Adaptive PDE-Based Reduced-Order Modeling for Fast Thermo-Mechanical Lifetime Assessment of Power Devices in Railway Traction Drives
Bibliographic record
Abstract
Power semiconductor devices in railway traction systems are frequently subjected to dynamic mission profiles characterized by rapid acceleration, regenerative braking, and load fluctuations.These conditions induce repetitive thermal cycles and complex thermomechanical stresses in device layers such as chips, solder joints, and substrates, eventually leading to fatigue and failure.This paper proposes a PDE-based thermo-mechanical modeling framework for the simulation and lifetime prediction of power semiconductor devices operating under railway traction profiles.The governing partial differential equations for transient heat conduction and coupled thermo-elastic stress are formulated and solved to capture temperature gradients and stress distributions across multilayer modules.An adaptive reduced-order modeling (A-ROM) approach with a-posteriori error control is introduced to accelerate simulations while preserving accuracy at thermal hotspots.A differentiable Rainflow algorithm combined with a Bayesian Miner model is integrated into the pipeline for end-to-end lifetime estimation with quantified uncertainty.The proposed framework is validated through representative traction duty cycles, demonstrating significant computational speed-up compared with full-order PDE simulations, while maintaining prediction accuracy.Results indicate that the methodology provides not only accurate field and fatigue life predictions but also a scalable digital twin concept for railway traction drives, enabling reliability-oriented design and proactive maintenance strategies.The integration of Bayesian Miner-based uncertainty quantification and digital twin adaptability ensures predictive reliability under mission variability.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".