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SU2 (formerly Stanford University Unstructured) is a suite of open-source software tools written in C++ and Python for the numerical solution of partial differential equations (PDE) and performing PDE-constrained optimization.[2] While initially developed for aerodynamics and compressible flow, it has evolved into a general-purpose multiphysics framework capable of simulating incompressible and compressible flows across all Mach regimes, species transport, conjugate heat transfer and combustion.

The framework is specialized for gradient-based design optimization using integrated continuous and discrete adjoint solvers. A distinguishing feature for researchers is its use of algorithmic differentiation (AD) to provide exact discrete adjoint sensitivities for complex multiphysics chains, including fluid-structure interaction (FSI) and conjugate heat transfer.[3] It supports unstructured meshes and offers extensibility through User Defined Functions (UDFs) and high-level Python wrappers.

To stimulate development and use of the software, the SU2 Foundation was established as a non-profit organization to coordinate the global community of users and developers. SU2 is released under the GNU Lesser General Public License (LGPL) version 2.1.


Developers

SU2 is being developed by individuals and organized teams around the world. The original SU2 Lead Developers are: Dr. Francisco Palacios and Dr. Thomas D. Economon.

The most active groups developing SU2 are:


Capabilities

SU2 is a general-purpose multiphysics suite designed for the simulation of partial differential equations (PDE) on unstructured meshes. The framework is built to handle complex multi-physics interactions through a multi-zone approach, allowing different physical models to be solved in connected domains.[2] Its current capabilities include:

  • Flow Regimes: Compressible and incompressible solvers for Euler, Navier-Stokes, and RANS equations across all Mach regimes (low-speed to hypersonic). For low Mach incompressible flow problems, preconditioning methods are used.
  • Turbulence & Transition Modeling:
  • Design Optimization: Gradient-based shape optimization using integrated continuous and discrete adjoint solvers. It utilizes algorithmic differentiation (via CoDiPack) for exact sensitivities in complex multiphysics chains.[10]
  • Topology Optimization: Gradient-based structural topology optimization with length scale control via black-white filters [11]
  • Multiphysics & Structures:
    • Solid Mechanics: Solvers for linear elasticity to model structural deformation.[2]
    • Thermal Analysis: Capability for conjugate heat transfer (CHT) to simulate heat exchange between fluid and solid regions.[12]
    • Fluid-Structure Interaction (FSI): Static and dynamic coupling between fluid and structural solvers.
  • Chemistry & Hypersonics:
    • Combustion: Reacting flow modeling using the Flamelet generated manifold (FGM) method.[13]
    • Hypersonics (NEMO): Simulation of high-enthalpy flows including thermo-chemical non-equilibrium and ionization with detailed chemistry modeling.[14]
  • Advanced Numerics: Support for high-order Discontinuous Galerkin Method (DG) for improved accuracy in vortex-dominated simulations.
  • User Interface & Ecosystem:
    • SU2-GUI: A graphical user interface for mesh importation and solver configuration.[15]
    • Automation: A high-level Python interface for workflow automation and support for User Defined Functions (UDFs).

License

SU2 is free and open source software, released under the GNU General Public License version 3 (SU2 v1.0 and v2.0) and GNU Lesser General Public License version 2.1 (SU2 v2.0.7 and later versions).

Alternative software

See also: List of computational fluid dynamics software

Free and open-source software

Proprietary software

References

  1. ^ “Release 8.5.0”. 27 April 2026. Retrieved 28 April 2026.
  2. ^ a b c Economon, Thomas D.; Palacios, Francisco; Copeland, Sean R.; Lukaczyk, Trent W.; Alonso, Juan J. (March 2016). “SU2: An Open-Source Suite for Multiphysics Simulation and Design”. AIAA Journal. 54 (3): 828–846. Bibcode:2016AIAAJ..54..828E. doi:10.2514/1.J053813.
  3. ^ Albring, M.; Sagebaum, M.; Gauger, N. R. (June 2016). “Efficient Aerodynamic Design using the Discrete Adjoint Method in SU2”. 17th AIAA/ISSMO MDAO Conference. doi:10.2514/6.2016-3518. ISBN 978-1-62410-439-8.
  4. ^ a b “SU2 Dev. Team at Stanford”. su2code.github.io. Retrieved 15 March 2025.
  5. ^ a b “SU2/AUTHORS.md at master · su2code/SU2”. GitHub. Retrieved 15 March 2025.
  6. ^ “SU2 Dev. Team at University of Kaiserslautern”. su2code.github.io. Retrieved 15 March 2025.
  7. ^ Rausa, A.; et al. (2025). “SU2 results for the Fifth High Lift Prediction Workshop”. AIAA SCITECH 2025 Forum. doi:10.2514/6.2025-0276.
  8. ^ Molina, E.; Zhou, B. Y.; Alonso, J. J.; Righi, M.; Silva, R. G. (2019). “Flow and Noise Predictions Around Tandem Cylinders using DDES approach with SU2”. AIAA Scitech 2019 Forum. doi:10.2514/6.2019-0326.
  9. ^ Rausa, A.; Guardone, A; Auteri, F. (2023). “Implementation of the $\gamma-Re_\theta$ and one-equation transition model within SU2: model validation and verification”. AIAA 2023. doi:10.2514/6.2023-1570. hdl:11311/1242117.
  10. ^ Albring, M.; Sagebaum, M.; Gauger, N. R. (June 2016). “Efficient Aerodynamic Design using the Discrete Adjoint Method in SU2”. 17th AIAA/ISSMO MDAO Conference. doi:10.2514/6.2016-3518. ISBN 978-1-62410-439-8.
  11. ^ Gomes, P., Palacios, R. Aerodynamic-driven topology optimization of compliant airfoils. Struct Multidisc Optim 62, 2117–2130 (2020). https://doi.org/10.1007/s00158-020-02600-9
  12. ^ Burghardt, O.; Gauger, N. (2019). “Coupled Adjoints for Conjugate Heat Transfer in Variable Density Incompressible Flows”. AIAA. doi:10.2514/6.2019-3668. ISBN 978-1-62410-589-0.
  13. ^ Mayer, D.; Beishuizen, N.; Pitsch, H.; Economon, T. D.; Carrigan, T. (August 2024). “Automatic adjoint-based design optimization for laminar combustion applications”. Fuel. 370 131751. Bibcode:2024Fuel..37031751M. doi:10.1016/j.fuel.2024.131751.
  14. ^ Maier, W.; Needles, J.; Garbacz, C.; Morgado, F.; Alonso, J. J.; Fossati, M. (2021). “SU2-NEMO: An Open-Source Framework for High-Mach Nonequilibrium Multi-Species Flows”. Aerospace. 8 (7): 193. Bibcode:2021Aeros…8..193M. doi:10.3390/aerospace8070193.
  15. ^ “SU2-GUI”. github.com. Retrieved 18 April 2026.

Official resources

Community resources

Further reading