Research Article
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.
References
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- Alic, E., Das, M., Kaska, O., (2019). Heat Flux Estimation at Pool Boiling Processes with Computational Intelligence Methods. Processes,7, 293.
- Ansari, H. R., Zarei, M. J., Sabbaghi, S., and Keshavarz, P., (2018). A new comprehensive model for relative viscosity of various nanofluids using feed-forward back-propagation MLP neural networks. International Communications in Heat and Mass Transfer, 91, 158-164.
- Chang, H., Tsung, T. T., Chen, L. C., Yang, Y. C., Lin, H. M., Lin, C. K., Jwo, C. S., (2005). Nanoparticle Suspension Preparation Using the Arc Spray Nanoparticle Synthesis System Combined with Ultrasonic Vibration and Rotating Electrode, The International Journal of Advanced Manufacturing Technology, 26, 552–558.
- Çifci H., (2014). Küresel Yüzeylerde Nanoakışkanlarda Kaynama Isı Transferinin Deneysel Olarak İncelenmesi, Yüksek Lisans Tezi.
- Demirpolat, A. B., Das, M., (2019). Prediction of Viscosity Values of Nanofluids at Different pH Values by Alternating Decision Tree and Multilayer Perceptron Methods. Applied Sciences, 9(7), 1288.
- Duangthongsuk, W., Wongwises, S., (2010). An experimental study on the heat transfer performance and pressure drop of TiO2-water nanofluids flowing under a turbulent flow regime, Int. J. Heat Mass Trans, 53, 334-344.
- 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.
- Findik T., Taşdemir Ş. and Şahin. I., (2010). The use of artificial neural network for prediction of grain size of 17-4 pH stainless steel powders. Sci. Research and Essays, 5, 11, 1274-1283.
- 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.
- Kang H.U., Kim S.H., Oh J.M., (2006). Estimation of thermal conductivity of nanofluid using experimental effective particle volume. Exp Heat Transfer, 19(3), 181–91.
- Karimi, H., Yousefi, F., and Rahimi, M. R., (2011). Correlation of viscosity in nanofluids using genetic algorithm-neural network (GA-NN). Heat and mass transfer. 47(11), 1417-1425.
- Kılıç, M., Yavuz, M., Yılmaz, İ. H., (2018). Numerical investigation of combined effect of nanofluids and impinging jets on heated surface. International Advanced Researches and Engineering Journal, 2(1), 14-19.
- Kumar, V., Tiwari, A. K., Ghosh, S. K., (2015). Application of nanofluids in plate heat exchanger: a review. Energy conversion and management, 105, 1017-1036.
- Liu , M., Lin , M., Huang , I., Wang, C., (2005). Enhancement of thermal conductivity with carbon nanotube for nanofluids, International Communications in Heat and Mass Transfer, 32, 1202–1210.
- Lo, C.-H., Tsung, T.-T., Chen, L.-C., Su, C.-H., Lin, H.-M., (2005). Fabrication of Copper Oxide Nanofluid Using Submerged Arc Nanoparticle Synthesis System (SANSS), Journal of Nanoparticle Research, 7, 313–320.
- Lo, C.-H., Tsung, T.-T., and Chen, L.-C., (2005). Shaped-Cntrolled Synthesis of Cu-Based Nanofluid Using Submerged Arc Nanoparticle Synthesis System (SANSS), Journal of Crystal Growth, 277, 636–642.
- Martin,K., McCarthy, G., (1991). North Dakota State Univ., Fargo, ND, USA.,ICDD Grant-in-Aid.
- Miller J. C., Serrato, R. J. M., (2004). Represas - Cardenas and G. Kundahl. The Handbook of Nanotechnology, John Wiley & Sons, Inc., Hoboken, New Jersy.
- Nguyen, C.T., Desgranges F., Gilles R., Nicolas G., Thierry M., Boucher S., (2007). Temperature and particle-size dependent viscosity data for water-based nanofluids–hysteresis phenomenon, Int. J. Heat Fluid Flow, 28(6), 1492–1506.
- Patel , H. E., Sundararajan , T., Das S. K., (2010). An experimental investigation into the thermal conductivity enhancement in oxide and metallic nanofluids, Journal of Nanoparticle Research, 12(3), 1015-1031.
- Patel, J., & Parekh, K., (2018). Effect of Size and Morphology on Stability and Thermal Conductivity of ZnO Nanofluid. Journal of Nanofluids, 7(2), 284-291.
- Suresh, R., Ponnuswamy, V., Mariappan, R., (2013). Effect of annealing temperature on the microstructural, optical and electrical properties of CeO2 nanoparticles by chemical precipitation method. Applied Surface Science, 273, 457-464.
