한국가스학회지 (Journal of the Korean Institute of Gas)
- 제10권2호
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- Pages.55-60
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- 2006
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- 1226-8402(pISSN)
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- 2713-6922(eISSN)
Time-Efficient, Repetitive Predictions of the Performance of PEMFCs Based on a Neural Network-Based, Reduced Order Model
- Shin Dong-Il (Department of Chemical Engineering, Myongji University) ;
- Oh Tae-Hoon (Department of Chemical Engineering, Myongji University) ;
- Park Myong-Nam (Department of Chemical Engineering, Myongji University) ;
- Rengaswamy Raghunathan (Department of Chemical Engineering, Clarkson University)
- 발행 : 2006.06.01
초록
Detailed modeling of PEMFCs has been getting considerable interest for predicting the fuel cell performance and also for use in various systems engineering activities. While CFD-based equipment models provide detailed analyses of the performance, they are very time-consuming to develop and run. The computations become quite complex when such models have to be embedded into the flowsheet-level optimization of fuel cell systems. In this paper, we present results about building and using NN-based reduced order models for quickly and repetitively predicting the flow of reactants in a PEMFC manifold.
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