Stiffness and damping characteristics of highly accurate rock simulation system based on gas–liquid coupling mechanism under nonharmonic impact
Bibliographic record
Abstract
To address the issue of the performance testing of hydraulic rock drills under specific working conditions, a stiffness–damping multi-stage adjustable rock simulation system has been proposed. Firstly, based on Kelvin rock theory, a rock vibration model under nonharmonic excitation is established. An integer-order model of the simulation system is created based on fluid mechanics theory. The stiffness–damping characteristics of granite and the system are studied, discussing the influencing factors of the stiffness–damping characteristics. Next, a drop hammer calibration experiment and an impact drilling experiment for the simulation system are designed. Finally, the stiffness–damping characteristics of the system in actual drilling are investigated, validating the effectiveness of the numerical model. Model simulations show that the smaller the stiffness of granite and the simulation system, the larger the drilling displacement. The larger the damping coefficient, the smaller the drilling displacement. Lower initial gas pressure and volume of the accumulator can provide greater stiffness. The greater the preload of the overflow valve and the cylinder diameter, the greater the damping produced. The experimental results show that the stiffness–damping error between the system and the granite does not exceed 10% with exceptionally high accuracy.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".