Part II - Challenges
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- Coarse Grained Simulation and Turbulent Mixing , pp. 105 - 190Publisher: Cambridge University PressPrint publication year: 2016
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References
Grinstein, F.F, Margolin, L.G., and Rider, W.J., editors, 2010, Implicit Large Eddy Simulation: Computing Turbulent Fluid Dynamics, 2nd printing, Cambridge University Press.Google Scholar
Spalart, P.R. 2000, “Strategies for turbulence modeling and simulations,” Heat and Fluid Flow, 21, 252–263.CrossRefGoogle Scholar
Spalart, P.R. 2009, “Detached Eddy Simulation,” Annu. Rev. Fluid Mech. 41, 181–202.CrossRefGoogle Scholar
Leschziner, M., Li, N., and Tessicini, F. 2009, “Simulating flow separation from continuous surfaces: Routes to overcoming the Reynolds number barrier,” Phil. Trans. R. Soc. A 367, 2885–2903.CrossRefGoogle ScholarPubMed
Grinstein, F.F. 2009, “On integrating large eddy simulation and laboratory turbulent flow experiments,” Phil. Trans. R. Soc. A, 67, 2931–2945.CrossRefGoogle Scholar
Hirt, C.W. 1969, “Computer studies of time-dependent turbulent flows,” Phys. Fluids II, 219–227.CrossRefGoogle Scholar
Ghosal, S. 1996, “An analysis of numerical errors in large-eddy simulations of turbulence,” J. Comp Phys., 125, 187–206.CrossRefGoogle Scholar
Fureby, C. and Grinstein, F.F., 1999, “Monotonically integrated large eddy simulation of free shear flows,” AIAA Journal, 37, 544–56.CrossRefGoogle Scholar
Margolin, L.G. and Rider, W.J. 2002, “A rationale for implicit turbulence modeling,” Int. J. Numer. Methods Fluids, 39, 821–841.CrossRefGoogle Scholar
Grinstein, F.F. and Fureby, C. 2007, “On flux-limiting-based implicit large eddy simulation,” J. Fluids Eng. 129, 1483–1492.CrossRefGoogle Scholar
Margolin, L.G. and Rider, W.J. 2010, “Numerical regularization: The numerical analysis of implicit subgrid models,” ch. 5 in Implicit Large Eddy Simulation: Computing Turbulent Fluid Dynamics, ed. by Grinstein, F.F., Margolin, L.G., and Rider, W.J., 2nd printing, Cambridge University Press.Google Scholar
Boris, J.P. 1990, “On Large Eddy Simulation Using Subgrid Turbulence Models,” in Whither Turbulence? Turbulence At the Crossroads, ed. by Lumley, J.L., Springer, 344.CrossRefGoogle Scholar
Boris, J.P., Grinstein, F.F., Oran, E.S., and Kolbe, R.J. 1992, “New insights into large eddy simulation,” Fluid Dyn. Res. 10, 199–228.CrossRefGoogle Scholar
Pope, S.B. 2004, “Ten questions concerning the large eddy simulation of turbulent flows,” New J. Phys, 6, 35.CrossRefGoogle Scholar
Harten, A. 1983, “High resolution schemes for hyperbolic conservation laws,” J. Comput. Phys. 49, 357–393.CrossRefGoogle Scholar
Fan, M., Wenren, Y., Dietz, W., Xiao, M., and Steinhoff, J. 2002, “Computing blunt body flows on coarse grids using vorticity confinement,” Journal Fluids Engineering 124, 876–885.CrossRefGoogle Scholar
Domaradzki, J.A. and Adams, N.A. 2002, “Direct modeling of subgrid scales of turbulence in large eddy simulations,” Journal of Turbulence, 3, 024.CrossRefGoogle Scholar
Stolz, S., Adams, N.A., and Kleiser, L. 2001, “The approximate deconvolution model for LES of compressible flows and its application to shock-boundary-layer interaction,” Phys. Fluids, 13(2985).Google Scholar
Visbal, M. and Rizzetta, D. 2002, “Large-eddy simulation on curvilinear grids using compact differencing and filtering schemes,” Journal of Fluids Engineering, 124, 836–847.CrossRefGoogle Scholar
