A permeability measurement method dedicated to a composite process family
Bibliographic record
Abstract
SUMMARY: During the last 30 years, Liquid Composite Molding (LCM) technologies have been intensively used to manufacture structural composite parts in a wide range of aeronautical applications. The development and optimization of these technologies as well as the associated numerical simulation require knowledge of physical properties of the fibrous material, called preform. The resistance to the resin flow is defined by the permeability tensor of the preform in the model of Darcy’s law. Considering the different industrial LCM processes, a complete identification of the permeability tensor is necessary. Material characterization for industrial applications requires using reliable, reproducible and realistic procedures. With this goal in view, different protocols of permeability measurements have been set up for several years by EADS Innovation works in collaboration with academic partners. A method of permeability measurement was developed in collaboration with Ecole Polytechnique of Montreal in order to identify the unsaturated in-plane permeability. This approach includes a built-in correlation with Darcy’s law, allows an estimation of both experimental and numerical errors and has been standardized within EADS with specific automation. In the same context, a through-thickness permeability measurement has been developed in collaboration with University of Le Havre. This system identifies saturated through-thickness permeability for a large range of fiber content. Finally, a specific protocol dedicated to the identification of a various and complete set of permeability values for LCM processes including a double porous medium was developed in collaboration with Ecole Polytechnique of Montreal. This approach consists of characterizing the in-plane and through-thickness unsaturated permeability tensor necessary for this family of processes. Numerical solutions were developed and applied to industrial parts. This clearly demonstrated how these different tasks highly contribute to the integration of composite process simulation in the development of aeronautical structural composites.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".