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The modeling of turbulence, especially the high-speed compressible turbulence encountered in aerospace engineering, has always being a significant challenge in terms of balancing efficiency and accuracy. Most traditional models typically show limitations in universality, accuracy, and reliance on past experience. The stochastic multi-scale models show great potential in addressing these issues by representing turbulence across all characteristic scales in a reduced-dimensional space, maintaining sufficient accuracy while reducing computational cost. This review systematically summarizes advances in methods related to a widely used and refined stochastic multi-scale model, the One-Dimensional Turbulence (ODT). The advancements in formulations are emphasized for stand-alone incompressible ODT models, stand-alone compressible ODT models, and coupling methods. Some diagrams are also provided to facilitate more readers to understand the ODT methods. Subsequently, the significant developments and applications of stand-alone ODT models and coupling methods are introduced and critically evaluated. Despite the extensively recognized effectiveness of ODT models in low-speed turbulent flows, it is crucial to emphasize that there is still a research gap in the field of ODT coupling methods that are capable of accurately and efficiently simulating complex, three-dimensional, high-speed compressible turbulent flows up to now. Based on an analysis of the advantages and limitations of existing ODT methods, the recent advancement in the conservative compressible ODT model is considered to have provided a promising approach to tackle the modeling challenges of high-speed compressible turbulence. Therefore, this review outlines several recommended new research subjects and challenging issues to inspire further research in simulating complex, three-dimensional, high-speed compressible turbulent flows using ODT models.
Leschziner MA, Drikakis D. Turbulence modelling and turbulent-flow computation in aeronautics. Aeronaut J 2002;106(1061):349–84.
Zhang L, Qiao HW, Liang JH, et al. Experimental study of scramjet cavity with rear edge slots and its performance in combustion enhancement. Acta Mechanica Sinica 2024;40:323135.
Fuentes JD, Chamecki M, Nascimento dos Santos RM, et al. Linking meteorology, turbulence, and air chemistry in the Amazon rain forest. Bull Am Meteorol Soc 2016;97(12):2329–42.
Guarini SE, Moser RD, Shariff K, et al. Direct numerical simulation of a supersonic turbulent boundary layer at Mach 2.5. J Fluid Mech 2000;414:1–33.
Wu XS, Liang JH, Zhao YX. Direct numerical simulation of a supersonic turbulent boundary layer subjected to a concave surface. Phys Rev Fluids 2019;4:044602.
Cogo M, Salvadore F, Picano F, et al. Direct numerical simulation of supersonic and hypersonic turbulent boundary layers at moderate-high Reynolds numbers and isothermal wall condition. J Fluid Mech 2022;945:A30.
Ciri U, Tubije JMB, Guzmán-Hernandez MA, et al. Direct numerical simulations of oscillatory boundary layers over rough walls. Int J Heat Fluid Flow 2023;103:109170.
Tong FL, Sun D, Li XL. Direct numerical simulation of impinging shock wave and turbulent boundary layer interaction over a wavy-wall. Chin J Aeronaut 2021;34(5):350–63.
Tian YY, Gao ZX, Jiang CW, et al. A correction for Reynolds-averaged-Navier–Stokes turbulence model under the effect of shock waves in hypersonic flows. Int J Numer Meth Fluids 2023;95(2):313–33.
Ugolotti M, Orkwis P, Wukie N. On the contribution of wall distance fields to the adjoint of a RANS model. Int J Comput Fluid Dyn 2022;36(8):687–704.
Romanelli M, Beneddine S, Mary I, et al. Data-driven wall models for Reynolds Averaged Navier-Stokes simulations. Int J Heat Fluid Flow 2023;99:109097.
Tangermann E, Klein M. On hybrid RANS-LES of transition in a separated boundary layer. Int J Heat Fluid Flow 2023;103:109188.
Belharizi M, Khorsi A, Yahiaoui T, et al. RANS and LES computations of natural convection in a square cavity. J Eng Phys Thermophys 2023;96(4):1017–27.
Li JP, Chen SS, Cai FJ, et al. Bayesian uncertainty analysis of SA turbulence model for supersonic jet interaction simulations. Chin J Aeronaut 2022;35(4):185–201.
Liu HK, Zhang SS, Zou Y, et al. Uncertainty analysis of turbulence model in capturing flows involving laminarization and retransition. Chin J Aeronaut 2022;35(10):148–64.
