Cryogenic Tank Modeling for the Saturn AS-203 Experiment
Bibliographic record
Abstract
A computational fluid dynamics (CFD) model is developed for the Saturn S-IVB liquid hydrogen (LH2) tank to simulate the 1966 AS-203 flight experiment. This significant experiment is the only known, adequately-instrumented, low-gravity, cryogenic self pressurization test that is well suited for CFD model validation. A 4000-cell, axisymmetric model predicts motion of the LH2 surface including boil-off and thermal stratification in the liquid and gas phases. The model is based on a modified version of the commercially available FLOW3D software. During the experiment, heat enters the LH2 tank through the tank forward dome, side wall, aft dome, and common bulkhead. In both model and test the liquid and gases thermally stratify in the low-gravity natural convection environment. LH2 boils at the free surface which in turn increases the pressure within the tank during the 5360 second experiment. The Saturn S-IVB tank model is shown to accurately simulate the self pressurization and thermal stratification in the 1966 AS-203 test. The average predicted pressurization rate is within 4% of the pressure rise rate suggested by test data. Ullage temperature results are also in good agreement with the test where the model predicts an ullage temperature rise rate within 6% of the measured data. The model is based on first principles only and includes no adjustments to bring the predictions closer to the test data. Although quantitative model validation is achieved or one specific case, a significant step is taken towards demonstrating general use of CFD for low-gravity cryogenic fluid modeling.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.006 | 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".