Fuel Estimation for Stardust-NExT Mission
Bibliographic record
Abstract
The success of the Stardust-NExT (New Exploration of Tempel 1) mission, which is a follow-on to the Stardust primary mission, depends upon an accurate knowledge of its remaining fuel. Measurements indicate that delaying the arrival of Stardust at Tempel 1 by at least 8 hours will maximize the probability of reaching the objective. Several techniques are used to measure the amount of remaining propellant in spacecraft. Bookkeeping, PVT (Pressure, Volume, and Temperature) and thermal Propellant Gauging System (PGS) are the most popular methods. The PGS method uses the temperature response of the tank to heating in order to infer the propellant load of the tank. Implementation of the PGS method for the Stardust spacecraft is discussed in the current paper. Along with the propellant estimation, an uncertainty analysis was conducted. The current paper compares fuel estimates made for Stardust by several techniques, including bookkeeping, PVT, and thermal PGS. These methods are described in detail, and their results and uncertainties for Stardust are compared. Based on these fuel estimates, project scientists have made their recommendations for the time-of-arrival adjustment. This paper shows how the PGS method can be useful for existing and future NASA/JPL missions. The accuracy of the fuel estimation by the thermal PGS method increases as the fuel load decreases due to the increased sensitivity of the temperature rise as the tank load decreases. The method can be used for mono- or bi-propellant propulsion systems with one-tank or multiple-tank configurations. Execution of the PGS method does not require model calibration during spacecraft Thermo-Vacuum Test.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".