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Record W3117529426 · doi:10.1115/omae2020-18845

Dynamic Analysis of a Progressing Cavity Pump System Using Bond Graphs

2020· article· en· W3117529426 on OpenAlexaff
Jeronimo de Moura, Geoff Rideout, Stephen Butt

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsBond graphPrime moverComputer scienceString (physics)StatorSystems modelingGraphAlgorithmControl theory (sociology)Control engineeringTopology (electrical circuits)SimulationMechanical engineeringEngineeringTheoretical computer scienceMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract One commonly used pump in the petroleum sector is the Progressing Cavity Pump (PCP). The PCP is a type of positive displacement pump that is used as an artificial lifting system which consists of a helical rotor and elastomeric stator. A mathematical solution to a PCP system model requires that we solve a partial differential equation system. The solution is inherently complex and requires considerable computational time. This paper uses the bond graph formalism, which is based on energy and information flow, to implement a model of a PCP system. Its purpose is to predict the dynamic response of the PCP system when it is subjected to a specific reservoir condition. Specifically focusing on the rod string, the torsional effects are captured by a lumped segment approximation. The software 20-Sim© was used to simulate a realistic PCP system application scenario. The model presented in this paper is able to determine the prime mover, rod string, and other component requirements. This paper shows that the multi-body lumped segment model is a useful way to simulate the rod string performance. The bond graph is effective at modeling the PCP system which contains elements from different energy domains.

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 score0.659
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.241
Teacher spread0.227 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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