Effect of inlet turbulence generation methods on Large-eddy Simulation results

For large eddy simulation, it is critical to choose the suitable turbulent inlet boundary condition as it significantly affects the calculated flow field. In the thesis, the effect of different inlet boundary conditions including random method (RAND), Lund method and divergence-free synthetic eddies method (DFSEM) on the flow in a channel with a hump are investigated through large-eddy simulation. The simulation results are further compared with experimental data. It has been found that turbulence is nearly fully developed in the case based on Lund method, not fully developed in the case based on DFSEM and not developed in the case based on RAND method. In the flow region before the hump, mean velocity profiles in the case applying Lund method gradually fit the law of the wall as main flow moves towards the hump, but the simulation results based on RAND and DFSEM methods cannot fit the wall function. In the flow region after the hump, cases applying Lund and DFSEM methods could relative precisely predict the size of turbulent bubble and turbulent statistics profiles. While the case based on RAND method cannot capture the positions of flow separation and re-attachment point and overestimates the turbulent bubble size. From this part, it could be found that different turbulent inflow generation methods have a manifested impact on the flow separation and re-attachment after the hump. If the coherent turbulence is maintained in the approach flow, even though turbulent intensity is not large enough, the simulation can still predict the flow separation and turbulent bubble size relative precisely. From the results, even if the simulation based on the Lund method and DFSEM have a better performance than the simulation based on the RAND method, the results still cannot agree well with the experimental data. There are some possible reasons that result in the big difference. Firstly, the spanwise width of the simulation domain is relatively small. Additionally, the LES sub-grid scale model has a slight impact on the results according to the previous research. Finally, the DFSEM is sensitive to the surface normal gradient schemes.

Beside the choice of the turbulent inlet boundary condition (IBC) methods, the settings of each turbulent IBC method are critical as well. In this thesis, the effect of setting different IBC methods in LES using the Lund method, the divergence-free synthetic eddies method (DFSEM) and the digital filter method (DFM) on the simulation of the boundary layer over a flat plate is investigated. This research also fully explained the influences of different IBC methods on the results of turbulent kinetic energy budget terms, and it is found that the DFM and the DFSEM both have good performance. In addition, for the DFM and DFSEM, the input parameter such as turbulent length scales are hard to set generally without prior knowledge of the flow field. It is found that the simulation results based on these two methods with constant turbulent length scales of 0.4-1.0 times of the value of boundary layer thickness could agree well with the DNS results to a great extent after about 10 boundary layer thickness along the streamwise direction. Overall, it could be recommended that the DFM and the DFSEM can be used in this case with the constant input turbulent length scale about 0.4-1.0 times of the value of boundary thickness.

