TAU

Overview of the hybrid RANS code TAU. A brief introduction is given which first describes the history of frame in which the TAU code was developed before explaining the main advantages which were the drivers for the selection of the approach. In the following an algorithmic overview describes shortly the code functionality before a section about the code design gives some more insight about the implementation and its scripting capability.


References in zbMATH (referenced in 74 articles , 1 standard article )

Showing results 1 to 20 of 74.
Sorted by year (citations)

1 2 3 4 next

  1. Garbo, A.; Bekemeyer, P.: Unsteady physics-based reduced order modeling for large-scale compressible aerodynamic applications (2022)
  2. Vila-Pérez, Jordi; Giacomini, Matteo; Sevilla, Ruben; Huerta, Antonio: A non-oscillatory face-centred finite volume method for compressible flows (2022)
  3. Abdelsamie, Abouelmagd; Lartigue, Ghislain; Frouzakis, Christos E.; Thévenin, Dominique: The Taylor-Green vortex as a benchmark for high-fidelity combustion simulations using low-Mach solvers (2021)
  4. He, Wei; Timme, Sebastian: Triglobal infinite-wing shock-buffet study (2021)
  5. Rueda-Ramírez, Andrés M.; Ferrer, Esteban; Kopriva, David A.; Rubio, Gonzalo; Valero, Eusebio: A statically condensed discontinuous Galerkin spectral element method on Gauss-Lobatto nodes for the compressible Navier-Stokes equations (2021)
  6. Sabater, Christian; Le Maître, Olivier; Congedo, Pietro Marco; Görtz, Stefan: A Bayesian approach for quantile optimization problems with high-dimensional uncertainty sources (2021)
  7. Komala-Sheshachala, Sanjay; Sevilla, Ruben; Hassan, Oubay: A coupled HDG-FV scheme for the simulation of transient inviscid compressible flows (2020)
  8. Timme, Sebastian: Global instability of wing shock-buffet onset (2020)
  9. Lozano, Carlos: Entropy and adjoint methods (2019)
  10. Dong, Yidao; Deng, Xiaogang; Gao, Xiang; Xiong, Min; Wang, Guangxue: A comparative study of boundary conditions for the density-based solvers in the framework of OpenFoam (2018)
  11. Lozano, Carlos: On mesh sensitivities and boundary formulas for discrete adjoint-based gradients in inviscid aerodynamic shape optimization (2017)
  12. Mößner, M.; Radespiel, R.: Flow simulations over porous media -- comparisons with experiments (2017)
  13. Pinto, Runa Nivea; Afzal, Asif; D’Souza, Loyan Vinson; Ansari, Zahid; Mohammed Samee, A. D.: Computational fluid dynamics in turbomachinery: a review of state of the art (2017)
  14. Semaan, R.: Optimal sensor placement using machine learning (2017)
  15. Stück, Arthur: Dual-consistency study for Green-Gauss gradient schemes in an unstructured Navier-Stokes method (2017)
  16. Xu, Shenren; Timme, Sebastian: Robust and efficient adjoint solver for complex flow conditions (2017)
  17. Semaan, Richard; Kumar, Pradeep; Burnazzi, Marco; Tissot, Gilles; Cordier, Laurent; Noack, Bernd R.: Reduced-order modelling of the flow around a high-lift configuration with unsteady Coanda blowing (2016)
  18. Wasserman, M.; Mor-Yossef, Y.; Greenberg, J. B.: A positivity-preserving, implicit defect-correction multigrid method for turbulent combustion (2016)
  19. Mößner, M.; Radespiel, R.: Modelling of turbulent flow over porous media using a volume averaging approach and a Reynolds stress model (2015)
  20. Schillings, C.; Schulz, V.: On the influence of robustness measures on shape optimization with stochastic uncertainties (2015)

1 2 3 4 next


Further publications can be found at: http://tau.dlr.de/literature/