Missile propulsion performance modeling in a visual simulation environment
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it