Topology optimization of structures under thermo-mechanical coupling by the improved parameterized level set method
Bibliographic record
Abstract
This article proposes an efficient parameterized level set method (PLSM) to achieve topology optimization of structures under thermo-mechanical coupling, with minimum compliance as the objective function and volume fraction as the constraint condition. By using the compactly-supported radial basis functions (CS-RBFs) to interpolate the level set function (LSF), it is more convenient and efficient to evolve the LSF while ensuring the smoothness of the boundary of the topology optimization results. Specifically, the thermo-mechanical coupling analysis is conducted on the structure and combined with the proposed PLSM to establish a topology optimization model. The method of moving asymptotes (MMA) is adopted to solve the topology optimization model, while incorporating the shape sensitivity constraint factor to enhance the computational efficiency. Furthermore, the approximate re-initialization scheme is adopted to prevent the gradient of the LSF boundary from being too large or too small, and to improve the numerical stability and convergence speed of the structural topology optimization process. The effectiveness and feasibility of this method have been demonstrated through several typical numerical examples.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".