Fluid Behavior in Stochastic Porous Structures
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2021-1443.vid Ablative materials in thermal protection systems (TPS) are used in aerospace applications to protect passengers and payload from critical degradation in high temperature conditions. The standard ablator, phenolic impregnated carbon ablator (PICA), is composed of carbon fibers embedded in a phenolic resin. It has a highly complex microstructure – a highly porous fibrous mat with its components oriented randomly in the transverse plane. As such, the calculation of its fluid behavior requires a robust approach. Here, we present a stochastic modeling approach that allows for assessment of properties as a function of local structure. The approach includes a computational toolkit for generating physically-motivated model representative volume elements (mRVE’s), which are confirmed to exhibit transverse isotropy (just as PICA). We also present a methodology for computing the distribution of fluid behavior for sets of mRVE’s using the lattice Boltzmann method (LBM). Based on our results, local variation in fiber geometry within fibrous ablators has little effect on fluid behavior but their global orientation has a significant one and must be a major design consideration moving forward.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".