Year 2019, Volume 5 , Issue 2, Pages 13 - 22 2019-12-11

PREDICTION OF NUSSELT NUMBER OF RECTANGULAR FINS USING ARTIFICIAL NEURAL NETWORK MODEL
PREDICTION OF NUSSELT NUMBER OF RECTANGULAR FINS USING ARTIFICIAL NEURAL NETWORK MODEL

Ece Ayli [1]


Pasif ısı transferi iyileştirme metodlarında ısı transferi kat sayısı ve Nusselt sayısını maximize ederken, basınç düşümünü minimize eden yaklaşımı tespit edebilmek için bir çok parametrenin optimizasyonunun yapılması gerekmektedir. Bu sebepten ötürü, deneysel ve sayısal çalışmalara bağlı olarak ampirik korelasyonlar elde edilmektedir. Bu çalışmada dikdörtgensel finlerin ısı transferi davranışı deneysel ve yapay sinir ağları metodları ile ortaya konmuştur. Yapay sinir ağları metodolojisi ile elde edilen sonuçlar korelasyon ile kıyaslanmıştır.  Ayrıca, tanımlanan problem için yapay sinir ağı uygulamasında farklı eğitim algoritmalarının ve katman sayısının sonuçlar üzerindeki etkisi araştırılmıştır. Elde edilen sonuçlara göre YSA yöntemi, korelasyon yönteminden daha hızlı ve daha doğru sonuç vermektedir. Diğer yandan YSA yaklaşımının doğruluğunun arttırılması için uygun eğitim algoritmasının seçimi, uygun katman sayısının tespiti yani uygun mimarinin elde edilmesi önem arz etmektedir.  Tanımlanan bu problem için, 10-5-1 ağına sahip Bayesian Regularization algoritması %7.6 ortalama yüzde hata ve 0.029 RMSE ile iyi senaryo olarak belirlenmiştir. Maximum ortalama hata %56.3 ile  Levenberg- Marquardt algoritmasında 10-12-1 ağı ile elde edilmiştir. 

In the passive heat transfer enhancement methods, there are several parameters which need to be optimized to maximize the heat transfer coefficient and Nusselt number while minimizing the pressure drop. For this purpose, empirical correlations are generated by experimental and numerical studies. In this study, a heat transfer analysis of rectangular fins with experimental data by an artificial neural network approach is performed. Artificial Neural Network method is compared with the classical regression model. Different networks with a different number of neurons in the hidden layer and several training algorithms are tested for the defined problem. The results show that the ANN model is found faster and more accurate than conventional techniques if the optimum architecture is generated and convenient training algorithm is chosen for the specific problem. For this   problem, 10-5-1 network with Bayesian Regularization training algorithm is selected as the best scenario with 7.6 % mean absolute percentage error (MAPE) and 0.029 RMSE value while maximum MAPE value is reached to 56.3 % with Levenberg- Marquardt training algorithm and with 10-12-1 network.

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Primary Language tr
Subjects Engineering
Journal Section Journals
Authors

Orcid: 0000-0002-6209-161X
Author: Ece Ayli (Primary Author)
Institution: ÇANKAYA ÜNİVERSİTESİ
Country: Turkey


Dates

Publication Date : December 11, 2019

Bibtex @research article { muglajsci529193, journal = {Mugla Journal of Science and Technology}, issn = {2149-3596}, address = {}, publisher = {Muğla Sıtkı Koçman Üniversitesi}, year = {2019}, volume = {5}, pages = {13 - 22}, doi = {10.22531/muglajsci.529193}, title = {PREDICTION OF NUSSELT NUMBER OF RECTANGULAR FINS USING ARTIFICIAL NEURAL NETWORK MODEL}, key = {cite}, author = {Ayli, Ece} }
APA Ayli, E . (2019). PREDICTION OF NUSSELT NUMBER OF RECTANGULAR FINS USING ARTIFICIAL NEURAL NETWORK MODEL. Mugla Journal of Science and Technology , 5 (2) , 13-22 . DOI: 10.22531/muglajsci.529193
MLA Ayli, E . "PREDICTION OF NUSSELT NUMBER OF RECTANGULAR FINS USING ARTIFICIAL NEURAL NETWORK MODEL". Mugla Journal of Science and Technology 5 (2019 ): 13-22 <https://dergipark.org.tr/en/pub/muglajsci/issue/49054/529193>
Chicago Ayli, E . "PREDICTION OF NUSSELT NUMBER OF RECTANGULAR FINS USING ARTIFICIAL NEURAL NETWORK MODEL". Mugla Journal of Science and Technology 5 (2019 ): 13-22
RIS TY - JOUR T1 - PREDICTION OF NUSSELT NUMBER OF RECTANGULAR FINS USING ARTIFICIAL NEURAL NETWORK MODEL AU - Ece Ayli Y1 - 2019 PY - 2019 N1 - doi: 10.22531/muglajsci.529193 DO - 10.22531/muglajsci.529193 T2 - Mugla Journal of Science and Technology JF - Journal JO - JOR SP - 13 EP - 22 VL - 5 IS - 2 SN - 2149-3596- M3 - doi: 10.22531/muglajsci.529193 UR - https://doi.org/10.22531/muglajsci.529193 Y2 - 2019 ER -
EndNote %0 Mugla Journal of Science and Technology PREDICTION OF NUSSELT NUMBER OF RECTANGULAR FINS USING ARTIFICIAL NEURAL NETWORK MODEL %A Ece Ayli %T PREDICTION OF NUSSELT NUMBER OF RECTANGULAR FINS USING ARTIFICIAL NEURAL NETWORK MODEL %D 2019 %J Mugla Journal of Science and Technology %P 2149-3596- %V 5 %N 2 %R doi: 10.22531/muglajsci.529193 %U 10.22531/muglajsci.529193
ISNAD Ayli, Ece . "PREDICTION OF NUSSELT NUMBER OF RECTANGULAR FINS USING ARTIFICIAL NEURAL NETWORK MODEL". Mugla Journal of Science and Technology 5 / 2 (December 2019): 13-22 . https://doi.org/10.22531/muglajsci.529193
AMA Ayli E . PREDICTION OF NUSSELT NUMBER OF RECTANGULAR FINS USING ARTIFICIAL NEURAL NETWORK MODEL. Mugla Journal of Science and Technology. 2019; 5(2): 13-22.
Vancouver Ayli E . PREDICTION OF NUSSELT NUMBER OF RECTANGULAR FINS USING ARTIFICIAL NEURAL NETWORK MODEL. Mugla Journal of Science and Technology. 2019; 5(2): 22-13.