Stress and frequency optimization of prismatic sandwich beams with structural joints: Improvements through accelerated topology optimization
Bibliographic record
Abstract
Steel sandwich beams and panels with prismatic cores offer a promising alternative to traditional structures in various industries because of their excellent mechanical characteristics. This research explores performance gains by optimizing the core of the beams using a topology optimization (TO) framework to improve stress distribution and natural frequency. The beams include structural joints to the surrounding structures, which has not been investigated before for these types of structures. To address computational demands, accelerated linear finite element (FE) solvers and eigensolvers are employed, specifically adapted for density-based TO to enhance efficiency and maintain accuracy. The inexact recycled implicitly restarted Lanczos method is proposed, providing a novel approach to efficiently solving eigenvalue problems by recycling eigenvectors and relaxing convergence tolerances, significantly speeding up the process. The topology optimized beams are compared to conventional prismatic sandwich beams (X-core, Y-core, corrugated-core, and web-core), which are optimized using a global evolutionary algorithm. Limits on design variables are used to ensure ease of production. The results show that topology optimized beams outperform conventional beams by up to 44% in terms of stress and 18% in terms of frequency, at higher mass levels. Although they resemble conventional beams, optimized core topologies with joints highlight additional improvements and underscore the importance of joint design in optimization. Accelerated solvers reduce computational time by up to 99%, enabling TO to generate Pareto fronts comparable to global sizing optimization. Certain limitations, such as reduced performance at volume fractions below 0.2, indicate potential areas for further study.
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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) | 0.001 | 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) | 0.002 | 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".