Strategically Coupled Inertial Flow and Interface Evolution Model for Cavern Development by Dissolution Mining
Bibliographic record
Abstract
Shape control is important for large-scale underground caverns developed by dissolution mining; however, it is greatly complicated by turbulent brine flow and natural and forced convection. An improved model for simulating dissolution mining of large caverns over long injection periods is presented. The brine flow is modeled using the Reynolds-Averaged Navier-Stokes equations coupled with a mass conservation equation governing the evolution of the cavern walls. It is demonstrated that cavern wall irregularities previously assumed to be exclusively due to mineral heterogeneity are also readily attributable to the turbulent flow. Two competing dissolution mechanisms are identified, one enhancing dissolution unevenness and one that smooths out irregular dissolution features on the cavern walls. Two cavern construction methods were investigated: reverse and direct dissolution methods, which tend towards a “morning glory” and a “wide bottom decanter” shaped cavern, respectively. Results suggest that, because of the buoyancy effect, large roof spans are unavoidable without using an oil/air blanket; however, blanket usage leads to more jagged boundaries and can decrease the cavern construction rate. This study opens a path to the development of robust models of large-scale cavern development for energy storage and has implications for similar processes such as leach mining or ice melting.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".