Data‐driven optimization of wind pressure sensor placement on low‐rise buildings using computational fluid dynamics and multi‐resolution dynamic mode decomposition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 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 | 0.000 | 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 it