Dikey Salınımlı Düz Bir Levhadan Karışık Taşınım Isı Transferinde Yığılmış Heterojen Topluluk Öğrenme Modeli

Year 2023, Volume: 6 Issue: 1, 635 - 654, 10.03.2023

Selma Akçay , Selim Buyrukoğlu , Ünal Akdağ

https://doi.org/10.47495/okufbed.1100651

Abstract

Bu çalışmada, hareketli dikey düz bir levhadan karışık taşınım ısı transferinin etkileri deneysel ve yığılmış heterojen topluluk öğrenme yaklaşımı ile analiz edildi. Deneysel çalışmada, boyutsuz salınım genliği (Ao), boyutsuz salınım frekansı (Wo) ve Rayleigh sayısının (Ra) doğal ve zorlanmış taşınım üzerindeki etkileri incelendi. Deneylerde, levhanın dikey hareketi volan-motor düzeneği ile sağlandı. Hareketli levha ve sabit levha yüzeyi üzerinde ortalama Nusselt sayıları (Nu) elde edildi. Ayrıca, bu çalışma, tek tabanlı algoritmalar (Gradient Boosting, AdaBoost, Multilayer Perceptron) ve yığılmış heterojen topluluk öğrenme modeli kullanarak hareketli bir düz plakanın ısı transfer tahminine odaklanmıştır. Tek tabanlı algoritmaların ve yığılmış topluluk modelinin istatistiksel performansı karışık taşınım ısı transferi tahmininde ölçülmüştür. Sonuçlar, yığılmış topluluk modelinin MSE = 2.01, RMSE = 1.42, MAE = 1.1 ve R2 = 0.99 değerlerini verdiğini göstermektedir. Genel olarak, bu çalışma, önerilen yığılmış topluluk makine öğrenme modelinin, hareketli bir levhanın taşınım ısı transferini modellemek için başarıyla kullanılabileceğini ortaya koymaktadır.

Keywords

Salınımlı düz levha, Isı transferi, Yığılmış topluluk, Makine öğrenmesi

Stacked Heterogeneous Ensemble Learning Model in Mixed Convection Heat Transfer from a Vertically Oscillating Flat Plate

Abstract

In this study, the effects of mixed convection heat transfer from a moving vertical flat plate with an experimental and stacked heterogeneous ensemble learning approach are analyzed. In the experimental work, the effects on both natural and forced convection of dimensionless oscillation amplitude (Ao), dimensionless oscillation frequency (Wo) and Rayleigh number (Ra) are investigated. In the experiments, the vertical movement of the plate is provided by a flywheel-motor assembly. The average Nusselt numbers (Nu) on the fixed plate and the moving plate surface were obtained. Additionally, this study is focused on the prediction of heat transfer of a moving flat plate using single-based algorithms (Gradient Boosting, AdaBoost, Multilayer Per-ceptron) and a stacked heterogeneous ensemble learning model. The statistical per-formance of the single-based algorithms and the stacked ensemble model is meas-ured in the prediction of mixed convection heat transfer. The results show that the stacked-based ensemble learning model yielded the MSE = 2.01, RMSE = 1.42, MAE = 1.1 and R2 = 0.99 values. Overall, this study reveals that the proposed stacked en-semble machine learning model can be used successfully for modeling convection heat transfer of a moving plate.

Keywords

Oscillation, Flat plate, Heat transfer, Stacked ensemble, Machine learning

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