Research on Development of a Simulation Model for an Aircraft Cryogenic LH2 Fuel Tank
Bibliographic record
Abstract
In applying hydrogen as an energy source for aircraft, storing hydrogen in liquid form can increase the energy density of the fuel.However, since hydrogen is liquefied at extremely low temperatures, fuel tanks storing liquid hydrogen (LH2) have the difficulty of having to maintain the storage temperature at cryogenic temperatures.Due to this, heat is easily introduced into the LH2, and when the LH2 is vaporized, a phenomenon occurs in which the pressure inside the tank rapidly increases.This phenomenon is a difference between LH2 fuel tanks and conventional aircraft fuel tanks using fossil fuel.Therefore, in order to design of aircraft fuel tanks using LH2, the phenomena that is ignored in conventional aircraft design such as evaporated fuel need to be considered.In this research, a thermal analysis model is developed to simulate the thermodynamic phenomena of LH2 fuel tanks.The development of the thermal analysis model takes into account tank shape, size, insulation, and fuel operating conditions such as fuel consumption and vent pressure.A simulation model for LH2 fuel tanks applicable to the aircraft's operating altitude and mission profile as wall as fuel operating conditions was developed.Finally, a cryogenic liquid hydrogen fuel tank simulation was performed to confirm that the pressure behavior inside the tank differs depending on the application of the mission profile, and the developed simulation model was confirmed to be applicable to the design of a cryogenic liquid hydrogen tank.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".