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Record W6959285532 · doi:10.11575/prism/27860

Stability Analysis and Numerical Simulation of Single and Double Diffusive Convection in Porous Media with Applications to Solvent-Aided Thermal Recovery of Bitumen

2016· other· en· W6959285532 on OpenAlexfundno aff

Bibliographic record

VenuePRISM (University of Calgary) · 2016
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaSuncor Energy IncorporatedUniversity of Calgary
KeywordsPorous mediumConvectionStability (learning theory)Computer simulationScalingAsphaltNatural convectionThermal

Abstract

fetched live from OpenAlex

Natural convection in porous media is a phenomenon in which the unstable setting of fluid layers leads to the formation of convective currents and is very important to different aspects of science and engineering including chemo-hydrodynamics, geological storage of CO2, and solvent-aided thermal recovery from bitumen reservoirs. This thesis analyzes the effect of viscosity variation in single and double diffusive convection using linear stability analysis and nonlinear numerical simulation. Universal scaling relations were developed to predict the onset of convective instabilities and their initial wavelengths. The developed numerical model was validated using a number of benchmark problems. The model results were compared with stability analysis predictions and good agreement was observed. Finally, linear stability analysis was employed to optimize the selection of n-alkane solvents in solvent-aided thermal recovery methods from bitumen reservoirs.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.198
Teacher spread0.184 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

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