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Record W4205192783 · doi:10.2514/6.2022-2129

Physics-Based Ejector Force Model

2022· article· en· W4205192783 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAIAA SCITECH 2022 Forum · 2022
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsInjectorAerodynamicsInertiaMechanicsMoment of inertiaAerodynamic forceFlow (mathematics)PhysicsHigh fidelityMechanical engineeringEngineeringClassical mechanicsAcoustics

Abstract

fetched live from OpenAlex

View Video Presentation: https://doi.org/10.2514/6.2022-2129.vid A critical part of the store separation analysis is the modeling of the ejector forces. Typically, such loading is included into separation simulation via a table look-up of the ejector forces either as a function of time or stroke of the ejector pistons. The values of the ejector forces are generally obtained by curve fitting experimental static ejection test data. Depending on the fidelity of the model, it may or may not consider the mass of the store in the force history, and such models virtually never consider the moments of inertia of the store or the aerodynamic loading at the time of ejection. This paper develops a model based on the physics of the flow within the ejector to account for the creation of ejector gases, losses in the system, and appropriate physical feedback of the store mass properties, aircraft loading, and the aerodynamics.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.870
Threshold uncertainty score0.736

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 head0.010
GPT teacher head0.200
Teacher spread0.191 · 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