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

Yıl 2019, Cilt: 12 Sayı: 2, 1056 - 1067, 31.08.2019

Mehmet Das , Ahmet Beyzade Demirpolat

https://doi.org/10.18185/erzifbed.552293

Cited By: 1

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

• Maxwell J. C., A Treatise on Electricity and Magnetism, 1881. Second ed., Clarendon Press, Oxford, UK.
• Gürmen, S. Ebin, B., 2008. Nanopartiküller ve Üretim Yöntemleri-1, Metalurji Dergisi, 150, 31-38. Choi, S.U.S., 1995. Enhancing thermal conductivity of fluids with nanoparticles, The Proceedings of the 1995 ASME International Mechanical Engineering Congress and Exposition, San Francisco, USA, ASME, FED 231/MD 66, 99–105.
• Xu J.F., Zhang J.R., Du Y.W., 1996, Ultrasonic velocity and attenuation in nano- structured Zn materials, Mater Lett; 29, 131–4.
• Verma P., Chaturvedi P., Rawat J.S.B.S., 2007. Elimination of currentnon-uniformity in carbon nanotube field emitters, J Mater Sci: Mater Electron, 18, 677–80.
• Fotukian, S.M., Esfahany, M.N., 2010. Experimental study of turbulent convective heat transfer and pressure drop of dilute CuO/water nanofluid inside a circular tube, Int. Commun. Heat Mass Trans, 37, 214-219.
• Wang, X., Xu, X., Choi, S.U.S., 1999. Thermal conductivity of nanoparticle-fluid mixture. J. Thermophys Heat. Transf, 13(4), 474–480.
• Pak, B.C., Cho, Y.I., 1998. Hydrodynamic and heat transfer study of dispersed fluids with submicron metallic oxide particles, Exp. Heat Transf Int. J., 11(2), 151–170.
• Eastman, J. A., Choi, S. U. S., Li, S., Yu,W., Thompson, L. J., 2001. Anomalously Increased Effective Thermal Conductivity of Ethylene Glycol-Based Nanofluids Containing Copper Nanoparticles, Applied Physics Letters, 78, 718–720.
• Xuan Y., Li Q., 2000. Heat transfer enhancement of nanofluids, International Journal of Heat and Fluid Flow, 21(1), 58–64.
• XuanY., Li Q., Hu W., 2003. Aggregation Structure and Thermal Conductivity of Nanofluids, AIChE Journal, Cilt 49, No 4, 1038-1043.
• Zhou, L.,P, Wang, B.X., Peng, X.,F, Du, X-Z, Yang, Y.P., 2010. On the specific heat capacity of CuO nanofluid. Adv Mech Eng, 172085, 1–4.
• Williams, W., Buongiorno, J., Hu, L.W., 2008. Experimental investigation of turbulent convective heat transfer and pressure loss of alumina/water and zirconia/ water nanoparticle colloids (nanofluids) in horizontal tubes, J. Heat Trans, 130, 042412.
• Fakoor, Pakdaman M., Akhavan-Behabadi M.A., Razi, P., 2012. An experimental investigation on thermo-physical properties and over all performance of MWCNT/ heat transfer oil nanofluid flow inside vertical helically coiled tubes, Exp Therm Fluid Sci 40(0),103–11.
• Sajadi, A. R., Kazemi, M. H., 2011. Investigation of turbulent convective heat transfer and pressure drop of TiO2/water nanofluid in circular tube. International Communications in Heat and Mass Transfer, 38(10), 1474-1478.
• Alade, I. O., Oyehan, T. A. et. al. 2018. Modeling thermal conductivity enhancement of metal and metallic oxide nanofluids using support vector regression, Advanced Powder Technology, vol.29(1), pp.157-167.
• Hemmati-Sarapardeh, A., Varamesh, A., Husein, M. M. and Karan, K., 2018. On the evaluation of the viscosity of nanofluid systems: Modeling and data assessment, Renewable and Sustainable Energy Reviews, vol.81, pp.313-329.
• Esfe, M. H. A.Tatar, Ahangar M.R.H. and Rostamian, H., 2018. A comparison of performance of several artificial intelligence methods for predicting the dynamic viscosity of TiO2/SAE 50 nano-lubricant, Physica E: Low-dimensional Systems and Nanostructures, vol. 96,pp. 85-93.
• Demirpolat, A.B. Das, M., 2019 Prediction of Viscosity Values of Nanofluids at Different pH Values by Alternating Decision Tree and Multilayer Perceptron Methods, Appl. Sci. vol.9, 1288.
• Afrand, M. A. Nadooshan, A. Hassani, M. Yarmand, H. and Dahari. M., 2016 Predicting the viscosity of multi-walled carbon nanotubes/water nanofluid by developing an optimal artificial neural network based on experimental data, International Communications in Heat and Mass Transfer, vol.77, pp.49-53.
• Ahmadi M. H., M. Ahmadi, A. Nazari M. A., Mahian O. and Ghasempour, R.,2019 A proposed model to predict thermal conductivity ratio of Al 2 O 3/EG nanofluid by applying least squares support vector machine (LSSVM) and genetic algorithm as a connectionist approach, Journal of Thermal Analysis and Calorimetry, vol.135(1), pp.271-281.
• Ahmadi, M. H., Tatar, A., Seifaddini P., et al. 2018. Thermal conductivity and dynamic viscosity modeling of Fe2O3/water nanofluid by applying various connectionist approaches, Numerical Heat Transfer, Part A: Applications, vol.74(6), pp.1301-1322.
• Gil, E. et. al. 2018. XPS and SEM analysis of the surface of gas atomized powder precursor of ODS ferritic steels obtained through the STARS route, Applied Surface Science, vol. 427: pp.182-191.
• Jothibas, M., Manoharan, C., Jeyakumar, S. J., Praveen, P., Punithavathy, I. K., & Richard, J. P. (2018). Synthesis and enhanced photocatalytic property of Ni doped ZnS nanoparticles. Solar Energy, 159, 434-443.
• Çengel Yunus A., 2010. Isı Ve Kütle Transferi Pratik bir Yaklaşım 3. Basım,467- 468.
• Safavian, S.R., Landgrebe, D., 1991. A survey of decision tree classifier methodology, IEEE Transactions on Systems Man and Cybernetics, vol. 21, pp.660-674.Pal, M., Mather, P.M., 2003. An assessment of the effectiveness of decision tree methods for land cover classification, Remote Sensing of Environment, vol.86, pp.554-565.
• Miller, J. C. Serrato, R. et.al., 2004. The Handbook of Nanotechnology, John Wiley & Sons, Inc., Hoboken, New Jersy.