A Novel Theoretical Model Development and Simulation of Melt‐Electrospinning Using Kane's and Udwadia–Kalaba Methods
Bibliographic record
Abstract
Abstract A novel analytical model development and simulation of melt‐electrospinning process is presented. Unconstrained equations of motion for the description of a discretized melt‐electrospun fiber are formulated using Kane's method. The motions of the spinneret and the collector plate are also incorporated into the kinematics formulation to simulate direct writing and three‐dimensional printing scenarios. Constraints describing viscoelastic joints in the system and the phenomenon of the melt‐electrospun fiber adhering to the collector plate are implemented using the Udwadia Kalaba method. Rotational viscoelastic elements are introduced to mimic the dampening of the whipping motion observed in the unstable region. A novel algorithm is devised to continuously run the simulation with no limit on the total duration and phase transitions. System responses such as fiber diameter, collection size, fiber elongations, and jet speeds are monitored for changes in control parameters including applied voltage, collector distance, and flowrate. The results demonstrate close agreement with experimental observations in the literature. The jet starts relatively straight on the onset of its trajectory and develops subtle whipping motion as it advances toward the collector plate. Reduction in the amplitude of the whipping motion is observed once the free end of the fiber adhered to the collector plate.
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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".