Large-eddy simulation of turbulent flow and dispersion within modeled urban environments
Bibliographic record
Abstract
In this thesis, wall-resolved and wall-modeled large-eddy simulation (LES) have been employed to investigate turbulent flow and dispersion around a single and a group of wall-mounted bluff bodies which are partially and fully submerged in developing boundary layers, respectively. The dispersion is caused by a continuous release of a passive scalar from a ground-level point source located within the matrix of obstacles. The results have been validated through comparisons against the available experimental measurement data. Thorough physical analysis including investigation of the spatial evolution and temporal cascades of the kinetic and scalar energies, flow structures and their influences on dispersion of the concentration plume in the context of highly disturbed flows, and study of turbulence statistics for the flow and concentration fields have been performed to provide deeper insights into turbulent flow and dispersion in domains with complex geometries. An in-house code based on FORTRAN programming language, parallelized with MPI libraries has been developed, modified and optimized for conducting the simulations. The simulations have been conducted on public-domain supercomputers ofWest-Grid, specifically Orcinus and Grex, and also the local 256-core cluster system of the CFD LAB at the University of Manitoba.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".