Thermomechanical Analysis of Composite Structures in Pyrolysis and Ablation Environments
Bibliographic record
Abstract
본 논문에서는 열분해 및 삭마 환경의 복합재 구조물에 대한 열기계적 연계 해석을 수행하였다. 열분해 과정의 재료 밀도 감소, 기공 가스 확산, 흡열 반응 에너지와 삭마 과정에서의 표면 침식 효과 등을 고려하였다. 상용 유한요소 코드에 교차 연계 알고리듬을 적용하여 완전 연계된 열 해석 및 구조 해석 인터페이스를 구성하였다. 수치 실험을 통해서 탄소/페놀릭 복합재료의 기본적인 열분해 및 삭마 특성을 분석하였다. 특히, 화학적 및 기계적 삭마에 영향을 미치는 주요 인자에 따른 표면 침식량 등을 비교하였다. 또한, 열분해 과정의 수축 또는 팽창 변형도가 재료의 열기계적 거동에 미치는 영향도 검토하였다. A coupled thermomechanical analysis of composite structures in pyrolysis and ablation environments is performed. The pyrolysis and ablation models include the effects of mass loss, pore gas diffusion, endothermic reaction energy, surface recession, etc. The thermal and structural analysis interface is based upon a staggered coupling algorithm by using a commercial finite element code. The characteristics of the proposed method are investigated through numerical experiments with carbon/phenolic composites. The numerical studies are carried out to examine the surface recession rate by chemical and mechanical ablation. In addition, the effects of shrinkage or intumescence during the pyrolysis process are shown.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".