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Record W7164827005 · doi:10.4050/sm_struct_2001-3417

Manpads Modeling And Simulation For Rotorcraft Structures Survivability

2001· article· W7164827005 on OpenAlexaff
Chad Sparks

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicMilitary Defense Systems Analysis
Canadian institutionsBell Helicopter Textron (Canada)
Fundersnot available
KeywordsSurvivabilityModeling and simulationStructural failureWeapon systemAviationProjectile

Abstract

fetched live from OpenAlex

Shoulder-launched Man Portable Air Defense Systems (MANPADS) missiles are fast becoming the weapon of choice for ground personnel against aircraft. Highly effective, economical, and widely proliferated worldwide, these weapons pose a serious threat to rotorcraft, which typically conduct operations that place them in harms way of MANPADS during all mission phases. In recent conflicts, data collected involving MANPADS and aircraft suggests that a hit does not necessarily equate to a kill. In addition, innovative structural design concepts may improve the chances of rotorcraft to survive MANPADS encounters, particularly for larger airframes. This paper will describe the methodology currently under development at Bell Helicopter to provide physics-based modeling and simulation of aircraft structural response to MANPADS and HEI projectile detonations. The paper will discuss how the technology will be used as a design tool to increase understanding of the damage mechanisms, and how results may be used to develop new, more survivable structural concepts in a costeffective manner.

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)0.000
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: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.022
GPT teacher head0.261
Teacher spread0.238 · 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
Published2001
Admission routes1
Has abstractyes

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