پیوندهای مفید
آمار
| تعداد نشریات | 7 |
| تعداد شماره‌ها | 399 |
| تعداد مقالات | 5,389 |
| تعداد مشاهده مقاله | 5,288,194 |
| تعداد دریافت فایل اصل مقاله | 4,882,937 |
Modeling the subgrid-scale kinetic energy in a turbulent channel flow using artificial neural network | ||
| AUT Journal of Mechanical Engineering | ||
| دوره 8، شماره 1، 2024، صفحه 19-30 اصل مقاله (1.6 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/ajme.2024.23091.6101 | ||
| نویسندگان | ||
| Amin Rasam* ؛ Ali Najarian | ||
| Faculty of Mechanical and Energy Engineering, Shahid Beheshti University, Tehran, Iran | ||
| چکیده | ||
| Modeling the subgrid-scale energy, , has important applications in large-eddy simulation, including the lattice Boltzmann method and formulation of advanced subgrid-scale models. In this study, a deep neural network is specifically developed to predict for large-eddy simulation of turbulent channel flow. To produce the training data for the neural network, a direct numerical simulation of turbulent channel flow at the friction Reynolds number is performed using an existing highly accurate pseudo-spectral method. The impact of the neural network configuration on its predictions is studied by examining the mean, probability density function and skewness of . Moreover, the correlation with the filtered data, its relative and root mean square error are also examined using a priori analysis. Appreciable improvements in the predictions were observed with increasing the number of neurons in the hidden layer, up to 64. Increasing the number of hidden layers to two and three showed small improvements in the predictions.The performance of the neural network is also compared with a dynamic subgrid-scale model. The comparison reveals that the neural network predictions reach correlation coefficients higher than 90% with the filtered direct numerical simulation data, whereas the dynamic subgrid-scale model predictions only reach up to about 50%. Also, a closer agreement was observed with the filtered data for the neural network predictions of , compared with the dynamic subgrid-scale model. | ||
| کلیدواژه‌ها | ||
| Subgrid-Sscale Tturbulent Kinetic Energy؛ Deep Neural Network؛ Direct Numerical simulation؛ Turbulent channel flow | ||
| مراجع | ||
|
[1] S.B. Pope, Turbulent flows, Cambridge university press, 2001.
[2] S.A. Orszag, Analytical theories of turbulence, Journal of Fluid Mechanics, 41(2) (1970) 363-386.
[3] P. Sagaut, Large eddy simulation for incompressible flows: an introduction, Springer Science & Business Media, 2005.
[4] L. Marstorp, G. Brethouwer, O. Grundestam, A.V. Johansson, Explicit algebraic subgrid stress models with application to rotating channel flow, Journal of Fluid Mechanics, 639 (2009) 403-432.
[5] Y. Kuwata, K. Suga, Wall-modeled large eddy simulation of turbulent heat transfer by the lattice Boltzmann method, Journal of Computational Physics, 433 (2021) 110186.
[6] S.L. Brunton, B.R. Noack, P. Koumoutsakos, Machine learning for fluid mechanics, Annual review of fluid mechanics, 52 (2020) 477-508.
[7] K. Duraisamy, G. Iaccarino, H. Xiao, Turbulence modeling in the age of data, Annual review of fluid mechanics, 51 (2019) 357-377.
[8] M. Gamahara, Y. Hattori, Searching for turbulence models by artificial neural network, Physical Review Fluids, 2(5) (2017) 054604.
[9] H. Frezat, G. Balarac, J. Le Sommer, R. Fablet, R. Lguensat, Physical invariance in neural networks for subgrid-scale scalar flux modeling, Physical Review Fluids, 6(2) (2021) 024607.
[10] Z. Wang, K. Luo, D. Li, J. Tan, J. Fan, Investigations of data-driven closure for subgrid-scale stress in large-eddy simulation, Physics of Fluids, 30(12) (2018) 125101.
