Application of Kinetics of Thermal Degradation for Time-Temperature Analysis of Automotive Components
Bibliographic record
Abstract
A fundamental problem in the development of automotive thermal protection strategies is the understanding of the effect of time and temperature on vehicle components life and their performance throughout the life of the vehicle. Due to restrictions on emissions and the stringent requirements for improved fuel economy, the use of polymers and synthetic materials has been widely adopted in automotive applications. It is therefore critical to develop a process to estimate life of engineering materials based on thermal testing and material physical properties. While a series of carefully selected vehicle tests can determine components temperatures during different testing conditions, a need still exists to determine the expected component life and performance throughout the life of the vehicle. Kinetic models have been widely used, in literature, to determine the aging of polymeric and composite materials over time. In this paper, bench test and literature data, for selected materials which are used in automotive applications, are analyzed to determine essential parameters for kinetic models of thermal degradation. The kinetic models are applied later to estimate the effect of time and temperature on the component life based on vehicle level thermal testing and the expected vehicle duty cycle.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".