Structural Topology Optimization for Multiple Load Cases While Avoiding Local Minima
Bibliographic record
Abstract
A new method for performing topology optimization while considering multiple load cases is presented. The technique is demonstrated using two classes of problems, the first of which seeks to minimize the maximum deflection under a series of fixed, point loads subject to a material volume constraint. The second is a classical weight minimization problem subject to constraints on the maximum deflection caused by each load case. Because the topology optimization problem involving SIMP materials is inherently non-convex, the optimized solution is highly sensitive to the starting point and search path followed during the optimization. Therefore, the proposed technique calls for the use of a composite objective function, which is defined as the Kreisselmeier–Steinhauser aggregate of the individual objectives corresponding to the different load cases. The technique is also applied to the weight minimization problem, in which case the KS function is used to construct an aggregate constraint function. In this way, sensitivity information from inactive load cases is taken into account throughout the optimization making the method less susceptible to local minima. Furthermore, by beginning with a low value for the
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".