Evaluation of Belleville Springs and Damping Through Static Cyclic Loading “Hysteresis” for pVARD Optimization: Part-I
Bibliographic record
Abstract
Abstract One unconventional application that researchers have been investigating for enhancing drilling performance, has been implemented through improving and stabilizing the most effective downhole drilling parameters including (i) increasing downhole dynamic weight on bit (DDWOB), (ii) stabilizing revolution per minutes (rpm), (iii) minimizing destructive downhole vibrations, among many others. As one portion of a three-part-research that consists of a comprehensive data analysis and evaluation of a static compression hysteresis, dynamic compression hysteresis, and corresponding drilling tests, this research investigates through static cyclic loading “Hysteresis” of individual and combined springs and damping the functionality of the passive Vibration Assisted Rotary Drilling (pVARD) tool that could be utilized towards enhancing the drilling performance. Tests are conducted on the two main pVARD tool sections that include (i) Belleville springs, which represent the elasticity portion and (ii) the damping section, which represents the viscous portion. Firstly, tests were conducted through static cyclic loading “Hysteresis” of (i) a mono elastic, (ii) a mono viscus, and (iii) dual elastic-viscus cyclic loading scenarios for the purpose of further examining pVARD functionality. For performing static compression tests, a calibrated geomechanics loading frame was utilized, and various spring stacking of different durometer damping were tested to seek a wide-range data and to provide a multi-angle analysis. Results involved analyzing loading and displacement relationships of individual and combined springs and damping are presented with detailed report of data analysis, discussion, and conclusions.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".