Ferziger JH. Large eddy numerical simulations of turbulent flows. AIAA J 1977;15(9):1261–7.
Hill DJ, Pantano C, Pullin DI. Large-eddy simulation and multiscale modelling of a Richtmyer-Meshkov instability with reshock. J Fluid Mech 2006;557:29–61.
Brandao FL, Mahesh K. Large-eddy simulation of cavitation inception in a shear flow. Int J Multiph Flow 2022;146:103865.
Zhang WJ, Shih TIP. Hybrid large-eddy simulation with adaptive downstream anisotropic eddy viscosity model. Int J Numer Meth Fluids 2022;94(11):1764–83.
Wu GJ, Lele SK, Jeun J, et al. Unstructured large-eddy simulations of rectangular jet screech: Assessment and validation. AIAA J 2023;61(3):1224–37.
Kamogawa R, Tamaki Y, Kawai S. Ordinary-differential-equation-based nonequilibrium wall modeling for large-eddy simulation. Phys Rev Fluids 2023;8(6):064605.
Meringolo DD, Lauria A, Aristodemo F, et al. Large eddy simulation within the smoothed particle hydrodynamics: Applications to multiphase flows. Phys Fluids 2023;35(6):063312.
Schlüter J, Zhao D, Sarkar A. Large eddy simulations of a turbulent mixing layer periodically excited with fundamental and third harmonic frequency. Chin J Aeronaut 2023;36(5):33–40.
Jones WP, Launder BE. The prediction of laminarization with a two-equation model of turbulence. Int J Heat Mass Transf 1972;15(2):301–14.
Yakhot V, Orszag SA. Renormalization group analysis of turbulence. I. Basic theory. J Sci Comput 1986;1(1):3–51.
Menter FR. Two-equation eddy-viscosity turbulence models for engineering applications. AIAA J 1994;32(8):1598–605.
Menter FR. Review of the shear-stress transport turbulence model experience from an industrial perspective. Int J Comput Fluid Dyn 2009;23(4):305–16.
Smagorinsky J. General circulation experiments with the primitive equations. Mon Weather Rev 1963;91(3):99–164.
Germano M, Piomelli U, Moin P, et al. A dynamic subgrid-scale eddy viscosity model. Phys Fluids A Fluid Dyn 1991;3(7):1760–5.
Kerstein AR. A linear- eddy model of turbulent scalar transport and mixing. Combust Sci Technol 1988;60(4–6):391–421.
Kerstein AR. One-dimensional turbulence: model formulation and application to homogeneous turbulence, shear flows, and buoyant stratified flows. J Fluid Mech 1999;392:277–334.
Kerstein AR. Hierarchical parcel-swapping representation of turbulent mixing. Part 1. Formulation and scaling properties. J Stat Phys 2013;153(1):142–61.
Kerstein AR. Hierarchical parcel-swapping representation of turbulent mixing. Part 2. Application to channel flow. J Fluid Mech 2014;750:421–63.
Kerstein AR. Hierarchical parcel-swapping representation of turbulent mixing. Ⅲ. Origins of correlation patterns observed in turbulent boundary layers. Phys Rev Fluids 2021;6(4):044611.
Kerstein AR, Ashurst WT, Wunsch S, et al. One-dimensional turbulence: Vector formulation and application to free shear flows. J Fluid Mech 2001;447:85–109.
Fistler M, Kerstein A, Lignell DO, et al. Turbulence modulation in particle-laden stationary homogeneous isotropic turbulence using one-dimensional turbulence. Phys Rev Fluids 2020;5(4):044308.
Echekki T, Kerstein AR, Dreeben TD, et al. ‘One-dimensional turbulence’ simulation of turbulent jet diffusion flames: Model formulation and illustrative applications. Combust Flame 2001;125(3):1083–105.
Ashurst WT, Kerstein AR. One-dimensional turbulence: Variable-density formulation and application to mixing layers. Phys Fluids 2005;17(2):025107.
Jiang Y, Qiu R, Dong G, et al. One dimensional turbulence simulation of diffusion flame in a hydrogen-air jet with detailed chemistry. J Combust Sci Technol 2006;12(5):401–7 [Chinese].
Wu SH, Qiu R, Zhu N, et al. One-dimensional turbulence simulation of jet H2/N2 diffusion flame. J Univ Sci Technol China 2006;36(1):49–55 [Chinese].
