Thermomechanical viscoelastic response of a unidirectional graphite/polyimide composite at elevated temperatures using a micromechanical approach
Bibliographic record
Abstract
In this work, the thermomechanical viscoelastic response of a high temperature polymer matrix composite system made up of T650-35 graphite fibers embedded in PMR-15 resin is studied through a micromechanical model based on the assumptions of simplified unit cell method within a temperature range of 250–300℃ corresponding to aerospace engine applications. The advantage of this particular micromechanical model lies in its ability to give closed-form expressions for the effective viscoelastic response of unidirectional composites as well as each of their constituents. Using the experimental data of the creep behavior of thermostable PMR-15 polyimide, the micromechanical model is first calibrated to account for the effect of temperature. The resulting elastic and viscoelastic responses are found to be in good agreement with the existing experimental data. The validated model is then used to predict the behavior of the composite material under different combinations of thermal and mechanical loadings. The results clearly demonstrate the importance of accounting for the viscoelastic effect of the matrix material as the temperature increases. Current works on modeling temperature-dependent viscoelastic behavior of polymer matrix composites are mainly based on the assumption of thermorheologically simple material. However, through the present approach where the matrix is modeled as a thermorheologically complex material, the effect of temperature on the elastic and viscoelastic response of the composite system can be individually investigated.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".