DEM Simulation of Enhancing Drilling Penetration using Vibration and Experimental Validation
Bibliographic record
Abstract
This paper details a study of Discrete Element Method (DEM) simulation of drilling penetration and is part of a broader investigation of the influence of bit vibration and rock-cutter compliance on enhancing drilling performance. It has been shown from laboratory experiments and field drilling trials that axial bit vibration (induced by modulated bit-rock compliance) can play a positive role in improving drilling rate of penetration (ROP), and the Drilling Technology Laboratory (DTL) at Memorial University of Newfoundland has incorporated this into passive Vibration Assisted Rotational Drilling (pVARD) technology and drilling tools. This paper focuses on DEM simulation of polycrystalline diamond compact (PDC) bit penetration and experimental validation of drilling with and without the pVARD technology, all other factors being equal, as a means of both evaluating the pVARD technology and understanding the basis of enhancing drilling performance. Simulated axial vibration properties such as amplitude and frequency were adjusted with different settings of spring compliance and dampening layers, simulating the physical configuration of the pVARD tool used for laboratory experiments. Analysis of Mechanic Specific Energy (MSE), Material Removal Rate (MRR) and Depth of Cut (DOC) are calculated to evaluate drilling performance and efficiency, and are used to compare the pVARD and non pVARD drilling results. In general, the DEM simulations agree with the experimental drilling results, and both indicate improved drilling performance using the pVARD technology.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".