MétaCan
Menu
Back to cohort
Record W3032055538 · doi:10.3390/en13112716

Influence of Rotation Speed and Air Pressure on the Down the Hole Drilling Velocity for Borehole Heat Exchanger Installation

2020· article· en· W3032055538 on OpenAlexaff
Tomasz Śliwa, Kinga Jarosz, Marc A. Rosen, Anna Sojczyńska, Aneta Sapińska-Śliwa, Andrzej Gonet, Karolina Fąfera, Tomasz Kowalski, Martyna Ciepielowska

Bibliographic record

VenueEnergies · 2020
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsDrillingBoreholeHeat exchangerGeologyLithologyMarlRotational speedRotation (mathematics)Drill bitDrillPetroleum engineeringGeotechnical engineeringMechanical engineeringEngineeringPetrologyGeomorphologyGeometry

Abstract

fetched live from OpenAlex

The relation between rotation speed, air pressure and the velocity of air-rotary drilling using the down the hole method is determined in an empirical manner. For the study, velocity measurements are obtained for combinations of the aforementioned parameters during fieldwork for the installation of borehole heat exchangers near Lublin, Poland. The tests consider three drill bit diameters—110, 127 and 140 mm; three rotational speeds—20, 40 and 60 1/min; and three air pressures—16, 20 and 24 bar. The borehole heat exchangers need 100 m deep wells. The lithology consists mainly of loess and clays to 24 m, sand and carbonate rocks to 36 m, and marls and limestone to 100 m. It is found that the highest drilling velocity is achieved when the greatest pressure is applied, while the lowest drilling velocity is connected to the lowest pressure. However, the relation between rotation speed and drilling velocity is more complex, as drilling velocity seems to be more affected by depth. Therefore, lithology can be a major factor. The results may find direct use in drilling, and provide a basis for further studies on the optimization of drilling technology.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.559
Threshold uncertainty score0.224

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.229
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueEnergiesSame topicGeothermal Energy Systems and ApplicationsFrench-language works237,207