Taguchi Yöntemi Kullanılarak Vorteks Tüpü Tasarım Parametrelerinin Optimizasyonu

Öz

Bu çalışmada, sabit tüp çapı ve sınır koşulları ile bir vorteks tüpünün (VT) optimizasyonu, Soğutma Performans Katsayısını (COPcooling) iyileştirmek amacıyla dört farklı tasarım faktörünün belirlenmesiyle gerçekleştirilmiştir: konik vana açısı (α), nozul sayısı (N), soğuk akış çıkışı çapı (Dcold exit) ve nozul giriş çapı (Dnozzle). Her belirlenen faktör için beş farklı seviye atanmış ve Taguchi yaklaşımı kullanılarak bir L25 ortogonal serisi oluşturulmuştur. 3D tasarlanmış vakalar, ANSYS Fluent yazılımında standart k-epsilon türbülans modeli kullanılarak sayısal analizlere tabi tutulmuştur. Tasarım parametrelerinin etki seviyeleri, Varyans Analizi (ANOVA) yöntemiyle belirlenmiştir. Ayrıca, COPcooling bağımsız değişkeni ile regresyon analizi yoluyla ampirik bir denklem elde edildikten sonra, bir doğrulama testi gerçekleştirilmiştir. Sonuçlar, COPcooling üzerindeki beş parametrenin etki sırasının N > Dnozzle > Dcold > α olduğunu göstermiştir; burada N parametresi VT içindeki COPcooling üzerinde en güçlü etkiye sahipken, α parametresi en düşük etkiye sahiptir. Ayrıca, optimum VT'nin, başlangıçtaki geometrik parametrelere sahip bir VT ile karşılaştırıldığında COPcooling değerinde %40.3'lük bir iyileşme sağladığı belirlenmiştir. Taguchi yaklaşımının VT geometri optimizasyonunda kullanılması, performansı önemli ölçüde artırmıştır.

Anahtar Kelimeler

• Soğutma Performans Katsayısı (COPcooling)
• Sıcaklık farkı
• Varyans Analizi (ANOVA)
• Hesaplamalı Akışkanlar Dinamiği (CFD)
• Regresyon analizi

Optimization of Vortex Tube Design Parameters Using the Taguchi Method

Öz

In this study, the optimization of a vortex tube (VT) with a fixed tube diameter and boundary conditions was attempted by determining four different design factors: the value of the conical valve degree (α), the number of nozzles (N), the cold flow exit diameter (Dcold exit), and the nozzle inlet diameter (Dnozzle), to improve the Cooling Coefficient of Performance (COPcooling). For each identified factor, five different levels were assigned, and an L25 orthogonal series was constructed using the Taguchi approach. The 3D-designed cases were subjected to numerical analysis in the ANSYS Fluent software program using the standard k-epsilon turbulence model. The effect levels of the design parameters were determined using the Analysis of variance (ANOVA) approach. Furthermore, after obtaining an empirical equation with COPcooling as the independent variable through Regression analysis, a confirmation test was conducted. The results indicated that the order of influence of the five parameters on COPcooling was N> Dnozzle> Dcold > α, with the N parameter having the strongest impact on the COPcooling in the VT, while the α parameter had the least effect. Additionally, the optimal VT showed a 40.3% improvement in COPcooling, when compared to a VT with initial geometric parameters. It has been identified that using the Taguchi approach for VT geometry optimization significantly enhanced performance

Anahtar Kelimeler

• Cooling Coefficient of Performance (COPcooling)
• Temperature difference
• Analysis of variance (ANOVA)
• Computational Fluid Dynamic (CFD)
• Regression analysis