Schilling, O. and Latini, M. 2010, “High-order WENO simulations of three-dimensional reshocked Richtmyer–Meshkov instability to late times: Dynamics, dependence on initial conditions, and comparisons to experimental data,” Acta Mathematica Scientia, 30B(2), 595–620.CrossRefGoogle Scholar
Margolin, L.G., 2009 “Finite-scale equations for compressible fluid flow,” Phil. Trans. R. Soc. A, 367, 2861–2871.CrossRefGoogle ScholarPubMed
Grinstein, F.F., Margolin, L.G., and Rider, W.J. 2010, “A rationale for implicit LES,” ch. 2 in Implicit Large Eddy Simulation: Computing Turbulent Fluid Dynamics, ed. by Grinstein, F.F, Margolin, L.G., and Rider, W.J., 2nd printing, Cambridge University Press.Google Scholar
Bose, S.T., Moin, P., and You, D. 2010, “Grid-independent large-eddy simulation using explicit filtering,” Phys. Fluids, 22, 1–11.CrossRefGoogle Scholar
Cohen, R.H., Dannevik, W.P., Dimits, A.M., Eliason, D.E., Mirin, A.A., Zhou, Y., Porter, D.H., and Woodward, P.R. 2002, “Three-dimensional simulation of a Ritchmyer–Meshkov instability with a two-scale initial perturbation,” Physics of Fluids, 14, 3692–3709.CrossRefGoogle Scholar
Drikakis, D., Fureby, C., Grinstein, F.F., and Youngs, D. 2007, “Simulation of transition and turbulence decay in the Taylor–Green vortex,” Journal of Turbulence, 8, 020.CrossRefGoogle Scholar
Wachtor, A.J., Grinstein, F.F., Devore, C.R., Ristorcelli, J.R., and Margolin, L.G. 2013, “Implicit large-eddy simulations of passive scalar mixing in statistically stationary isotropic turbulence,” Physics of Fluids, 25, 025101.CrossRefGoogle Scholar
Zhou, Y., Grinstein, F.F., Wachtor, A.J., and Haines, B.M., 2014, “Estimating the effective Reynolds number in implicit large eddy simulation,” Physical Review E, 89, 013303.CrossRefGoogle ScholarPubMed
Dimotakis, P.E. 2000, “The mixing transition in turbulent flows,” J. Fluid Mech., 409, 69–98.CrossRefGoogle Scholar
Zhou, Y. 2007, “Unification and extension of the similarity scaling criteria and mixing transition for studying astrophysics using high energy density laboratory experiments or numerical simulations,” Phys. of Plasmas, 14, 082701.CrossRefGoogle Scholar
Brachet, M.E., Meiron, D.I., Orszag, S.A., Nickel, B.G., Morg, R.H., and Frisch, U.J. 1983, “Small-scale structure of the Taylor-Green vortex,” J. Fluid. Mech. 130, 411.CrossRefGoogle Scholar
Brachet, M.E. 1991, “Direct numerical simulation of three-dimensional turbulence in the Taylor–Green vortex,” Fluid Dyn. Res. 8(1).CrossRefGoogle Scholar
Grinstein, F.F., Gowardhan, A.A., and Wachtor, A.J. 2011, “Simulations of Richtmyer–Meshkov instabilities in planar shock-tube experiments,” Physics of Fluids 23, 034106.CrossRefGoogle Scholar
Kaneda, Y., Ishihara, T., Yokokawa, M., Itakura, K., and Uno, A. 2003, “Energy dissipation rate and energy spectrum in high resolution direct numerical simulations of turbulence in a periodic box,” Phys. Fluids, 15, L21.CrossRefGoogle Scholar
Grinstein, F.F., editor, 2004, “Boundary conditions for large eddy simulation, special section,” AIAA Journal, 42, 437–492.Google Scholar
Hussain, A.K.M.F. and Zedan, M.F. 1978, “Effects of the initial condition on the axisymmetric free shear layer: effect of the initial fluctuation level,” Phys. Fluids, 21, 1475–1481.CrossRefGoogle Scholar
Gutmark, E. and Ho, C.M. 1983, “Preferred modes and the spreading rates of jets,” Phys. Fluids, 26, 2932.CrossRefGoogle Scholar
Wygnanski, I., Champagne, F., and Marasli, B., 1986, “On the large-scale structures in two-dimensional, small-deficit, turbulent wakes,” Journal Of Fluid Mechanics 168, 31–71.CrossRefGoogle Scholar
