Improvement to the Analytical Predictor-Corrector Guidance Algorithm Applied to Mars Aerocapture
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Bibliographic record
Abstract
Introduction O NE of the state-of-the-art technologies considered to reduce the cost of planetary exploration is aerocapture. This technique allows the reduction of fuel cost for planetary insertion by using atmospheric drag to decrease the total orbital energy of the vehicle. It consists in a reduction of velocity from a hyperbolic orbit or highly elliptical orbit to a low-altitude near-circular planetary orbit. It has previously been demonstrated that aerocapture would be beneficial for human exploration of Mars.1 The purpose of an aerocapture maneuver is to bring the vehicle from given atmospheric entry conditions to desired atmospheric exit conditions. The desired exit conditions are typically expressed as a given apoapsis radius of the unperturbed orbit once the vehicle is out of the atmosphere. This apoapsis radius is chosen to minimize the velocity impulse that is required to reach the final mission orbit. Up to now, several types of algorithms, such as the analytical predictor-corrector,2−6 the energy controller,4,7 the numerical predictor-corrector,4,8−10 and the terminal point controller6,11 have been developed, considering only the vehicle bank angle as control parameter. As shown in Fig. 1, the authors classify these algorithms in three main categories: the analytical algorithms, the numerical algorithms, and the predefined-trajectory algorithms. Firstly, the analytical predictor corrector (APC) and the energy controller are part of the first category. These algorithms make certain assumptions that lead to an analytical guidance solution to the exit conditions for the current vehicle state. Secondly, the numerical predictor corrector numerically integrates the remaining part of the trajectory to predict the atmospheric exit conditions from the current position and updates the commanded bank angle for the remaining part of the trajectory. It is therefore part of the second category. Finally, the terminal point controller, part of the third category, uses a predefined optimal trajectory. In this case, the vehicle tries to remain on the optimal trajectory at any moment in time.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it