MétaCan
Menu
Back to cohort

Preliminary Study on Supersonic Two-phase Expansion Refrigeration Technology in Liquid Hydrogen Temperature Region

2023· preprint· en· W4321790163 on OpenAlexaboutno aff
Aihong Zou, Ercang Luo

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicCombustion and Detonation Processes
Canadian institutionsnot available
FundersNational Key Research and Development Program of China
KeywordsRefrigerationSupersonic speedLiquid hydrogenLiquid phaseMaterials scienceRefrigerantThermodynamicsHydrogenPhase changePhase (matter)ChemistryPhysicsHeat exchangerOrganic chemistry

Abstract

fetched live from OpenAlex

As a clean energy carrier, hydrogen has attracted extensive international attention.Hydrogen liquefaction is the key solution of large-scale utilization of hydrogen energy.How to realize the high-efficiency and low-cost liquefaction of hydrogen is one of the key technologies that need to be solved urgently.In the current mainstream hydrogen liquefaction technology, the highspeed rotating turbine may have an adverse impact on the stable operation of the bearing.Therefore, the supersonic two-phase expander in liquid hydrogen temperature zone is innovatively employed for the first time to complete expansion refrigeration, condensation phase change, gas-liquid separation and pressure recovery in a compact space.It has the advantages of gas-liquid two-phase operation, direct liquefaction, easy high power, simple structure and low processing cost.In the hydrogen supersonic two-phase expander, Laval nozzle is the key component.The main research contents in this paper include: (1) Establish the design criteria of hydrogen Laval nozzles.(2) The design law of hydrogen Laval nozzles under different working conditions.(3) The cooling characteristics of hydrogen Laval nozzles under different operating conditions.This paper preliminarily investigates the liquefaction possibility of supersonic two-phase expansion refrigeration technology in liquid hydrogen temperature region, and supports the development of new hydrogen liquefaction technology.It has important strategic value for promoting the realization of carbon neutralization goal in clean energy industry.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.003
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.031
GPT teacher head0.301
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same topicCombustion and Detonation ProcessesFrench-language works237,207