George, W.K. 1990, “Governing equations, experiments, and the experimentalist,” Exp. Thermal Fluid Sc. 3, 557–566.CrossRefGoogle Scholar
Li, G. and Gutmark, E.J. 2006, “Experimental study of boundary condition effects on non-reacting and reacting flow in a multi-swirl gas turbine combustor,” AIAA. J. 44, 444.CrossRefGoogle Scholar
Poinsot, T.J. and Lele, S.K. 1992, “Boundary conditions for direct simulations of compressible viscous flows,” J. Comp. Phys. 101, 104.CrossRefGoogle Scholar
Colonius, T., Lele, S.K., and Moin, P. 1993, “Boundary conditions for direct computations of aerodynamic sound,” AIAA J., 31, 1574.CrossRefGoogle Scholar
Grinstein, F.F. 1994, “Open boundary conditions in the simulation of subsonic turbulent shear flows,” J. Comp. Phys. 115, 43–55.CrossRefGoogle Scholar
Spalart, P.R., Jou, W.H., Strelets, M., and Allmaras, S.R. 1997, “Comments on the Feasibility of LES for Wings, and on Hybrid RANS/LES Approach,” in Advances in DNS/LES, First AFOSR International Conference in DNS/LES, Greyden Press.Google Scholar
Kong, H., Choi, H., and Lee, J.S. 2000, “Direct numerical simulation of turbulent thermal boundary layers,” Phys. Fluids, 12, 2555.CrossRefGoogle Scholar
Fureby, C., Alin, N., Wikström, N., Menon, S., Persson, L., and Svanstedt, N. 2004, “On large eddy simulations of high Re-number wall bounded flows,” AIAA. J. 42, 457.CrossRefGoogle Scholar
Sagaut, P., Garnier, E., Tromeur, E., Larchevêque, L., and Labourasse, E. 2004, “Turbulent inflow conditions for large-eddy simulation of compressible wall-bounded flows,” AIAA J. 42, 469.CrossRefGoogle Scholar
Sidwell, T., Richards, G., Casleton, K., Straub, D., Maloney, D., Strakey, P., Ferguson, D., Beer, S., and Woodruff, S. 2006, “Optically accessible pressurized research combustor for computational fluid dynamics model validation,” AIAA. J, 44, 434.CrossRefGoogle Scholar
Thompson, K. 1990, “Time dependent boundary conditions for hyperbolic systems, II,” J. Comp. Phys. 89, 439.CrossRefGoogle Scholar
Slessor, M.D., Bond, C.L., and Dimotakis, P.E. 1998, “Turbulent shear-layer mixing at high Reynolds numbers: effects of inflow conditions,” J. Fluid Mech., 376, 115–138.CrossRefGoogle Scholar
George, W.K. and Davidson, L., 2004, “Role of initial conditions in establishing asymptotic flow behavior,” AIAA Journal 42, 438–446.CrossRefGoogle Scholar
Ramaprabhu, P., Dimonte, G., and Andrews, M.J. 2005, “A numerical study of the influence of initial perturbations on the turbulent Rayleigh–Taylor instability,” J. Fluid Mech. 536, 285-319-23.CrossRefGoogle Scholar
Strikwerda, J.C. 1977, “Initial value boundary value problems for incompletely parabolic systems,” Comm. Pure Appl. Math. 30, 797.CrossRefGoogle Scholar
Oliger, J. and Sundstrom, A. 1978, “Theoretical and practical aspects of some initial boundary value problems in fluid dynamics,” SIAM J. Appl. Math. 35, 419.CrossRefGoogle Scholar
Kim, W.-W., Menon, S., and Mongia, H.C. 1999, “Large-eddy simulation of a gas turbine combustor flow,” Combust. Sci. Tech. 143, 25–62.CrossRefGoogle Scholar
Grinstein, F.F. and Fureby, C. 2004, “LES studies of the flow in a swirl gas combustor,” Proc. of the Comb. Inst. 30, 1791.CrossRefGoogle Scholar
Druault, P., Lardeau, S., Bonnet, J.P., Coiffet, F., Delville, J., Lamballais, E., Largeau, J.F., and Perret, L. 2004, “Generation of three-dimensional turbulent inlet conditions for large-eddy simulation,” AIAA Journal, 42, 447–456.CrossRefGoogle Scholar
Patnaik, G., Boris, J.P., Young, T.R., and Grinstein, F.F. 2007, “Large scale urban contaminant transport simulations with MILES,” J. Fluids Eng. 129, 1524–1532.CrossRefGoogle Scholar
