Hardware and instrumentation to investigate massive releases of dense phase CO<sub>2</sub>
Bibliographic record
Abstract
CCS (carbon capture and storage) is seen as a possibility to mitigate the global warming effect. The practical implementation of this technique faces a few challenges like safety issues. It is wondered if a massive release affecting the pipeline (may be the most vulnerable part of the CCS chain) would not lead to a disaster remembering what happened in Africa about 28 years ago (about Nyos Lake accident see Eos, 2009). Few experimental observations of large‐scale CO2 releases have been made, and the physics and thermochemistry involved are not fully understood, even if considerable progress has been made theoretically.2 In this paper, the experimental techniques used to investigate this specific problem are described and illustrated with some key results extracted from various projects. Innovative techniques were employed to control the mass flowrate, blowdown, nearfield, and farfield dispersion in the atmosphere. A 2 m3 spherical vessel able to store up to 1000 kg of CO2 at a pressure above 8 MPa was used. Dense CO2 was allowed to spill out via a 50 mm pipe. The temperature, CO2 concentration, and density field of the outside cloud were monitored using thermocouples and concentration probes. Among other results, it was in particular shown that when the mass flowrate is large enough, body forces become significant forcing the cloud to stay on the ground.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".