Topology optimization of structures with heat dissipation and thermo-mechanical coupling based on the EPTO method
Bibliographic record
Abstract
The heat dissipation performance of the structure has an important impact on the normal operation of the structure in the practical application of engineering. Topology optimization of structures considering heat dissipation performance and under thermo-mechanical coupling is achieved by the enhanced proportional topology optimization (EPTO) method. Firstly, the EPTO method is combined with the steady-state heat conduction theory to solve topology optimization of structures with heat dissipation, where the distribution of element density is determined by the proportion of the heat dissipation weakness of the element in the heat dissipation weakness of the structure. Then, the EPTO method is combined with the structural thermo-mechanical coupling analysis theory to solve topology optimization of structures in the thermo-mechanical coupling environment, where the thermal stress coefficient is introduced to reduce the calculation cost during the process of thermal stress solution, and the comprehensive performance of the structures is optimized by weighting the average of structural compliance and heat dissipation weakness. The results of numerical examples show that the proposed method can not only accelerate the iterative convergence of optimization, but also obtain optimization results with smaller values of objective function and better topological configurations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".