Optimal design of coolant jacket for cryogen transfer pipelines
Bibliographic record
Abstract
Abstract Cryogenic liquids such as liquid oxygen and liquid hydrogen are extensively used in many processes and manufacturing industries. In these industries, transferring cryogens via pipelines is a routine phenomenon. As the boiling points and latent heat of cryogens are low, excessive vaporization of these cryogens is innate. Therefore, ensuring that the cryogen reaches the utility in its liquid form is challenging. In the case of liquid hydrogen and liquid helium, the pipelines are jacketed with a high boiling cryogen like nitrogen. The idea is to dump most of the heat into cheap nitrogen to limit the loss of precious hydrogen or helium. From a heat inleak point, maximizing the amount of nitrogen in the jacket is advantageous by choosing large cross‐sectional areas. Also, larger flow cross sections would lower pressure drops and, therefore, lower pumping costs. However, such a choice would add to the mass of the pipeline. An increase in the mass of the pipeline increases the need for better structural support of the pipeline assembly. Therefore, the design of cryogen jackets for limiting heat inleak is a multi‐objective optimization problem. In this work, we model the heat leak into the hydrogen via the nitrogen jacket and the pressure drop of liquid nitrogen, and we find the mass of the pipeline assembly. Then, we optimize the design of nitrogen jackets fitted over hydrogen pipelines. We employ the evolutionary optimization technique, genetic algorithm (GA), to perform this optimization.cryogen; genetic algorithm; heat inleak; liquid hydrogen; optimization.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 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".