Thermo-mechanical fatigue damage constrained topology optimization
Bibliographic record
Abstract
• Consider thermo-mechanical fatigue damage constraints in topology optimization. • Evaluate thermal effects on fatigue via temperature-dependent Basquin equation. • Verify the method’s feasibility and effectiveness via four numerical examples. • Achieve lightweight designs to raise fatigue life under coupled thermo-elastic loads. • Confirm fatigue life improvements by the experimental results of optimized beams. Structures in extreme thermal environments are susceptible to thermo-mechanical fatigue damage under cyclic thermal and mechanical loading. To effectively address this issue via structural optimization design, this study proposes a thermo-mechanical fatigue damage-constrained topology optimization (TMFDCTO) method, aiming to achieve lightweight designs while rigorously accounting for fatigue damage induced by coupled thermal and mechanical loads. A nonlinear fatigue damage analysis framework is established, incorporating a temperature-dependent Basquin equation to quantify thermal effects on S-N curve degradation. Then Morrow criterion is employed to evaluate nonlinear fatigue damage accumulation under non-proportional thermo-mechanical loading conditions. The TMFDCTO formulation integrates a modified p -norm aggregation function to efficiently handle the inherent multi-constraint nature of fatigue damage optimization. Additionally, sensitivity equations are derived for both the volume objective function and the thermo-mechanical fatigue damage constraint function with respect to design variables. Meanwhile, several 2D and 3D numerical examples are tested to demonstrate the feasibility and effectiveness of the proposed TMFDCTO method. Finally, experimental validation under thermo-elastic loading is conducted using laser-cut MBB beam prototypes, with comparative analysis between simulation and experimental results confirming the effectiveness of the TMFDCTO method. The experimental results demonstrate that the TMFDCTO method enhances fatigue life by 12.02% to 613.25% compared to the method that does not account for thermal loading effects.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".