Numerical study on structured sandwich panels exposed to spherical air explosions
Bibliographic record
Abstract
There is a need to develop innovative protective shield structures to withstand extreme loads, such as impact and blast loading. Sandwich structures that absorb significant kinetic energy as strain energy through plastic deformation offer superior protection. This study conducts a numerical analysis of structured sandwich protective structures subjected to airblast loads using finite element modeling. First, an experimental result from the literature was used to validate and verify finite element models of an architected sandwich structure modeled in Abaqus/Explicit software. Second, parametric studies were conducted on sandwich structures with additional layers of insert plates and newly proposed core topologies for viable shield protection against airblast loading. The finite element analysis results indicated that, under the same impulsive load, the control sandwich panel exhibited higher kinetic energy, demanding a proportionally larger internal energy. Conversely, sandwich structures with additional inner core insert plates dissipated the imposed kinetic energy more efficiently, due to the inelastic plastic deformation of the proposed core configurations. Moreover, the energy absorption capacity and back sheet displacement time-history were significantly improved by dense-hierarchical inner core configurations. Additionally, the parametric study analysis showed that increasing the number of insert plates and designing the core topology of cellular walls to be redundant, dense-hierarchical, and braced against buckling significantly reduced core collapse mechanisms such as folding, buckling, and crushing. However, despite these benefits, a reversed effect on the areal specific energy absorption index was observed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".