[Aboshosha, H., Elshaer, A., Bitsuamlak, G.T., El Damatty, A., 2015. Consistent inflow turbulence generator for LES evaluation of wind-induced responses for tall buildings. J. Wind Eng. Ind. Aerodyn. 142, 198–216.Aliabadi, A.A., Veriotes, N., Pedro, G., 2018. A Very Large-Eddy Simulation (VLES) model for the investigation of the neutral atmospheric boundary layer. J. Wind Eng. Ind. Aerodyn. 183, 152–171.Allegrini, J., Carmeliet, J., 2017. Evaluation of the Filtered Noise Turbulent Inflow Generation Method. Flow, Turbul. Combust. 98, 1087–1115.Araya, G., Castillo, L., Meneveau, C., Jansen, K., 2011. A dynamic multi-scale approach for turbulent inflow boundary conditions in spatially developing flows. J. Fluid Mech. 670, 581–605.Asgari, E., Tadjfar, M., 2017. Assessment of four inflow conditions on large-eddy simulation of a gently curved backward-facing step. J. Turbul. 18, 61–86.Bechmann, A., Sørensen, N.N., Johansen, J., 2007. Atmospheric flow over terrain using hybrid RANS/LES. Eur. Wind Energy Conf. Exhib. 2007, EWEC 2007 1, 107–113.Benedicto, A., Rives, T., Soliva, R., 2014. The 3D Fault Segmentation Development - A Conceptual Model. Implications of Fault Sealing.Blackman, K., Perret, L., Calmet, I., Rivet, C., 2017. Turbulent kinetic energy budget in the boundary layer developing over an urban-like rough wall using PIV. Phys. Fluids 29.Boris, C., 2012. Wind resource accessment in complex terrain by wind tunnel modelling.Cao, S.J., 2019. Challenges of using CFD simulation for the design and online control of ventilation systems. Indoor Built Environ. 28, 3–6.Castro, H.G., Paz, R.R., Sonzogni, V.E., 2011. Generation of Turbulent Inlet Velocity Conditions XXX, 1–4.CFD Direct, 2020. OpenFOAM v6 User Guide: 4.4 Numerical schemes (date accessed: 13.02.2020) [WWW Document]. URL https://cfd.direct/openfoam/user-guide/v6-fvschemes/Classic Collection All cases no.83 [WWW Document], n.d. URL http://cfd.mace.manchester.ac.uk/ercoftac/index.htmlDavidson, L., 2022. Synthetic turbulence generator for lattice Boltzmann method at the interface between Synthetic turbulence generator for lattice Boltzmann method at the interface between RANS and LES 055118.Davidson, L., Billson, M., 2006. Hybrid LES-RANS using synthesized turbulent fluctuations for forcing in the interface region. Int. J. Heat Fluid Flow 27, 1028–1042.Dhamankar, N.S., Blaisdell, G.A., Lyrintzis, A.S., 2018. Overview of turbulent inflow boundary conditions for large-eddy simulations. AIAA J. 56, 1317–1334.di Mare, L., Klein, M., Jones, W.P., Janicka, J., 2006. Synthetic turbulence inflow conditions for large-eddy simulation. Phys. Fluids 18.Druault, P., Lardeau, S., Bonnet, J.P., Coiffet, F., Delville, J., Lamballais, E., Largeau, J.F., Perret, L., 2004. Generation of Three-Dimensional Turbulent Inlet Conditions for Large-Eddy Simulation. AIAA J. 42, 447–456.Druilhet, A., Durand, P., 1997. Experimental investigation of atmospheric boundary layer turbulence. Atmos. Res. 43, 345–388.Drummond, I.T., Duane, S., Horgan, R.R., 1984. Scalar diffusion in simulated helical turbulence with molecular diffusivity. J. Fluid Mech. 138, 75–91.Egolf, P.W., 1994. Difference-quotient turbulence model: A generalization of Prandtl’s mixing-length theory. Phys. Rev. E 49, 1260–1268.Elghorab, M., Ahmed, M., 2020. Numerical analysis for active control of drag over flat plate using sinusoidal travelling wave method.Fagbade Afagbade@Uwyo.Edu, A., Heinz, S., 2022. Application of Mode-Controlled Hybrid RANS-LES to the NASA Wall-Mounted Hump Flow. AIAA Sci. Technol. Forum Expo. AIAA SciTech Forum 2022.Fluent Inc, 2006. FLUENT 6.3 User’s Guide.Fröhlich, J., Mellen, C.P., Rodi, W., Temmerman, L., Leschziner, M.A., 2005. Highly resolved large-eddy simulation of separated flow in a channel with streamwise periodic constrictions. J. Fluid Mech. 526, 19–66.Fukami, K., Nabae, Y., Kawai, K., Fukagata, K., 