Low-Velocity Impact Resistance of Double-Layer Folded Sandwich Structure
Bibliographic record
Abstract
The folded sandwich structure might inevitably be impacted at low velocity when it is working, which will lead to the decline of the mechanical properties. The low-velocity impact resistance of the double-layer V-shaped aluminium folded sandwich structure is researched by the finite element method. It was found that the damage mode and the proportion of energy absorption of the double-layer and single-layer folded sandwich structures are different under low-velocity impact, and the impact stiffness and energy-absorbing capacity of the double-layer structure are better than those of the single-layer structure when the impact energy is small. In addition, in view of the low-velocity impact response characteristics of the double-layer V-shaped aluminum folded sandwich structure, two methods are proposed to improve its impact stiffness. Both methods ensure that the total mass of the structure remains unchanged. One method keeps the inner panel still and changes the wall thickness distribution of the top and bottom cores, and the wall thickness of the top core is increased. The other method keeps the wall thickness of the cores unchanged, and the inner panel is moved upward. From the finite element results, it can be seen that after increasing the wall thickness of the top core from 0.25 mm to 0.4 mm, the maximum impact distance of the impactor decreases by 30.3% when the impact energy is 5 J, and it decreases by 23.1% when the impact energy is 10 J, and with 20 J, it decreases by 14.5%. After reducing the height of the top core from 12.5 mm to 5 mm, the maximum impact distance of the impactor is reduced by 22.86% when the impact energy is 5 J, 21.85% when the impact energy is 10 J, and 20.51% when the impact energy is 40 J. The improvement is obvious. The two methods can increase the equivalent density of the top core, which can increase the stiffness of the top core. For the double-layer structure, the stiffness of the top core near the impact point has a greater influence on the low-velocity impact resistance of the entire 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".