| { |
| "field_id": "F039", |
| "field": "computational fluid dynamics", |
| "directions": [ |
| { |
| "direction_id": "G0", |
| "name": "Flow-informed geometry design", |
| "scope": "Use CFD to evaluate or guide design search, selecting and optimizing the persistent geometry of shapes, channels, and fluid devices to improve flow-related performance." |
| }, |
| { |
| "direction_id": "G1", |
| "name": "Learned flow-result prediction and generation", |
| "scope": "Learn to produce flow fields, flow-state evolution, or flow-performance results to replace expensive CFD result acquisition, or to characterize the distribution of variation in plausible flow states; encompasses both rapid prediction for specified inputs and conditional or unconditional sampling of flow states." |
| }, |
| { |
| "direction_id": "G2", |
| "name": "Flow reconstruction from incomplete observations", |
| "scope": "Infer unobserved fluid motion and field information from sparse, missing, coarse-grained, or visual observations." |
| }, |
| { |
| "direction_id": "G3", |
| "name": "Turbulence closure modeling and assessment", |
| "scope": "Develop, update, or calibrate in-solver closure representations for flow physics that cannot be directly resolved, and assess associated model error and uncertainty; includes subgrid-scale and near-wall closures in coarse-scale simulations, as well as RANS closure calibration and probabilistic assessment." |
| }, |
| { |
| "direction_id": "G4", |
| "name": "Learned spatial operations for CFD solvers", |
| "scope": "Improve meshes or local spatial representations and decisions to support accurate, stable numerical solutions while a CFD solver continues to advance the computation." |
| } |
| ] |
| } |
|
|
