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Record W2334322111 · doi:10.2514/6.2013-3922

Study on Alternative Cargo Launch Options from the Lunar Surface

2013· article· en· W2334322111 on OpenAlexaff
Cheryl A. Blomberg, Zamir A. Zulkefli, Spencer Rich, Steven D. Howe

Bibliographic record

Venue49th AIAA/ASME/SAE/ASEE Joint Propulsion Conference · 2013
Typearticle
Languageen
FieldEngineering
TopicSpacecraft and Cryogenic Technologies
Canadian institutionsWestern University
Fundersnot available
KeywordsAerospace engineeringAstrobiologyMoon landingAeronauticsEnvironmental scienceComputer scienceEngineeringPhysicsApollo

Abstract

fetched live from OpenAlex

In the future, there will be a need for constant cargo launches from Earth to Mars in order to build, and then sustain, a Martian base. Currently, chemical rockets are used for space launches. These are expensive and heavy due to the amount of necessary propellant. Nuclear thermal rockets (NTRs) are the next step in rocket design. Another alternative is to create a launcher on the lunar surface that uses magnetic levitation to launch cargo to Mars in order to minimize the amount of necessary propellant per mission. This paper investigates using nuclear power for six different cargo launching alternatives, as well as the orbital mechanics involved in launching cargo to a Martian base from the moon. Each alternative is compared to the other alternative launchers, as well as compared to using an NTR instead. This comparison is done on the basis of mass that must be shipped from Earth, the amount of necessary propellant, and the number of equivalent NTR launches. Of the options, a lunar coil launcher had a ship mass that is 12.7% less than the next best option and 17 NTR equivalent launches, making it the best of the presented six options.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.047
GPT teacher head0.248
Teacher spread0.201 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2013
Admission routes1
Has abstractyes

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