Parallel AMR Scheme for Turbulent Multi-Phase Rocket Motor Core Flows
Bibliographic record
Abstract
The development of a parallel adaptive mesh refinement (AMR) scheme is described for solving the governing equations for turbulent multi-phase (gas-particle) core flows in solid propellant rocket motors (SRMs). The Favre-Averaged Navier-Stokes equations are solved for the gas-phase. Turbulence closure is achieved by using a two equation turbulence model. An Eulerian formulation is used to describe the motion of the inert, dilute, and disperse particle-phase. A cell-centred upwind finite-volume discretization and the use of limited solution reconstruction, Riemann solver based flux functions to determine the inviscid flux for the gas and particle phases at cell interfaces. Green-Gauss integration over the diamond-path defined at cell interfaces is used to determine the primitive-variable gradients for evaluation of the viscous fluxes. A parallel multigrid method coupled with an explicit optimally-smoothing multi-stage time-stepping scheme is used to obtain steady state solutions. Unsteady calculations are achieved through the use of a dual time-stepping approach. The propagation of the propellant-core flow interface is tracked using the level set method and a mesh adjustment scheme is used to fit the computational mesh to the location of the burning interface. Application of block-based AMR accurately resolves the multiple solution scales of the fluid flow and enables efficient and scalable parallel implementations on distributed memory multi-processor architectures. High-scalability of the model has been achieved on a parallel cluster computer consisting of 276 processors. Various numerical test cases are presented to verify the validity of the scheme as well as demonstrate the capabilities of the approach for predicting SRM core flows. I.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".