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Record W2062630614 · doi:10.5539/mas.v6n3p100

Lateral Entry Guidance for Lunar Return Vehicles

2012· article· en· W2062630614 on OpenAlexvenueno aff
zunshi shui, Jun Zhou, Zhilei Ge

Bibliographic record

VenueModern Applied Science · 2012
Typearticle
Languageen
FieldEngineering
TopicSpace Satellite Systems and Control
Canadian institutionsnot available
Fundersnot available
KeywordsAzimuthComputer scienceRotation (mathematics)Sign (mathematics)Point (geometry)Monte Carlo methodSimulationControl theory (sociology)PhysicsGeologyGeodesyGeometryOpticsMathematicsControl (management)Artificial intelligenceStatisticsMathematical analysis

Abstract

fetched live from OpenAlex

A lateral entry guidance is designed based on azimuth error and crossrange error for a low L/D ratio lunar return vehicle. The conventional technique to determine the bank sign is according to the crossrange error, which might cause large crossrange deviation during Kepler phase if azimuth error at skip out point is large. This paper develops a combined lateral guidance logic to minish accumulate crossrange error caused by azimuth error during Kepler phase. The lateral logic decides the value of crossrange threshold by constantly predicting the crossrange at skip out point. The azimuth error at skip out point is regulated to a small value by only one bank reverse through online adjusting reversal threshold. The effect of earth rotation is compensated by moving the landing site to opposite direction. During the second entry, the lateral logical is designed based on the crossrange error to achieve precise lateral control. The lateral guidance logic is validated by numerical simulations. Monte Carlo simulations show that the proposed lateral guidance logic can deliver the vehicle to the desired landing site in the presence of large initial dispersions and disturbance.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.880
Threshold uncertainty score0.431

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.215
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2012
Admission routes1
Has abstractyes

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