Study of Belleville springs and bond graph analysis of passive vibration assisted rotary drilling tool.
Bibliographic record
Abstract
Since 2008, the effect of active/passive sources of drilling vibration has been a topic of research at the Drilling Technology Laboratory of Memorial University of Newfoundland. Drill-off tests have been conducted in many different settings using fully instrumented lab-scale drill rigs (Small Drilling Simulator (SDS) and Large Drilling Simulator (LDS)) and through field trials in order to validate these effects and the researchers have been able to acquire substantially prominent results, which is evident from improved drilling rates and increased dynamic downhole weight on bit (DDWOB). One of the outcomes from those experiments is the development of novel passive Vibration assisted Rotary Drilling (p-VARD) Tool, which harnesses the vibration generated at bit-rock interface to improve the drilling rate and the measured DDWOB. The p-VARD tool uses different configuration of Belleville springs and elastomer to produce desired value of tool compliance and modify the damping. The research outcomes in this thesis explain the dynamic behaviour of the p-VARD tool and helps us understand the actual phenomenon inside the tool, which results in this behaviour. Mass-spring-damper schematics of the rigid and compliant drilling systems are analyzed with the help of bond graphs in 20-sim. Results show that the p-VARD can lower the drill string natural frequency significantly enabling it to resonate at the frequencies of the top drive. In order to facilitate the p-VARD experiments in a more controlled environment, the tool has been designed for the LDS, which is more advanced than the SDS. The dynamic simulation of the Belleville springs is in itself, a separate area of research that is used for the dynamic analysis of the p-VARD tool design. The LDS offers a wide range of rotary speeds and weight on bit and can be operated fully automatically or manually. P-VARD experiments in LDS will enable us to characterize the tool properties in more depth. The LDS p-VARD tool is designed with a minimum safety factor of 3.4 against the maximum torque generated by the LDS. It utilizes a uniformly grooved sensor plates with five identical and one distinct cross-section, which is attached to the outer shell to track the axial movement of the tool. The tool is designed such that the torque is transmitted from the inner shaft to the outer shell through four uniformly spaced keys and for fluid transmission, a 13 mm through hole is made through the middle of the tool.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".