OpenCMP: An Open-Source Computational MultiphysicsPackage
Bibliographic record
Abstract
OpenCMP is a computational multiphysics software package based on the finite element method (Ferziger & Perić, 2002).It is primarily intended for physicochemical processes in which fluid convection plays a significant role.OpenCMP uses the NGSolve finite element library (Schöberl, n.d.) for spatial discretization and provides a configuration file-based interface for pre-implemented models and time discretization schemes.It also integrates with Netgen (Schöberl, n.d.) and Gmsh (Geuzaine & Remacle, 2009) for geometry construction and meshing.Additionally, it provides users with built-in functionality for post-processing, error analysis, and data export for visualisation using Netgen (Schöberl, n.d.) or ParaView (Ahrens et al., 2005).OpenCMP development follows the principles of ease of use, performance, and extensibility.The configuration file-based user interface is intended to be concise, readable, and intuitive.Furthermore, the code base is structured and documented (Monte, Elizabeth J, 2021) such that experienced users with appropriate background can add their own models with minimal modifications to existing code.The finite element method enables the use of high-order polynomial interpolants for increased simulation accuracy, however, continuous finite element methods suffer from stability and accuracy (conservation) for fluid convection-dominated problems.OpenCMP addresses this by providing discontinuous Galerkin method (Cockburn et al., 2000) solvers, which are locally conservative and improve simulation stability for convectiondominated problems.Finally, OpenCMP implements the diffuse interface or diffuse domain method (Monte et al., 2021;Nguyen et al., 2018), which a type of continuous immersed boundary method (Mittal & Iaccarino, 2005).This method enables complex domains to be meshed by non-conforming structured meshes for improved simulation stability and reduced computational complexity, under certain conditions (Monte et al., 2021).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.084 | 0.031 |
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".