References
- Eça L, Dowding K, Roache PJ. On the interpretation and scope of the V&V 20 standard for verification and validation in computational fluid dynamics and heat transfer. J Verif Valid Uncertain Quantif. 2022;7:021005.
- Oberkampf WL, Roy CJ. Verification and validation in scientific computing. Cambrigde: Cambridge University Press; 2010.
- García-Mayoral R, Chung D, Durbin P, et al. Challenges and perspective on the modelling of high-Re, incompressible, non-equilibrium, rough-wall boundary layer. J Turbul. 2024.
- Klewicki J, Sandberg R, Knopp T, et al. On the physical structure, modeling and computation-based prediction of two-dimensional, smooth-wall turbulent boundary layers subjected to streamwise pressure gradients. J Turbul. 2024.
- Lowe KT, Smits A, Visonneau M, et al. Effects of streamline curvature and three dimensionality). J Turbul. 2024.
- Spalart PR, Allmaras SR. A one-equation turbulence model for aerodynamics flows. In: AIAA Paper 1992-0439, Reno, NV; 1992.
- Menter FR. Two-equation eddy-viscosity turbulence models for engineering applications. AIAA J. 1994;32:1598–1605. doi: 10.2514/3.12149
- Eisfeld B, Rumsey C, Togiti V. Verification and validation of a second-moment-closure model. AIAA J. 2016;54:1524–1541. doi: 10.2514/1.J054718
- Shur ML, Strelets MK, Travin AK. Turbulence modeling in rotating and curved channels: assessing the Spalart-Shur correction. AIAA J. 2000;38:784–792. doi: 10.2514/2.1058
- Spalart PR. Strategies for turbulence modelling and simulation. Int J Heat Fluid Flow. 2000;21:252–263. doi: 10.1016/S0142-727X(00)00007-2
- Fritsch D, Vishwanathan V, Roy CJ, et al. Experimental and computational study of 2D smooth wall turbulent boundary layers in pressure gradient. In: AIAA Paper 2022-0696, San Diego (CA); 2022.
- Knopp T, Reuther N, Novara M, et al. Experimental analysis of the log law at adverse pressure gradient. J Fluid Mech. 2021;918:A17 24A17–1–A17 –32. doi: 10.1017/jfm.2021.331
- Gargiulo A, Duetsch-Patel JE, Borgoltz A, et al. Strategies for computational fluid dynamics validation experiments. J Verif Valid Uncertain Quantif. 2023;8(3):Paper no. VVUQ 24Paper no. VVUQ–23–1011, 1–25 .
- Ding L, Saxton-Fox T, Hultmark M, et al. Effect of pressure gradient and streamline curvature on the statistics of a turbulent pipe flow. In: Eleventh International Symposium on Turbulence and Shear Flow Phenomena (TSFP11); Southampton (UK): University of Southampton; 2019. p. 353–358.
- Visonneau M, Deng G, Guilmineau E, et al. Bodies-of-revolution in turbulent flow: comparing computation with experiment. In: AIAA Paper 2022-0694, San Diego (CA); 2022.
- Knopp T, Schanz D, Novara M, et al. Experimental and numerical investigation of turbulent boundary layers with strong pressure gradients. In: AIAA Paper 2022-1035, San Diego (CA); 2022.
- Gargiulo A, Ozoroski TA, Hallock T, et al. Computations of the BeVERLI Hill three-dimensional separating flow model validation cases. In: AIAA Paper 2022-1034, San Diego (CA); 2022.
- Oberkampf WL, Trucano TG. Verification and validation in computational fluid dynamics. Albuquerque and Livermore: Sandia National Laboratories; 2002. (Technical report).
- Roache PJ. Quantification of uncertainty in computational fluid dynamics. Annu Rev Fluid Mech. 1997;29:123–160. doi: 10.1146/fluid.1997.29.issue-1
- Roache PJ. Error bars for CFD. In: AIAA Paper 2003-408, Reno, NV; 2003.
- Roache PJ. Fundamentals of verification and validation. Socorro, New Mexico: Hermosa Publishers; 2009.
- Roy CL. Errors and uncertainties: their sources and treatment. In: Beisbart C, Saam NJ, editors. Computer Simulation Validation: Fundamental Concepts, Methodological Frameworks, and Philosophical Perspectives; Cham: Springer; 2019. p. 119–141.
- Oberkampf WL, Smith BL. Assessment criteria for computational fluid dynamics model validation experiments. J Verif Valid Uncertain Quantif. 2017;2:031002. doi: 10.1115/1.4037887
- Rumsey CL. Insights and lessons learned from the NASA juncture flow experiment. J Aircr. 2022;59(6):1493–1499. doi: 10.2514/1.C036838
- NATO AVT-246 Task Group. Progress and challenges in validation testing for computational fluid dynamics. 2016 Sep. (NATO MP-AVT-246 Report).
- Patel VC. Calibration of the Preston tube and limitations on its use in pressure gradients. J Fluid Mech. 1965;23:185–208. doi: 10.1017/S0022112065001301
- Heyse JF, Mishra AA, Iaccarino G. Estimating RANS model uncertainty using machine learning. J Glob Power Propuls Soc. 2021;1–14. doi: 10.33737/jgpps/134643
- Oliver TA, Moser RD. Bayesian uncertainty quantification applied to RANS turbulence model. J Phys Conf Ser. 2011;318:042032. doi: 10.1088/1742-6596/318/4/042032
- Wang Q, Dow EA. Quantification of structural uncertainties in the k-ω turbulence model. Phys Fluids. 2010;14:2043–2051. doi: 10.1063/1.1476668
- Xiao H, Cinnella P. Quantification of model uncertainty in RANS simulations: A review. Prog Aerosp Sci. 2019;108:1–31. doi: 10.1016/j.paerosci.2018.10.001
- Volino R, Fritsch D, Devenport W, et al. Effects of roughness on non-equilibrium turbulent boundary layers. J Turbul. 2024.