Kaynakça

1. [1] Ranque GJ. Experiments on Expansion in a Vortex with Simultaneous Exhaust of Hot Air and Cold Air. Journal de Physique et le Radium. 1933; 4:112–114.
2. [2] Hilsch R. The Use of the Expansion of Gases in a Centrifugal Field as Cooling Process. Review of Scientific Instruments. 1947; 18:108–113.
3. [3] Wang Z, Suen KO. Numerical comparisons of the thermal behaviour of air and refrigerants in the vortex tube. Applied Thermal Engineering. 2020; 164:114515.
4. [4] Hartnett JP, Eckert ERG. Experimental Study of the Velocity and Temperature Distribution in a High-Velocity Vortex-Type Flow Journal of Fluids Engineering. 1957; 79(4):751–758.
5. [5] Kurosaka M. Acoustic streaming in swirling flow and the Ranque—Hilsch (vortex-tube) effect. Journal of Fluid Mechanics. 1982; 124:139–172.
6. [6] Takahama H. Studies on Vortex Tubes : (1) Experiments on Efficiency of Energy Separation : (2) On Profiles of Velocity and Temperature. Bulletin of JSME. 1965; 8:433–440.
7. [7] Takahama H, Yokosawa H. Energy Separation in Vortex Tubes with a Divergent Chamber. ASME Journal of Heat and Mass Transfer.1981; 103:196–203.
8. [8] Dincer K, Avci A, Baskaya S, Berber A. Experimental investigation and exergy analysis of the performance of a counter flow Ranque–Hilsch vortex tube with regard to nozzle cross-section areas. International Journal of Refrigeration. 2010; 33:954–962.

