The paper presents use of an artificial neural network (ANN) for predicting the thermal-flow behavior of a solid oxide fuel cell with no algorithmic solution merely by utilizing available experimental data. The error backpropagation algorithm was used for an ANN training procedure.
Artificial Neural Network-Based Model for Calculating the Flow Composition Influence of Solid Oxide Fuel Cell
Contributed by the Advanced Energy Systems Division of ASME for publication in the JOURNAL OF FUEL CELL SCIENCE AND TECHNOLOGY. Manuscript received November 20, 2012; final manuscript received July 30, 2013; published online December 4, 2013. Editor: Nigel M. Sammes.
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Milewski, J., and Świrski, K. (December 4, 2013). "Artificial Neural Network-Based Model for Calculating the Flow Composition Influence of Solid Oxide Fuel Cell." ASME. J. Fuel Cell Sci. Technol. April 2014; 11(2): 021001. https://doi.org/10.1115/1.4025922
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