Grinstein, F.F., Young, T.R., Gutmark, E.J., Li, G., Hsiao, G., and Mongia, H.C. 2002, “Flow dynamics in a swirl combustor,” J. of Turbulence 3, 1468.CrossRefGoogle Scholar
Fureby, C., Grinstein, F.F., Li, G., and Gutmark, E.J. 2007, “An experimental and computational study of a multi-swirl gas turbine combustor,” Proc. of the Combustion Institute, 31, 3107–3114.CrossRefGoogle Scholar
Verfaillie, S., Gutmark, E.J., Bonnet, J.P., and Grinstein, F.F. 2006, “Linear stochastic estimation of a swirling jet,” AIAA J., 44, 457–468.Google Scholar
Brouillete, M. 2002, “The Ritchmyer–Meshkov instability,” Annu. Rev. Fluid Mech., 34, 445–468.CrossRefGoogle Scholar
Hill, D.J., Pantano, C., and Pullin, D.I. 2006, “Large-eddy simulation and multiscale modelling of a Ritchmyer–Meshkov instability with reshock,” J. Fluid Mech. 557, 29–61.CrossRefGoogle Scholar
Youngs, D.L. 2007, Rayleigh–Taylor and Richtmyer–Meshkov mixing,” ch. 13 in Implicit Large Eddy Simulation: Computing Turbulent Fluid Dynamics, ed. by Grinstein, F.F., Margolin, L.G., and Rider, W.J., 2nd printing, Cambridge University Press, 2010.Google Scholar
Rider, W. and Kothe, D. 1998, “Reconstructing volume tracking,” J. Comp. Phys. 141, 112–152.CrossRefGoogle Scholar
Lax, P. and Wendroff, B. 1960, “Systems of conservation laws,” Comm. Pure Appl. Math. 13, 217–237.CrossRefGoogle Scholar
Leveque, R.J. 1990, Finite Volume Methods for Hyperbolic Problems, Cambridge University Press.Google Scholar
Drikakis, D.,Grinstein, F.F., and Youngs, D. 2005, “On the computation of instabilities and symmetry-breaking in fluid mechanics,” Progress in Aerospace Sciences 41(8), 609–641.CrossRefGoogle Scholar
Dimonte, G. 1999 “Nonlinear evolution of the Rayleigh–Taylor and Richtmyer–Meshkov instabilities,” Phys. Plasma 6(5), 2009–2015.CrossRefGoogle Scholar
Vetter, M. and Sturtevant, B. 1995, “Experiments on the Richtmyer–Meshkov instability of an air/ SF6 interface,” Shock Waves 4, 247–252.CrossRefGoogle Scholar
Gowardhan, A.A., Ristorcelli, J.R. and Grinstein, F.F. 2011, “The bipolar behavior of the Richtmyer–Meshkov instability,” Physics of Fluids 23 (Letters), 071701.CrossRefGoogle Scholar
Richtmyer, R.D. 1960, “Taylor instability in shock acceleration of compressible fluids,” Commun. Pure Appl. Math. 13, 297.CrossRefGoogle Scholar
Lombardini, M., Hill, D.J., Pullin, D.I., and Meiron, D.I. 2011, “Atwood ratio dependence of Richtmyer–Meshkov flows under reshock conditions using large-eddy simulations,” J. Fluid Mech., 670, 439–480.CrossRefGoogle Scholar
Thornber, B., Drikakis, D., Youngs, D.L., and Williams, R.J.R. 2012, “Physics of the single-shocked and reshocked Richtmyer–Meshkov instability,” J. of Turbulence 13(10), 1–17.CrossRefGoogle Scholar
Orlicz, S.G.C., Prestridge, K.P., and Balakumar, B.J. 2012, “Experimental study of initial condition dependence on Richtmyer-Meshkov instability in the presence of reshock,” Physics of Fluids 24, 034103.Google Scholar
Gowardhan, A.A. and Grinstein, F.F. 2011, “Numerical simulation of Richtmyer–Meshkov instabilities in shocked gas curtains,” Journal of Turbulence 12(43), 1–24.CrossRefGoogle Scholar
Ristorcelli, J.R., Gowardhan, A.A., and Grinstein, F.F. 2013, “Two classes of Richtmyer–Meshkov instabilities: A detailed statistical look,” Phys. Fluids 25, 044106.CrossRefGoogle Scholar
Leinov, E.,Malamud, G., Elbaz, Y., Levin, I.A., Ben-dor, G., Shvarts, D., and Sadot, O. 2009, “Experimental and numerical investigation of the Richtmyer–Meshkov instability under re-shock conditions,” J. Fluid Mech. 626, 449–475.CrossRefGoogle Scholar
