A Java-Based Graphical User Interface for A 2-D Discrete Element Program
Bibliographic record
Abstract
Many discrete element programs are not user friendly. They are usually run in batch mode that does not allow user interaction. Problems during an analysis simply result in abortion of the execution. Input procedures are usually scripts. Command syntax has to be memorized and results are seen only after program execution has been terminated. Even some commercial programs that have Windows-style pre- and post-processors are not easy to understand, and require a rather steep learning curve in their use. Input files can be saved, avoiding re-typing, however all input calculations, such as element generation, contact model calculation, etc., are repeated every time the program is started. Output typically consists of animation of element motion, shown simultaneously during computation. Additional analyses can be performed later after the calculation is finished. For an average technically educated user, the lack of click, drag-and-drop mouse activities is typically taken as a disadvantage and, in our opinion, presents one of the main obstacles for wider spreading use of DEM, not only in industrial application but also within the academic and research community. In an attempt to overcome this problem, a graphical user interface for a two-dimensional distinct element program DEMEOS has been developed. This program is developed to model the excavation of the Alberta oil sands, a rather peculiar viscous-frictional material with time and temperature dependent behaviour. DEMEOS is coded in Sun Java JDK1.3.1. Its functional flowchart is shown in Figure 1. Java was chosen because of its increased portability, but it has its pros and cons. It is a language under development, fast changing, and requires frequent re-coding from developers in order to keep pace with the current trends and capabilities. Comparison between various programming solutions in DEMEOS (especially the development of customised Java data structures) and an exemplary C++ DEM program like PFC will provide an interesting discussion in determining the advantages and disadvantages of one over the other.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.125 | 0.043 |
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".