ANOMALOUS EFFECTS DURING THERMAL DISPLACEMENT IN POROUS MEDIA UNDER NON-LOCAL THERMAL EQUILIBRIUM
Bibliographic record
Abstract
Robust and accurate mathematical models describing fluid and heat transport in naturally occurring geological media can be challenging to formulate due to the spatial heterogeneities occurring at many different scales. Thus, a majority of the widely presented continuum-based mathematical models may not be completely adequate for predicting fluid or/and heat transport in such systems. In this work, two nonlocal temporal constitutive flux relationships are employed to present a novel mathematical model describing the fluid flow and heat transport through a porous medium. Subsequently, existing numerical schemes and well-established numerical discretization methods are applied to solve the resulting set of fractional equations. Parameter sensitivity analysis is presented to illustrate the effect of introduced phenomenological parameters on the fluid and heat transport behavior in the porous medium. Results show that the order(s) of fractional derivative plays a significant role in the pressure and rock temperature evolution with a minimal effect observed in the fluid temperature evolution. Furthermore, the magnitude of the heat transfer coefficient between the fluid and rock phase determines how fast the rock temperature approaches the fluid temperature. The presented mathematical model would find widespread applications in geothermal reservoirs and analyzing temperature profiles in fractured reservoir rocks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".