SU2

The SU2 suite is an open-source collection of C++ based software tools for performing Partial Differential Equation (PDE) analysis and solving PDE-constrained optimization problems. The toolset is designed with Computational Fluid Dynamics (CFD) and aerodynamic shape optimization in mind, but is extensible to treat arbitrary sets of governing equations such as potential flow, elasticity, electrodynamics, chemically-reacting flows, and many others. SU2 is under active development by the Aerospace Design Lab (ADL) of the Department of Aeronautics and Astronautics at Stanford University and many members of the community, and is released under an open-source license.


References in zbMATH (referenced in 32 articles )

Showing results 1 to 20 of 32.
Sorted by year (citations)

1 2 next

  1. Chen, Liming; Qiu, Haobo; Gao, Liang; Jiang, Chen; Yang, Zan: Optimization of expensive black-box problems via gradient-enhanced Kriging (2020)
  2. Ekici, Kivanc; Djeddi, Reza; Li, Hang; Frankel, Jay I.: Modeling periodic and non-periodic response of dynamical systems using an efficient Chebyshev-based time-spectral approach (2020)
  3. Gori, G.; Zocca, M.; Cammi, G.; Spinelli, A.; Congedo, P. M.; Guardone, A.: Accuracy assessment of the non-ideal computational fluid dynamics model for siloxane MDM from the open-source SU2 suite (2020)
  4. Gori, G.; Zocca, M.; Guardone, A.; Le Maître, O. P.; Congedo, P. M.: Bayesian inference of thermodynamic models from vapor flow experiments (2020)
  5. Lam, Remi R.; Zahm, Olivier; Marzouk, Youssef M.; Willcox, Karen E.: Multifidelity dimension reduction via active subspaces (2020)
  6. Mohanamuraly, P.; Hascoët, L.; Müller, J.-D.: Seeding and adjoining zero-halo partitioned parallel scientific codes (2020)
  7. Razaaly, Nassim; Persico, Giacomo; Gori, Giulio; Congedo, Pietro Marco: Quantile-based robust optimization of a supersonic nozzle for organic rankine cycle turbines (2020)
  8. van den Bos, Laurent; Sanderse, Benjamin; Bierbooms, Wim; van Bussel, Gerard: Generating nested quadrature rules with positive weights based on arbitrary sample sets (2020)
  9. Vassilevski, Yuri; Terekhov, Kirill; Nikitin, Kirill; Kapyrin, Ivan: Parallel finite volume computation on general meshes (2020)
  10. Zhang, Xin-Lei; Michelén-Ströfer, Carlos; Xiao, Heng: Regularized ensemble Kalman methods for inverse problems (2020)
  11. Cheng, Kai; Lu, Zhenzhou; Zhen, Ying: Multi-level multi-fidelity sparse polynomial chaos expansion based on Gaussian process regression (2019)
  12. Chen, Liming; Qiu, Haobo; Gao, Liang; Jiang, Chen; Yang, Zan: A screening-based gradient-enhanced Kriging modeling method for high-dimensional problems (2019)
  13. Fossati, M.; Mogavero, A.; Herrera-Montojo, J.; Scoggins, J. B.; Magin, T.: A kinetic BGK edge-based scheme including vibrational and electronic energy modes for high-Mach flows (2019)
  14. Pascarella, G.; Fossati, M.; Barrenechea, G.: Adaptive reduced basis method for the reconstruction of unsteady vortex-dominated flows (2019)
  15. Sagebaum, Max; Albring, Tim; Gauger, Nicolas R.: High-performance derivative computations using CoDiPack (2019)
  16. Swischuk, Renee; Mainini, Laura; Peherstorfer, Benjamin; Willcox, Karen: Projection-based model reduction: formulations for physics-based machine learning (2019)
  17. Yirtici, Ozcan; Cengiz, Kenan; Ozgen, Serkan; Tuncer, Ismail H.: Aerodynamic validation studies on the performance analysis of iced wind turbine blades (2019)
  18. Gori, Giulio; Guardone, Alberto: Virtuaschlieren: a hybrid GPU/CPU-based schlieren simulator for ideal and non-ideal compressible-fluid flows (2018)
  19. Hokanson, Jeffrey M.; Constantine, Paul G.: Data-driven polynomial ridge approximation using variable projection (2018)
  20. Kusch, Lisa; Albring, T.; Walther, A.; Gauger, N. R.: A one-shot optimization framework with additional equality constraints applied to multi-objective aerodynamic shape optimization (2018)

1 2 next