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

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.

Anahtar Kelimeler:

Yapay Sinir Ağları, Isı Transferi, Nusselt Sayısı, Isı Transferi Katsayısı

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

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.

Keywords:

Artificial Neural Network, Heat Transfer, Nusselt Number, Overall Heat Transfer Coefficient,

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