• Vol 9, No 7 (2018)
  • Mechanical Engineering

Turbulence Models Application in Air Flow of Crossflow Turbine

Gun Gun R. Gunadi, Ahmad Indra Siswantara, Budiarso

Corresponding email: gungun.rg@mesin.pnj.ac.id


Published at : 21 Dec 2018
IJtech : IJtech Vol 9, No 7 (2018)
DOI : https://doi.org/10.14716/ijtech.v9i7.2636

Cite this article as:
Gunadi, G.G.R., Siswantara, A.I., Budiarso., 2018. Turbulence Models Application in Air Flow of Crossflow Turbine. International Journal of Technology. Volume 9(7), pp. 1490-1497
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Gun Gun R. Gunadi Department of Mechanical Engineering, Politeknik Negeri Jakarta, Depok 16424, Indonesia
Ahmad Indra Siswantara Department of Mechanical Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia
Budiarso Department of Mechanical Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia
Email to Corresponding Author

Abstract
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Using the CFD method as the initial analysis for experiments has more benefits, including saving time and costs. The variable of flow parameters and geometry can be easily developed to get the desired results. However, research is needed to improve the accuracy of the results and the optimality of the calculation process; the study of complex turbulent flow modelling becomes very important. The k-? model and renormalization group (RNG) k-? model are widely used in research to produce the appropriate models and develop the constants value. This turbulent flow modelling research was conducted to improve the result accuracy and the calculation process optimality in the turbulent flow of crossflow turbine. Research was done by comparing the simulation results of k-? model with different constants and RNG k-? model. The k-? model with kinetic Prandtl 0.8, 0.9, 1, 1.1, 1.2 and the RNG k-? model show different results for predicting the average pressure and velocity distribution in the turbulent flow of crossflow turbine, and likewise for turbulent parameters. The RNG k-? model has more accuracy than the k-? model, although the k-? model’s simulation time is quite short. Therefore, complex fluid flow recommends RNG k-? model.

k-? model; RNG k-? model; Turbulent flow

Introduction

Using the CFD method as the initial analysis for experiments has more benefits, including saving time and costs. For example, the variable of flow parameters and geometry can be easily developed to get the desired results. CFD simulations are used in digesters with baffle clearance variations, indicating that the baffle clearance 50 mm has the largest recirculation, which leads to better slurry mixing (Siswantara et al., 2016). A CFD method was used in the net power coefficient study of wind turbines with crossflow runners, resulting in optimal work located in a narrow band of low TSR and ? reaching a value of Cp < 0.2 only (Pujol et al., 2018).

The flow behavior is random and chaotic. Motion becomes intrinsically unsteady, even with constant imposed boundary conditions. The velocity and all other flow properties vary in a random and chaotic way. A lot of turbulence model development occurs in CFD, so the model is in the RANS group.

The most widely used turbulence models are the k-? and RNG k-? models; the former is one of the simplest turbulence models, only requiring the input processes of boundary conditions. The k-? model is widely used for technical analysis in industry because it is quite stable and widely validated. However, the model’s weakness is that it produces unfavorable results when used for simulating non-walled flow, large strain flow, rotating flow, and flow developed in a non-circular channel. Two additional equations in the k-? model for turbulent flow are the kinetic energy transport equation k and the dissipation transport equation ? (Versteeg & Malalasekera, 1995). The RNG k-? model is improved from the k-? model (Mohammadi & Pironneau, 1993). Developed by Yakhot and Orszag, and based on the renormalization group (RNG) statistical theory, the RNG k-? model adds some equations into the k-? model.

Both models are widely used in research to produce the appropriate models and develop the constants value. The RNG model k-? with the model characteristics is used to analyze cross-flow runners (Darmawan et al., 2015). The value of inverse-turbulent Prandtl number (?) 1.1 is best used to simulate turbulent flow in a curved pipe using the RNG k-? model at Re 63800 and the r/D 1,607 (Budiarso et al., 2015). k-? and RNG k-? could be used to represent the combustion process phenomenon without any significant differences for the numerical analysis of gas flow in the annular combustion chamber of a Proto X-3 (Daryus et al., 2016). Three turbulence models compared in wind tunnels to predict turbulence parameters are validated with test data, revealing that the k-? model is effective because its results are comparable to the RSM model (Gunadi et al., 2016).

This research will compare the k-? model with different constants and the RNG k-? model to analyze flow characteristics to improve the result accuracy and the calculation process optimality in crossflow turbines.

Conclusion

This turbulent flow modelling research was conducted to improve the result accuracy and the calculation process optimality in the turbulent flow of crossflow turbine by comparing the simulation results of the k-? and RNG k-? models. Both model gave different results for the average pressure and velocity distribution, and for turbulent parameters.  The RNG k-? model was more accurate than the k-? model, which requires a shorter simulation time; therefore, the RNG k-? model is recommended for complex fluid flow.

Acknowledgement

The authors would like to thank DRPM Universitas Indonesia for funding this research through “Penelitian Dasar Unggulan Perguruan Tinggi Kementerian Riset Teknologi dan Pendidikan Tinggi Republik Indonesia Tahun Anggaran 2018” and  PT. CCIT Group Indonesia for CFDSOF® software license.

References

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Budiarso., Siswantara, A.I., Darmawan, S., Tanujaya, H., 2015. Inverse-turbulent Prandtl Number Effects on Reynolds Numbers of RNG k-? Turbulence Model on Cylindrical-Curved Pipe. Applied Mechanics and Materials, Volume 758, pp. 35–44

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Daryus, A., Siswantara, A.I., Darmawan, S., Gunadi, G.G.R., Camalia, R., 2016. CFD Simulation of Turbulent Flows in Proto X-3 Bioenergy Micro Gas Turbine Combustor using STD k-? and RNG k-? Model for Green Building Application. International Journal of Technology, Volume 7(2), pp. 204–211

Gunadi, G.G.R., Siswantara, A.I., Budiarso, B., Daryus, A., Pujowidodo, H., 2016. Turbulence Model and Validation of Air Flow in Wind Tunnel. International Journal of Technology, Volume 7(8), pp. 1362–1371

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Siswantara, A. I., Daryus, A., Darmawan, S., Gunadi, G.G.R., Camalia, R., 2016. CFD Analysis of Slurry Flow in an Anaerobic Digester. International Journal of Technology, Volume 7(2), pp. 197–203

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