Orlicz, G.C., Balakumar, B.J., Tomkins, C.D., and Prestridge, K.P. 2009 “A Mach number study of the Richtmyer–Meshkov instability in a varicose, heavy-gas curtain,” Phys. Fluids 21, 064102.CrossRefGoogle Scholar
Wang, H.L. and George, W.K. 2002, “The integral scale in homogeneous isotropic turbulence,” J. Fluid Mech. 459, 429–443.CrossRefGoogle Scholar
References
Andrejczuk, M., Grabowski, W., Reisner, J., and Gadian, A., “Cloud-aerosol interactions for boundary-layer stratocumulus in the Lagrangian cloud model,” J. Geophys. Res, 115, 2010.CrossRefGoogle Scholar
Andrejczuk, M., Reisner, J., Henson, B., Dubey, M., and Jeffery, C., “The potential impacts of pollution on a non-drizzling stratus deck: Does aerosol number matter more than type?,” J. Geophys. Res, 113, 2008.CrossRefGoogle Scholar
Crowe, C., Sommerfeld, M., and Tsuji, Y., Multiphase Flows with Droplets and Particles, CRC Press, New York, 1998.Google Scholar
DeMaria, M., Sampson, C., Knaff, J., and Musgrave, K., “Is tropical cyclone intensity guidance improving?,” Bull. Amer. Meteor. Soc., 95:387–398, 2014.CrossRefGoogle Scholar
Emanual, K., “Global warming effects on U.S. hurricane damage,” Weather, Climate, and Society, 3:261–268, 2011.CrossRefGoogle Scholar
Fierro, A. and Reisner, J., “High resolution simulation of the electrification and lightning of hurricane Rita during the period of rapid intensification,” J. Atmos. Sci., 137:477–494, 2010.Google Scholar
Godinez, H. and Moulton, D., “An efficient matrix-free implementation of the ensemble kalman filter,” Comput. Geosci., 2012.CrossRefGoogle Scholar
Godinez, H., Reisner, J., Fierro, A., Guimond, S., and Kao, J., “Optimally estimating key model parameters of rapidly intensifying hurricane Guillermo (1997) using the ensemble kalman filter,” J. Atmos. Sci., 2848–2868, 2012.Google Scholar
Guimond, S., Bourassa, M., and Reasor, P., “A latent heat retrieval and its effects on the intensity and structure of hurricane Guillermo (1997). part 1: The alogithm and observations,” J. Atmos. Sci., 68:1549–1567, 2011.CrossRefGoogle Scholar
Hall, W., “A detailed microphysical model within a two-dimensional dynamic framework: Model description and preliminary results,” J. Atmos. Sci., 37:2486–2507, 1980.2.0.CO;2>CrossRefGoogle Scholar
Harlow, F., “Particle-in-cell method for two-dimensional hydrodynamic problems,” LA-02082-MS, 1956.Google Scholar
Harlow, F., “The particle-in-cell computing method for fluid dynamics,” Meth. Comput. Physics, 3:319–342, 1964.Google Scholar
Hu, C. and Shu, C.-W., “Weighted essentially non-oscillatory schemes on triangle meshes,” J. Comput. Physics, 150:97–127, 1999.CrossRefGoogle Scholar
Kao, C., Hang, Y., Reisner, J., and Smith, W., “Test of the volume-of-fluid method on marine boundary layer clouds,” Mon. Wea. Rev., 128:1960–1970, 1999.2.0.CO;2>CrossRefGoogle Scholar
Magnusson, L., Bidlot, J.-R., Lang, S., Thorpe, A., Wedi, N., and Yamaguchi, M., “Evaluation of medium-range forecasts for hurricane Sandy,” Mon. Wea. Rev., 142:1962–1981, 2014.CrossRefGoogle Scholar
Margolin, L., Reisner, J., and Smolarkiewicz, P., “Application of the volume-of-fluid method to the advection-condensation problem,” Mon. Wea. Rev., 125:2265–2273, 1997.2.0.CO;2>CrossRefGoogle Scholar
Meyers, M., DeMott, P., and Cotton, W., “New primary ice-nucleation parameterizations in an explicit cloud model,” J. Appl. Meteor., 31:708–721, 1992.2.0.CO;2>CrossRefGoogle Scholar
