Machine learning comparison of cooling performance of a Counterflow Ranque–Hilsch Vortex Tube system using different fluids

Yıl 2024, , 621 - 631, 15.04.2024

Murat Korkmaz , Ayhan Doğan , Volkan Kırmacı

https://doi.org/10.28948/ngumuh.1310811

Öz

Today, when heating or cooling processes are carried out, environmentally harmful refrigerants are used. The Counterflow Ranque-Hilsch Vortex Tube (CFRHVT) consists of a simple tube. In RHVT, the heating and cooling process is done with the help of a pressurized fluid, causing little harm to the environment. In this study, 2, 3, 4, 5 and 6 orifice nozzles made of polyamide and brass materials were used in all experiments. At the same time, data were taken with air and oxygen gases as pressurized fluids during the experiments, and at every 0.5 bar, variables between 1.5bar and 7 bar. With the results obtained in the experiments, predictions were made with Support vector machines (SVM), Gaussian process regression (GPR), Linear regression (LR), Ensemble of Tree (ET) and Regression Trees (RT) models from machine learning methods. Five-fold analyses were performed with the k-fold cross-validation method for the validation processes as a result of the investigation. They obtained the best estimation result with the GSR method with 0.99 in the comparisons made with pressured fluids, oxygen and air, and polyamide and brass as the materials. The results show that the cost of experimental setup can be reduced and significant time savings can be achieved using machine learning.

Anahtar Kelimeler

Cooling, Heating, Vortex Tube, Machine Learning

Farklı akışkanlar kullanılan Karşıt Akışlı Ranque–Hilsch Vorteks Tüp sisteminin soğutma performansının makine öğrenimiyle karşılaştırılması

Öz

Günümüzde ısıtma veya soğutma işlemleri yapıldığında çevreye zarar veren soğutucu akışkanlar kullanılmaktadır. Karşıt Akışlı Ranque-Hilsch Vorteks Tüpü (KARHVT) basit bir borudan oluşmaktadır. RHVT de ısıtma ve soğutma işlemi basınçlı bir akışkan yardımıyla yaparak çevreye çok az zarar vermektedir. Bu çalışmada, tüm deneylerde polyamid ve pirinç malzemelerden yapılan 2, 3, 4, 5 ve 6 orfisli nozullar kullanılmıştır. Aynı zamanda deneyler esnasında basınçlı akışkan olarak hava ve oksijen gazları ile 1.5 bar ile 7 bar arasında her 0.5 bar değişkenlerinde veriler alınmıştır. Deneylerde alınan sonuçlar ile makine öğrenimi yöntemlerinden Destek vektör makineleri (DVM), Gauss süreç regresyonu (GSR), Lineer regresyonu (LR), Ağaç toplulukları (AT) ve Regresyon Ağaçları (RA) modelleri tahmin edilmiştir. Doğrulama işlemleri için k-fold çapraz doğrulama yöntemi ile beş katlı olarak analizler yapılmıştır. Analizler sonucunda basınçlı akışkanlar oksijen ve hava, malzemelerden polyamid ve pirinç ile yapılan karşılaştırmalarda en iyi tahmin sonucunu 0,99 ile GSR metoduyla elde edilmiştir. Sonuçlar makine öğrenimi kullanılarak deney düzeneği kurulum maliyetlerinin azaltılabileceğini ve önemli zaman tasarrufu elde edilebileceğini göstermektedir.

Anahtar Kelimeler

Soğutma, Isıtma, Vorteks Tüp, Makine Öğrenme

Kaynakça

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