Effects of Different Turbulators on Heat Transfer in Smoke Tube Boilers and Modeling of These Effects with Machine Learning Algorithms

Öz

In smoke pipe boilers, the thermal efficiency of the boiler depends on the smoke pipe diameter, smoke pipe length and the heat transfer between the smoke pipe and the outlet chimney. If the heat in the smoke pipes is effectively transported through the pipes, the heat distribution on the surfaces is balanced and the thermal efficiency of the boiler increases. In this study, the improvement of heat transfer in a solid fuel boiler with 125,000 kcal / h heat capacity with a diameter of 42 mm, chimney diameter of 230 mm and water inlet and outlet diameters of 65 mm was investigated by using 4 different types of strip turbulators. Experiments were carried out with turbulators placed in all the smoke pipes in the boiler. Firstly, experiments were carried out without placing a turbulator inside. In the second step, by placing turbulators in the smoke pipes, experiments were made for each type and heat transfer was calculated. In the experiments, the flow rate of the fan was changed with the help of damper and the reynolds number was calculated between 18000 and 28000. Turbulator experiments for heat transfer improvement have increased by at least %15 and at most %41 compared to turbulator free experiments. For the heat transfer increase values obtained because of calculations, predictive models were obtained using machine learning algorithms SVM (support vector machine) and decision tree (M5P model tree). The resulting models have been analyzed for error analysis and have been shown to successfully predict heat transfer increase values.

Anahtar Kelimeler

• smoke tube boiler,
• heat transfer,
• turbulator,
• pressure difference
• support vector machine
• M5P model tree

Kaynakça

1. Abadi SMANR, Mehrabi M, Meyer JP, 2018, Prediction and optimization of condensation heat transfer coefficients and pressure drops of R134a inside an inclined smooth tube. International Journal of Heat and Mass Transfer Volume 124, September, Pages 953-966.
2. Akeel AM, Bashar AM and Raheem JM, 2014. Heat Transfer Enhancement in a Tube Fitted with NozzleTurbulators, Perforated Nozzle-Turbulators with Different hole shap. Eng. Tech.Journal, Vol. 32, Part (A), No.10.
3. Alic E, Das M, Kaska O, 2019. Heat Flux Estimation at Pool Boiling Processes with Computational Intelligence Methods. Processes, 7(5), 293.
4. Argunhan Z, Yıldız C, 2011. Dairesel Kesitli Bir Borunun Girişine Yerleştirilen Delikli Sabit Kanatçıklı Dönme Üreticinin Isı Geçişi Ve Basınç Düşüşüne Etkileri. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 12(2), 217-223.
5. Chokkıyee MKP, Balasuramanaın R, Velusamy DD, 2020. Predictive Analysıs of Heat Transfer Characterıstıcs of Nanofluıds ın Helıcally Coıled Tube Heat Exchanger Usıng Regressıon Approach. Thermal Scıence International Scientific Journal, Volume 24, Issue 1, Pages: 505 – 513.
6. Çakmak G, 2000. Boru Girişinde Enjektörlü Türbülans Üreticisi Bulunan Isı Değiştirgeçlerinde Isı Transferinin ve Basınç Düşüşünün İncelenmesi, Yüksek Lisans Tezi. F.Ü. Fen Bilimleri Enstitüsü, Elazığ.
7. Çerçi K N, Daş M, 2019. Modeling of Heat Transfer Coefficient in Solar Greenhouse Type Drying Systems. Sustainability, 11(18), 5127.
8. Çirak B, Korcak S, 2017. Isı Transferinde Isı Kayıplarının Yapay Sinir Ağları Yöntemi ile İncelenmesi. Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 7(2), 185-197.

