Short answer

When simulating complex aerodynamic designs with potential flow separation, select the Menter SST turbulence model for more reliable and stable CFD results.

Field: Modelling
Source: Fluids (2019)
Method: Numerical simulation and benchmarking
Evidence: Strong effect

The Menter Shear Stress Transport (SST) turbulence model provides the best balance of accuracy and numerical stability for simulating complex, separated compressible flows compared to Spalart-Allmaras and Wilcox k-ω models. This modelling research insight is drawn from a 2019 study published in Fluids. Using Numerical simulation and benchmarking, researchers explored how this design variable affects real-world outcomes. The key design takeaway: When simulating complex aerodynamic designs with potential flow separation, select the Menter SST turbulence model for more reliable and stable CFD results.

Study

ModellingHigh ImpactStrong effect

Menter SST turbulence model excels in simulating separated flows over complex geometries

Fluids · 2019

Key Findings

01All tested turbulence models performed consistently for attached or mildly separated flows.
02Significant differences in performance were observed in separated flows with recirculation zones.
03The Menter SST model demonstrated the best compromise between accurately capturing flow physics and maintaining numerical stability in complex flow scenarios.

Application

Design takeaway

When simulating complex aerodynamic designs with potential flow separation, select the Menter SST turbulence model for more reliable and stable CFD results.

How to apply

When undertaking CFD analysis for aircraft wings, vehicle aerodynamics, or turbomachinery, consider the Menter SST model if flow separation is anticipated.

Project actions

01When choosing a CFD model for your design project, research which turbulence models are best suited for the expected flow conditions (e.g., attached vs. separated flow).
02Always compare your simulation results to experimental data or established benchmarks to validate your chosen model's performance.

Method & Evidence

AimTo assess the performance of different turbulence models (Spalart-Allmaras, Wilcox k-ω, Menter SST) in simulating steady, compressible, turbulent flow over complex geometries with concave and convex curvatures using unstructured grids.

MethodNumerical simulation and benchmarking

ProcedureThe study employed the FLITE3D flow solver to simulate various CFD cases, including supersonic flow over a flat plate, transonic flow over an airfoil, and flow around wing and aircraft configurations. The performance of the Spalart-Allmaras, Wilcox k-ω, and Menter SST turbulence models was evaluated against experimental data and Direct Numerical Simulation (DNS) results.

ContextAerospace engineering, Computational Fluid Dynamics (CFD)

Variables

IVTurbulence model (Spalart-Allmaras, Wilcox k-ω, Menter SST)

DVAccuracy of flow prediction (e.g., separation point, drag coefficient), numerical stability

CVFlow conditions (compressible, turbulent), geometry complexity (concave/convex curvatures), grid type (unstructured), solver (FLITE3D)

Strengths & Limitations

Strengths

+Benchmarking against established CFD cases and experimental data.
+Evaluation across a range of complex geometries and flow conditions.

Limitations

The computational cost of running multiple turbulence models can be high. The accuracy of the results is also dependent on the quality of the mesh and the solver settings.

Reliability & validity

The study's validity is supported by comparison with experimental data and DNS. Reliability is enhanced by testing multiple models across various standard CFD cases.

Think critically

How might the choice of turbulence model impact the perceived 'success' or 'failure' of a design in simulation, and what are the potential real-world consequences of relying on an inappropriate model?

Design Principles

"The choice of turbulence model in CFD simulations must be validated against flow complexity, particularly in regions of flow separation, to ensure predictive accuracy and numerical stability."

Accurate simulation of turbulent flow is crucial for designing aerodynamic surfaces, engines, and other systems operating at high speeds. Selecting the right turbulence model directly impacts the reliability of computational fluid dynamics (CFD) predictions, influencing design iterations and performance outcomes.

What This Means for Your Design

When using computer simulations to test designs that might cause air or fluid to separate (like a bent wing edge), the Menter SST model is the most reliable choice for getting accurate results without the simulation crashing.

How to use in your project

1.Reference this study when justifying the selection of a specific turbulence model in your CFD simulations, especially if your design involves complex geometries or potential flow separation.

Add to My Project

Quick Cite

Paragraph starter

The selection of an appropriate turbulence model is critical for accurate CFD simulations, particularly in complex flow scenarios involving separation. Research by Araya (2019) highlights that while models like Spalart-Allmaras and Wilcox k-ω perform adequately for attached flows, the Menter SST model offers a superior balance of accuracy and numerical stability when simulating separated flows over complex geometries, making it a preferred choice for designs where flow detachment is a concern.

Source

Fluids

Turbulence Model Assessment in Compressible Flows around Complex Geometries with Unstructured Grids

journal · 2019

View source

Questions About This Research

What does the research say about menter sst turbulence model excels in simulating separated flows over complex geometries?: When simulating complex aerodynamic designs with potential flow separation, select the Menter SST turbulence model for more reliable and stable CFD results. Evidence: Fluids (2019).
Why does "Menter SST turbulence model excels in simulating separated flows over complex geometries" matter for design?: Accurate simulation of turbulent flow is crucial for designing aerodynamic surfaces, engines, and other systems operating at high speeds. Selecting the right turbulence model directly impacts the reliability of computational fluid dynamics (CFD) predictions, influencing design iterations and performance outcomes.
How can designers apply this research?: When simulating complex aerodynamic designs with potential flow separation, select the Menter SST turbulence model for more reliable and stable CFD results.
What were the main findings?: All tested turbulence models performed consistently for attached or mildly separated flows.. Significant differences in performance were observed in separated flows with recirculation zones.. The Menter SST model demonstrated the best compromise between accurately capturing flow physics and maintaining numerical stability in complex flow scenarios.
What research method was used?: Numerical simulation and benchmarking.
How strong is the evidence?: Evidence strength is rated Strong effect, based on a 2019 journal from Fluids.
What should I do differently in my next project?: When undertaking CFD analysis for aircraft wings, vehicle aerodynamics, or turbomachinery, consider the Menter SST model if flow separation is anticipated.
What are the limitations?: The study focused on steady RANS equations, which may not fully capture transient turbulent phenomena. The performance of models can also be dependent on the specific solver and grid resolution used.