Computational Modelling and Simulation of Proton-Exchange Membrane Fuel Cells (Keynote)
Bibliographic record
Abstract
Fuel cells (FC’s) are electrochemical devices that convert directly into electricity the chemical energy of reaction of a fuel (usually hydrogen) with an oxidant (usually oxygen from ambient air). The only by-products in a hydrogen fuel cell are heat and water, making this emerging technology the leading candidate for quiet, zero emission energy production. Several types of fuel cell are currently undergoing intense research and development for applications ranging from portable electronics and appliances to residential power generation and transportation. The focus of this lecture is Proton-Exchange Membrane Fuel Cells (PEMFC’s). An electrolyte consisting of a “solid” polymer membrane, low operating temperatures (typically below 90 °C) and a relatively simple design combine to make PEMFC’s particularly well suited to automotive and portable applications. The operation of a fuel cell relies on electrochemical reactions and an array of coupled transport phenomena, including multi-component gas flow, two phase-flow, heat and mass transfer, phase change and transport of charged species. The transport processes take place in variety of media, including porous gas diffusion electrodes and polymer membranes. The fuel cell environment makes it impossible to measure in-situ the quantities of interest to understand and quantify these phenomena, and computational modelling and simulations are therefore poised to play a central role in the development and optimization of fuel cell technology. We provide an overview of the role of various transport phenomena in fuel cell operation and some of the physical and computational modelling challenges they present. The processes will be illustrated through examples of multi-dimensional numerical simulations of Proton-Exchange Membrane Fuel Cells. We close with a perspective on some of the many remaining challenges and future development opportunities.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".