Flow-Induced Deformation of Poroelastic Tissues and Gels: A New Perspective on Equilibrium Pressure-Flow-Thickness Relations
Bibliographic record
Abstract
Hydrostatic pressure-driven flows through soft tissues and gels cause deformations of the solid network to occur, due to drag from the flowing fluid. This phenomenon occurs in many contexts including physiological flows and infusions through soft tissues, in mechanically stimulated engineered tissues, and in direct permeation measurements of hydraulic permeability. Existing theoretical descriptions are satisfactory in particular cases, but none provide a description which is easy to generalize for the design and interpretation of permeation experiments involving a range of different boundary conditions and gel properties. Here a theoretical description of flow-induced permeation is developed using a relatively simple approximate constitutive law for strain-dependent permeability and an assumed constant elastic modulus, using dimensionless parameters which emerge naturally. Analytical solutions are obtained for relationships between fundamental variables, such as flow rate and pressure drop, which were not previously available. Guidelines are provided for assuring that direct measurements of hydraulic permeability are performed accurately, and suggestions emerge for alternative measurement protocols. Insights obtained may be applied to interpretation of flow-induced deformation and related phenomena in many contexts.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".