9. [9] Arjomandi M, Xue YP. An Investigation on the Effect of the Hot End Plugs on the Efficiency of the Ranque-Hilsch Vortex Tube. Fifth International Conference on Fluid Mechanics. Aug.15-19, 2007 Shanghai, China
10. [10] Gao CM, Bosschaart KJ, Zeegers JCH, De Waele ATAM. Experimental study on a simple Ranque–Hilsch vortex tube. Cryogenics. 2005; 45(3):173–183.
11. [11] Eiamsa-ard S, Promvonge P. Numerical investigation of the thermal separation in a Ranque–Hilsch vortex tube. International Journal of Heat and Mass Transfer. 2007; 50(5-6):821–832.
12. [12] Liu X, Liu Z. Investigation of the energy separation effect and flow mechanism inside a vortex tube. Applied Thermal Engineering. 2014; 67(1-2):494–506.
13. [13] Bagre N, Parekh AD, Patel VK. Exergy analysis and experimental investigation of various vortex tube material with different combination of vortex generators. International Journal of Refrigeration 2023; 150:113–124.
14. [14] Chen W, Luo Z, Li X, Lu S, Guo F. Numerical study of temperature separation characteristics of vortex tubes: Effects of structural parameters and modeling of cooling performance correlations. Thermal Science and Engineering Progress 2023; 39:101715.
15. [15] Rafiee SE, Sadeghiazad MM. Experimental and 3D CFD analysis on optimization of geometrical parameters of parallel vortex tube cyclone separator. Aerospace Science and Technology. 2017; 63:110–122.
16. [16] Im SY, Yu SS. Effects of geometric parameters on the separated air flow temperature of a vortex tube for design optimization. Energy. 2012; 37(1):154–160.
17. [17] Bazgir A, Khosravi-Nikou M, Heydari A. Numerical CFD analysis and experimental investigation of the geometric performance parameter influences on the counter-flow Ranque-Hilsch vortex tube (C-RHVT) by using optimized turbulence model. Heat and Mass Transfer. 2019; 55:2559–2591.
18. [18] Pinar AM, Uluer O, Kırmacı V. Statistical Assessment of Counter-Flow Vortex Tube Performance for Different Nozzle Numbers, Cold Mass Fractions, and Inlet Pressures Via Taguchi Method. Experimental Heat Transfer. 2009; 22:271–282.
19. [19] Pinar AM, Uluer O, Kirmaci V. Optimization of counter flow Ranque–Hilsch vortex tube performance using Taguchi method. International Journal of Refrigeration. 2009; 32(6):1487–1494.
20. [20] Bramo AR, Pourmahmoud N. CFD simulation of length to diameter ratio effects on the energy separation in a vortex tube. Thermal Science. 2011; 15(3):833–848.
21. [21] Pourmahmoud Nb, Abdol Reza. The effect of L/D ratio on the temperature separation in the counter-flow vortex tube. International Journal of Research and Reviews in Applied Sciences. 2011;6(1):59-68.
22. [22] Dutta T, Sinhamahapatra KP, Bandyopdhyay SS. Comparison of different turbulence models in predicting the temperature separation in a Ranque–Hilsch vortex tube. International Journal of Refrigeration. 2010; 33(4):783–792.
23. [23] Shamsoddini R, Abolpour B. A geometric model for a vortex tube based on numerical analysis to reduce the effect of nozzle number. International Journal of Refrigeration. 2018; 94:49–58.
24. [24] Suresh Kumar G, Padmanabhan G, Dattatreya Sarma B. Optimizing the Temperature of Hot outlet Air of Vortex Tube using Taguchi Method. Procedia Engineering. 2014; 97:828–836.
25. [25] Kirmaci V. Exergy analysis and performance of a counter flow Ranque–Hilsch vortex tube having various nozzle numbers at different inlet pressures of oxygen and air. International Journal of Refrigeration. 2009; 32(7):1626–1633.
26. [26] Ahlborn BK, Gordon JM. The vortex tube as a classic thermodynamic refrigeration cycle. Journal of Applied Physics. 2000; 88(6):3645–3653.
27. [27] Chen K, Liang S, Shen Z, Xu X, Wang B, Zhu Y. Thermodynamic assessment of a novel and efficient self-condensing transcritical CO2 power cycle with a vortex tube. Applied Thermal Engineering. 2024; 245:122825.
28. [28] Shaji K, Lee KK, Salmani F, Kim HD. Numerical analysis and an approach for optimization of the Ranque–Hilsch vortex tube for a compressible flow. Applied Thermal Engineering. 2024; 243:122590.
29. [29] Xue Y, Arjomandi M. The effect of vortex angle on the efficiency of the Ranque–Hilsch vortex tube. Experimental Thermal and Fluid Science. 2008; 33(1):54–57.
30. [30] Wang Z, Suen KO. Numerical comparisons of the thermal behaviour of air and refrigerants in the vortex tube. Applied Thermal Engineering. 2020; 164:114515.
31. [31] Prabakaran J, Vaidyanathan S. Effect of orifice on vortex tube" Prabakaran & Vaidyanathan Effect of orifice and pressure of counter flow vortex tube. Indian Journal of Science and Technology. 2010; 3(4):374–376.
32. [32] Behera U, Paul PJ, Kasthurirengan S, Karunanithi R, Ram SN, Dinesh K, Jacob S. CFD analysis and experimental investigations towards optimizing the parameters of Ranque–Hilsch vortex tube. International Journal of Heat and Mass Transfer. 2005(10); 48:1961–1973.
33. [33] Tanürün HE. Improvement of vertical axis wind turbine performance by using the optimized adaptive flap by the Taguchi method. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. 2024; 46(1):71–90.