9. Çitlak A, Demirpolat AB, Das M, 2019. Katı Yakıtlı Bir Kazanda Isı Transferi İyileştirmeleri ve Basınç Farkının Yapay Sinir Ağı ile Modellenmesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 31 (2), 371-380. DOI: 10.35234/fumbd.509198.
10. Das M, Akpinar E K, 2018. Investigation of pear drying performance by different methods and regression of convective heat transfer coefficient with support vector machine. Applied Sciences, 8(2), 215.
11. Das M, Demirpolat AB, 2019. Bir Nanoakışkanın Farklı pH Değerlerindeki Isı Transfer Katsayılarının Belirlenmesi ve Karar Ağacı Algoritması ile Modellenmesi. Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 12 (2), 1056-1067. DOI: 10.18185/erzifbed.552293.
12. Daş M, Balpetek N, Kavak Akpinar E, Akpinar S, 2019. Türkiye’de bulunan farklı illerin rüzgâr enerjisi potansiyelinin incelenmesi ve sonuçların destek vektör makinesi regresyon ile tahminsel modelinin oluşturulması. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 34 (4).
13. 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.
14. Golgiyaz S, Talu M F, Onat C, 2019. Görüntü İşleme ve Makine Öğrenmesi Yöntemleri ile Baca Gazı Sıcaklığının Tahmin Edilmesi. Avrupa Bilim ve Teknoloji Dergisi, (16), 283-291.
15. Guoqiang DSX Z, 2007. Heat transfer analysis using ANN with experimental data of 180° turn channels with rib turbulators. Journal of Beijing University of Aeronautics and Astronautics, 4.
16. Holman JP, 1989. Experimental Methods for Engineers, 5th edition Mc-Graw Hill Company, New York.
17. Ibrikçi T,Saçma S,Yıldırım V, Koca T, 2010. Application of Artificial Neural Networks in the Prediction of Critical Buckling Loads of Helical Compression Springs. Strojniški vestnik - Journal of Mechanical Engineering 56,6, 409-417.
18. Kakaç S, 1987. Isı İletimi, ODTÜ Mühendislik Fakültesi Yayınları, Yayın No: 52, Ankara.
19. Karagöz Ş, Abdi H, Ömeroğlu G, 2017. Experimental InvestigationOf The Effect Of Turbulators On Heat Transfer In Horizontal Tubes. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, 17(2), 810-817.
20. Karagöz Ş, Çiltaş S, Yıldırım O, Erdoğan S, 2019. Yatay Borularda Türbülatörlerin Isı Transferine Olan Etkisinin Deneysel Araştırılması. Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 12(1), 306-316.
21. Karakaya H, Durmuş A, 2013. Heat transfer and exergy loss in conical spring turbulators. International Journal of Heat and Mass Transfer, 60, 756-762.
22. Kayataş N, İlbaş M, 2005. İç İçe Borulu Model Bir Isı Değiştiricisinde Isı Transferinin İyileştirilmesinin Sayısal Olarak İncelenmesi. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, 21(1), 128-139.
23. Kikuyama K, Murakami M, Nishibori K, Maeda K, 1983. Flow in an Axially Rotating Pipe, Bulletin of The JSME Vol.26, No.214, April, 506-513.
24. Kline SJ and McClintock FA,1953. Describing Uncertainties in Single-Sample Experiments, Mechanical engineering, 75, 3-8.
25. Koca T, Zedeli A, 2020. Helisel İç Borulu Isı Değiştiricilerde Isı Transferi ve Basınç Düşümü Analizinin Deneysel Olarak İncelenmesi. Journal of the Institute of Science and Technology, 10(3): 1943-1955, 2020 ISSN: 2146-0574, eISSN: 2536-4618. DOI: 10.21597/jist.739873.
26. Kuzay TM, Scott CJ, 1977. Turbulent Heat Transfer Studies in Annulus With inner Cylinder Rotation, Journal of Heat Transfer, February 12-19.
27. Migay V K, Golubev LK, 1970. Friction and Heat Transfer in Turbulent Swirl Flow with a Variable Swirl Generator in A Pipe, Int. J. Heat Mass Transfer, Vol 2 No:3, May 68-73.
28. Moya-Rico JD, Molina AE, Belmonte J, Tendero JC, Almendros-Ibáñez JA, 2019. Characterization of a triple concentric-tube heat exchanger with corrugated tubes using Artificial Neural Networks (ANN). Applied Thermal Engineering; 147, 1036-1046.
29. Narezhnyy EG, Sudarev AV, 1971. Local Heat Transfer in Air Flowing in Tubes with a Turbulence Prometer at The Inlet, Int. J. Heat Mass Transfer, Vol.3 No:2, March-April 62-66.
30. Pal M, Mather PM, 2003. An assessment of the effectiveness of decision tree methods for land cover classification, Remote Sensing of Environment, vol.86, pp.554-565.
31. Panahi D, Zamzamian K, 2017. Heat transfer enhancement of shell-and-coiled tube heat exchanger utilizing helical wire turbulator. Applied Thermal Engineering, 115, 607-615.
32. Smithberg E, Landis F, 1964. Friction and Forced Convection Heat Transfer Characteristics in Tubes with Twisted Tape Swirl Generators, Journal of Heat Transfer, February 39-49.
33. Smola AJ and Schölkopf BA, 2004. Tutorial on Support Vector Regression, Statistics and Computing., 14. 199-222.
34. Sparrow EM, Chaboki A, 1984. Turbulent Fluid Flow and Heat Transfer in a Circular Tube. ASME Journal of Heat Transfer; 106, 766-773.
35. Sungur B, Topaloğlu B, 2018. Boru İçine Yerleştirilen Konik Türbülatör Sayısının Nümerik Optimizasyonu. Technological Applied Sciences, 13(3), 208-218.
36. Sungur B, Topaloglu B, Ozcan H, Namli L, 2018. Numerical analysis of the effect of conical turbulators to heat transfer performance of a liquid fuelled boiler. Research on Engineering Structures and Materials, 4(2), 127.
37. Şahin H, Dal A, Özkaya M, 2020. İç İçe Borulu Yay Tip Türbülatörlü Bir Isı Değiştiricisinin RNG k-ε Türbülans Modeli ile Sayısal Analizi. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 8 (1), 64-78. DOI: 10.29109/gujsc.625585.
38. Tokgoz N, Alıç E, Kaşka Ö, Aksoy MM, 2018. The Numerical Study of Heat Transfer Enhancement Usıng AL2O3-Water Nanofluid in Corrugated Duct Application. Journal of Thermal Engineering, Vol. 4, No. 3, pp. 1984-1997, April, Yildiz Technical University Press, Istanbul, Turkey
39. Uğurlubilek N, Nuralcan İY, 2011. Halisel Türbülatörün Isı Geçmişine Etkisinin Sayısal İncelenmesi. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi, 24(2), 71-84.
40. Vapnik V, 1995. The Nature of Statistical Learning Theory. Springer-Verlag, New York.
41. Vapnik, VN, 1998. Statistical learning theory. New York: Wiley.
42. Verma TN, Nashine P, Singh DV, Singh TS, Panwar D ANN, 2017. Prediction of an experimental heat transfer analysis of concentric tube heat exchanger with corrugated inner tubes. Applied Thermal Engineering; 120, 219-227.
43. Yıldız C, Biçer Y, Pehlivan D, 1998. Effect of Twisted Strips on Heat Transfer and Pressure Drop in Heat Exchanger. Energy Conversion and Management; 39, 331-336.
44. Yılmaz T, Ayhan T, 1983. Birbirleriyle Bağ1antılı Daralan -Genişleyen Kanallarda Isı Transferi, Isı Bilimi ve Tekniği 4. Ulusal Kongresi; 133-149.