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Tempered fractional LES modeling. (English) Zbl 1514.76046

Summary: The presence of non-local interactions and intermittent signals in the homogeneous isotropic turbulence grant multi-point statistical functions a key role in formulating a new generation of large-eddy simulation (LES) models of higher fidelity. We establish a tempered fractional-order modelling framework for developing non-local LES subgrid-scale models, starting from the kinetic transport. We employ a tempered Lévy-stable distribution to represent the source of turbulent effects at the kinetic level, and we rigorously show that the corresponding turbulence closure term emerges as the tempered fractional Laplacian, \((\varDelta +\lambda )^{\alpha}(\cdot )\), for \(\alpha \in (0,1)\), \(\alpha \neq \frac{1}{2}\) and \(\lambda>0\) in the filtered Navier-Stokes equations. Moreover, we prove the frame invariant properties of the proposed model, complying with the subgrid-scale stresses. To characterize the optimum values of model parameters and infer the enhanced efficiency of the tempered fractional subgrid-scale model, we develop a robust algorithm, involving two-point structure functions and conventional correlation coefficients. In an a priori statistical study, we evaluate the capabilities of the developed model in fulfilling the closed essential requirements, obtained for a weaker sense of the ideal LES model [C. Meneveau, Phys. Fluids 6, No. 2, pt. 2, 815–833 (1994; Zbl 0825.76279)]. Finally, the model undergoes the a posteriori analysis to ensure the numerical stability and pragmatic efficiency of the model.

MSC:

76F65 Direct numerical and large eddy simulation of turbulence
76F25 Turbulent transport, mixing
76F05 Isotropic turbulence; homogeneous turbulence
26A33 Fractional derivatives and integrals

Citations:

Zbl 0825.76279

References:

