IFP-C3D: an Unstructured Parallel Solver for Reactive Compressible Gas Flow with Spray
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Bibliographic record
Abstract
IFP-C3D, a hexahedral unstructured parallel solver dedicated to multiphysics calculation, is being developed at IFP to compute the compressible combustion in internal engines. IFP-C3D uses an unstructured formalism, the finite volume method on staggered grids, time splitting, SIMPLE loop, sub-cycled advection, turbulent and Lagrangian spray and a liquid film model. Original algorithms and models such as the conditional temporal interpolation methodology for moving grids, the remapping algorithm for transferring quantities on different meshes during the computation enable IFP-C3D to deal with complex moving geometries with large volume deformation induced by all moving geometrical parts (intake/exhaust valve, piston). The Van Leer and Superbee slop limiters are used for advective fluxes and the wall law for the heat transfer model. Physical models developed at IFP for combustion (ECFM gasoline combustion model and ECFM3Z for Diesel combustion model), for ignition (TKI for auto-ignition and AKTIM for spark plug ignition) and for spray modelling enable the simulation of a large variety of innovative engine configurations from non-conventional Diesel engines using for instance HCCI combustion mode, to direct injection hydrogen internal combustion engines. Large super-scalar machines up to 1 000 processors are being widely used and IFP-C3D has been optimized for running on these Cluster machines. IFP-C3D is parallelized using the Message Passing Interface (MPI) library to distribute calculation over a large number of processors. Moreover, IFP-C3D uses an optimized linear algebraic library to solve linear matrix systems and the METIS partitionner library to distribute the computational load equally for all meshes used during the calculation and in particular during the remap stage when new meshes are loaded. Numerical results and timing are presented to demonstrate the computational efficiency of the code.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it