SU2 CFD Solver

Category: 解析 | Integrated 2026-04-06
CAE visualization for su2 cfd theory - technical simulation diagram
SU2 CFDソルバー

Theory and Physics

Overview

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Professor! Today's topic is about the SU2 CFD solver, right? What kind of thing is it?


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SU2 is an open-source CFD suite originating from Stanford University. It analyzes compressible/incompressible flows using the finite volume method on unstructured meshes. Sensitivity analysis via the adjoint method and shape optimization are particularly powerful features.


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Now that I've heard this, I finally understand why Stanford University is so important!


Governing Equations


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Expressing this with equations, it looks like this.


$$\frac{\partial \mathbf{U}}{\partial t} + \nabla\cdot\mathbf{F}^c - \nabla\cdot\mathbf{F}^v = \mathbf{Q}$$

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Hmm, just the equation alone doesn't really click... What does it represent?


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Adjoint equation:



$$\left(\frac{\partial \mathbf{R}}{\partial \mathbf{U}}\right)^T \boldsymbol{\psi} = -\left(\frac{\partial J}{\partial \mathbf{U}}\right)^T$$

Theoretical Foundation

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I've heard of "theoretical foundation," but I might not have properly understood it...


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The numerical solution method of the SU2 CFD solver is based on the Finite Volume Method (FVM) or the Finite Element Method (FEM). Being open-source, its greatest advantage is that algorithm details can be verified and modified at the source code level. Discretization schemes and convergence determination logic, which are black boxes in commercial solvers, can be directly verified, making it particularly suitable for academic research and method development. Continuous improvement and bug fixes by the community guarantee its quality.


🧑‍🎓

Wow, the talk about the solver's numerical solution method is super interesting! Please tell me more.


Theoretical Background of Numerical Solution Methods

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Professor, please teach me about the "theoretical background of numerical solution methods"!


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Explains the theoretical foundation of the numerical solution methods implemented by open-source CAE tools.



Variational Principle of the Finite Element Method (FEM)

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Please teach me about the "Finite Element Method"!


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The principle of minimum potential energy, which is the foundation of structural analysis:



$$ \Pi(\mathbf{u}) = \frac{1}{2} \int_{\Omega} \boldsymbol{\sigma} : \boldsymbol{\varepsilon} \, d\Omega - \int_{\Omega} \mathbf{f} \cdot \mathbf{u} \, d\Omega - \int_{\Gamma_t} \mathbf{t} \cdot \mathbf{u} \, d\Gamma $$


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The displacement field $\mathbf{u}$ that makes $\Pi$ stationary is the equilibrium solution. CalculiX and Code_Aster implement the Galerkin method based on this variational principle.




Conservation Law of the Finite Volume Method (FVM)

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Please teach me about the "Finite Volume Method"!


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The FVM adopted by OpenFOAM is based on the integral conservation law for a control volume:



$$ \frac{\partial}{\partial t} \int_{V} \rho \phi \, dV + \oint_{S} \rho \phi \mathbf{u} \cdot d\mathbf{S} = \oint_{S} \Gamma \nabla \phi \cdot d\mathbf{S} + \int_{V} S_\phi \, dV $$


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Discrete equations are obtained by applying this integral form to each control volume and numerically evaluating the fluxes on the faces.



License and Quality Assurance

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Please teach me about "License and Quality Assurance"!


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Open-source CAE allows algorithm verification by third parties because the source code is public. On the other hand, since there is no vendor support like with commercial tools, information sharing within the user community and forums is important.



Application Conditions and Precautions

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I've heard of "application conditions and precautions," but I might not have properly understood it...


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  • OSS tool results must always be verified with known benchmark problems.
  • Be aware of incompatibility between versions (especially differences between OpenFOAM forks).
  • It is recommended to confirm OSS accuracy by comparing results with commercial tools.
  • When documentation is lacking, direct reference to the source code may be necessary.

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So, if you cut corners on the tool's results, you'll pay for it later. I'll keep that in mind!


Dimensionless Parameters and Dominant Scales

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Professor, please teach me about "dimensionless parameters and dominant scales"!


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Understanding the dimensionless parameters governing the physical phenomenon being analyzed is the foundation for appropriate model selection and parameter setting.


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  • Peclet number Pe: Relative importance of convection and diffusion. For Pe >> 1, convection dominates (stabilization methods are needed).
  • Reynolds number Re: Ratio of inertial forces to viscous forces. A fundamental parameter for fluid problems.
  • Biot number Bi: Ratio of internal conduction to surface convection. For Bi < 0.1, the lumped capacitance method is applicable.
  • Courant number CFL: Indicator of numerical stability. For explicit methods, CFL ≤ 1 is required.

