A coupled phase‐field method (PFM) and thermo‐hydro‐mechanics (THM) based framework for analyzing saturated ice‐rich porous materials
Bibliographic record
Abstract
Abstract This study proposes a novel framework for ice‐rich saturated porous media using the phase‐field method (PFM) coupled with a thermo‐hydro‐mechanical (THM) formulation. By incorporating the PFM and THM approaches based on the continuum theory, we focus on the mechanical responses of fully saturated porous media under freeze‐thaw conditions. The phase transition between liquid water and crystalline ice can be explicitly expressed as captured by evaluating the internal energy and implementing thermal, mechanical, and hydraulic couplings at a diffused interface using PFM. Accurately modeling the coupled mechanical behaviors of ice and soil presents significant challenges. Therefore, in previous numerical frameworks, ad hoc constitutive models were adopted to phenomenologically estimate the overall behavior of frozen soil. To address this, we employ a method that differentiates between the kinematics of the solid and ice constituents, enabling our framework to accommodate distinct constitutive models for each constituent. Within this framework, we naturally introduce anisotropy of frozen soil as it undergoes the freezing process by integrating a transversely isotropic plastic constitutive model for ice. We illustrate the capabilities of our proposed approach through numerical examples, demonstrating its effectiveness in modeling the phase transition process and revealing the overall anisotropic responses of frozen soil.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".