A level set-based extended finite element description for THM couplings in heterogeneous geomaterials
Bibliographic record
Abstract
Most geomaterials are porous and heterogeneous in nature which affects their complex behaviour under mechanical, hydraulic and thermal loading. Such heterogeneities have to be accounted for in poromechanical simulations used to investigate their coupled response. Taking into account the heterogeneity of natural materials in simulations however remains difficult as the geometrical arrangement of the constituent phases has to be properly described and incorporated within the discretization of the governing equations. The concept of level set functions can be used to achieve these two objectives. A level set-based geometry generation procedure will be presented allowing a flexible generation of geometrical data. This procedure can be adapted to produce geometrical data for various applications, ranging from lab-scale samples (inclusion-based systems) to regional scale problems. This geometrical description has the advantage to be seamlessly incorporated within an extended finite element (XFEM) implementation of the poroelasticity governing equations. This allows simulations in which the discretization of the unknown fields is kept independent from the complex geometrical description of the material boundaries. The use of these tools for the simulation will be illustrated on benchmark problems, as well as consolidation phenomena in heterogeneous material structures involving coupled thermo-hydro-mechanical processes, showing the versatility of the obtained computational scheme.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".