Empirical Evaluation of Influence of pVARD on Drilling Performance Through Static, Dynamic, and Drilling Applications
Bibliographic record
Abstract
Abstract The research of this paper is a continuation on the evaluation of Belleville Springs and Dampening for the passive Vibration Assisted Rotary Drilling (pVARD) tool optimization for drilling operations. The research involves a set of mono versus dual compression tests through involving rotational loading on Belleville Springs and Damping to simulate the rotational drilling performance using pVARD. The reason for involving rotational compression loading during rock drilling is to capture the associated bit-rock interactions and evaluate its relation to the implementation of pVARD versus conventional rotational drilling (no-pVARD). Comprehensive experimental sets on natural isotropic rock drilling using pVARD vs. no-pVARD are conducted under the same conditions of weight on bit (WOB), revolution per minute (rpm), rock type, water flow rate, drill bit. All drilling experiments were conducted at atmospheric pressure using a large-scale and fully instrumented rotary drilling simulator. At first, a full set of static compression tests on Belleville Springs and dampening in combination as well as in individual sets were conducted and displacement versus load relationships were generated. Dynamic motions of pVARD while drilling were also captured and reported. Relationships between pVARD static and dynamic compressions were linked to the drilling performance resulted from pVARD vs. no-pVARD drilling.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".