A Novel Computational Approach for an Improved Expression of the Spectral Content in the Lower Atmospheric Boundary Layer
Bibliographic record
Abstract
The paper presents an innovative methodology for the simulation of incoming wind conditions in computational domains that use large eddy simulation (LES) for the evaluation of wind loads on low-rise buildings. Simulating the atmospheric boundary layer has proven to be a challenging process for computational wind engineering, especially to correctly introduce the fluctuations in the high-frequency spectral domain. Experiments in the Concordia University Building Aerodynamics Laboratory were carried out to estimate the evolution of turbulence features in the along-wind direction of a generic profile. Virtual probes were placed inside the computational domain to match the location of those in the wind tunnel and compare mean speed values, turbulence intensity, integral length scales, and spectral content. Synthetic methods were applied for the incoming wind velocity to estimate their capacity to capture the evolution of these turbulence features in the streamwise direction. The inability of these synthetic methods to represent the spectral domain, particularly for heights of interest for low-rise buildings, was examined, and the so-called dynamic terrain method in which velocity time histories are extracted from wind tunnel measurements and reconstructed to fit certain statistical parameters was implemented. The proposed method represents the frequency domain’s spectral content with good agreement with the theoretical von Karman spectrum and the experimental results. Advantages of the method include time efficiency and relative simplicity, which makes it attractive to the practitioners for the design of a neutral atmospheric boundary layer respecting the turbulent wind characteristics.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".