2019. Synthetic turbulent inflow generator using machine learning. Phys. Rev. Fluids 4, 1–26.Germano, M., 1992. Turbulence : the filtering approach. J. Fluid Mech. 238, 325–336.Greenblatt, D., Paschal, K.B., Yao, C., Harris, J., Schaeffler, N.W., Washburn, A.E., 2006. Part 1 : Baseline & Steady Suction. AIAA J. 44, 1–17.Hepffner, J., Naka, Y., Fukagata, K., 2011. Realizing turbulent statistics. J. Fluid Mech. 676, 54–80.Holzmann, T., 2017. Mathematics, Numerics, Derivations and OpenFOAM. Config. Distrib. Syst. 1992., Int. Work. 68–79.Honnert, R., Masson, V., Lac, C., Nagel, T., 2021. A Theoretical Analysis of Mixing Length for Atmospheric Models From Micro to Large Scales. Front. Earth Sci. 8, 1–15.Huang, S.H., Li, Q.S., Wu, J.R., 2010. A general inflow turbulence generator for large eddy simulation. J. Wind Eng. Ind. Aerodyn. 98, 600–617.Hunt, J.C.R., Wray, a a, Moin, P., 1988. Eddies, streams, and convergence zones in turbulent flows. Cent. Turbul. Res. Proc. Summer Progr. 193–208.Hutchinson, A.J., Hale, N., Born, K., Mason, D.P., 2021. Prandtl’s extended mixing length model applied to the two-dimensional turbulent classical far wake. Proc. R. Soc. A Math. Phys. Eng. Sci. 477.I.Park, G., 2015. Wall-modeled large-eddy simulation of a separated flow over the NASA wall-mounted hump. Annu. Res. Briefs (Center Turbul. Res. 145–160.Immer, M.C., 2016. Time-resolved measurement and simulation of local scale turbulent urban flow.Inoue, M., 2012. Large-Eddy Simulation of the Flat-plate Turbulent Boundary Layer at High Reynolds numbers. Caltech Dr. thethis 2012, 1–111.Iwamoto, K., 2002. Database for fully developed channel flow [WWW Document]. THTLAB Intern. Rep. (ILR- 0201).Jarrin, N., Benhamadouche, S., Laurence, D., Prosser, R., 2006. A synthetic-eddy-method for generating inflow conditions for large-eddy simulations. Int. J. Heat Fluid Flow 27, 585–593.Ji, B., Lei, W., Xiong, Q., 2022. An inflow turbulence generation method for large eddy simulation and its application on a standard high-rise building. J. Wind Eng. Ind. Aerodyn. 226, 105048.Kanchi, H., Sengupta, K., Mashayek, F., 2013. Effect of turbulent inflow boundary condition in les of flow over a backward-facing step using spectral element method. Int. J. Heat Mass Transf. 62, 782–793.Karras, T., Aila, T., Laine, S., Lehtinen, J., 2018. Progressive growing of GANs for improved quality, stability, and variation. 6th Int. Conf. Learn. Represent. ICLR 2018 - Conf. Track Proc. 1–26.Karras, T., Laine, S., Aila, T., 2021. A Style-Based Generator Architecture for Generative Adversarial Networks. IEEE Trans. Pattern Anal. Mach. Intell. 43, 4217–4228.Keating, A., Piomelli, U., Balaras, E., Kaltenbach, H.J., 2004. A priori and a posteriori tests of inflow conditions for large-eddy simulation. Phys. Fluids 16, 4696–4712.Khosronejad, A., Flora, K., Kang, S., 2020. Effect of Inlet Turbulent Boundary Conditions on Scour Predictions of Coupled LES and Morphodynamics in a Field-Scale River: Bankfull Flow Conditions. J. Hydraul. Eng. 146, 1–24.Kim, J., Lee, C., 2020. Deep unsupervised learning of turbulence for inflow generation at various Reynolds numbers. J. Comput. Phys. 406, 1–26.Kim, W.W., Menon, S., 1995. A new dynamic one-equation subgrid-scale model for large eddy simulations. 33rd Aerosp. Sci. Meet. Exhib.Kim, Y., Castro, I.P., Xie, Z.T., 2013. Divergence-free turbulence inflow conditions for large-eddy simulations with incompressible flow solvers. Comput. Fluids 84, 56–68.Klein, M., Sadiki, A., Janicka, J., 2003. A digital filter based generation of inflow data for spatially developing direct numerical or large eddy simulations. J. Comput. Phys. 186, 652–665.Kondo, K., Murakami, S., Mochida, A., 1997. Generation of velocity fluctuations for inflow boundary condition of LES. J. Wind Eng. Ind. Aerodyn. 