Bibliographic record
Abstract
The Cu-Cl thermochemical cycle is a promising method to generate hydrogen \nas a clean fuel for human use in the future. The cycle can be coupled to nuclear \nreactors to supply its heat requirements. The cycle generates hydrogen by splitting \nwater molecules through a series of chemical reactions. Thermal management within \nthe cycle is crucial for improving its thermal efficiency. The cycle has an average \ntheoretical efficiency of around 46% without any heat recovery. The efficiency may \nincrease up to 74%, if all heat associated with the products of the cycle???s steps is \nrecycled internally. The products of the different processes that transfer heat are; \noxygen, hydrogen, and molten CuCl. The heat carried by oxygen and hydrogen can be \nrecovered by the use of conventional heat exchangers. However, recovering heat from \nmolten CuCl is very challenging due to the phase transformations that molten CuCl \nundergoes, as it cools down from liquid to solid states. This thesis presents a new \nmodel that predicts the fluid flow and heat transfer in a direct contact heat exchanger, \ndesigned to recover the heat from molten CuCl, through the physical interaction \nbetween CuCl droplets and air. Numerical results for the variations of temperature, \nvelocity, heat transfer rate, and so forth, are given for two cases of CuCl flow. The \npredicted dimensions of the heat exchanger were found to be a diameter of 0.13 m, \nand a height of 0.6 and 0.8 m for 1 and 0.5 mm droplet diameters, respectively. The \nresults obtained provide valuable insights for the equipment design and scale-up of \nthe Cu-Cl cycle.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".