Neumann, J. V. and Richtmyer, R., “A method for the numerical calculation of hydrodynamic shocks,” J. of Appl. Physics, 21:232–237, 1950.CrossRefGoogle Scholar
Phillips, V., Pokrovsky, A., and Khain, A., “The influence of time-dependent melting on the dynamics and precipitation production in maritime and continental storm clouds,” J. Atmos. Sci., 64:338–359, 2007.CrossRefGoogle Scholar
Reasor, P., Eastin, M., and Gamache, J., “Rapidly intensifying hurricane Guillermo (1997). Part 1: Low-wavenumber structure and evolution,” Mon. Wea. Rev., 137:603–631, 2009.CrossRefGoogle Scholar
Reisner, J., Bruintjes, R., and Rasmussen, R., “An examination on the utility of forecasting supercooled liquid water in a mesoscale model,” Q. J. R. Meteorol. Soc., 124:1071–1107, 1998.CrossRefGoogle Scholar
Reisner, J. and Jeffery, J., “A smooth cloud model,” Mon. Wea. Rev., 137:1825–1843, 2009.CrossRefGoogle Scholar
Reisner, J., Wyszogrodzki, A., Mousseau, V., and Knoll, D., “An efficient physics-based preconditioner for the fully implicit solution of small-scale thermally driven atmospheric flows,” J. Comput. Phys., 189:30–44, 2003.CrossRefGoogle Scholar
Rider, W. and Kothe, B., “Reconstructing volume tracking,” J. Comput. Physics, 141:112–152, 1998.CrossRefGoogle Scholar
Rutledge, S. and Hobbs, P., “The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. VIII: A model fo the seeder-feeder process in warm frontal rainbands,” J. Atmos. Sci., 40:1185–1206, 1983.2.0.CO;2>CrossRefGoogle Scholar
Rutledge, S. and Hobbs, P., “The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. XI: A diagnostic modeling study of precipitation development in narrow cold-frontal rainbands,” J. Atmos. Sci., 41:2949–2972, 1984.2.0.CO;2>CrossRefGoogle Scholar
Shu, C.-W. and Osher, S., “Efficient implementation of essentially non-oscillatory shock-capturing schemes, II” J. Comput. Physics, 83:97–127, 1999.Google Scholar
Skamarock, W. and Klemp, J., “A time-split non-hydrostatic atmospheric model for weather research and forecasting applications,” J. Comput. Physics, 227:3465–3485, 2008.CrossRefGoogle Scholar
Stevens, B., Moeng, C.-H., Ackerman, A., Bretherton, C., Chlond, A., Roode, S., Edwards, J., Golaz, J.-C., Jiang, H., Khairoutdinov, M., Kirkpatrick, M., Lewellen, D., Lock, A., Muller, F., Stevens, D., Whelan, E., and Zhu, P., “Evaluation of large-eddy simulations via observations of nocturnal marine stratocumulus,” Mon. Wea. Rev., 133:1443–1462, 2005.CrossRefGoogle Scholar
Stevens, B. and Schwartz, S., “Observing and modeling earth’s energy flows,” Surveys in Geophysics, 41:779–816, 2012.CrossRefGoogle Scholar
Thompson, G., Rasmussen, R., and Manning, K., “Explicit forecasts of winter precipitation using an improved bulk microphyiscs scheme. part ii: Implementation of a new snow parameterization,” Mon. Wea. Rev., 136:5095–5115, 2008.CrossRefGoogle Scholar
Wicker, L. and Skamarock, W., “Time-splitting methods for elastic models using forward time schemes,” Mon. Wea. Rev., 130:2088–2097, 2002.2.0.CO;2>CrossRefGoogle Scholar
Zalesak, S., “Fully multidimensional flux-corrected transport algorithm for fluids,” J. Comput. Phys., 31:335–362, 1979.CrossRefGoogle Scholar
References
AIAA, Guide for the Verification and Validation of Computational Fluid Dynamics Simulations, American Institute of Aeronautics and Astronautics, Reston, VA, AIAA-G-077–1998 (1998).Google Scholar
ASME, V&V 10 - 2006 Guide for Verification and Validation in Computational Solid Mechanics, American Society of Mechanical Engineers, New York (2006).Google Scholar
