Parallel Computing of a Meshfree Method and Its Application in Geotechnical Engineering
Bibliographic record
Abstract
Among meshfree methods to overcome the ineffectiveness in handling extreme material deformation, Reproducing Kernel Particle Method (RKPM) has demonstrated its great suitability for structural analysis. This paper presents applications of RKPM in elasto-plastic problems after a review of meshfree methods and an introduction to RKPM. Different from the graph-based partitioning methods such as Metis, which is quite popular for mesh-based analysis, a geometry-based partitioner is developed and used in parallel computing of the meshfree method. The effectiveness and performance with different partitions are then compared and a comparison of the meshfree method with finite element methods is presented. A slope stability problem in geotechnical engineering is analyzed as an illustrative case. The parallel simulations are conducted on an SGI Onyx3900 supercomputer and a Dawning TC4000L PC cluster, with MPI message passing statements used for all communications among processors for various partitions. The comparison between the RKPM and the FEM under identical conditions shows that the RKPM is more suitable for problems, where there exists extremely large strain such as in the case of slope sliding.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".