67–68, 51–64.Kornev, N., Hassel, E., 2003. A new method for generation of artificial turbulent inflow data with prescribed statistic properties for LES and DNS simulations. Schiffbauforschung 42, 35–44.Kraichnan, R.H., 1970. Diffusion by a Random Velocity Field 22.Krishna, V., Squires, K.D., Forsythe, J.R., 2004. Prediction of separated flow characteristics over a hump using RANS and DES. 2nd AIAA Flow Control Conf. 44.Kröger, H., Kornev, N., 2018. Generation of divergence free synthetic inflow turbulence with arbitrary anisotropy. Comput. Fluids 165, 78–88.Lamberti, G., García-Sánchez, C., Sousa, J., Gorlé, C., 2018a. Optimizing turbulent inflow conditions for large-eddy simulations of the atmospheric boundary layer. J. Wind Eng. Ind. Aerodyn. 177, 32–44.Lamberti, G., García-Sánchez, C., Sousa, J., Gorlé, C., 2018b. Optimizing turbulent inflow conditions for large-eddy simulations of the atmospheric boundary layer. J. Wind Eng. Ind. Aerodyn. 177, 32–44.Lee, Y.T., Gutti, L.K., Lim, H.C., 2021. Numerical study of the influence of the inlet turbulence length scale on the turbulent boundary layer. Appl. Sci. 11.Leonard, A., 1975. Energy cascade in large-eddy simulations of turbulent fluid flows. Adv. Geophys. 18, 237–248.Li, N., Balaras, E., Piomelli, U., 2000. Inflow conditions for large-eddy simulations of mixing layers. Phys. Fluids 12, 935–938.Lilly, D.K., 1992. A proposed modification of the German0 closure method h J axi. Phys. Fluids 4, 633–635.Liu, K., Pletcher, R.H., 2006. Inflow conditions for the large eddy simulation of turbulent boundary layers: A dynamic recycling procedure. J. Comput. Phys. 219, 1–6.Lund, T.S., Wu, X., Squires, K.D., 1998. Generation of Turbulent Inflow Data for Spatially-Developing Boundary Layer Simulations. J. Comput. Phys. 140, 233–258.Luo, Y., Liu, H., Huang, Q., Xue, H., Lin, K., 2017. A multi-scale synthetic eddy method for generating inflow data for LES. Comput. Fluids 156, 103–112.M. R. Maxey, 1987. The gravitational settling of aerosol particles in homogeneous turbulence and random flow fields. J. Fluid Mech. 174, 441–465.Mahdizadehaghdam, S., Panahi, A., Krim, H., 2019. Sparse generative adversarial network. Proc. - 2019 Int. Conf. Comput. Vis. Work. ICCVW 2019 3063–3071.Mansouri, Z., Panneer, R., Gan, A., 2022. Journal of Wind Engineering & Industrial Aerodynamics Performance of different inflow turbulence methods for wind engineering applications. J. Wind Eng. Ind. Aerodyn. 229, 105141.Martínez, J., Piscaglia, F., Montorfano, A., Onorati, A., Aithal, S.M., 2015. Influence of spatial discretization schemes on accuracy of explicit LES: Canonical problems to engine-like geometries. Comput. Fluids 117, 62–78.McMullan, W.A., Gao, S., Coats, C.M., 2009. The effect of inflow conditions on the transition to turbulence in large eddy simulations of spatially developing mixing layers. Int. J. Heat Fluid Flow 30, 1054–1066.Minguez, M., Pasquetti, R., Serre, E., 2008. High-order large-eddy simulation of flow over the “Ahmed body” car model. Phys. Fluids 20.Moin, P., Mahesh, K., 1998. DIRECT NUMERICAL SIMULATION: A Tool in Turbulence Research. Annu. Rev. Fluid Mech. 30, 539–578.Montorfano, A., Piscaglia, F., Ferrari, G., 2013a. Inlet boundary conditions for incompressible LES: A comparative study. Math. Comput. Model. 57, 1640–1647.Montorfano, A., Piscaglia, F., Ferrari, G., 2013b. Inlet boundary conditions for incompressible LES: A comparative study. Math. Comput. Model. 