[11] Q. Meng, Z. Jiang, J. Wang, Artificial neural network-based subgrid-scale models for LES of compressible turbulent channel flow, Theoretical and Applied Mechanics Letters, 13(100399) (2023) 1-12.
[12] D. Xu, J. Wang, C. Yu, S. Chen, Artificial-neural-network-based nonlinear algebraic models for large-eddy simulation of compressible wall-bounded turbulence, Journal of Fluid Mechanics, 960 (2023) A4.
[13] A. Rasam, M. Shirazi, Subgrid-scale flux modeling of a passive scalar in turbulent channel flow using artificial neural network, Iranian Journal of Mechanical Engineering Transactions of the ISME, 24(2) (2024) 157-172.
[14] M. Chevalier, P. Schlatter, A. Lundbladh, D.S. Henningson, SIMSON: A pseudo-spectral solver for incompressible boundary layer flows, KTH, 2007.
[15] J. Kim, P. Moin, R. Moser, Turbulence statistics in fully developed channel flow at low Reynolds number, Journal of fluid mechanics, 177 (1987) 133-166.
[16] A. Rasam, G. Brethouwer, A. Johansson, A stochastic extension of the explicit algebraic subgrid-scale models, Physics of fluids, 26(5) (2014) 055113.
[17] A. Rasam, G. Brethouwer, A.V. Johansson, An explicit algebraic model for the subgrid-scale passive scalar flux, Journal of Fluid Mechanics, 721 (2013) 541-577.
[18] A. Rasam, G. Brethouwer, P. Schlatter, Q. Li, A.V. Johansson, Effects of modelling, resolution and anisotropy of subgrid-scales on large eddy simulations of channel flow, Journal of turbulence, (12) (2011) N10.
[19] M.Y. Hussaini, T.A. Zang, Spectral methods in fluid dynamics, Annual review of fluid mechanics, 19(1) (1987) 339-367.
[20] R.D. Moser, J. Kim, N.N. Mansour, Direct numerical simulation of turbulent channel flow up to Re τ= 590, Physics of fluids, 11(4) (1999) 943-945.
[21] J. Park, H. Choi, Toward neural-network-based large eddy simulation: Application to turbulent channel flow, Journal of Fluid Mechanics, 914 (2021) A16.
[22] P.C. Di Leoni, T.A. Zaki, G. Karniadakis, C. Meneveau, Two-point stress–strain-rate correlation structure and non-local eddy viscosity in turbulent flows, Journal of Fluid Mechanics, 914 (2021) A6.
[23] N. Park, S. Lee, J. Lee, H. Choi, A dynamic subgrid-scale eddy viscosity model with a global model coefficient, Physics of Fluids, 18(12) (2006) 125109.
[24] S. Völker, R.D. Moser, P. Venugopal, Optimal large eddy simulation of turbulent channel flow based on direct numerical simulation statistical data, Physics of Fluids, 14(10) (2002) 3675-3691.
[25] M. Germano, U. Piomelli, P. Moin, W.H. Cabot, A dynamic subgrid-scale eddy viscosity model, Physics of Fluids, 3 (1991) 1760-1765.
[26] M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, TensorFlow: a system for Large-Scale machine learning, 12th USENIX symposium on operating systems design and implementation (OSDI 16), (2016) 265-283.
[27] S. Haykin, Neural networks and learning machines, 3/E, Pearson Education India, 2009.
[28] D.P. Kingma, J. Ba, Adam: A method for stochastic optimization, arXiv, 1412.6980 (2014) 1-15.
[29] M. Kang, Y. Jeon, D. You, Neural-network-based mixed subgrid-scale model for turbulent flow, Journal of Fluid Mechanics, 962 (2023) A38.
[30] F. Sarghini, G. de Felice, S. Santini, Neural networks based subgrid scale modeling in large eddy simulations, Computers & fluids, 32(1) (2003) 97-108. | ||
|
آمار تعداد مشاهده مقاله: 260 تعداد دریافت فایل اصل مقاله: 131 |
||