Multiphase Modeling of a Flowing Electrolyte-Direct Methanol Fuel Cell
Bibliographic record
Abstract
Direct methanol fuel cells (DMFCs) are considered one of the leading contenders for low power applications due to their energy dense, liquid fuel as well as low greenhouse gas emissions.However, DMFCs have lower than predicted performance due to methanol crossover.One proposed solution is to allow a liquid electrolyte, such as diluted sulfuric acid, to flow between the anode and cathode, thereby removing any methanol that attempts to crossover to the cathode.The corresponding fuel cell is named the flowing electrolyte -direct methanol fuel cell, or FE-DMFC.So far few researchers have examined the effectiveness of this fuel cell and none have explored the multiphase flow within the membrane electrode assembly (MEA) of this fuel cell.In this study, the well-known Multiphase Mixture Model (MMM) was improved with a new single domain approach which was used to model the flow behaviour and performance of the FE-DMFC.Unlike the existing methods, the proposed model only requires the mixture variables, thereby removing the requirement for information about the gaseous state, when attempting to couple the porous and electrolyte layers together.Furthermore, the model's formulation gives the capability to resolve liquid saturation jumps in a single domain manner.The proposed approach is sufficiently flexible that it could be applied to other modeling methods, such as the Multi-Fluid Model (MFM).The corresponding derivation for the MFM is provided.The fidelity of the improved MMM is examined through 3 test cases, which include a comparison to: the analytical liquid saturation jump solution, the analytical single phase solution for the FE-DMFC, and to in-house FE-DMFC experimental iii First and foremost, I would like to thank my supervisors, Dr. Edgar Matida and Dr. Cynthia Ann Cruickshank, for giving me the opportunity to work on this project.Their patience, support and guidance towards my work and their open door policy are greatly appreciated.Whenever we met, they always displayed enormous enthusiasm and passion towards teaching and research and I found it very contagious.I would also like to thank Dr. Feridun Hamdullahpur for his support and generosity in the initial years of my work and Dr. Glenn McRae for his invaluable input and for our many fruitful discussions in the area of electrochemistry.We would frequently lose track of time during these discussions.Over the years, he has been an incredible wealth of information in seemingly everything.I would also like to thank the technologists and machinists at Carleton University for their help and guidance in my experimental work.As well as Neil McFadyen for his help in giving me access to the computational resources on campus and for his technical support.I would also like to extend a special thanks to
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".