Development and Validation of a Reduced Critical Radius Model for Cryogenic Cavitation
Bibliographic record
Abstract
Cryogenic fluids such as liquid hydrogen, liquid oxygen, and liquid methane have often been used as liquid rocket propellants, and it is well known that the suction performance of turbopump inducers is better in cryogenic fluids than it is in cold water due to the so-called “thermodynamic effect.” The origin of the thermodynamic effect is the temperature change inside a cavity region that arises from the latent heat transfer across the interface of a cavity. To better understand the suction performance of cavitating cryogenic inducers, we must take into account the temperature changes that take place due to the thermodynamic effect; computational fluid dynamics (CFD) analysis coupled with an energy equation is one of the most powerful tools for this purpose. The computational cost, however, becomes an obstacle for its application to the design phase, so a reduction in the number of governing equations is often preferable. In the present study, a cryogenic cavitation model that does not need to solve an energy equation is proposed as a reduced model; the model is named the “reduced critical radius model.” This model assumes that the temperature change due to the latent heat transfer can be analytically well estimated on the basis of an approximation of the local equilibrium when the pressure inside a cavity is always kept at a saturation vapor pressure at every temperature (at least on the time scale of the flow field). The proposed method was validated carefully for a variety of objects: blunt headforms, hydrofoils, a two-dimensional blunt wing, and Laval nozzles. The results obtained during the validation were in good agreement with the experimental results, except in the case of strong unsteady cavitation. This indicates that the present method, which does not involve solving an energy equation, offers good potential for application to the design phase of cryogenic cavitating inducers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".