[1] Akhavan-Safaei, A., Samiee, M. & Zayernouri, M.2021Data-driven fractional subgrid-scale modeling for scalar turbulence: a nonlocal LES approach. J. Comput. Phys.446, 110571. · Zbl 07516447
[2] Akhavan-Safaei, A., Seyedi, S.H. & Zayernouri, M.2020Anomalous features in internal cylinder flow instabilities subject to uncertain rotational effects. Phys. Fluids32 (9), 094107.
[3] Akhavan-Safaei, A. & Zayernouri, M.2020 A parallel integrated computational-statistical platform for turbulent transport phenomena. arXiv:2012.04838.
[4] Beck, A. & Kurz, M.2020 A perspective on machine learning methods in turbulence modelling. arXiv:2010.12226.
[5] Bouffanais, R.2010Advances and challenges of applied large-eddy simulation. Comput. Fluids39 (5), 735-738. · Zbl 1242.76073
[6] Briard, A., Gomez, T. & Cambon, C.2016Spectral modelling for passive scalar dynamics in homogeneous anisotropic turbulence. J. Fluid Mech.799, 159-199. · Zbl 1460.76373
[7] Buaria, D., Pumir, A. & Bodenschatz, E.2020Self-attenuation of extreme events in Navier-Stokes turbulence. Nat. Commun.11 (1), 5852.
[8] Burkovska, O., Glusa, C. & D’Elia, M.2020 An optimization-based approach to parameter learning for fractional type nonlocal models. arXiv:2010.03666.
[9] Burton, G.C. & Dahm, W.J.A.2005Multifractal subgrid-scale modeling for large-eddy simulation. II. Backscatter limiting and a posteriori evaluation. Phys. Fluids17 (7), 075112. · Zbl 1187.76079
[10] Cairoli, A.2016 Towards a comprehensive framework for the analysis of anomalous diffusive systems. PhD thesis, Queen Mary University of London.
[11] Cambon, C. & Scott, J.F.1999Linear and nonlinear models of anisotropic turbulence. Annu. Rev. Fluid Mech.31 (1), 1-53.
[12] Cerutti, S., Meneveau, C. & Knio, O.M.2000Spectral and hyper eddy viscosity in high-Reynolds-number turbulence. J. Fluid Mech.421, 307-338. · Zbl 0958.76507
[13] Chao, M.A., Kulkarni, C., Goebel, K. & Fink, O.2020 Fusing physics-based and deep learning models for prognostics. arXiv:2003.00732.
[14] Chen, H., Orszag, S.A., Staroselsky, I. & Succi, S.2004Expanded analogy between Boltzmann kinetic theory of fluids and turbulence. J. Fluid Mech.519, 301-314. · Zbl 1065.76172
[15] Deng, W., Li, B., Tian, W. & Zhang, P.2018Boundary problems for the fractional and tempered fractional operators. Multiscale Model. Simul.16 (1), 125-149. · Zbl 1391.60104
[16] Di Leoni, P.C., Zaki, T.A., Karniadakis, G. & Meneveau, C.2020 Two-point stress-strain rate correlation structure and non-local eddy viscosity in turbulent flows. arXiv:2006.02280. · Zbl 1461.76181
[17] Di Nezza, E., Palatucci, G. & Valdinoci, E.2012Hitchhiker’s guide to the fractional Sobolev spaces. Bull. Sci. Mathématiques136 (5), 521-573. · Zbl 1252.46023
[18] Egolf, P.W. & Hutter, K.2017 Fractional turbulence models. In Progress in Turbulence VII, pp. 123-131.
[19] Egolf, P.W. & Hutter, K.2020Nonlinear, Nonlocal and Fractional Turbulence, Graduate Studies in Mathematics. Springer. · Zbl 1433.76003
[20] Epps, B.P. & Cushman-Roisin, B.2018 Turbulence modeling via the fractional Laplacian. arXiv:1803.05286.
[21] Evin, G., Blanchet, J., Paquet, E., Garavaglia, F. & Penot, D.2016A regional model for extreme rainfall based on weather patterns subsampling. J. Hydrol.541, 1185-1198.
[22] Girimaji, S.S.2007Boltzmann kinetic equation for filtered fluid turbulence. Phys. Rev. Lett.99 (3), 034501.
[23] Hamlington, P.E. & Dahm, W.J.A.2008Reynolds stress closure for nonequilibrium effects in turbulent flows. Phys. Fluids20 (11), 115101. · Zbl 1182.76305
[24] Henderson, D.W. & Taimina, D.2000Experiencing Geometry. Prentice Hall.
[25] Hill, R.J.2002Exact second-order structure-function relationships. J. Fluid Mech.468, 317-326. · Zbl 1062.76028
[26] Holgate, J., Skillen, A., Craft, T. & Revell, A.2019A review of embedded large eddy simulation for internal flows. Arch. Comput. Methods Engng26 (4), 865-882.
[27] Huang, L.2015 Density estimates for SDEs driven by tempered stable processes. arXiv:1504.04183.
[28] Ionescu, C., Lopes, A., Copot, D., Machado, J.A.T. & Bates, J.H.T.2017The role of fractional calculus in modeling biological phenomena: a review. Commun. Nonlinear Sci. Numer. Simul.51, 141-159. · Zbl 1467.92050
