OP2A: A Multiphysics Fluid Simulation Framework
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2022-0826.vid A new, modular, open-source framework is presented for the simulation of hypersonic aerothermodynamics. The Open-source multiPhysics framework for Plasma Applications (OP2A) allows for the construction of multiphysics simulations, including all the necessary components for a comprehensive simulation of a body in hypersonic flow. Components currently included allow for the evolution of the Euler or Navier-Stokes equations with finite volume discretisation on linear or axisymmetric block-structured grids. Solutions can be computed to either first or second order accuracy using various numerical methods, together with flux limiting routines to maintain total variation diminishing (TVD) properties and minimise potentially spurious results. OP2A is written in object oriented C++, so as to facilitate the implementation of new modules, allowing OP2A to be forward looking with respect to new methods. The framework currently utilises the MPI and OpenMP libraries for parallel computation, METIS for mesh decomposition, and PLATO (PLAsma in Thermodynamic nOn-equilibrium) for plasma and non-equilibrium chemistry calculations. Other than these specific dependencies, only standard C++ libraries are used in OP2A, meaning that it is highly portable between different hardware architectures. An overview of the OP2A framework, and of the numerical methods included in OP2A is provided. This is followed by details of the simulations – including validation cases – that have been run to date, as well as planned future studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.052 | 0.016 |
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".