- Şahin, B., (2010). Nanokışkanların Isı Transferi ve Akış Karakteristiklerinin İncelenmesi, TÜBİTAK Proje No: 105M292, Erzurum.
- Şahin, B., Çomaklı, K., Çomaklı, Ö., Yılmaz, M., Karslı, S., Özyurt, Ö., Karagöz, Ş., Kaya, M., (2010). Nanokışkanların Isı Transferi ve Akış Karakteristiklerinin İncelenmesi”, Tübitak, Proje No: 105M292.
- Tavman, I., Turgut, A., (2010). An ınvestigation on thermal conductivity and viscosity of water based nanofluids, Microfluidics Based Microsystems NATO Science for Peace and Security Series A: Chemistry and Biology, 139-162.
- Uysal, C., Korkmaz, M. E., (2018). Estimation of entropy generation for Ag-MgO/water hybrid nanofluid flow through rectangular minichannel by using artificial neural network. Politeknik Dergisi, ISSN: 2147-9429.
- Weerapun, D., Somchai, W., (2009). Measurement of temperaturedependent thermal conductivity and viscosity of TiO2 water nanofluids, Exp. Therm. Fluid Sci., 33(4), 706–714. Wen, D., Ding, Y., (2004). Experimental investigation into convective heat transfer of nanofluids at the entrance region under laminar flow conditions, International Journal of Heat and Mass Transfer, 47, 5181–5188.
- Xie H, Wang J, Xi T, Liu Y., (2001). Study on the thermal conductivity of SiC nanofluids. J Chin Ceram Soc, 29(4), 361–364.
- Xie H.Q., Wang J.C., Xi T.G., Liu Y., Ai F., Wu Q.R., (2002). Thermal conductivity enhancement of suspensions containing nanosized alumina particles, J Appl Phys, 91, 4568–4572.
- Zhu, H., Lin, Y., Yin, Y., (2004). A novel one-step chemical method for preparation of copper nanofluids, Journal of Colloid and Interface Science, 277, 100–103.
Cuo ve Zno İçeren Nanoakışkanların Termofiziksel Özelliklerinin Belirlenmesi ve Yapay Sinir Ağı İle Modellenmesi
Abstract
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.
References
- Afrand, M., Nadooshan, A. A., Hassani, M., Yarmand, H., 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, 77, 49-53.
- Alic, E., Das, M., Kaska, O., (2019). Heat Flux Estimation at Pool Boiling Processes with Computational Intelligence Methods. Processes,7, 293.
- Ansari, H. R., Zarei, M. J., Sabbaghi, S., and Keshavarz, P., (2018). A new comprehensive model for relative viscosity of various nanofluids using feed-forward back-propagation MLP neural networks. International Communications in Heat and Mass Transfer, 91, 158-164.
- Chang, H., Tsung, T. T., Chen, L. C., Yang, Y. C., Lin, H. M., Lin, C. K., Jwo, C. S., (2005). Nanoparticle Suspension Preparation Using the Arc Spray Nanoparticle Synthesis System Combined with Ultrasonic Vibration and Rotating Electrode, The International Journal of Advanced Manufacturing Technology, 26, 552–558.
- Çifci H., (2014). Küresel Yüzeylerde Nanoakışkanlarda Kaynama Isı Transferinin Deneysel Olarak İncelenmesi, Yüksek Lisans Tezi.
- Demirpolat, A. B., Das, M., (2019). Prediction of Viscosity Values of Nanofluids at Different pH Values by Alternating Decision Tree and Multilayer Perceptron Methods. Applied Sciences, 9(7), 1288.
- Duangthongsuk, W., Wongwises, S., (2010). An experimental study on the heat transfer performance and pressure drop of TiO2-water nanofluids flowing under a turbulent flow regime, Int. J. Heat Mass Trans, 53, 334-344.
- 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.
- Findik T., Taşdemir Ş. and Şahin. I., (2010). The use of artificial neural network for prediction of grain size of 17-4 pH stainless steel powders. Sci. Research and Essays, 5, 11, 1274-1283.
- 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.
- Kang H.U., Kim S.H., Oh J.M., (2006). Estimation of thermal conductivity of nanofluid using experimental effective particle volume. Exp Heat Transfer, 19(3), 181–91.
- Karimi, H., Yousefi, F., and Rahimi, M. R., (2011). Correlation of viscosity in nanofluids using genetic algorithm-neural network (GA-NN). Heat and mass transfer. 47(11), 1417-1425.
- Kılıç, M., Yavuz, M., Yılmaz, İ. H., (2018). Numerical investigation of combined effect of nanofluids and impinging jets on heated surface. International Advanced Researches and Engineering Journal, 2(1), 14-19.
