The role of fluid viscosity in the interaction between individual fluid-filled rock joints and high-intensity stress waves 
Bibliographic record
Abstract
Understanding the interaction of stress waves with fluid-filled rock joints is crucial for seismic hazard assessment, hydrocarbon extraction, geological CO2 storage, geothermal energy exploration, and wastewater disposal. This study investigates dynamic mechanical behaviors (including elastic modulus and initial joint stiffness) and wave propagation characteristics (i.e., transmission and reflection coefficients, energy attenuation) of single fluid-filled rock joints under the normal incidence of high-intensity stress waves, with a focus on the effects of liquid content and viscosity. Dynamic compression tests were conducted using the split Hopkinson pressure bar (SHPB) technique combined with high-speed photography on rock joints with varying liquid content and viscosity. The results demonstrate that higher liquid content and viscosity increase the dynamic elastic modulus and initial joint stiffness of the joints. Increasing joint stiffness leads to an increase in wave transmission but a decrease in wave reflection. Besides, the increasing liquid viscosity reduces both wave transmission and reflection but enhances wave attenuation by individual fluid-filled rock joints. High-speed imaging revealed a transition from turbulent to laminar jet behavior with increasing liquid viscosity. These findings advance the understanding of fluid-rock interaction under dynamic conditions, offering valuable insights for theoretical development and practical applications in geophysical and geomechanical engineering.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".