Accelerated Degradation for Hardware in the Loop Simulation of Fuel Cell-Gas Turbine Hybrid
Bibliographic record
Abstract
Solid oxide fuel cells (SOFCs) are a promising technology for clean power generation, however their implementation has been limited by several degradation mechanisms, which significantly reduce its lifetime under constant output power and inhibits the technology for commercialization in the near future. With the purpose of harnessing the capabilities offered by SOFCs, the U.S. DOE-National Energy Technology Laboratory (NETL) in Morgantown, WV has developed the Hybrid Performance (HyPer) project in which a SOFC 1D, real-time operating model is coupled to a gas turbine hardware system by utilizing hardware-in-the-loop simulation (HiLS). More recently, in order to assess the long-term stability of the SOFC part of the system, electrochemical degradation due to operating conditions such as current density and fuel utilization have been incorporated into the SOFC model and successfully recreated in real time for standalone and hybrid operation. The mathematical expression for degradation rate was obtained through the analysis of empirical voltage versus time plots for different current densities and fuel utilizations at 750, 800, and 850°C. Simulation results well reflected the behavior of SOFC degradation rates from which the long-term stability of the cell under various conditions was assessed. Distributed fuel cell parameters are presented for both standalone and hybrid configurations. The incorporation of the electrochemical degradation rate into the SOFC model provides a framework to study more realistically Fuel Cell-hybrid systems and set forth a mechanism to improve the long-term stability of SOFCs through the hybridization of such technology.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".