Experimental dataset on aluminium wedge slamming: Measurements of acceleration, pressure, strain, and video data
Bibliographic record
Abstract
This paper presents data from three experimental campaigns investigating slamming loads on a three-dimensional non-prismatic aluminium wedge, complementing the original research article "Slamming loads and responses on a non-prismatic stiffened aluminium wedge: Part I. Experimental study [1]." The experiments were designed to investigate the effects of slamming loads on structural responses through a series of free-fall drop tests. These tests included wedges with stiffened and unstiffened bottom plates to examine the influence of flexural rigidity on hydroelastic slamming. The experimental setup utilized three accelerometers for vertical acceleration measurement, sixteen pressure sensors for slamming pressure capture, and twenty strain gauges for recording structural responses. Detailed information on wedge geometry, material properties, and test plans is provided. Symmetric impact tests were conducted at drop heights from 25 cm to 200 cm with two different wedge masses. Asymmetric impact tests were carried out at three drop heights with heel angles ranging from 5 to 25°. The dataset includes time histories of sensor records, the geometry of the wedge section, and video footage from various runs. This comprehensive data offers insights into the effects of water impact velocity, deadrise angle, wedge mass, and bending stiffness on hydrodynamic pressures and structural responses on V-shaped sections. The experiments provide a valuable benchmark for future slamming impact research, aiding in the refinement of experiments, validation of numerical methods, and enhancement of mathematical models.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".