Kare ve yuvarlak silindir etrafındaki ısı transferinin modellenmesi: yeni YSA temelli bir yaklaşım

Yıl 2021, , 807 - 814, 27.07.2021

Özge Yetik

https://doi.org/10.28948/ngumuh.771650

Öz

Yapay sinir ağları (ANN) insan beyninin bir modellemesidir. Bu çalışmanın amacı da ANN ile farklı silindirler etrafındaki ısı dağılımını tahmin etmektir. Çalışma kapsamında kare ve daire silindirler ele alınmıştır. Bu çalışmada, silindirlerin sıcaklık analizi, tam arka planda ne yaptığı bilinmeyen bir paket program kullanılmadan, fortran dili kullanılarak gerçekleştirilmiştir. Kod yazarak elde edilen sonuçlar daha güvenilir sonuçlardır. Çalışma iki boyutlu olarak ele alınmıştır. ANN ile verilerin eğitilmesinde en yaygın kullanılan 3 farklı algoritma (Levenberg–Marguardt (LM), Pola-Ribiere Conjugate Gradient (CGP) ve Scaled Conjugate Gradient (SCG)) kullanılmıştır. Silindir tipi, x-koordinatı ve y koordinatı girdi verileri, sıcaklık ise çıktı verisidir. En uygun algoritma LM-18 algoritması olarak bulunmuştur. Yapay sinir ağları ile gerçek değerlerin birbirine yakınlığı istatistiksel olarak değerlendirilmiştir. Bunlar R2, CoV ve RMSE'dir. LM-18 algritmasının eğitim aşamasındaki R2, CoV ve RMSE değerleri sırasıyla 0.9939, 0.0044, 0.0107; test aşamasındaki değerleri sırasıyla 0.9850, 0.0043, 0.0190'dir. R2 değerinin 1'e bu kadar yakın olması ANN'nin çok iyi çalıştığının da göstergesidir.

Anahtar Kelimeler

Levenberg-Marquardt, Levenberg-Marquardt, Isı transferi, Sayısal benzetim, Yapay sinir ağları

Modeling of heat transfer around a square and a circle: a novel ANN-based approach

Öz

Artificial neural network (ANN) is a modelling of the human brain. The aim of this study is to estimate the heat distribution with ANN around different cylinders. Within the scope of the study, square and circular cylinders were discussed. In this study, the temperature analysis of the cylinders was carried out with a program written in Fortran, not using a package program whose exact background is unknown. The results obtained from a writing code are more reliable. The study is discussed in two dimensions. The 3 most commonly used algorithms (Levenberg – Marguardt (LM), Pola-Ribiere Conjugate Gradient (CGP) and Scaled Conjugate Gradient (SCG)) were used to train with ANN. Cylinder type, x-coordinate and y-coordinate were the input variables; and temperature was the output variable. The most suitable algorithm was found to be LM-18 algorithm. The proximity of artificial neural networks and real values was evaluated statistically. These were R2, CoV and RMSE. R2, CoV and RMSE values in the training phase of Levenberg – Marguardt -18 neuron were determined to be 0.9939, 0.0044, 0.0107 while their values in the test phase were 0.9850, 0.0043 and 0.0190 respectively. The fact that the R2 value is so close to 1 is an indication that ANN is working very well.

Anahtar Kelimeler

Levenberg-Marquardt, heat transfer, numerical simulation, artificial neural network.

Kaynakça

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