Spring-Based Approach for Rapid Modeling of Ejector-Store Interaction
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-2074.vid Store separation analyses is a highly important part of the weapon development process. Considerable effort is expended to verify the safe separation and capture of aircraft released stores. As a result, the topic has been comprehensively researched to improve predictions of the weapons behavior post-ejection, ensure it follows a safe trajectory by distancing itself from the aircraft, and maintains sufficient flight attitudes to ensure capture. Store separation simulations expend considerable time, money, and effort to create accurate freestream and aircraft interference aerodynamic models through the use of Computational Fluid Dynamics (CFD) and wind tunnel tests. However, the interaction between the store and ejector piston is often overlooked and predicted with a simple point-force model. For cases when the lateral Center of Gravity offset (CG) is small, the point-force application model can perform adequately. On the other hand, when the lateral CG offset is large, the model tends to generate an unrealistic rolling moment due to the larger moment arm resulting from the CG offset. To overcome this challenge, a model has been developed to account for multiple contact point loads of an ejection system. Each location is modeled as a damped spring generating a reaction load in response to the ejector push, the inertia of the store, the aircraft maneuvers, the interference aerodynamics, and gravity. The comprehensive loading more accurately models the push of complex ejection systems and stores with arbitrary mass properties.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".