Simulation of hemp fibre bundle and cores using discrete element method
Bibliographic record
Abstract
Demands for high-grade hemp fibre are increasing for various industrial applications. To obtain high-grade fibre, it is important to understand the mechanical behaviour of hemp fibre and core. Modelling using discrete element method is a promising approach to simulate mechanical behaviour of any materials, including hemp fibre and core. In this study, a commercial discrete element software, Particle Flow Code - Three Dimension (PFC 3D ) was used to simulate hemp fibre and core. Because the basic PFC 3D particles, named balls, are spherical. Individual virtual hemp fibres were defined as strings of balls held together by PFC 3D parallel bonds. The results showed that the resulting virtual fibre was flexible and could be bended and broken by forces, which appropriately reflect the characteristics of hemp fibre. Using the clump logic of PFC 3D , the virtual hemp core was defined as a rigid and unbreakable body, which reflect the characteristics of the core. The virtual fibre and core were defined with several microproperties, some of which were previously calibrated. The five PFC 3D bond properties including normal and shear stiffness, pb_kn and pb_ks; normal and shear strength, σc and τc and bond disk radius, R of the virtual fibre were calibrated in this study. The calibration started with developing a PFC 3D model to simulate fibre tensile test. The microproperties of virtual fibre and core were calibrated through running the PFC 3D model. The simulations were compared with literature data from fibre tensile tests. The results showed that normal and shear strength of the bond could be considered equal to the external stress applied on the fibre due to axial load during tensile test. Shear stiffness value could be assumed low to make the fibre flexible. The normal stiffness of the bond was determined to be 9e20 N/m by trial and error method.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".