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Record W2165882021 · doi:10.5589/q12-009

Development of the shock simulation methodology for spacecraft components

2012· article· en· W2165882021 on OpenAlexafffundvenue
M. Nejad Ensan, D. G. Zimcik

Bibliographic record

VenueCanadian aeronautics and space journal · 2012
Typearticle
Languageen
FieldMaterials Science
TopicHigh-Velocity Impact and Material Behavior
Canadian institutionsNational Research Council Canada
FundersUniversity of Calgary
KeywordsSpacecraftShock response spectrumRobustness (evolution)Shock (circulatory)Component (thermodynamics)Transient (computer programming)Shock waveComputer scienceAerospace engineeringEngineeringPhysicsAcceleration

Abstract

fetched live from OpenAlex

Spacecraft components encounter mechanical shock from a variety of sources. Components must withstand a series of flight shock pulses and must be designed and tested accordingly to ensure reliability. This paper presents a “Design for Shock” methodology to accurately analyze the response to shock for components under the short duration dynamic loading to assess component structural integrity and demonstrate design robustness. This methodology uses a more realistic simulation of the response of the component to shock loading using a wave propagation solution based on explicit time integration of the transient shock event rather than a stationary random and (or) quasi-static approach which has been traditionally used for this analysis. This approach was applied to a sensitive scientific instrument to demonstrate its effectiveness. The method is shown to provide a more accurate simulation of the shock event thereby reducing the unknown conservatism of the stationary random and (or) quasi-static approach while providing accurate simulation of the transient dynamic response of the structure.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.120
GPT teacher head0.329
Teacher spread0.209 · 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
GenreMethods

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
Published2012
Admission routes3
Has abstractyes

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Same venueCanadian aeronautics and space journalSame topicHigh-Velocity Impact and Material BehaviorFrench-language works237,207