A CFD Analysis of Thrust Losses in a Solid Rocket Motor Nozzle
Bibliographic record
Abstract
Rocket propulsion is an ancient discipline built on centuries of innovation. Yet, the internal physics of rocket motors remains incompletely understood, with modern development still relying heavily on idealized models and empirical correlations. Recent advances in computational power have enabled a new approach that offers the potential to uncover the complex flow phenomena inside rocket motors: Computational Fluid Dynamics (CFD). This work presents a comprehensive numerical framework for predicting the performance of a composite propellant rocket motor with a converging-diverging de Laval nozzle. The simulation captures compressible, reactive flow and the effects of suspended liquid aluminium oxide particles, employing Large Eddy Simulation with an implicit sub-grid formulation (ILES) to resolve the unsteady three-dimensional dynamics. Chemical kinetics are governed by a 97-reaction mechanism model and integrated using a third-order Rosenbrock ODE solver. Dispersed alumina particles in the nozzle flow are tracked with a Lagrangian Particle Tracking (LPT) module that exchanges momentum and heat with the gas phase. The method is validated against experimental data from a static-fire test of a Ballistic Test Motor (BTM) conducted at the Swedish Defence Research Agency (FOI). Simulated nozzle efficiency reaches 97.12%, matching the experimental result of 96.0%. Beyond serving as an accurate physics-based alternative to traditional one-dimensional ballistics codes, the framework provides spatially and temporally resolved flow fields that enhance physical insight. Though applied here to a lab-scale BTM, the methodology is extendable to full-scale motors, supporting future propulsion design and test-data interpretation.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".