A macroscopic model for the impregnation process of composite material by a concentrated suspension
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Bibliographic record
Abstract
In order to improve thermal, mechanical behavior and weight of our turbine blades, we need to use a new composite material. The manufacturing process to obtain this composite is intricate and requires a fluid densification process consisted of two parts. Firstly, particles are introduced in the reinforcement thanks to a pressure-driven flow, where they're retained by a filtration membrane. By reducing porosity, we improve the capillarity infiltration of a melted metal which can react with particles (second part). In this present study, we carry out a model that can describe physics of particles' introduction in our material. Given that we wanted to simulate flow at fibers scale and considering average particles' size is about a micrometer, we decided to use the volume fraction of particles Φ to describe our colloidal suspension. Thus, suspension flow can be resolved with the Navier-Stokes equations of mass and momentum conservation. To evaluate the particle's concentration field, a diffusion equation is introduced. Originally developed by Leighton et al [1], then improved by Phillips et al [2] this equation describes the migration of particles in a sheared flow. At last, the viscosity dependence of volume fraction is given by Krieger [3]: μ (Φ)= (1−Φ /Φ max) η Φ max Due to the filtration membrane presence, our process is similar to the dead-end filtration developed in microfiltration process [4]. Thus, we easily observe the sieving mechanism with formation of a growing cake that can be seen as a porous media. In the cake, our model describes a macroscopic flow of aqueous fluid in a porous media composed of rigid spheres. Microfiltration process can also provide theoretical law over temporal evolution of the cake-layer thickness. Before testing our model over realistic geometries, it was evaluated with experiments [5]. Then, our work consisted of two parts: 2D parametric studies and strong 3D simulations over RVE. References [1] Leighton, D. and Acrivos, A. (1987). The shear-induced migration of particles in concentrated suspensions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it