Quality assessment of peak pressure estimates in high-rise building using large eddy simulation
Bibliographic record
Abstract
• Quantifies numerical and modelling errors in LES-predicted peak pressures. • Applies systematic grid and model variation to map error estimates. • Uses 96-cells-per-breadth resolution to achieve low grid-induced uncertainty. • Shows grid-resolution sensitivity exceeds that of the sub-grid-scale model constant. • Confirms 95 % of peak pressures match wind-tunnel data within ±30 % tolerance. Analysis of peak surface pressures is essential for specifying wind-resistant cladding of tall buildings. Large eddy simulation (LES) holds promise for predicting peak wind action, yet its reliability is challenged by uncertainties arising from extreme value analysis and grid-controlled scale filtering. This study postulates that quantifying numerical and modelling errors in the LES pressure solutions improves design reliability and guides grid resolution specifications for high-fidelity peak pressure prediction. Thus, the key objectives were to ( i ) map magnitudes of numerical (truncation) and modelling errors along facades—achieved here through the novel application of the systematic grid and model variation method—and ( ii ) assess the accuracy of low-uncertainty peak pressure solutions against experimental measurements. Simulations reproduced a benchmark from the Tokyo Polytechnic University, modelling a square-based building with height-to-breadth ratio of 5:1 subjected to orthogonal and oblique wind incidences. Peak pressure coefficients at 500 locations were extracted from a Gumbel distribution of extremes, recorded during 30 min of equivalent full-scale exposure to 100-year return wind speeds. Using an isotropic wall-tangent resolution of 96 cells per building breadth, the LES peak pressure results showed generally low uncertainty, with area-averaged numerical and modelling errors of 7.3 % and 4.1 %, respectively. This configuration demonstrated respectable accuracy: 55 %, 85 %, and 95 % of peak pressure predictions fell within ±10 %, ±20 %, and ±30 % of the experimental references. Ultimately, this work presents a systematic LES framework for reliable peak pressure prediction, well-suited for third-party reproduction.
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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 | 0.000 |
| 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".