Wu SH, Qiu R, Jiang Y. Local extinction and reignition in non-premixed turbulent jet H2/N2 diffusion flames. J Combust Sci Technol 2007;13(4):347–54 [Chinese].
Chen CP, Liang JH, Guan QD, et al. Conservative compressible one-dimensional turbulence method and its application in supersonic scalar mixing layer. Acta Aeronautica et Astronautica Sinica 2021;42(S1):726364 [Chinese].
Klein M, Schmidt H, Lignell DO. Stochastic modeling of surface scalar-flux fluctuations in turbulent channel flow using one-dimensional turbulence. Int J Heat Fluid Flow 2022;93:108889.
Jozefik Z, Kerstein AR, Schmidt H. Simulation of shock–turbulence interaction in non-reactive flow and in turbulent deflagration and detonation regimes using one-dimensional turbulence. Combust Flame 2016;164:53–67.
Chen CP, Liang JH, Gao TY, et al. Conservative compressible one-dimensional turbulence formulation and application to high-Reynolds-number compressible turbulent channel flows. Phys Fluids 2022;34(6):065121.
Gao TY, Schmidt H, Klein M, et al. One-dimensional turbulence modeling of compressible flows. Ⅰ. Conservative Eulerian formulation and application to supersonic channel flow. Phys Fluids 2023;35(3):035115.
Gao TY, Schmidt H, Klein M, et al. One-dimensional turbulence modeling of compressible flows: Ⅱ. Full compressible modification and application to shock-turbulence interaction. Phys Fluids 2023;35(3):035116.
Hoffie AF, Echekki T. A coupled LES-ODT model for spatially-developing turbulent reacting shear layers. Int J Heat Mass Transf 2018;127:458–73.
Cao SF, Echekki T. A low-dimensional stochastic closure model for combustion large-eddy simulation. J Turbul 2008;9:N2.
Ranganath B, Echekki T. One-dimensional turbulence-based closure with extinction and reignition. Combust Flame 2008;154(1):23–46.
Park J, Echekki T. LES–ODT study of turbulent premixed interacting flames. Combust Flame 2012;159(2):609–20.
Lignell DO, Kerstein AR, Sun G, et al. Mesh adaption for efficient multiscale implementation of one-dimensional turbulence. Theor Comput Fluid Dyn 2013;27:273–95.
Ashurst WT, Kerstein AR. Erratum: “one-dimensional turbulence: variable-density formulation and application to mixing layers”. Phys Fluids 2009;21(11):119901.
Movaghar A, Linne M, Oevermann M, et al. Numerical investigation of turbulent-jet primary breakup using one-dimensional turbulence. Int J Multiph Flow 2017;89:241–54.
Freire LS, Chamecki M. Large-Eddy Simulation of smooth and rough channel flows using a one-dimensional stochastic wall model. Comput Fluids 2021;230:105135.
Zhuo L, Jiang Y, Xu W, et al. Local extinction and reignition in nonpremixed turbulent jet C3H8/H2/air diffusion flames. Fire Saf Sci 2014;23(2):85–91 [Chinese].
Wang L, Jiang Y, Pan LW, et al. Lagrangian investigation and chemical explosive mode analysis of extinction and re-ignition in H2/CO/N2 syngas non-premixed flame. Int J Hydrog Energy 2016;41(8):4820–30.
Wu YX, Qiu XL, Zhang DL, et al. Numerical simulation of instantaneous coal gasification flames with one dimension turbulence model. J Eng Thermophys 2012;33(9):1619–22 [Chinese].
Qiu XL, Wu YX, Zhang H, et al. Simulation of He plumes and CH4/H2/N2 flames with one dimensional turbulence model. J Eng Thermophys 2012;33(6):1073–6 [Chinese].
Echekki T, Gupta KG. Hydrogen autoignition in a turbulent jet with preheated co-flow air. Int J Hydrog Energy 2009;34(19):8352–77.
Gupta KG, Echekki T. One-dimensional turbulence model simulations of autoignition of hydrogen/carbon monoxide fuel mixtures in a turbulent jet. Combust Flame 2011;158(2):327–44.
Gowda BD, Echekki T. One-dimensional turbulence simulations of hydrogen-fueled HCCI combustion. Int J Hydrog Energy 2012;37(9):7912–24.
Lignell DO, Rappleye DS. One-dimensional-turbulence simulation of flame extinction and reignition in planar ethylene jet flames. Combust Flame 2012;159(9):2930–43.
