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 17 articles )

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  1. 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)
  2. 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)
  3. Pascarella, G.; Fossati, M.; Barrenechea, G.: Adaptive reduced basis method for the reconstruction of unsteady vortex-dominated flows (2019)
  4. Swischuk, Renee; Mainini, Laura; Peherstorfer, Benjamin; Willcox, Karen: Projection-based model reduction: formulations for physics-based machine learning (2019)
  5. Yirtici, Ozcan; Cengiz, Kenan; Ozgen, Serkan; Tuncer, Ismail H.: Aerodynamic validation studies on the performance analysis of iced wind turbine blades (2019)
  6. Gori, Giulio; Guardone, Alberto: Virtuaschlieren: a hybrid GPU/CPU-based schlieren simulator for ideal and non-ideal compressible-fluid flows (2018)
  7. Hokanson, Jeffrey M.; Constantine, Paul G.: Data-driven polynomial ridge approximation using variable projection (2018)
  8. 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)
  9. P. Cardiff, A. Karač, P. De Jaeger, H. Jasak, J. Nagy, A. Ivanković, Ž. Tuković: An open-source finite volume toolbox for solid mechanics and fluid-solid interaction simulations (2018) arXiv
  10. Razaaly, Nassim; Congedo, Pietro Marco: Novel algorithm using active metamodel learning and importance sampling: application to multiple failure regions of low probability (2018)
  11. Rubino, A.; Pini, M.; Colonna, P.; Albring, T.; Nimmagadda, S.; Economon, T.; Alonso, J.: Adjoint-based fluid dynamic design optimization in quasi-periodic unsteady flow problems using a harmonic balance method (2018)
  12. Sagebaum, Max; Albring, T.; Gauger, N. R.: Expression templates for primal value taping in the reverse mode of algorithmic differentiation (2018)
  13. Lin, Tao; Liu, G. R.: A development of a GSM-CFD solver for non-Newtonian flows (2017)
  14. 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)
  15. Economon, Thomas D.; Mudigere, Dheevatsa; Bansal, Gaurav; Heinecke, Alexander; Palacios, Francisco; Park, Jongsoo; Smelyanskiy, Mikhail; Alonso, Juan J.; Dubey, Pradeep: Performance optimizations for scalable implicit RANS calculations with SU2 (2016)
  16. Vogeltanz, Tomáš: A survey of free software for the design, analysis, modelling, and simulation of an unmanned aerial vehicle (2016)
  17. Yao, Jianyao; Liu, G. R.: A matrix-form GSM-CFD solver for incompressible fluids and its application to hemodynamics (2014)