Numerical Two-Phase Simulations of Alkaline Water Electrolyzers
Bibliographic record
Abstract
Alkaline water electrolyzers (AWE) have several advantages over other types of electrolyzers, including their high efficiency and especially their relatively low cost due to the usage of non-precious metal catalysts, such as nickel and iron, for the electrodes. Information about local quantities and physical phenomena such as the formation of gas bubbles, current densities, temperatures or local species concentrations within a running cell are important for their improvement. Multiphysical computational fluid dynamics (CFD) simulations of electrochemical components using detailed three-dimensional models can provide valuable insight on local behaviors and characteristics that are difficult or impossible to measure experimentally. This work extends the CFD library openFuelCell2 1 , which has been implemented using the open-source platform OpenFOAM®, to simulate AWE cells. The model considers the major transport phenomena, including two-phase fluid flow, heat and mass transfer, electrochemical reactions, species transfer and charge transfer in the various functional regions of the cell. It employs an Eulerian-Eulerian approach to characterize the behavior of each phase comprising interphase mass transport, momentum exchange and heat transfer. Appropriate mapping functions are used to couple the physically distinct regions together. A Butler-Volmer equation characterizes the electrochemical reactions that are assumed to occur in electrodes of finite thickness. This model is used to simulate a single zero-gap AWE cell, depicted in Fig. 1, for different operating conditions such as varying temperatures and volumetric flow rates. The conducted studies provide insight into the local formation of the created gas phase (bubbles), the distribution of species within the gas and electrolyte and their impact towards the performance of the running cell. These numerically obtained results are compared to in-house available and gathered experimental data. Figure 1 demonstrates that the polarization curves obtained at various temperatures are in good agreement with the experimental data. Figure 1
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.001 |
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 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".