subgridscalemodeller

Subgridscalemodeller refers to a computational component used in grid-based numerical simulations to represent phenomena that occur at scales smaller than the computational grid. It provides a closure for unresolved physics, allowing the simulation to account for the net effect of small-scale processes without resolving them directly. The term is commonly encountered in turbulence modelling, climate and weather prediction, ocean modelling, combustion, and engineering fluid dynamics.

Typical targets of a subgridscalemodeller include turbulent momentum transfer, scalar mixing, heat flux, and cloud microphysical processes in climate models. In large-eddy simulation, the subgridscalemodeller estimates the subgrid Reynolds stresses and scalar fluxes, enabling an effective eddy viscosity or alternative closures. In climate models, it may parameterize convection, subgrid cloud formation, and turbulent mixing. Common modelling approaches are eddy-viscosity closures (e.g., Smagorinsky, dynamic Smagorinsky, WALE), gradient models, scale-similarity models, and stochastic closures. Some models couple multiple processes and depend on local grid size, strain rates, stability, and other local diagnostics.

Applications rely on calibration and validation against higher-resolution simulations or experimental data. Limitations include sensitivity to grid resolution, numerical schemes, and model coefficients; potential biases; difficulty in representing flow regions with strong intermittency or transitional regimes. Recent trends include dynamic, adaptive, and hybrid SGS models, and data-driven approaches using machine learning to infer closures from high-fidelity data.

Related concepts include large-eddy simulation, Reynolds-averaged Navier–Stokes, and parameterization.