CFD and Flow Network Analysis of Manifolding in a PEMFC
Bibliographic record
Abstract
Near-uniform flow distribution in a fuel cell stack is essential to stack performance and overall system efficiency. The gradients induced by the non-uniformity of the flow within each of the unit cells also have a significant impact on stack durability. In typical configurations, the oxidant and fuel are fed into a stack through manifolds and then enter each unit cell through secondary inlet port. After flowing through the unit cells, the spent gases as well as possible liquid water then enter the outlet header to leave the stack. The objective of this paper is to develop a practical model to predict cell-to-cell flow distribution in a proton exchange membrane fuel cell (PEMFC) stack. The flow distribution is first simulated using a computational fluid dynamics (CFD) tool, CFD-ACE+, in a 3D computational domain for single-phase gas flows. The simulations use a domain encompassing the flow from the inlet header through an array of unit cells to the outlet header. The CFD simulations show that in the outlet header, the flow injected from the unit cells to the header changes the flow pattern considerably, which results in a reduced cross section area for the flow in the axial direction. A circulation zone is seen near the low velocity end of the header, which may potentially become a region where liquid water accumulates. Increasing static pressure along the flow direction is observed in the inlet header. The simulated results are validated and found to be in good agreement with experimentally measured pressures in a fuel cell stack. Based on the observations in the CFD simulations, a flow network model is developed to provide quick estimates of the flow distribution as a function of stack dimensions including header and unit cell geometry. In essence, the flow network model solves for the pressure at each junction of the unit cell and the header. Three fitting parameters are introduced to account for effects of surface roughness of the headers, reduced effective header area in the outlet header, and pressure drop in the unit cell. The flow network model is shown to capture the characteristics of pressure variation and flow distribution obtained in the CFD simulations. The flow network model can effectively match experimental data and be used as a fast tool for initial design of a PEMFC stack.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".