[29] Jacob, J., Malaspinas, O. & Sagaut, P.2018A new hybrid recursive regularised Bhatnagar-Gross-Krook collision model for Lattice Boltzmann method-based large eddy simulation. J. Turbul.19 (11-12), 1051-1076.
[30] Jin, G., Wang, S., Wang, Y. & He, G.2018Lattice Boltzmann simulations of high-order statistics in isotropic turbulent flows. Z. Angew. Math. Mech.39 (1), 21-30.
[31] Jouybari, M.A., Yuan, J., Brereton, G.J. & Murillo, M.S.2020 Data-driven prediction of the equivalent sand-grain height in rough-wall turbulent flows. arXiv:2002.01515. · Zbl 1461.76301
[32] Kaleta, K. & Sztonyk, P.2015Estimates of transition densities and their derivatives for jump Lévy processes. J. Math. Anal. Appl.431 (1), 260-282. · Zbl 1317.60056
[33] Kassinos, S.C., Reynolds, W.C. & Rogers, M.M.2001One-point turbulence structure tensors. J. Fluid Mech.428, 213-248. · Zbl 0983.76035
[34] Kharazmi, E. & Zayernouri, M.2019Fractional sensitivity equation method: application to fractional model construction. J. Sci. Comput.80 (1), 110-140. · Zbl 1448.35550
[35] Kurz, M. & Beck, A.2020 A machine learning framework for LES closure terms. arXiv:2010.03030.
[36] Laval, J.P., Dubrulle, B. & Nazarenko, S.2001Nonlocality and intermittency in three-dimensional turbulence. Phys. Fluids13 (7), 1995-2012. · Zbl 1184.76311
[37] Malaspinas, O. & Sagaut, P.2012Consistent subgrid scale modelling for lattice Boltzmann methods. J. Fluid Mech.700, 514-542. · Zbl 1248.76120
[38] Meneveau, C.1994Statistics of turbulence subgrid-scale stresses: necessary conditions and experimental tests. Phys. Fluids6 (2), 815-833. · Zbl 0825.76279
[39] Meral, F.C., Royston, T.J. & Magin, R.2010Fractional calculus in viscoelasticity: an experimental study. Commun. Nonlinear Sci. Numer. Simul.15 (4), 939-945. · Zbl 1221.74012
[40] Mishra, A.A. & Girimaji, S.2019Linear analysis of non-local physics in homogeneous turbulent flows. Phys. Fluids31 (3), 035102.
[41] Mishra, A.A. & Girimaji, S.S.2017Toward approximating non-local dynamics in single-point pressure-strain correlation closures. J. Fluid Mech.811, 168-188. · Zbl 1383.76270
[42] Mortensen, M. & Langtangen, H.P.2016High performance python for direct numerical simulations of turbulent flows. Comput. Phys. Commun.203, 53-65. · Zbl 1375.76072
[43] Moser, R.D., Haering, S.W. & Yalla, G.R.2021Statistical properties of subgrid-scale turbulence models. Annu. Rev. Fluid Mech.53, 255-286. · Zbl 1459.76067
[44] Naghibolhosseini, M. & Long, G.R.2018Fractional-order modelling and simulation of human ear. Intl J. Comput. Maths95 (6-7), 1257-1273. · Zbl 1499.92004
[45] Pang, G., D’Elia, M., Parks, M. & Karniadakis, G.E.2020nPINNs: nonlocal physics-informed neural networks for a parametrized nonlocal universal Laplacian operator. Algorithms and applications. J. Comput. Phys.422, 109760. · Zbl 07508384
[46] Patra, A.K., Bevilacqua, A. & Safaei, A.A.2018 Analyzing complex models using data and statistics. In International Conference on Computational Science, pp. 724-736. Springer.
[47] Pawar, S., San, O., Rasheed, A. & Vedula, P.2020A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence. Theor. Comput. Fluid Dyn.34 (4), 429-455.
[48] Piomelli, U.2014Large eddy simulations in 2030 and beyond. Phil. Trans. R. Soc. Lond. A372 (2022), 20130320.
[49] Pope, S.B.2000Turbulent Flows. Cambridge University Press. · Zbl 0966.76002
[50] Portwood, G.D., Nadiga, B.T., Saenz, J.A. & Livescu, D.2021Interpreting neural network models of residual scalar flux. J. Fluid Mech.907, A23. · Zbl 1461.76311
[51] Premnath, K.N., Pattison, M.J. & Banerjee, S.2009Dynamic subgrid scale modeling of turbulent flows using lattice-Boltzmann method. Physica A: Stat. Mech. Applics.388 (13), 2640-2658.
[52] Sabzikar, F., Meerschaert, M.M. & Chen, J.2015Tempered fractional calculus. J. Comput. Phys.293, 14-28. · Zbl 1349.26017
[53] Sagaut, P.2010Toward advanced subgrid models for Lattice-Boltzmann-based large-eddy simulation: theoretical formulations. Comput. Maths Applics.59 (7), 2194-2199. · Zbl 1193.76115
[54] Sagaut, P. & Cambon, C.2008Homogeneous Turbulence Dynamics, vol. 10. Springer. · Zbl 1154.76003