57, 1640–1647.Mordant, N., Metz, P., Michel, O., Pinton, J.F., 2001. Measurement of lagrangian velocity in fully developed turbulence. Phys. Rev. Lett. 87, 214501-1-214501–4.Mukha, T., Liefvendahl, M., 2017. The generation of turbulent inflow boundary conditions using precursor channel flow simulations. Comput. Fluids 156, 21–33.Nicoud, F., Ducros, F., 2006. Subgrid-scale stress modelling based on the square of the velocity. Agric. Econ. Res. Rev. 19, 37–48.OpenFOAM-v2006, n.d. Turbulent inlet boundary condition based on pseudo-random numbers and a given spatiotemporal-invariant mean field [WWW Document]. URL https://www.openfoam.com/documentation/guides/latest/api/classFoam_1_1turbulentInletFvPatchField.htmlPamiès, M., Weiss, P.É., Garnier, E., Deck, S., Sagaut, P., 2009. Generation of synthetic turbulent inflow data for large eddy simulation of spatially evolving wall-bounded flows. Phys. Fluids 21.Patruno, L., Ricci, M., 2017. On the generation of synthetic divergence-free homogeneous anisotropic turbulence. Comput. Methods Appl. Mech. Eng. 315, 396–417.Perret, L., Delville, J., Manceau, R., Bonnet, J.P., 2008. Turbulent inflow conditions for large-eddy simulation based on low-order empirical model. Phys. Fluids 20.Philipp, S., 2011. Boundary Layer DNS/LES Data [WWW Document]. 7th Int. Symp. Turbul. Shear Flow Phenom. URL https://www.mech.kth.se/~pschlatt/DATA/Piomelli, U., 1999. Large-eddy simulation: achievements and challenges. Prog. Aerosp. Sci. 35, 335–362.Poletto, R., Craft, T., Revell, A., 2013. A new divergence free synthetic eddy method for the reproduction of inlet flow conditions for les. In: Flow, Turbulence and Combustion. pp. 519–539.Pope, S., 2001. Turbulent flows.Pope, S.B., 2001. Turbulent flows.Quon, E.W., Ghate, A.S., Lele, S.K., 2018. Enrichment methods for inflow turbulence generation in the atmospheric boundary layer. In: Journal of Physics: Conference Series. Institute of Physics Publishing.R.I.Issa, 1991. Solution of the Implicity Discretized Reacting Flow Equations by Operator-Splitting. J. Comput. Phys. 388–410.Radford, A., Metz, L., Chintala, S., 2016. Unsupervised representation learning with deep convolutional generative adversarial networks. 4th Int. Conf. Learn. Represent. ICLR 2016 - Conf. Track Proc. 1–16.Rai, M.M., Parviz, M., 1993. Direct Numerical Simulation of Transition and Turbulence in a Spatially Evolving Boundary Layer. J. Comput. Phys. 109, 169–192.Roach, P., Brierlay, D., 1989. The influence of a turbulent freestream on zero pressure gradient transitional boundary layer development including the condition test cases T3A and T3B. Cambridge Univ. Press 319–347.Roth, K., Lucchi, A., Nowozin, S., Hofmann, T., 2017. Stabilizing training of generative adversarial networks through regularization. Adv. Neural Inf. Process. Syst. 2017-Decem, 2019–2029.Sagaut, P., 2006. Large eddy simulation for incompressible flows: an introduction. Springer Science & Business Media.Sagaut, P., Garnier, E., Tromeurz, E., Larchevêque, L., Labourasse, E., 2003. Turbulent inflow conditions for LES of supersonic and subsonic wall bounded flows. 41st Aerosp. Sci. Meet. Exhib. 1–11.Šarić, S., Jakirlić, S., Tropea, C., 2005. A periodically perturbed backward-facing step flow by means of LES, des and T-RANS: An example of flow separation control. J. Fluids Eng. Trans. ASME 127, 879–887.Schlatter, P., Örlü, R., 2012. Turbulent boundary layers at moderate Reynolds numbers: Inflow length and tripping effects. J. Fluid Mech. 710, 5–34.Shahinfar, S., Fransson, J.H.M., Sattarzadeh, S.S., Talamelli, A., 2013. Scaling of streamwise boundary layer streaks and their ability to reduce skin-friction drag. J. Fluid Mech. 733, 1–32.Smagorinsky, J., 1963. General circulation