Methodology for multiscale design and optimization of lattice core sandwich structures for lightweight hopper railcars
Bibliographic record
Abstract
This research is focused on developing new lightweight structures for railcars based on a pre-selected material, i.e. Al 2099. The goal is to design a new sandwich structure with an octet truss lattice core for a floor panel of a hopper freight railcar designed to meet North American standards. For that, mesoscale to macroscale design of the sandwich panel was performed. In mesoscale design, relative density, elastic properties, strength properties, and failure criterion of the lattice unit cell were investigated. In the next step, these properties were used as inputs for macroscale design, i.e. design of the whole sandwich structure. Multiple failure modes associated with the lateral loading of a sandwich panel were analyzed. These equations in conjunction with the minimum weight target led to an optimization problem, and the minimum required thicknesses were obtained. Finally, the new optimized design was validated by comparing different finite element simulations with the exact analytical equations. By using this type of structure, a 53% weight reduction was achieved on the floor panel which ultimately led to an estimated 12.5% reduction in the weight of the whole freight railcar body.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".