Uncertainty quantification and identification of. (English) Zbl 07882697
Keywords:
analysis of universality; Bayesian optimization; maximum posterior calibration; near wall flow pressure; SST turbulence model; supersonic cavityReferences:
| [1] | BarnesFW, SegalC. Cavity‐based flameholding for chemically‐reacting supersonic flows. Prog Aerosp Sci. 2015;76:24‐41. |
| [2] | LiXP, LiuWD, PanY, et al. Characterization of kerosene distribution around the ignition cavity in a scramjet combustor. Acta Astronaut. 2017;134:11‐16. |
| [3] | SunL, AnW, LiuX, et al. On developing data‐driven turbulence model for DG solution of RANS. Chin J Aeronaut. 2019;32(8):1869‐1884. |
| [4] | EdelingWN, CinnellaP, DwightRP, et al. Bayesian estimates of parameter variability in the [( k-\varepsilon \]\) turbulence model. J Comput Phys. 2014;258:73‐94. · Zbl 1349.76110 |
| [5] | SpalartPR, AllmarasSR. A one‐equation turbulence model for aerodynamic flows. AIAA Paper. 1992;92:0439. |
| [6] | MenterFR. Two‐equation eddy‐viscosity turbulence models for engineering applications. AIAA J. 1994;32(8):1598‐1605. |
| [7] | GodfreyAG, CliffEM. Sensitivity equations for turbulent flows. 39th AIAA Aerospace Sciences Meeting and Exhibit, AIAA Paper 2001‐1060; 2001. |
| [8] | TurgeonE, PelletierD, BorggaardJ. A general continuous sensitivity equation formulation for the [( k-\varepsilon \]\) model of turbulence. Int J Comput Fluid Dyn. 2004;18(1):29‐46. · Zbl 1063.76574 |
| [9] | SchaeferJA, CaryAW, ManiM, et al. Uncertainty quantification and sensitivity analysis of SA turbulence model coefficients in two and three dimensions. In Proceedings of the 55th AIAA Aerospace Sciences Meeting, Grapevine, TX, USA; 2017. |
| [10] | SchaeferJ, HosderS, WestT, RumseyC, CarlsonJR, KlebW. Uncertainty quantification of turbulence model closure coefficients for transonic wall‐bounded flows. AIAA J. 2017;55(1):19‐213. |
| [11] | CheungSH, OliverTA, PrudencioEE, PrudhommeS, MoserRD. Bayesian uncertainty analysis with applications to turbulence modeling. Reliab Eng Syst Saf. 2011;96(9):1137‐1149. |
| [12] | EmoryM, PecnikR, IaccarinoG. Modeling structural uncertainties in Reynolds‐averaged computations of shock/boundary layer interactions. AIAA Paper 2011‐0479; 2011. |
| [13] | ZhangJC, SongF. An efficient approach for quantifying parameter uncertainty in the SST turbulence model. Comput Fluids. 2019;181:173‐187. · Zbl 1410.76140 |
| [14] | ZhangJC, SongF. An efficient Bayesian uncertainty quantification approach with application to [( k-w-\gamma \]\) transition modeling. Comput Fluids. 2018;161:211‐224. · Zbl 1390.76121 |
| [15] | MartinA, SerhatH, RobertA, et al. Effect of turbulence model uncertainty on scramjet strut injector flow field analysis. Comput Fluids. 2021;229:105104. · Zbl 1521.76413 |
| [16] | ZhangKL, ZhaoYT, WangQ, LiJP, ZengFZ, YanC. Uncertainty analysis and calibration of SST turbulence model for free shear layer in cavity‐ramp flow. Acta Astronaut. 2022;192:168‐181. |
| [17] | TangDG, LiJP, ZengFZ, et al. Bayesian parameter estimation of SST model for shock wave‐boundary layer interaction flows with different strengths. Chin J Aeronaut. 2023;36(4):217‐236. |
| [18] | LiJP, ZengFZ, JiangZH, LiY, YanC. Investigations on turbulence model uncertainty for hypersonic shock‐wave/boundary‐layer interaction flows. AIAA J. 2022;60(8):4509‐4522. |
| [19] | ZhangKL, LiJP, ZengFZ, et al. Uncertainty analysis of parameters in SST turbulence model for shock wave‐boundary layer interaction. Aerospace. 2022;9(2):55. |
| [20] | XiaoH, CinnellaP. Quantification of model uncertainty in RANS simulations: a review. Prog Aerosp Sci. 2019;108:1‐31. |
| [21] | LiJP, ChenSS, CaiFG, et al. Bayesian uncertainty analysis of SA turbulence model for supersonic jet interaction simulations. Chin J Aeronaut. 2022;35(4):185‐201. |
| [22] | HosderS, WaltersRW, BalchM. Point‐collocation nonintrusive polynomial chaos method for stochastic computational fluid dynamics. AIAA J. 2010;48:2721‐2730. |
| [23] | SargsyanK, NajmHN, GhanemR. On the statistical calibration of physical models. Int J Chem Kinet. 2015;47(4):246‐276. |
| [24] | ManokaranK, RamakrishnaM, JayachandranT. Application of flux vector splitting methods with SST turbulence model to wall‐bounded flows. Comput Fluids. 2020;208:104611. · Zbl 1502.76052 |
| [25] | WangD, TanD, LiuL. Particle swarm optimization algorithm: an overview. Soft Comput. 2018;22:387‐408. |
| [26] | WagnerJG, FlorentM. Shape optimization for the noise induced by the flow over compact bluff bodies. Comput Fluids. 2020;198:104400. · Zbl 1519.76308 |
| [27] | AdyaS, HanD, HosderS. Uncertainty quantification integrated to the CFD modeling of synthetic jet actuators. AIAA Paper 2010‐4411; 2010. |
| [28] | HosderS, WaltersRW, BalchM. Efficient uncertainty quantification applied to the aeroelastic analysis of a transonic wing. AIAA Paper 2008‐729; 2008. |
| [29] | NajmHN. Uncertainty quantification and polynomial chaos techniques in computational fluid dynamics. Annu Rev Fluid Mech. 2009;41:35‐52. · Zbl 1168.76041 |
| [30] | YuA, YangD, WuJ, et al. The numerical simulation techniques research and preliminary experimental validation of the start characteristics for a two‐dimensional hypersonic inlet. Progr Propuls Phys. 2019;11:729‐746. |
| [31] | SettlesGS, WilliamsDR, BacaBK, BogdonoffSM. Reattachment of a compressible turbulent free shear layer. AIAA J. 1982;20(1):60‐67. |
| [32] | KnightDD, DegrezG. Shock wave boundary layer interactions in high speed flows: a critical survey of current numerical prediction capabilities. Advisory Rep. 1998;319:2‐35. |
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.
