Electrohydrodynamic melting of paraffin wax with autonomous and non-autonomous charge injection models
Bibliographic record
Abstract
The present study investigates how different charge injection functions impact electrohydrodynamic-driven flows within a latent heat thermal storage system (LHTSS). To this end, three injection models are examined: the Heaviside step function, the Schottky injection, and autonomous injection. Previous research has largely utilized autonomous charge injection, which neglects the influence of the electric field, thus producing space charge density distributions that do not mimic the realistic charge injection phenomena. In this work, the lattice Boltzmann method (LBM) is applied to simulate the behavior of paraffin wax in an LHTSS under both autonomous and non-autonomous charge injection based on an experimental current–voltage curve. The governing equations were solved using an LBM solver, with the results being verified against multiple experimental and numerical benchmarks. Initially, the paraffin wax began to melt due to thermal conduction from the hot top wall. Electro-convection was then generated by injecting charges via a central circular electrode in the LHTSS, thereby enhancing the heat transfer rate. LBM results demonstrated an electrohydrodynamic enhancement factor of 1.57 using the experimental current–voltage curve presented by Hassan and Cotton [Int. J. Heat Mass Transfer 204, 123831 (May 2023)]. The liquid fraction, heat transfer coefficient, and velocity for the Schottky and Heaviside step functions of injection were very close; however, despite using identical current in all cases, the results for autonomous injection showed deviations of up to 30%, 16%, and 42%, respectively. Furthermore, increasing the material's permittivity worsens the deviations. This study provides insights into how to use charge injection to design LHTSSs and predict heat transfer augmentation in LHTSSs.
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 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 | 0.001 |
| 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".