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Record W1604797751 · doi:10.1063/1.2845051

In-Situ Propellant Supplied Lunar Lander Concept

2008· article· en· W1604797751 on OpenAlexfundno aff
Benjamin Donahue, Curtis Maulsby, Mohamed S. El‐Genk

Bibliographic record

VenueAIP conference proceedings · 2008
Typearticle
Languageen
FieldEngineering
TopicSpacecraft and Cryogenic Technologies
Canadian institutionsnot available
FundersInstitute of Population and Public Health
KeywordsPropellantPayload (computing)SpacecraftLunar orbitAerospace engineeringPropulsionMoon landingCrewDescent (aeronautics)Environmental scienceConsumablesAeronauticsAstrobiologySystems engineeringComputer scienceEngineeringApolloPhysics

Abstract

fetched live from OpenAlex

Future NASA and commercial Lunar missions will require innovative spacecraft configurations incorporating reliable, sustainable propulsion, propellant storage, power and crew life support technologies that can evolve into long duration, partially autonomous systems that can be used to emplace and sustain the massive supplies required for a permanently occupied lunar base. Ambitious surface science missions will require efficient Lunar transfer systems to provide the consumables, science equipment, energy generation systems, habitation systems and crew provisions necessary for lengthy tours on the surface. Lunar lander descent and ascent stages become significantly more efficient when they can be refueled on the Lunar surface and operated numerous times. Landers enabled by Lunar In‐Situ Propellant Production (ISPP) facilities will greatly ease constraints on spacecraft mass and payload delivery capability, and may operate much more affordably (in the long term) then landers that are dependant on Earth supplied propellants. In this paper, a Lander concept that leverages ISPP is described and its performance is quantified. Landers, operating as sortie vehicles from Low Lunar Orbit, with efficiencies facilitated by ISPP will enable economical utilization and enhancements that will provide increasingly valuable science yields from Lunar Bases.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.668

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 head0.019
GPT teacher head0.209
Teacher spread0.190 · 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

Citations0
Published2008
Admission routes1
Has abstractyes

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