Missile propulsion performance modeling in a visual simulation environment
Bibliographic record
Abstract
The Defence Research Establishment Valcartier (DREV) in Canada and the TNO Prins Maurits Laboratory (TNO-PML) in the Netherlands are investigating ducted rocket propulsion technology and its impact on missile performance in a collaborative research program. One key component of this collaboration is the development of a Modeling and Simulation (M&S) capability to evaluate the applicability, benefits and limitations of the ducted rocket for air-to-air missiles in realistic mission engagement scenarios. Since the engagement simulation is used specifically to assess the impact of missile propulsion on overall weapon performance, the selection of the components of the missile model and their level of fidelity have been purposely tailored to focus on those performance drivers having a dependence on the propulsion system.The engagement model includes a six-degree-of-freedom (6DOF) representation of the missile flight dynamics as well as component models of suitable fidelity for the seeker, guidance and autopilot. The core component of the missile model is the standalone Fortran-based TNO DREV ducted rocket engine model. The complete engagement model including the launcher aircraft, missile and target, was implemented in Matlab/Simulink to take advantage of the wide range of features available. Visual environments provide an integrated capability for fast prototyping of dynamic systems, facilitate team development through a standard approach for model implementation, and offer a flexible mechanism for the re-use of legacy models. Sample results of simulated missile-target engagements illustrate the application of this simulation capability to missile propulsion trade-off studies and analysis of system concepts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".