A comprehensive computer model for thermal flows of moist air through porous media
Bibliographic record
Abstract
Accurately simulating the flow and thermal behaviors of moist air in porous media is essential for many natural processes and industrial applications. One particular example is that the Creighton and Kidd Creek Mines (Sudbury, Canada) use the fragmented rock as a cooling or heating source to regulate the temperature and humidity of the air for underground mine ventilation. To model such systems, an integrated model is established to incorporate fluid flow, heat transfer, mass transport, and phase change in a porous material. The model adopts a two-temperature representation, which allows us to account for convective heat transfer due to air flow, the thermal conduction between air and rock due to the temperature difference, as well as the latent heat effect during phase change. The multiple-relaxation-time (MRT) lattice Boltzmann method (LBM) is selected to solve the governing equations. In specific, the D3Q19 lattice model is used for the porous flow with the Darcy and Forchheimer forces considered, and the D3Q7 model is utilized for the solid and fluid temperatures as well as the vapor concentration transport process. Furthermore, the vapor-liquid phase change process is included based on the vapor concentration and temperature, and the associated heat and mass transfer are implemented as source terms in the corresponding governing equations.In this presentation, more details of the theoretical model and numerical techniques will be presented. Several validation tests will be discussed, where simulation results are compared to analytical solutions to demonstrate the correctness and accuracy of our programs. Example simulations will also be described for the potential applications of our model in future studies.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".