Prediction procedure for hail impact
Bibliographic record
Abstract
The constant increase of composite materials’ performances makes them more and more used in recent aircrafts. Structures, as the wings or the fuselage, may suffer from hail impacts that can make critical damages or even perforate them. In order to guaranty the safety of passengers, aircrafts have to be certified and simulations have to demonstrate good agreements with real behaviour of the structures and the hail projectile. The aim of this work is to propose a procedure to analyse the home made manufacturing of the ice generally performed in laboratories, its mechanical characterization and a mechanical model that can predict the time-space profile of the impact force on a rigid structure. Because of the high strain level of the hail during the impact, the Smooth Particle Hydrodynamics (SPH) method will be used. Indeed, the finite elements method needs heavy remeshing that are time consuming to avoid mesh distortion. The SPH is a numerical meshless method that calculates interactions between particles at every time increment. Models available in the literature have been studied and the model of J.D. Tippmann (Tippmann, Kim, et Jennifer D. Rhymer 2013) is chosen. In this paper, the Tippman model is presented with its solving using the SPH. A parametric study is proposed in order to catch the relevant parts of this model. A simple experimental procedure is then proposed to feed the model and the results of impact simulations at different velocities are compared to experimental measurements realized in the laboratory.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.018 | 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".