Pace Regresyon Algoritması İle Kaynama Isı Transferinde Isı Akısının Modellenmesi

Yıl 2020, Ejosat Özel Sayı 2020 (ISMSIT), 43 - 49, 30.11.2020

Erdem Alıç , Mehmet Daş

https://doi.org/10.31590/ejosat.819017

Öz

Küçük sıcaklık farkları ile yüksek miktarda enerji dönüşümüne imkân sağlayan kaynama ısı transferi, buhar kazanları, ısı değiştiricileri, enerji sistemleri ve nükleer santral reaktörleri gibi birçok alanda araştırılmaktadır. Bu çalışmada daha önce deneysel olarak çalışılmış silindirik metal yüzey üzerinde izole buhar kabarcığı bölgesinde gerçekleşen kaynama ısı transferi incelenmiştir. Yüzey malzemesi olarak pürüzsüz çelik seçilmiştir. Deneysel verilerle hesaplanmış metal malzemenin yüzeyinde gerçekleşen havuz kaynama ısı transferi sonucu ortaya çıkan ısı akısı değerleri, bir makine öğrenmesi algoritması olan Pace regresyon algoritması ile ilk kez modellenmiştir. Pace regresyonda 2 farklı metot sonucu üretilen veriler ile deneyler sonucunda elde edilen veriler karşılaştırılmıştır. Çelik malzeme için 0.132 (RAE) hata oranı ile ısı akısı başarılı bir şekilde PG algoritması OLS metodu tarafından modellenmiştir.

Anahtar Kelimeler

Havuz kaynama, Isı transferi, Isı akısı, Pace regresyon

Modeling of Heat Flux in Boiling Heat Transfer with Pace Regression Algorithm

Öz

Boiling heat transfer, which allows a large amount of energy conversion with small temperature differences, has been investigated in many areas such as steam boilers, heat exchangers, energy systems and nuclear power plant reactors. In this study, the boiling heat transfer occurring in the isolated vapor bubble region on the cylindrical metal surface, which was experimentally studied before, was investigated. Smooth steel has been chosen as the surface material. The heat flux values resulting from pool boiling heat transfer on the surface of the metal material calculated with experimental data were modeled for the first time with the Pace regression algorithm, which is a machine learning algorithm. The data obtained as a result of 2 different methods in pace regression were compared with the data obtained from the experiments. With an error rate of 4.06 (RMSE) for steel, the heat flux was successfully modeled by the PG algorithm OLS method.

Anahtar Kelimeler

Pool boiling, Heat transfer, Heat flux, Pace regresyon

Kaynakça

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