Quasilinear Transport Models
Quasilinear models estimate turbulent transport quickly by combining linear gyrokinetic growth rates with a saturation rule, bridging first-principles codes and transport solvers.
The speed problem
Full nonlinear gyrokinetics is too expensive to run inside a predictive transport loop that needs thousands of evaluations. Quasilinear models solve this by computing only the linear stability of the turbulent modes, which is far cheaper, and then applying a saturation model to estimate the nonlinear transport level.
How they work
The model runs a fast linear gyrokinetic solver to get growth rates, frequencies, and mode structures across a spectrum of wavelengths. It weights each mode by a saturation rule, often anchored to nonlinear simulations, to estimate the quasilinear flux. The result is a set of diffusivities and pinches as functions of the local gradients.
Calibration and validity
- Saturation rules are tuned against databases of nonlinear runs
- Models are validated against measured profiles in interpretive analysis
- Each has a documented range of gradients, collisionality, and geometry where it is trusted
Role in the code ecosystem
Quasilinear models are the practical link between first-principles turbulence physics and engineering-scale prediction. They give transport solvers a physically grounded, stiff transport model that runs fast enough for scenario design, while remaining traceable back to the gyrokinetic simulations that calibrated them.
Cautions
Because they omit the full nonlinear dynamics, quasilinear models can miss effects like turbulence spreading or strong zonal-flow regulation. Responsible use brackets predictions with the model's known uncertainties and, where stakes are high, spot-checks against nonlinear simulations.
In Kronos design work, quasilinear estimates support rapid scans, with results reported alongside their confidence range rather than as guarantees.