Combustion of reactive metal particles in high-speed flow of detonation products
Bibliographic record
Abstract
It is common practice to add reactive metal particles to high-explosive formulations in order to increase the total energy output. The present study is an effort to increase the understanding of metal particle combustion in detonation products. Aluminum and magnesium particles ranging from 2 to 100 μm are subjected to the flow of detonation products of a stoichiometric mixture of hydrogen and oxygen. Luminosity emitted from the reacting particles is used to determine the reaction delay and duration. It is found that the reaction duration increases as d^n with n ≈ 0.5, more consistent with kinetically controlled reaction rather than the classical diffusion controlled regime. Emission spectroscopy is used to estimate the combustion temperature, which is found to be well below the flow temperature. This fact also suggests combustion in the kinetic regime. Finally, the flow field is modelled with a CFD code and the results are used to model analytically the behaviour of the aluminum particles. Furthermore, a simple analytical model is developed to predict ignition of magnesium particles in nitromethane detonation products. The flow field is simplified by considering the detonation products as a perfect gas expanding in a vacuum in a planar geometry. This simplification allows the flow field tobe solved analytically. A single particle is then introduced in this flow field. Its trajectory and heating history are computed. It is found that most of the particle heating occurs in the Taylor wave and in the quiescent flow region behind it, shortly after which the particle cools. By considering only these regions, thereby considerably simplifying the problem, the flow field ca
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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 | 0.000 |
| 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 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".