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Results for “Solid Oxide Cells”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Heavily neodymium doped ceria as an effective barrier layer in solid oxide electrochemical cells

10 mol% gadolinium doped ceria (GDC10) is widely used as a barrier layer between oxygen electrode and electrolyte to prevent interfacial reactions. A 50 mol% neodymium doped ceria (NDC50) barrier layer has been proposed and studied in this paper. Symmetrical cells with NDC50 and GDC10 barrier layers, Nd 2 NiO 4+δ (NNO)–Ce 0.5 Nd 0.5 O 2-δ (NDC50) electrode, and YSZ electrolyte have been systematically studied using impedance spectroscopy (EIS) at various temperatures and oxygen partial pressure (pO 2 ). The NDC50 barrier layer has significantly decreased polarization resistance across a wide temperature and pO 2 range compared to the GDC10 barrier layer. The rare earth C-type structure of the NDC50 barrier layer causes barrier free migration of oxygen ions resulting in improved ionic conductivity compared to GDC10. Finally, distribution of relaxation time (DRT) modeling has been used to obtain insights into the electrode processes.

08 HYDROGEN↗
DOE OSTI2020

Multiobjective Optimal Controlled Variable Selection for a Gas Turbine–Solid Oxide Fuel Cell System Using a Multiagent Optimization Platform

Hybrid gas turbine–fuel cell systems have immense potential for high efficiency in electrical power generation with cleaner emissions compared with fossil-fueled power generation. We report a systematic controlled variable (CV) selection method is deployed for a hybrid gas turbine–fuel cell system in the HyPer (hybrid performance) facility at the U.S. Department of Energy’s National Energy Technology Laboratory (NETL) for maximizing its economic and control performance. A three-stage approach is used for the CV selection comprising a priori analysis, multiobjective optimization, and a posteriori analysis. The a priori analysis helps to screen off several candidate CVs, thus reducing the size of the combinatorial optimization problem for multiobjective CV selection. For optimal CV selection, a transfer function model of the HyPer facility is identified. By considering several candidate models, the final transfer function model is selected using Akaike’s Final Prediction Error criterion. Experimental data from the HyPer facility are used to estimate the noise in the measurement data. For solving the combinatorial multiobjective optimization problem for CV selection, a multiagent optimization platform comprising simulated annealing, genetic algorithm, and efficient ant colony optimization algorithms is used. Pareto-optimal CV sets exhibit a high trade-off between the economic and control objective. The a posteriori analysis is undertaken for several top Pareto-optimal CV sets. An optimal CV set is selected that shows the best compromise between process economics and controllability under both nominal and off-design conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