MIN3P-HPC: A High-Performance Unstructured Grid Code for Subsurface Flow and Reactive Transport Simulation
Bibliographic record
Abstract
Abstract The numerical simulation of flow and reactive transport in porous media with complex domains is nontrivial. This paper presents a method to implement fully unstructured grid capabilities into the well-established software ParMIN3P-THCm, a process-based numerical model designed for the investigation of subsurface fluid flow and multicomponent reactive transport in variably saturated porous media with parallelization capability. The enhanced code, MIN3P-HPC, is modularized to support different cell types, spatial discretization methods and gradient reconstruction methods. MIN3P-HPC uses a vertex-centered control volume method with consideration of both vertex-based and cell-based material properties (e.g., permeability). A flexible parallelization scheme based on domain decomposition and thread acceleration was implemented, which allows the use of OpenMP, MPI and hybrid MPI-OpenMP, making optimized use of computer resources ranging from desktop PCs to distributed memory supercomputers. The code was verified by comparing the results obtained with the unstructured grid version to those produced by the structured grid version. Numerical accuracy was also verified against analytical solutions for 2D and 3D solute transport, and by comparison with third-party software using different cell types. Parallel efficiency of OpenMP, MPI and hybrid MPI-OpenMP versions was examined through a series of solute transport and reactive transport test cases. The results demonstrate the versatility and enhanced performance of MIN3P-HPC for reactive transport simulation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".