A coupled multigrid solver with wall functions for<scp>three‐dimensional</scp>turbulent flows over urban‐like obstacles
Bibliographic record
Abstract
Abstract One of the objectives for rapid operational tools for urban atmospheric events is fast calculation of the computational fluid dynamics (CFD) models for multiphase flows to respond to deliberate or accidental chemical, biological, and radiological (CBR) releases. This article addresses the implementation of a coupled multigrid (CMG) method in an in‐house pressure‐based low‐speed CFD solver called STREAM in detail, including its validation against several 2D/3D benchmark test problems pertinent to urban flows. It was identified that solving the advection–diffusion equation of concentration for the prediction of dispersion of CBR agents is computationally very efficient, provided that the input data of turbulence viscosity and flow fields can be supplied in a timely fashion. Since most pressure‐based solvers adopt the segregated SIMPLE algorithm, and the coupling among different (linearized) equations is established via outer iterations on a single grid, its convergence rate is generally poor, particularly for turbulent flows in urban environments involving massive flow separation. The proposed approach and main contribution here is to adopt CMG to solve turbulent flows over urban‐like obstacles efficiently in 2D and particularly 3D, in which the standard Reynolds‐averaged Navier–Stokes turbulence model in conjunction with wall functions is employed. The results presented in this paper demonstrate a speedup ratio based on work unit (WU) for 3D laminar cavity flows of roughly 100, and 25 for 3D turbulent urban flow, depending on grid sizes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".