Data‐driven optimization of wind pressure sensor placement on low‐rise buildings using computational fluid dynamics and multi‐resolution dynamic mode decomposition
Bibliographic record
Abstract
This study presents a novel hybrid framework for optimal sensor placement to evaluate wind loads on low-rise buildings. Recognizing the challenges of deploying dense sensor arrays in turbulent atmospheric boundary layer wind tunnel tests, the proposed method integrates large eddy simulation with multi-resolution dynamic mode decomposition (mrDMD) to isolate spatiotemporally dominant flow features. Unlike traditional DMD-based approaches that capture global modes, the use of mrDMD enables scale-separated modal analysis, enhancing sensitivity to transient and localized flow dynamics. These modes guide a QR pivoting algorithm, which efficiently selects sensor locations that maximize information content. The framework demonstrates a sensor count reduction of over 80%, from 1426 candidates to just 182 sensors, while preserving high reconstruction accuracy (R > 90%) for both mean and fluctuating pressure fields. This distinction enables robust and cost-effective wind load assessment without compromising fidelity. The methodology is validated using wind tunnel experiments and is shown to be applicable for generalized wind scenarios through an angle-of-attack-unified sensor configuration. By combining modal decomposition with informed optimization, this framework advances state-of-the-art techniques in structural monitoring, offering practical utility in experimental and real-world applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".