On Application of Artificial Neural Network Methods in Large-eddy Simulations with Unresolved Urban Surfaces
Bibliographic record
Abstract
Micro-meteorological aspects of city comfort, land use management and air quality monitoring are rapidly growing areas of applications where environmental turbulence-resolving or large-eddy simulation (LES) models play the central role. Complex details of the urban surface morphology however remain unresolved or poorly resolved in the state-of-the-art LES due to severe limitations from computer facilities. The LES code LESNIC is applied in this study to simulate turbulent flow interaction with elements of the urban surface morphology. The study investigates a possibility to utilize a trained three layers’ artificial neural network (ANN) to parameterize the flow-to-surface interactions in the coarse LES where surface features are unresolved. It is concluded that the ANN can be a robust predictor for scalar concentrations and components of the surface stress tensor in the urban sub-layer with unresolved scalar sources and surface morphology. It has been noted however that dynamic training of the ANN may require more computational resources than adequate refinement of the LES resolution.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".