[55] Samiee, M.2021Data-Infused Fractional Modeling and Spectral Numerical Analysis for Anomalous Transport and Turbulence. Michigan State University.
[56] Samiee, M., Akhavan-Safaei, A. & Zayernouri, M.2020aA fractional subgrid-scale model for turbulent flows: theoretical formulation and a priori study. Phys. Fluids32 (5), 055102.
[57] Samiee, M., Kharazmi, E., Meerschaert, M.M. & Zayernouri, M.2020bA unified Petrov-Galerkin spectral method and fast solver for distributed-order partial differential equations. Commun. Appl. Math. Comput.3, 61-90. · Zbl 1476.65272
[58] Samiee, M., Zayernouri, M. & Meerschaert, M.M.2019A unified spectral method for FPDEs with two-sided derivatives; part I: a fast solver. J. Comput. Phys.385, 225-243. · Zbl 1451.65160
[59] She, Z.-S., Jackson, E. & Orszag, S.A.1990Intermittent vortex structures in homogeneous isotropic turbulence. Nature344 (6263), 226-228.
[60] Shivamoggi, B.K. & Tuovila, N.2019Direct interaction approximation for non-Markovianized stochastic models in the turbulence problem. Chaos29 (6), 063124. · Zbl 1433.76057
[61] Sirignano, J., Macart, J.F. & Freund, J.B.2020DPM: a deep learning PDE augmentation method with application to large-eddy simulation. J. Comput. Phys.423, 109811. · Zbl 07508424
[62] Smagorinsky, J.1963General circulation experiments with the primitive equations: I. The basic experiment. Mon. Weath. Rev.91 (3), 99-164.
[63] Soto, R.2016Kinetic Theory and Transport Phenomena, vol. 25. Oxford University Press. · Zbl 1374.82002
[64] Stein, E.M.1970Singular Integrals and Differentiability Properties of Functions, vol. 2. Princeton University Press. · Zbl 0207.13501
[65] Suzuki, J., Zhou, Y., D’Elia, M. & Zayernouri, M.2021aA thermodynamically consistent fractional visco-elasto-plastic model with memory-dependent damage for anomalous materials. Comput. Meth. Appl. Mech. Engng373, 113494. · Zbl 1506.74074
[66] Suzuki, J.L., Kharazmi, E., Varghaei, P., Naghibolhosseini, M. & Zayernouri, M.2021bAnomalous nonlinear dynamics behavior of fractional viscoelastic beams. J. Comput. Nonlinear Dyn.16 (11), 111005.
[67] Suzuki, J.L. & Zayernouri, M.2021A self-singularity-capturing scheme for fractional differential equations. Intl J. Comput. Maths98 (5), 933-960. · Zbl 1480.65171
[68] Taghizadeh, S., Witherden, F.D. & Girimaji, S.S.2020Turbulence closure modeling with data-driven techniques: physical compatibility and consistency considerations. New J. Phys.22 (9), 093023.
[69] Vincent, A. & Meneguzzi, M.1991The spatial structure and statistical properties of homogeneous turbulence. J. Fluid Mech.225, 1-20. · Zbl 0721.76036
[70] Weron, R.2001Levy-stable distributions revisited: tail \(\text{index} > 2\) does not exclude the Levy-stable regime. Intl J. Mod. Phys. C12 (02), 209-223.
[71] Willard, J., Jia, X., Xu, S., Steinbach, M. & Kumar, V.2020 Integrating physics-based modeling with machine learning: a survey. arXiv:2003.04919.
[72] Xie, C. & Fang, S.2019A second-order finite difference method for fractional diffusion equation with Dirichlet and fractional boundary conditions. Numer. Meth. Partial Differ. Equ.35 (4), 1383-1395. · Zbl 1416.65285
[73] Yang, X.I.A. & Lozano-Durán, A.2017A multifractal model for the momentum transfer process in wall-bounded flows. J. Fluid Mech.824, R2. · Zbl 1374.76090
[74] You, H., Yu, Y., Trask, N., Gulian, M. & D’Elia, M.2021Data-driven learning of nonlocal physics from high-fidelity synthetic data. Comput. Meth. Appl. Mech. Engng374, 113553. · Zbl 1506.74505
[75] Zaky, M.A., Hendy, A.S. & Macías-Díaz, J.E.2020Semi-implicit Galerkin-Legendre spectral schemes for nonlinear time-space fractional diffusion-reaction equations with smooth and nonsmooth solutions. J. Sci. Comput.82 (1), 13. · Zbl 1433.65247
[76] Zayernouri, M., Ainsworth, M. & Karniadakis, G.Em.2015Tempered fractional Sturm-Liouville eigenproblems. SIAM J. Sci. Comput.37 (4), A1777-A1800. · Zbl 1323.34012
[77] Zhang, Z., Deng, W. & Karniadakis, G.Em.2018A Riesz basis Galerkin method for the tempered fractional Laplacian. SIAM J. Numer. Anal.56 (5), 3010-3039. · Zbl 1402.65120
[78] Zhiyin, Y.2015Large-eddy simulation: past, present and the future. Chin. J. Aeronaut.28 (1), 11-24.
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