experiments with the primitive equations: I. The basic equations. Mon. Weather Rev. 91,99-164.Smirnov, A., Shi, S., Celik, I., 2001. Random Flow Generation Technique for Large Eddy Simulations and Particle-Dynamics Modeling. J. Fluid Mech. 123, 359–371.Smith, F.T., Scheichl, B., Kluwick, A., 2010. On turbulent separation. J. Eng. Math. 68, 373–400.Source, O., Consulting, C.F.D., 2014. OpenFoam Utilities.Spalart, P.R., 1988. Direct simulation of a turbulent boundary layer up to Rθ= 1410. J. Fluid Mech. 187, 61–98.Spalding, D.B., 1960. A single formula for the “law of the wall.” J. Appl. Mech. Trans. ASME 28, 455–458.Tabor, G.R., Baba-Ahmadi, M.H., 2010. Inlet conditions for large eddy simulation: A review. Comput. Fluids 39, 553–567.Tominaga, Y., Stathopoulos, T., 2011. CFD modeling of pollution dispersion in a street canyon: Comparison between LES and RANS. J. Wind Eng. Ind. Aerodyn. 99, 340–348.Treleaven, N.C.W., Staufer, M., Spencer, A., Garmory, A., Page, G.J., 2020. Application of the PODFS method to inlet turbulence generated using the digital filter technique. J. Comput. Phys. 1, 109541.Tyacke, J.C., Tucker, P.G., 2015. Future use of large eddy simulation in aero-engines. J. Turbomach. 137, 1–16.Uzun, A., Malik, M.R., 2018. Large-eddy simulation of flow over a wall-mounted hump with separation and reattachment. AIAA J. 56, 715–730.Vasaturo, R., Kalkman, I., Blocken, B., van Wesemael, P.J.V., 2018. Large eddy simulation of the neutral atmospheric boundary layer: performance evaluation of three inflow methods for terrains with different roughness. J. Wind Eng. Ind. Aerodyn. 173, 241–261.Veloudis, I., Yang, Z., McGuirk, J.J., Page, G.J., Spencer, A., 2007. Novel implementation and assessment of a digital filter based approach for the generation of les inlet conditions. Flow, Turbul. Combust. 79, 1–24.Vita, G., Hemida, H., Andrianne, T., Baniotopoulos, C., 2020. The effect of the integral length scale of turbulence on a wind turbine aerofoil. J. Wind Eng. Ind. Aerodyn. 204, 104235.Wu, X., 2016. Inflow Turbulence Generation Methods. Annu. Rev. Fluid Mech. 49, 23–49.Wu, X., Moin, P., 2009. Direct numerical simulation of turbulence in a nominally zero-pressure-gradient flat-plate boundary layer. J. Fluid Mech. 630, 5–41.Xiao, F., Dianat, M., McGuirk, J.J., 2013. Large eddy simulation of liquid-jet primary breakup in air crossflow. AIAA J. 51, 2878–2893.Xie, Z.T., Castro, I.P., 2008. Efficient generation of inflow conditions for large eddy simulation of street-scale flows. Flow, Turbul. Combust. 81, 449–470.Yan, B.W., Li, Q.S., 2015. Computers & Fluids Inflow turbulence generation methods with large eddy simulation for wind effects on tall buildings 116, 158–175.Yang, Q., Zhou, T., Yan, B., Van Phuc, P., Hu, W., 2020. LES study of turbulent flow fields over hilly terrains — Comparisons of inflow turbulence generation methods and SGS models. J. Wind Eng. Ind. Aerodyn. 204.You, D., Moin, P., 2007. Application of a dynamic global-coefficient subgrid-scale model for large-eddy simulation in complex geometries. 2007 Proc. 5th Jt. ASME/JSME Fluids Eng. Summer Conf. FEDSM 2007 1 SYMPOSIA, 1429–1437.You, D., Wang, M., Moin, P., 2006. Large-eddy simulation of flow over a wall-mounted hump with separation control. AIAA J. 44, 2571–2577.Yu, Y., Yang, Y., Xie, Z., 2018. A new inflow turbulence generator for large eddy simulation evaluation of wind effects on a standard high-rise building. Build. Environ. 138, 300–313.Zhong, J., Cai, X., Xie, Z.-T., 2019. Implementation of a synthetic inflow turbulence generator in idealised WRF v3.6.1 large eddy simulations under neutral atmospheric conditions. Geosci. Model Dev. Discuss. 1–24.