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Record W4392382874 · doi:10.32920/25336312.v1

Design and Analysis of a Whipple Shield

2024· preprint· en· W4392382874 on OpenAlexaff
Artin Sarkezians

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMaterials Science
TopicHigh-Velocity Impact and Material Behavior
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSpacecraftProjectileShieldObservableRange (aeronautics)Ballistic limitWork (physics)Computer scienceMeteoroidSpace debrisLimit (mathematics)ComputationAerospace engineeringMechanicsPhysicsMathematicsAlgorithmEngineeringGeologyMechanical engineeringMathematical analysisAstronomy

Abstract

fetched live from OpenAlex

<p>The Whipple shield, an innovation first proposed in the 1940s, is a common way by which spacecraft are protected from the threat of meteoroid impact. Engineers and scientists have learned how to track and respond to potential collisions with other spacecraft and observable objects, so it is now the smallest untraceable debris that pose the greatest risk. This work explores the design and optimisation of a two-layer metallic Whipple shield. The ‘new’ Cour-Palais ballistic limit equations are used in conjunction with various correction factors and modifiers from additional works and assembled into a model found to be 78.9% accurate against a bank of test data. A brute-force optimisation algorithm analyses a broad range of configurations against a minimum critical projectile diameter and selects three solutions based on minimum mass and proposed “performance” and “value” factors. The relationship between the performance factor and the shield’s configuration are considered. Finite element (FE) and smoothed particle hydrodynamics (SPH) methods are explored as numerical solutions, but ultimately relinquished and recommended for future work.</p>

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.008
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.046
GPT teacher head0.312
Teacher spread0.266 · 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.

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
Published2024
Admission routes1
Has abstractyes

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