- Kumar, V., Tiwari, A. K., Ghosh, S. K., (2015). Application of nanofluids in plate heat exchanger: a review. Energy conversion and management, 105, 1017-1036.
- Liu , M., Lin , M., Huang , I., Wang, C., (2005). Enhancement of thermal conductivity with carbon nanotube for nanofluids, International Communications in Heat and Mass Transfer, 32, 1202–1210.
- Lo, C.-H., Tsung, T.-T., Chen, L.-C., Su, C.-H., Lin, H.-M., (2005). Fabrication of Copper Oxide Nanofluid Using Submerged Arc Nanoparticle Synthesis System (SANSS), Journal of Nanoparticle Research, 7, 313–320.
- Lo, C.-H., Tsung, T.-T., and Chen, L.-C., (2005). Shaped-Cntrolled Synthesis of Cu-Based Nanofluid Using Submerged Arc Nanoparticle Synthesis System (SANSS), Journal of Crystal Growth, 277, 636–642.
- Martin,K., McCarthy, G., (1991). North Dakota State Univ., Fargo, ND, USA.,ICDD Grant-in-Aid.
- Miller J. C., Serrato, R. J. M., (2004). Represas - Cardenas and G. Kundahl. The Handbook of Nanotechnology, John Wiley & Sons, Inc., Hoboken, New Jersy.
- Nguyen, C.T., Desgranges F., Gilles R., Nicolas G., Thierry M., Boucher S., (2007). Temperature and particle-size dependent viscosity data for water-based nanofluids–hysteresis phenomenon, Int. J. Heat Fluid Flow, 28(6), 1492–1506.
- Patel , H. E., Sundararajan , T., Das S. K., (2010). An experimental investigation into the thermal conductivity enhancement in oxide and metallic nanofluids, Journal of Nanoparticle Research, 12(3), 1015-1031.
- Patel, J., & Parekh, K., (2018). Effect of Size and Morphology on Stability and Thermal Conductivity of ZnO Nanofluid. Journal of Nanofluids, 7(2), 284-291.
- Suresh, R., Ponnuswamy, V., Mariappan, R., (2013). Effect of annealing temperature on the microstructural, optical and electrical properties of CeO2 nanoparticles by chemical precipitation method. Applied Surface Science, 273, 457-464.
- Şahin, B., (2010). Nanokışkanların Isı Transferi ve Akış Karakteristiklerinin İncelenmesi, TÜBİTAK Proje No: 105M292, Erzurum.
- Şahin, B., Çomaklı, K., Çomaklı, Ö., Yılmaz, M., Karslı, S., Özyurt, Ö., Karagöz, Ş., Kaya, M., (2010). Nanokışkanların Isı Transferi ve Akış Karakteristiklerinin İncelenmesi”, Tübitak, Proje No: 105M292.
- Tavman, I., Turgut, A., (2010). An ınvestigation on thermal conductivity and viscosity of water based nanofluids, Microfluidics Based Microsystems NATO Science for Peace and Security Series A: Chemistry and Biology, 139-162.
- Uysal, C., Korkmaz, M. E., (2018). Estimation of entropy generation for Ag-MgO/water hybrid nanofluid flow through rectangular minichannel by using artificial neural network. Politeknik Dergisi, ISSN: 2147-9429.
- Weerapun, D., Somchai, W., (2009). Measurement of temperaturedependent thermal conductivity and viscosity of TiO2 water nanofluids, Exp. Therm. Fluid Sci., 33(4), 706–714. Wen, D., Ding, Y., (2004). Experimental investigation into convective heat transfer of nanofluids at the entrance region under laminar flow conditions, International Journal of Heat and Mass Transfer, 47, 5181–5188.
- Xie H, Wang J, Xi T, Liu Y., (2001). Study on the thermal conductivity of SiC nanofluids. J Chin Ceram Soc, 29(4), 361–364.
- Xie H.Q., Wang J.C., Xi T.G., Liu Y., Ai F., Wu Q.R., (2002). Thermal conductivity enhancement of suspensions containing nanosized alumina particles, J Appl Phys, 91, 4568–4572.
- Zhu, H., Lin, Y., Yin, Y., (2004). A novel one-step chemical method for preparation of copper nanofluids, Journal of Colloid and Interface Science, 277, 100–103.
There are 31 citations in total.
Details
| Primary Language | Turkish |
|---|---|
| Journal Section | Articles |
| Authors | |
| Publication Date | March 27, 2020 |
| Submission Date | April 17, 2019 |
| Published in Issue | Year 2020 Volume: 11 Issue: 1 |
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