Topology Optimization of Joints Between Prismatic Sandwich Panels and Girders Under In-Plane and Bending Loads
Bibliographic record
Abstract
Abstract Prismatic sandwich structures offer improved mechanical properties over traditional structures used in ships and other vehicles. While research on their response and strength under local and hull girder loads has been advancing, insufficient attention has been placed on their joints, i.e. connections to surrounding structure, such as girders and bulkheads. It is important to seek appropriate topology of the joints to minimize stress they experience. We conduct this investigation via topology optimization, minimizing stress for a given volume fraction of a material. Aggregation approach is used where a p-norm function approximates the maximum stress in the finite element analysis to reduce the number of iterations to reach converged solution. Optimization is performed using sequential convex approximations via Method of Moving Asymptotes (MMA). The optimization is performed in Matlab. Maximum stress of optimized designs is validated using Abaqus. Joints are optimized for three load cases, combining moment on the joint (coming from lateral pressure on the panel) with in-plane tension and compression. Several topologies of the joint were found which feature 2–3 times smaller maximum stress than the conventional joint, even for lower structural weight (material volume fraction). However, the complexity of the joint is increased, which can be controlled effectively by the filter radius and passive elements.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".