34. [34] Tanürün HE, Acır A. Investigation of the hydrogen production potential of the H-Darrieus turbines combined with various wind-lens. International Journal of Hydrogen Energy. 2022; 47(55):23118–23138.
35. [35] Rafiee SE, Rahimi M. Experimental study and three-dimensional (3D) computational fluid dynamics (CFD) analysis on the effect of the convergence ratio, pressure inlet and number of nozzle intake on vortex tube performance–Validation and CFD optimization. Energy. 2013; 63:195–204.
36. [36] Tanürün HE, Ata İ, Canlı ME, Acır A. Farklı Açıklık Oranlarındaki NACA-0018 Rüzgâr Türbini Kanat Modeli Performansının Sayısal ve Deneysel İncelenmesi. Politeknik Dergisi. 2020; 23(2):371–381.
37. [37] Tanürün HE, Akın AG, Acır A, Şahin. Experimental and Numerical Investigation of Roughness Structure in Wind Turbine Airfoil at Low Reynolds Number. International Journal of Thermodynamics 2024; 27(3):26–36.
38. [38] Tanürün HE, Acır A. Modifiye Edilmiş NACA-0015 Kanat Yapısında Tüberkül Etkisinin Sayısal Analizi. Journal of Polytechnic. 2018; 22(1):185–195.
39. [39] Tanürün HE, Akın AG, Acır A. Rüzgâr Türbinlerinde Kiriş Yapısının Performansa Etkisinin Sayısal Olarak İncelenmesi. Politeknik Dergisi. 2021; 24(3):1219–1226.
40. [40] Doğan A, Korkmaz M, Kirmaci V. Estimation of Ranque-Hilsch vortex tube performance by machine learning techniques. International Journal of Refrigeration. 2023; 150:77–88.
41. [41] Subudhi S, Sen M. Review of Ranque–Hilsch vortex tube experiments using air. Renewable and Sustainable Energy Reviews. 2015; 52:172–178.
42. [42] Ic YT, Yurdakul M, Dengiz B, Sasmaz T. Investigation of the Importance of Machine Sequence Flexibility on A Flexible Manufacturing System Performance. Gazi University Journal of Science. 2023; 36(2):735–750.
43. [43] Özoğlu Y. Genetic Algorithm and Fuzzy Based on The Taguchi Optimization to Improve The Torque Behavior of An Outer-Rotor Permanent-Magnet Machine. Gazi University Journal of Science. 2018; 31(1):82–98.
44. [44] Alsaghir AM, Hamdan MO, Orhan MF, Awad M. Numerical and sensitivity analyses of various design parameters to maximize performance of a Vortex Tube. International Journal of Thermofluids. 2022; 13:100133.
45. [45] Hu Z, Wang D, Gao F, Cao Y, Wu H. Experimental investigation on cooling performance of vortex tube with rectifier using Taguchi method. Case Studies in Thermal Engineering. 2023; 49:103373.
46. [46] Kang MS, Park SG, Dinh CT. Heat transfer enhancement by a pair of asymmetric flexible vortex generators and thermal performance prediction using machine learning algorithms International Journal of Heat and Mass Transfer. 2023; 200:123518.
47. [47] Rafiee SE, Sadeghiazad MM. Effect of Conical Valve Angle on Cold-Exit Temperature of Vortex Tube. Journal of Thermophysics and Heat Transfer. 2014; 28(4):785–794.
48. [48] Markal B, Aydin O, Avci M. An experimental study on the effect of the valve angle of counter-flow Ranque–Hilsch vortex tubes on thermal energy separation. Experimental Thermal and Fluid Science. 2010; 34(7):966–971.
49. [49] Devade KD, Pise AT. Effect of Mach number, valve angle and length to diameter ratio on thermal performance in flow of air through Ranque Hilsch vortex tube. Heat and Mass Transfer. 2017; 53:161–168.
50. [50] Nimbalkar SU, Muller MR. An experimental investigation of the optimum geometry for the cold end orifice of a vortex tube. Appl Therm Eng. 2009; 29:509–514.
51. [51] Liu X, Liu Z. Investigation of the energy separation effect and flow mechanism inside a vortex tube. Applied Thermal Engineering. 2014; 67(2-3):494–506.
52. [52] Dincer K, Baskaya S, Uysal BZ. Experimental investigation of the effects of length to diameter ratio and nozzle number on the performance of counter flow Ranque-Hilsch vortex tubes. Heat and Mass Transfer. 2008; 44:367–373.
53. [53] Zhang K, Liu Z, Li Y, Li Q, Zhang J, Liu H. Experimental analysis of a Ranque–Hilsch vortex tube for optimizing nozzle numbers and diameter. Applied Thermal Engineering. 2013; 61(2):500–506.
54. [54] Tanürün HE. Taguchi Yöntemiyle Sağlamlık Oranının Dikey Eksenli Rüzgâr Türbini Performansına Etkisinin Sayısal Olarak İncelenmesi. Journal of Materials and Mechatronics: A (JournalMM). 2023; 4(2):355–372.
55. [55] Seyhan M, Es HA, and Sarioglu M. Overall aerodynamic performance of the airfoils with different amplitudes via a fuzzy decision making based Taguchi methodology. Applied Soft Computing. 2024; 165: 112057.
56. [56] Mola E, Ünsal Bayrak O, İrfan Baş F, Ferit Bayata H. Investigating the usability of kevlar and steel fibers as a hybrid in concrete pavements. Sigma Journal of Engineering and Natural Sciences. 2024; 42(2):344–355.
57. [57] Çakıroğlu R, Tanürün HE, Acır A, Üçgül F, Olkun S. Optimization of NACA 4412 augmented with a gurney flap by using grey relational analysis. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 2023; 45:1–18.
58. [58] Taguchi GH, ElSayed EA. Quality Engineering in Production Systems. 1989;1-173.
59. [59] Sarıoglu M, Seyhan M, Akansu YE. Drag force estimation of a truck trailer model using artificial neural network. International Journal of Automotive Engineering and Technologies. 2016; 5(4): 168-175.