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.
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:
RANS Models: Includes several variants of the Spalart-Allmaras (SA) and Menter's Shear Stress Transport (SST) models, including curvature and rotation corrections (QCR).[7]. The turbulence models include classical wall functions.
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.
123Economon, 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. doi:10.2514/1.J053813.
↑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.
↑Rausa, A.; etal. (2025). "SU2 results for the Fifth High Lift Prediction Workshop". AIAA SCITECH 2025 Forum. doi:10.2514/6.2025-0276.
↑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.
↑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.
↑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.
↑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
↑Burghardt, O.; Gauger, N. (2019). "Coupled Adjoints for Conjugate Heat Transfer in Variable Density Incompressible Flows". AIAA. doi:10.2514/6.2019-3668.
↑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. doi:10.1016/j.fuel.2024.131751.
↑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: 193. doi:10.3390/aerospace8070193.{{cite journal}}: CS1 maint: unflagged free DOI (link)
Economon, T. D.; Palacios, F.; Copeland, S. R.; Lukaczyk, T. W.; Alonso, J. J. (March 2016). "SU2: An Open-Source Suite for Multiphysics Simulation and Design". AIAA Journal. 54 (3): 828–846. doi:10.2514/1.J053813.
Bluhdorn, J.; Gomes, P.; Aehle, M.; Gauger, N. (March 2025). "Hybrid parallel discrete adjoints in SU2". Computers & Fluids. 289: 106528. doi:10.1016/j.compfluid.2024.106528.{{cite journal}}: CS1 maint: article number as page number (link)
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. doi:10.1016/j.fuel.2024.131751.{{cite journal}}: CS1 maint: article number as page number (link)
Rubino, A.; Vitale, S.; Colonna, P.; Pini, M. (2020). "Fully-turbulent adjoint method for the unsteady shape optimization of multi-row turbomachinery". Aerospace Science and Technology. 106: 106132. doi:10.1016/j.ast.2020.106132.{{cite journal}}: CS1 maint: article number as page number (link)
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