Introducing a novel method for determining the effective thermal conductivity at moderate and high Péclet numbers
Bibliographic record
Abstract
Abstract Flow and heat transfer in porous materials is a common topic throughout many fields, including environmental and petroleum engineering. Using a coupled approach of experimental and simulation methods, this study presents a novel method for calculating thermal conductivity. Specifically, a new approach for the measurement of thermal dispersivity with both conduction and forced convection drives is proposed. In our experiments, temperature was monitored at different points within the porous medium, providing detailed spatial temperature distributions. These measurements allowed us to calculate and report effective thermal conductivity, enhancing the accuracy of our model. Experiments with various injection rates and temperatures were conducted on a sand pack. There is a relationship between the composition and connectivity of the solid in the geometry and heat transfer. However, in the case of forced convection, the key factor is the Péclet number which is important for optimal extraction of the heat inside the geothermal reservoir according to the cooling rate. When the Péclet number is high, the permeability of the porous medium plays a significant role. The velocity of the fluid can change the effective thermal conductivity up to four orders of magnitude. Due to the thermal resistance of solid and fluid, the temperature gradient between the boundary and the centre of the geometry was seen and temperature peaks were observed in the initial stages of the experiments. The size and number of peaks at the initial stage of the experiments are highly dependent on the matrix properties, such as thermal conductivity and surface area.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".