CFD Modeling of Gas-Liquid Flow and Heat Transfer in a High Pressure Water Electrolysis System
Bibliographic record
Abstract
A high-pressure water electrolysis system has been investigated numerically and experimentally. The advanced CFD model of two-phase flow, which calculated the 3D distributions of pressure, gas and liquid velocities and gas and liquid volume fractions, has been developed to account for all the major components in the system, and appropriate constitutive equations for two-phase flow parameters were selected for various parts of the system, such as the cell stack, riser, separator and downcomer. Heat transfer between the two phases, and between the gas-liquid mixture and cooling coils located in the gas-liquid separator was also accounted for. The model was validated using comparisons of predicted liquid flow rate with the liquid flow rate measured in the downcomer, where a single-phase liquid flow existed. The effects of pressure, current density, number of cells, and bubble size were investigated with the numerical model. The numerical predictions matched the general trends obtained from the experimental results with regard to the effects of pressure and current density on the liquid flow rate. The validated CFD model is being used as a cell design tool at Hydrogenics Corporation.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".