Brock, J.S., Kamm, J.R., Rider, W.J., Brandon, S.T., Woodward, C., Knupp, P., and Trucano, T.G., Verification Test Suite for Physics Simulation Codes, Los Alamos National Laboratory report LA-UR-06–8421 (2006).CrossRefGoogle Scholar
Boyack, B.E., “Quantifying reactor safety margins Part 1: An overview of the code scaling, applicability, and uncertainty evaluation methodology,” Nucl. Engrng Design, 119, 1–15 (1990).CrossRefGoogle Scholar
Hanson, K.M. and Hemez, F.M., “A framework for assessing confidence in computational predictions,” Exp. Tech., 25, 50–55 (2001).CrossRefGoogle Scholar
Helton, J., Conceptual and Computational Basis for the Quantification of Margins and Uncertainty, Sandia National Laboratories report SAND2009-3055 (2009).CrossRefGoogle Scholar
Hills, R., Witkowski, W., Rider, W., Trucano, T., and Urbina, A., “Development of a fourth generation predictive capability maturity model,” Sandia National Laboratories report SAND2013-8051 (2013).CrossRefGoogle Scholar
Hemez, F.M., Non-Linear Error Ansatz Models for Solution Verification in Computational Physics, Los Alamos National Laboratory report LA-UR-05–8228 (2005).Google Scholar
Kamm, J.R., Brock, J.S., Brandon, S.T., Cotrell, D.L., Johnson, B., Knupp, P., Rider, W.J., Trucano, T.G., and Weirs, V.G., Enhanced Verification Test Suite for Physics Simulation Codes, Los Alamos National Laboratory report LA-14379, Lawrence Livermore National Laboratory report LLNL-TR-411291, Sandia National Laboratories report SAND2008-7813 (2008).Google Scholar
Knupp, P. and Salari, K., Verification of Computer Codes in Computational Science and Engineering, Chapman & Hall/CRC, Boca Raton, FL (2003).Google Scholar
Knupp, P., Ober, C., and Bond, R., “Measuring progress in order-verification within software development projects,” Engrng. Comp., 23, 283–294 (2007).Google Scholar
Mahadevan, S. and Rebba, R., “Validation of reliability computational models using Bayes networks,” Reliability Engineering & System Safety 87(2), 223–232 (2005).CrossRefGoogle Scholar
Nelson, R., Unal, C., Stewart, J., and Williams, B., Using Error and Uncertainty Quantification to Verify and Validation Modeling and Simulation, Los Alamos National Laboratories report LA-UR-10-06125 (2010).Google Scholar
Oberkampf, W.L., “What are validation experiments?,” Exp. Tech., 25, 35–40 (2001).CrossRefGoogle Scholar
Oberkampf, W.L. and Trucano, T.G., “Verification and validation in computational fluid dynamics,” Prog. Aerospace Sci., 38, 209–272 (2002).CrossRefGoogle Scholar
Oberkampf, W.L., Trucano, T.G., and Hirsch, C., “Verification, validation, and predictive capability in computational engineering and physics,” Appl. Mech. Rev., 57, 345–384 (2004).CrossRefGoogle Scholar
Oberkampf, W.L. and Trucano, T.G., “Verification and validation benchmarks,” Nucl. Design Engrng, 23, 716–743 (2007); also available as Sandia National Laboratories report SAND2007-0853 (2007).Google Scholar
Oberkampf, W. L., Pilch, M., and Trucano, T. G., Predictive Capability Maturity Model for Computational Modeling and Simulation, Sandia National Laboratories report SAND 2007–5948 (2007).CrossRefGoogle Scholar
Oberkampf, W.L. and Roy, C.J., Verification and Validation in Scientific Computing, Cambridge University Press, New York (2010).CrossRefGoogle Scholar
Oberkamf, W.L. and Smith, B., Assessment criteria for computational fluid dynamic validation benchmark experiments, 52nd Aerospace Sciences Meeting, AIAA 2014-0205, January 2014.CrossRefGoogle Scholar
Pilch, M., Trucano, T.G., and Helton, J.C., Ideas Underlying Quantification of Margins and Uncertainties (QMU): A White Paper, Sandia National Laboratories report SAND2006-5001 (2006).Google Scholar