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Ah, I see! So that's how the mechanism of the physical phenomenon being analyzed works.



Verification by Dimensional Analysis

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Please teach me about "verification by dimensional analysis"!


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Dimensional analysis based on Buckingham's Π theorem is effective for order-of-magnitude estimation of analysis results. Using characteristic length $L$, characteristic velocity $U$, and characteristic time $T = L/U$, the order of each physical quantity is estimated in advance to confirm the validity of the analysis results.


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I see. So if you can do that for the physical phenomenon being analyzed, you're basically okay to start?


Classification and Mathematical Characteristics of Boundary Conditions

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I've heard that if you get the boundary conditions wrong, everything goes wrong...


TypeMathematical ExpressionPhysical MeaningExample
Dirichlet condition$u = u_0$ on $\Gamma_D$Specification of variable valueFixed wall, specified temperature
Neumann condition$\partial u/\partial n = g$ on $\Gamma_N$Specification of gradient (flux)Heat flux, force
Robin condition$\alpha u + \beta \partial u/\partial n = h$Linear combination of variable and gradientConvective heat transfer
Periodic boundary condition$u(x) = u(x+L)$Spatial periodicityUnit cell analysis
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Choosing appropriate boundary conditions is directly linked to solution uniqueness and physical validity. Insufficient boundary conditions lead to an ill-posed problem, while excessive boundary conditions cause contradictions.



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Wow, the SU2 CFD solver is really deep... But thanks to your explanation, I've been able to organize my thoughts a lot!


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Yeah, you're doing great! Actually getting hands-on is the best way to learn. If you don't understand something, feel free to ask anytime.


Coffee Break Casual Talk

From Euler Equations to Navier-Stokes—The Scope of Fluid Mechanics Handled by SU2

The equation systems that SU2 can solve are extremely broad, ranging from inviscid Euler equations to full Reynolds-Averaged Navier-Stokes (RANS), and even including linearized Euler equations and adjoint equations. This breadth is a characteristic of SU2, with the development background at Stanford University's Department of Aeronautics and Astronautics having the goal of "completing the entire design optimization workflow within a single framework." Shape optimization using the adjoint method (continuous adjoint / discrete adjoint) is one of SU2's most powerful features, enabling airfoil optimization that maintains lift while minimizing drag in just a few dozen analyses. This dual design of "CFD solver + optimization engine" is SU2's unique strength, going beyond its role as a standalone CFD solver.

Physical Meaning of Each Term
  • Time derivative of conserved quantity: Represents the rate of change over time of the physical quantity in question. Becomes zero for steady-state problems. 【Image】When filling a bathtub with hot water, the water level rises over time—this "rate of change per time" is the time derivative. The state where the valve is closed and the water level is constant is "steady," and the time derivative is zero.
  • Flux term: Describes the spatial transport/diffusion of a physical quantity. Broadly divided into convection and diffusion. 【Image】Convection is like "a river's flow carrying a boat," where things are carried along by the flow. Diffusion is like "ink naturally spreading in still water," where things move due to concentration differences. The competition between these two transport mechanisms governs many physical phenomena.
  • Source term (generation/destruction term): Represents the local generation or destruction of a physical quantity due to external forces/reactions. 【Image】When a heater is turned on in a room, thermal energy is "generated" at that location. When fuel is consumed in a chemical reaction, mass is "destroyed." A term representing physical quantities injected into the system from the outside.
Assumptions and Applicability Limits
  • The continuum assumption holds for the spatial scale.
  • The constitutive laws of materials/fluids (stress-strain relationship, Newtonian fluid law, etc.) are within the applicable range.
  • Boundary conditions are physically valid and mathematically well-defined.
Dimensional Analysis and Unit Systems
VariableSI UnitNotes / Conversion Memo
Characteristic length $L$mMust match the unit system of the CAD model.
Characteristic time $t$sFor transient analysis, time step should consider CFL condition and physical time constant.

Numerical Solution Methods and Implementation

Details of Numerical Methods

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Specifically, what kind of algorithm is used to solve the SU2 CFD solver?


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Explains the key points of the numerical solution methods and implementation of the SU2 CFD solver.


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Now that I've heard this, I finally understand why the solver's numerical solution methods are so important!


Compilation and Build

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I've heard of "compilation and build," but I might not have properly understood it...


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