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Time-Efficient, Repetitive Predictions of the Performance of PEMFCs Based on a Neural Network-Based, Reduced Order Model
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  • Time-Efficient, Repetitive Predictions of the Performance of PEMFCs Based on a Neural Network-Based, Reduced Order Model
  • Time-Efficient, Repetitive Predictions of the Performance of PEMFCs Based on a Neural Network-Based, Reduced Order Model
저자명
Shin. Dong-Il,Oh. Tae-Hoon,Park. Myong-Nam,Rengaswamy. Raghunathan
간행물명
한국가스학회지
권/호정보
2006년|10권 2호|pp.55-60 (6 pages)
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한국가스학회
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정기간행물|ENG|
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이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
서지반출

기타언어초록

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.