Finite Element Bounds to the Mass Flow Rate for Electro-Osmotic Flows in Two Space Dimensions
Bibliographic record
Abstract
The micro pumping technology is one of the major and growing research fields in microfluidics. The use an electric voltage to induce electro-kinetic fluid flow is an efficient and reliable mechanism for micro pumping systems. The prediction of quantities such as the mass flow rate or the mean species concentration for this type of flow, termed the Electro-Osmotic flow, plays a crucial role in the design and control process of the entire microfluidic system. To this end, numerical techniques are efficient to evaluate these quantities but accuracy depends on the mesh utilizes. The a posteriori finite element output bound method is used to calculate these quantities while offering information regarding accuracy. The bound method applied here-in is based on the flux-free approach and provides relevant, inexpensive, and asymptotic lower and upper bounds to the mass flow rate of an Electro-Osmotic flow in a cross-intersection of a two-dimensional microchannel. To obtain shaper bounds, the flux-free approach is further enhanced by an adaptive mesh refinement strategy. This work focuses on the development of the numerical procedure for Electro-Osmotic flows and reports performance of the method in terms of numerical accuracy and computational cost.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".