- Nikuradse J. Strömungsgesetze in rauhen Rohren. Berlin: 1933. (Technical report, VDI-Forschungsheft 361).
- V&V-20-Committee. Standard for verification and validation in computational fluid dynamics and heat transfer. American Society of Mechanical Engineers; 2009. (Technical report).
- Eça L, Kerkvliet M, Toxopeus SL. Comparison of RANS turbulence models for the simulation of smooth wall boundary-layers in pressure gradient at moderate and high Reynolds numbers. In: Garcia-Espinosa J, Gonzalez L, Gutierrez JE, Servan-Camas B, editors. 10th International Conference on Computational Methods in Marine Engineering (MARINE 2023); Madrid, Spain; 2023.
- Eça L, Hoekstra M, Windt J. Practical grid generation tools with applications to ship hydrodynamics. In: 8th International Conference on Numerical Grid Generation in Computational Field Simulations; Honolulu, Hawaii, USA; 2002.
- Eça L, Kerkvliet M, Toxopeus SL. Turbulent boundary layers with variable pressure gradient using RANS. IST, Lisboa, Portugal: Universidad de Lisboa; 2022. (Technical report, Tech. Rep. M-10).
- Knopp T, Reuther N, Novara M, et al. Modification of the SSG/LRR-omega model for adverse pressure gradients. Flow Turbul Combust. 2023;111:409–438. doi: 10.1007/s10494-023-00457-8
- Bailey SCC, Hultmark M, Monty JP, et al. Obtaining accurate mean velocity measurements in high Reynolds number turbulent boundary layers using pitot tubes. J Fluid Mech. 2013;715:642–670. doi: 10.1017/jfm.2012.538
- Atkinson C, Buchmann NA, Amili O, et al. On the appropriate filtering of PIV measurements of turbulent shear flows. Exp Fluids. 2014;55:1654. doi: 10.1007/s00348-013-1654-8
- Kähler CJ, Scharnowski S, Cierpka C. On the resolution limit of digital particle image velocimetry. Exp Fluids. 2012;52:1629–1639. doi: 10.1007/s00348-012-1280-x
- Kähler CJ, Scharnowski S, Cierpka C. On the uncertainty of digital PIV and PTV near walls. Exp Fluids. 2012;52:1641–1656. doi: 10.1007/s00348-012-1307-3
- Herpin S, Wong CY, Stanislas M, et al. Stereoscopic PIV measurements of a turbulent boundary layer with a large spatial dynamic range. Exp Fluids. 2008;45:745–763. doi: 10.1007/s00348-008-0533-1
- Vishwanathan V, Fritsch DJ, Devenport WJ, et al. Spatial resolution limitations of particle image velocimetry in high Reynolds number turbulent boundary layers. Preprint Version 1. Research Square, 2022. doi: 10.21203/rs.3.rs-2384035/v1.
- de Silva CM, Gnanamanickam EG, Atkinson C, et al. High spatial range velocity measurements in a high Reynolds number turbulent boundary layer. Phys Fluids. 2014;26:025117. doi: 10.1063/1.4866458
- Novara M, Schanz D, Reuther N, et al. Lagrangian 3D particle tracking in high-speed flows: shake-the-box for multi-pulse systems. Exp Fluids. 2016;57:Article ID 128, 1 24Article ID 128, 1–20 . doi: 10.1007/s00348-016-2216-7
- Bailey SCC, Vallikivi M, Hultmark M, et al. Estimating the value of von Kármán's constant in turbulent pipe flow. J Fluid Mech. 2014;749:79–98. doi: 10.1017/jfm.2014.208
- Thibault R, Poitras GJ. Uncertainty evaluation of friction velocity measurements by oil-film interferometry. J Fluids Eng. 2017;139:051401. doi: 10.1115/1.4035461
- Skare PE, Krogstad PA. A turbulent equilibrium boundary layer near separation. J Fluid Mech. 1994;272:319–348. doi: 10.1017/S0022112094004489
- Sporschill G. Improved Reynolds-stress modeling for adverse-pressure-gradient turbulent boundary layers in industrial aeronautical flow [PhD thesis]. Universite de Pau et des Pays de l'Adour; 2021.
- Phillips TS, Roy CJ. Richardson extrapolation-based discretization uncertainty estimation for computational fluid dynamics. J Fluids Eng. 2014;136(12):121401. doi: 10.1115/1.4027353
- Menter FR, Kuntz M, Langtry R. Ten years of industrial experience with the SST turbulence model. In: Turbulence, Heat and Mass Transfer 4; West Redding: Begell House; 2003. p. 625–632.
- Durbin PA, Petterson Reif BA. Statistical theory and modelling for turbulent flows. Chichester: John Wiley & Sons; 2001.
- Menter FR, Matyushenko A, Lechner R. Development of a generalized k-ω two-equation turbulence model. In: Dillmann A, Heller G, Krämer E, Wagner C, Tropea C, Jakirlic S, editors. New Results in Numerical and Experimental Fluid Mechanics. Contributions to the 21st STAB/DGLR Symposium Darmstadt, Germany 2018; Vol. 142; Cham: Springer; 2018. p. 101–109.
- Marusic I, Chauhan KA, Kulandaivelu V, et al. Evolution of zero-pressure-gradient boundary layers from different tripping conditions. J Fluid Mech. 2015;783:379–411. doi: 10.1017/jfm.2015.556
- NATO AVT-301 Task Group.Flowfield prediction for manoeuvring underwater vehicles. 2022 Jun. (NATO TR-AVT-301 Report).