Thermal and Mechanical Stress Analysis in Aircraft Hybrid Panels with Multi-Bolt Joints
Bibliographic record
Abstract
This study investigates the thermal stress and bolt load distribution in a hybrid panel structure of an aircraft mechanical joint under extreme temperatures. The hybrid panel structure comprises two aluminum alloy splices, six T-shaped composite stringers, and two composite skins, secured together with 96 bolts. This study analyzed the strain induced by thermal stress on composite materials and metals within the structure across temperatures, employing temperature environment tests ranging from room temperature to -54 °C, alongside a carrying capacity test at -54 °C. Furthermore, a three-dimensional simulation model of the panel structure was developed, incorporating considerations for contact, metal elastoplasticity, and the progressive damage failure of composite materials. This model facilitated the determination of thermal stress and bolt load distribution patterns. The results indicate a strong consistency between the finite element analysis outcomes and the experimental data. Temperature variations exacerbate the uneven distribution of bolt loads, concentrating the load near the edges of the hybrid structure while diminishing it in the center. The bolt load distribution parallel to the mechanical load direction forms an "M" shape, with a maximum load magnitude of approximately 31 kN. Perpendicular to the mechanical load, the bolt load undergoes significant changes, especially at the edges, reaching a maximum of about 20 kN, which warrants attention. The bolt-load distribution of the structure with the increase in mechanical load at -54 °C tends to be consistent with that at room temperature.
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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 | 0.000 |
| 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".