A coupled hydro-thermo-mechanical model based on TLF-SPH for simulating crack propagation in fractured rock mass
Bibliographic record
Abstract
Abstract In this paper, a hydro-thermo-mechanical coupling model based on the smoothed particle hydrodynamics with total Lagrangian formula (HTM-TLF-SPH) is proposed to simulate the crack propagation and instability process of fractured rock mass. TLF-SPH uses the Lagrangian kernel approximation, that is, the kernel function and its gradient need only be calculated once in the initial configuration, which is much more efficient than the smoothed particle hydrodynamics (SPH) based on the Euler kernel approximation. In TLF-SPH, particles interact with each other through virtual link, and the crack propagation path of rock mass is tracked dynamically by capturing the fracture of virtual link. Firstly, the accuracy and robustness of the HTM-TLF-SPH coupling model are verified by a reference example of drilling cold shock, and the simulation results agree well with the analytical solutions. Then, the crack propagation law of surrounding rock and the evolution characteristics of physical fields (displacement, seepage and temperature fields) after excavation and unloading of deep roadway under the coupling condition of hydro-thermo-mechanical are investigated. In addition, the seepage and heat transfer processes of the surrounding rock of Daqiang coal mine under different coupling conditions are successfully simulated. Meanwhile, the effect of the boundary water pressure difference on the temperature and seepage fields under the hydro-thermal coupling condition is quantitatively analyzed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".