Development of a Characterization Mold to Measure the Transverse Thermal Conductivity of a Composite Material by Inverse Analysis
Bibliographic record
Abstract
In the present work, the transverse thermal conductivity of a composite material is determined by inverse analysis of the heat conduction phenomenon. Knowing the evolution of boundary conditions in time, i.e., the bottom and top surface temperatures of a specific sample, the thermal conductivity can be deduced from transient temperature measurements at three given positions through the thickness of the part. Starting from an initial estimate of thermal conductivity, the inverse method begins by solving the direct problem, i.e., the heat equation. The solution gives the transient temperature field everywhere in the composite sample. Calculated temperatures are then compared with transient experimental measurements based on a criterion evaluating the integral in time of the square of the distance between the measured and predicted temperatures. Conductivity is modified iteratively so as to minimize this criterion until the desired accuracy is achieved. The inverse methodology is tested in this article for a composite part made out of unsaturated polyester resin and unidirectional glass fibers. A special multifunctional mold was designed to produce the composite test plates by the Resin Transfer Molding Process (RTM) and at the same time, measure the transverse thermal conductivity as a function of time and resin degree of polymerization. The mold has an adjustable cavity depth to accommodate various experimental configurations and allows easy testing of a series of different fiber volume contents. It is equipped with controlled heating and cooling devices. Numerical simulations were performed to determine the dimensions of the mold and to optimize the positions of temperature and pressure sensors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".