THERMO-ELECTRO-MECHANICAL INVESTIGATION OF VOLTAGE DROP IN ANODE ASSEMBLY USING FINITE ELEMENT METHOD
Bibliographic record
Abstract
In today's context, aluminum producers strive to improve their position regarding energy consumption and production costs. To do so, mathematical modeling offers a good way to study the behavior of the cell during its life. In this paper, a fully-coupled, thermoelectro-mechanical (TEM) model of a whole hexapod anode assembly is presented. The parametric finite element (FE) model was developed using APDL (ANSYS Parametric Design Language) and was solved with a FESh++ TEM application to simulate the thermal and electrical effects of the reduction process on the anode assembly. A half block submodel of the full anode assembly model was used for the analysis in order to achieve a better understanding of the system and to evaluate the hypothesis of lowering the anodic voltage drop in the anode assembly through a simple change in the stub diameter. After calibration with the experimental results, sensitivity analysis (SA) was carried out to investigate the impact of changes of some critical parameters on the initial air gap at the cast iron to carbon interface and consequently on the total voltage drop in the system. These critical parameters were: effective stub temperature at cast iron solidification, stub diameter and coefficient of thermal expansion (CTE) which showed they have marginal or remarkable impact on the total voltage drop depending on the operating temperature.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".