Lignell DO, Lansinger VB, Medina J, et al. One-dimensional turbulence modeling for cylindrical and spherical flows: model formulation and application. Theor Comput Fluid Dyn 2018;32(4):495–520.
Sharma S, Klein M, Schmidt H. Features of far-downstream asymptotic velocity fluctuations in a round jet: a one-dimensional turbulence study. Phys Fluids 2022;34(8):085134.
Medina Méndez JA, Schmidt H, Bacher C, et al. Electrohydrodynamic-enhanced internal pipe flows from a One-Dimensional Turbulence perspective. PAMM 2021;20(1):e202000132.
Klein M, Schmidt H. Investigating Schmidt number effects in turbulent electroconvection using one-dimensional turbulence. PAMM 2021;21(1):e202100147.
Kerstein AR. One-Dimensional Turbulence: A new approach to high-fidelity subgrid closure of turbulent flow simulations. Comput Phys Commun 2002;148(1):1–16.
Schmidt RC, Kerstein AR, Wunsch S, et al. Near-wall LES closure based on one-dimensional turbulence modeling. J Comput Phys 2003;186:317–55.
Gonzalez-Juez ED, Schmidt RC, Kerstein AR. ODTLES simulations of wall-bounded flows. Phys Fluids 2011;23(12):125102.
Glawe C, Medina MJA, Schmidt H. IMEX based multi-scale time advancement in ODTLES. Z Angew Math Mech 2018;98(11):1907–23.
Zhang ZS, Cui GX, Xu CX. Theory and modeling of turbulence. Beijing: Tsinghua University Press; 2005. p. 49–51 [Chinese].
Spalart PR. Detached-eddy simulation. Annu Rev Fluid Mech 2009;41:181–202.
Spalart PR, Deck S, Shur ML, et al. A new version of detached-eddy simulation, resistant to ambiguous grid densities. Theor Comput Fluid Dyn 2006;20(3):181–95.
Fröhlichi J, von Terzi D. Hybrid LES/RANS methods for the simulation of turbulent flows. Prog Aerosp Sci 2008;44(5):349–77.
Chen CP, Gao TY, Liang JH. Separation induced low-frequency unsteadiness in a supersonic combustor with single-side expansion. Phys Fluids 2019;31(5):056103.
Friedrichs MS, Eastman P, Vaidyanathan V, et al. Accelerating molecular dynamic simulation on graphics processing units. J Comput Chem 2009;30(6):864–72.
Vu VT, Cats G, Wolters L. Graphics processing unit optimizations for the dynamics of the HIRLAM weather forecast model. Concurr Comput: Pract Exp 2013;25(10):1376–93.
Mielikainen J, Huang B, Huang HLA, et al. Improved GPU/CUDA based parallel weather and research forecast (WRF) single moment 5-class (WSM5) cloud microphysics. IEEE J Sel Top Appl Earth Obs Remote Sens 2012;5(4):1256–65.
Jambunathan R, Levin DA. Advanced parallelization strategies using hybrid MPI-CUDA octree DSMC method for modeling flow through porous media. Comput Fluids 2017;149:70–87.
Kijsipongse E, Piyatumrong A, U-ruekolan S. A hybrid GPU cluster and volunteer computing platform for scalable deep learning. J Supercomput 2018;74(7):3236–63.
Sawant SS, Tumuklu O, Jambunathan R, et al. Application of adaptively refined unstructured grids in DSMC to shock wave simulations. Comput Fluids 2018;170:197–212.
Stegailov V, Dlinnova E, Ismagilov T, et al. Angara interconnect makes GPU-based Desmos supercomputer an efficient tool for molecular dynamics calculations. Int J High Perform Comput Appl 2019;33(3):507–21.
Nguyen G, Dlugolinsky S, Bobák M, et al. Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey. Artif Intell Rev 2019;52(1):77–124.
Zhong HS, Wang H, Deng YH, et al. Quantum computational advantage using photons. Science 2020;370(6523):1460–3.
Xu GL, Daley AJ, Givi P, et al. Quantum algorithm for the computation of the reactant conversion rate in homogeneous turbulence. Combust Theory Model 2019;23(6):1090–104.
Xu GL, Daley AJ, Givi P, et al. Turbulent mixing simulation via a quantum algorithm. AIAA J 2018;56(2):687–99.
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