Transition SST
Four-equation transition model - Most advanced transition prediction with γ-Reθ method
Use Transition SST When (Industry Focus)
| Situation | Why It Works | Advice |
|---|---|---|
| General transition applications (most versatile) | Most advanced and widely validated transition model | Default choice for any transition-critical application |
| Turbomachinery (compressors, turbines) | Handles complex transition in rotating machinery | Industry standard for modern turbomachinery |
| Automotive aerodynamics (transition-sensitive) | Accurate transition prediction affects drag | Use when transition location affects performance |
| Research transition studies (highest accuracy) | Most sophisticated transition physics | Best for fundamental transition research |
| Complex transition scenarios (mixed conditions) | Handles natural, bypass, and separation-induced transition | Most comprehensive transition coverage |
Avoid Transition SST When (Industry Focus)
| Situation | Why It Fails | Industry Advice |
|---|---|---|
| Fully turbulent flows (Re > 10⁶, rough surfaces) | Overkill, no transition occurs, higher cost | Use k-ω SST for fully turbulent flows |
| Basic industrial flows (pumps, fans, HVAC) | Too sophisticated and expensive for routine analysis | Use k-ε for basic industrial equipment |
| Fully laminar flows (Re < 1,000) | No transition expected, unnecessary complexity | Use laminar model for low Re flows |
| Large-scale industrial systems (computational cost) | 4 equations significantly more expensive | Use simpler models for large systems |
| Preliminary design studies (fast turnaround) | Too detailed and slow for initial design phases | Start with k-ω SST, upgrade if transition critical |
| Limited computational resources | High memory and CPU requirements | Use k-kL-ω or k-ω SST for resource constraints |
Industry Takeaway
✅ Most Advanced
Use Transition SST when transition is critical and you need the most accurate prediction available.
⚡ Highest Cost
4 equations = highest computational cost - only use when transition physics are essential.
🎯 Industry Standard
Becoming standard for turbomachinery and transition-critical applications.
Practical Applications
🌀 Turbomachinery
Compressors, turbines, modern design
🚗 Automotive Aero
Transition-sensitive drag applications
🔬 Advanced Research
Highest fidelity transition studies
🎯 General Transition
Most versatile transition model
Transport Equations
Intermittency (γ)
$$\frac{\partial \gamma}{\partial t} + U_j \frac{\partial \gamma}{\partial x_j} = P_{\gamma} - D_{\gamma} + \frac{\partial}{\partial x_j}\left[(\nu + \frac{\nu_t}{\sigma_{\gamma}})\frac{\partial \gamma}{\partial x_j}\right]$$
Controls transition from laminar to turbulent
Transition Momentum Thickness Reynolds Number (Reθt)
$$\frac{\partial \tilde{Re}_{\theta t}}{\partial t} + U_j \frac{\partial \tilde{Re}_{\theta t}}{\partial x_j} = P_{\theta t} + \frac{\partial}{\partial x_j}\left[(\nu + 2.0\nu_t)\frac{\partial \tilde{Re}_{\theta t}}{\partial x_j}\right]$$
Tracks transition onset criteria
Modified k-ω SST Base
$$\frac{\partial k}{\partial t} + U_j \frac{\partial k}{\partial x_j} = \gamma P_k - \beta^* k \omega + \frac{\partial}{\partial x_j}\left[(\nu + \sigma_k \nu_t)\frac{\partial k}{\partial x_j}\right]$$
k-ω SST modified with intermittency γ
Engineering Analogy
🎛️ Advanced AI Traffic Control
Transition SST is like an advanced AI traffic control system:
- γ (intermittency): "How much of the traffic is chaotic vs orderly"
- Reθt (transition criteria): "Predicts when traffic will become chaotic"
- k-ω SST base: "Best traffic management system when fully chaotic"
- Automatic prediction: "AI predicts traffic jams before they happen"
- Most sophisticated: "Like having the most advanced traffic AI available"
vs Other Models
| Model | Transition | Equations | Cost | Best Use |
|---|---|---|---|---|
| Transition SST | Most Advanced | 4 | Highest | General transition, turbomachinery |
| k-kL-ω | Good | 3 | High | LPT, natural transition |
| k-ω SST | None | 2 | Medium | Fully turbulent |
| Laminar | None | 1 (NS) | Low | Re < 2,300 |