değeri sırasıyla 349,821 (W/m²°C) ve 374,253 (W/m²°C) olarak hesaplanmıştır. pH değeri 7.84 ve 9.95 iken ısı transfer katsayısı değeri sırasıyla 349,821 (W/m²°C) ve 374,253 (W/m²°C) olarak hesaplanmıştır. Deney çalışmaları ile hesaplanan ısı transfer katsayıları için karar ağacı (KA) algoritmaları kullanılarak tahminsel modeller elde edilmiştir. Elde edilen modellerin geçerliliğini belirlemek için, ortalama karesel hata (MSE), kök ortalama karesel hata (RMSE), ortalama mutlak yüzde hata (MAPE) analizleri yapılmıştır. Sonuç olarak pH değerleri arttıkça da nanoakışkanın ısı transfer katsayısı değerinin azaldığı gözlemlenmiştir. Farklı Reynolds değerlerinde elde edilen nanoakışkanın ısı transfer katsayısı, Saf suya ait ısı transfer katsayından yaklaşım %13.3 oranında daha yüksek olduğu belirtilmiştir.  Hesaplamalı zeka yöntemi olan KA algoritmasının nanoakışkanların termofiziksel özelliğini tahminlemesinde 0.891 MAPE değerine göre başarılı olduğu gösterilmiştir.

Anahtar Kelimeler

• Nanoakışkan,Nanomalzeme,Isı transfer katsayısı,Karar ağacı

Kaynakça

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