An XFEM-based computational homogenization framework for thermal conductivity evaluation of composites with imperfectly bonded inclusions
Bibliographic record
Abstract
Abstract In many engineering applications, such as thermal coating and structural insulation, 2D modelling is deemed sufficient to identify design parameters. For the effective thermal conductivity evaluation of heterogeneous composites, a 2D computational homogenization procedure is developed in this study. The inclusions are distributed randomly within the plane. The boundary conditions tested for homogenization procedure introduced include the periodic temperature, uniform temperature, and uniform flux boundary conditions. The bond between the inclusions and the surrounding matrix is assumed imperfect allowing partial heat transfer at the interface. Randomly distributed inclusions can be introduced without altering the underlying regular mesh of the matrix by using the XFEM, providing an efficient way of introducing the inclusions. Implementation details of the proposed computational homogenization scheme based on the XFEM are provided. The results are validated by comparisons with available analytical solutions. Effect of assumed boundary conditions on the results are shown. Parametric studies illustrate the influence of the interface properties, volume ratio of inclusions as well as the distributions of the inclusions on the effective thermal conductivity of composites.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".