Paralel Bağlı Vorteks Tüplerinin Performansı için Yapay Sinir Ağları Analizi

Year 2020, , 1509 - 1517, 30.09.2020

Hüseyin Kaya

https://doi.org/10.31202/ecjse.774448

Cited By: 2

Abstract

Bu çalışmada iki adet aynı özelliklere sahip karşıt akışlı Ranque-Hilsch Vorteks Tüpü (RHVT) paralel bağlanarak performansı deneysel olarak incelenmiş ve vorteks tüpünün performans göstergesi olan sıcak akışkan çıkışı ile soğuk akışkan çıkışı arasındaki sıcaklık farkı (ΔT) değerleri elde edilmiştir. Oksijen ve hava ile yapılan deneylerde polyamid ve pirinç malzemeden imal edilmiş 3,4 ve 5 orifisli nozullar kullanılmıştır. Farklı akışkanlar, nozul malzemeleri ve nozul sayıları için elde edilen ΔT değerlerinin modellenmesi için bir yapay sinir ağları (YSA) çalışması yapılmış ve paralel bağlı iki vorteks tüpü sistemi için genelleştirilebilir modelleme elde edilmiştir. Nozullar için ısıl iletkenlik ve orifis sayısı, çalışma akışkanları için özgül ısı ve yoğunluk parametreleri ile RHVT giriş basıncı ( 5 girdi) girdi parametreleri olarak kullanılmıştır. YSA için veriler eğitim ve test grubu olarak ayrılmış ve eğitilen model test grubu ile test edilmiştir. Regresyon analizinde eğitim gurubu için R2 değeri %99.8, test grubu için %99.6 olarak hesaplanıştır

Keywords

Vorteks tüpü, Modelleme, YSA

Artificial Neural Network Analysis for Performance of Parallel Connected Vortex Tubes

Abstract

In this study, two counter-flow Ranque-Hilsch Vortex Tubes (RHVTs) were connected in parallel, and their performance was investigated experimentally, and the temperature difference (ΔT) values the vortex tube's performance indicator between hot and the cold fluid outlet were obtained. In experiments with oxygen and air, 3,4 and 5 orifice nozzles made of polyamide and brass are used. An artificial neural network (ANN) study was conducted to model the ΔT values obtained for different fluids, nozzle materials and nozzle numbers, and generalizable modeling was obtained for two parallel vortex tube systems. Thermal conductivity and orifice number for nozzles, specific heat and density parameters for working fluids and RHVT inlet pressure (5 input) are used as input parameters. Data for ANN was separated as a training and test group and the trained model was tested with the test group. In regression analysis, R2 value was calculated as 99.8% for the education group and 99.6% for the test group.

Keywords

Vortex tube, Modelling, ANN

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