Rider, W.J., Kamm, J.R., and Weirs, V.G., Code Verification Workflow in CASL, Sandia National Laboratories report SAND2010-7060P (2010).Google Scholar
Roache, P., “Code verification by the method of manufactured solutions,” J. Fluids Engrng, 124, 4–10 (2002).CrossRefGoogle Scholar
Roache, P., “Building PDE Codes to be verifiable and validatable,” Comput. Sci. Engrng., 6, 30–38 (2004).CrossRefGoogle Scholar
Roache, P., Fundamentals of Verification and Validation, Hermosa Publishers, Albuquerque, NM (2009).Google Scholar
Roy, C.J., “Review of code and solution verification procedures for computational simulation,” J. Comput. Phys., 205, 131–156 (2005).CrossRefGoogle Scholar
Roy, C.J. and Oberkampf, W.L., “A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing,” submitted to Comp. Meth. Appl. Mech. Engrng. (2010).CrossRefGoogle Scholar
Salari, K. and Knupp, P., Code Verification by the Method of Manufactured Solutions, Sandia National Laboratories report SAND2000-14444 (2000).CrossRefGoogle Scholar
Sargent, R.G., “Verification and validation of simulation models,” in Proceedings of the 1998 Winter Simulation Conference, ed. by Medeiros, D.J., Watson, E.F., Carson, J.S., and Manivannan, M.S., 121–130 (1998).Google Scholar
Sargent, R.G., “Some approaches and pardigms for verifying and validation simulation models,” in Proceedings of the 2001 Winter Simulation Conference, ed. by Peters, B.A., Smith, J.S., Medeiros, D.J., and Rohrer, M.W., 106–114 (2001).Google Scholar
Schwer, L.E., “An overview of the PTC 60 / V&V 10 guide for verification and validation in computational solid mechanics,” Reprint by ASME, available at http://cstools.asme.org/csconnect/pdf/CommitteeFiles/24816.pdf (2006).CrossRefGoogle Scholar
Sornette, D., Davis, A.B., Ide, K., Vixie, K.R., Pisarenko, V., and Kamm, J. R., “Algorithm for model validation: Theory and applications,” Proc. Nat. Acad. Sci. USA 104, 6562–6567 (2007).CrossRefGoogle ScholarPubMed
Stern, F., Wilson, R.V., Coleman, H.W., Paterson, E.G., “Comprehensive approach to verification and validation of CFD simulations—Part 1: Methodology and procedures,” J. Fluids Engrng, 123, 793–802 (2001).CrossRefGoogle Scholar
Stern, F., Wilson, R.V., and Shao, J., “Quantitative V&V of CFD simulations and certification Of CFD codes,” Int. J. Num. Meth. Fluids, 50, 1335–1355 (2005).CrossRefGoogle Scholar
Stewart, J.W., Measures of Progress in Verification, Sandia National Laboratories report SAND2005-4021P (2005).Google Scholar
Szilard, R., Zhang, H., Kothe, D., and Turinsky, P., The Consortium for Advanced Simulation of Light Water Reactors, Idaho National Laboratory (United States): Funding organization: DOE-NE (United States) (2011).Google Scholar
Trucano, T.G., Pilch, M., and Oberkampf, W.L., General Concepts for Experimental Validation of ASCI Code Applications, Sandia National Laboratories report SAND2002-0341 (2002).CrossRefGoogle Scholar
Trucano, T.G., Pilch, M., and Oberkampf, W.L., On the Role of Code Comparisons in Verification and Validation, Sandia National Laboratories report SAND2003-2752 (2003).Google Scholar
Trucano, T.G., Swiler, L.P., Igusa, T., Oberkampf, W.L., and Pilch, M., “Calibration, validation, and sensitivity analysis: What’s what,” Reliab. Engrng. Syst. Safety, 92, 1331–1357 (2006).CrossRefGoogle Scholar
Zhang, R. and Mahadevan, S., “Bayesian methodology for reliability model acceptance,” Reliability Engineering & System Safety 80(10, 95–103 (2003).CrossRefGoogle Scholar