Downburst-Induced Loads on a Six-Row Array of Ground-Mounted Solar Trackers
Bibliographic record
Abstract
Abstract This work presents the preliminary analysis of the tests conducted at the WindEEE Dome in the framework of the ERIES-SOLAR project for downburst flow. The wind tunnel characteristics, the instrumentation and reduced scale model of a six-row array of solar trackers is briefly described. The flow, similar to a vertical downburst with impinging jet velocity U ij = 8.6 m, is characterized based on the velocity records at various heights and distances from the downburst axis, obtaining the ensembles of several repetitions, PDFs, spectra and Morlet wavelets, identifying the ramp-up, plateau and decay stages. The downburst action on the array of solar trackers is assessed based on the time-dependent pressure coefficients recorded simultaneously by 576 pressure taps. In this work, the results are presented for one case of a vertical downburst, with the center of the six-row array model located at a distance 0.7 D from the downburst vertical axis. Strong increments in the net pressure coefficients are identified, associated with the effect of the deflected flow and the primary vortex reaching the vicinity of the panels. The detailed analysis of the large amount of data generated will be instrumental to identify the peak actions on solar trackers due to downburst occurrences, enabling the recommendation of load cases to be included in the best practice guidelines and structural codes for storm-prone regions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".