SOFT BODY IMPACT SIMULATION ON COMPOSITE STRUCTURES
Bibliographic record
Abstract
The paper describes recent progress on numerical simulation of soft body impact onto a fibre reinforced composite wing leading edge structure. The work is based on the application of non-linear explicit finite element analysis to simulate the response of composite wing structures under soft body impact loads. Soft body impactors such as gelatine (substitute bird) or ice (hailstones) are highly deformable on impact and flow over the structure spreading the impact load. Therefore, first benchmark simulations were carried out for soft body impact onto a rigid target. Soft body impactor was modeled using the Arbitrary Lagrangian-Eulerian (ALE) method. The results obtained using this impact model for different velocity were compared to the experimental test results in terms of local pressure, including Hugoniot and stagnation pressures, and global load to validate the accuracy of the model. Then, the impact of soft body onto a composite wing structures was described. A composite failure model which includes ply damage and interplay delamination model has been used to predict impact damage in the structure modeled using shell elements. The simulation tool predicts the impact damage in leading edge structure.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".