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Reduced Modelling of Planar Fuel Cells

- Spatial Smoothing and Asymptotic Reduction

Über Reduced Modelling of Planar Fuel Cells

This book focuses on novel reduced cell and stack models for proton exchange membrane fuel cells (PEMFCs) and planar solid oxide fuel cells (P-SOFCs) that serve to reduce the computational cost by two orders of magnitude or more with desired numerical accuracy, while capturing both the average properties and the variability of the dependent variables in the 3D counterparts. The information provided can also be applied to other kinds of plate-type fuel cells whose flow fields consist of parallel plain channels separated by solid ribs. These fast and efficient models allow statistical sensitivity analysis for a sample size in the order of 1000 without prohibitive computational cost to be performed to investigate not only the individual, but also the simultaneous effects of a group of varying geometrical, material, and operational parameters. This provides important information for cell/stack design, and to illustrate this, Monte Carlo simulation of the reduced P-SOFC model is conducted at both the single-cell and stack levels.

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  • Sprache:
  • Englisch
  • ISBN:
  • 9783319826271
  • Einband:
  • Taschenbuch
  • Seitenzahl:
  • 291
  • Veröffentlicht:
  • 7. Juli 2018
  • Ausgabe:
  • 12017
  • Abmessungen:
  • 155x235x0 mm.
  • Gewicht:
  • 486 g.
  Versandkostenfrei
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Verlängerte Rückgabefrist bis 31. Januar 2025

Beschreibung von Reduced Modelling of Planar Fuel Cells

This book focuses on novel reduced cell and stack models for proton exchange membrane fuel cells (PEMFCs) and planar solid oxide fuel cells (P-SOFCs) that serve to reduce the computational cost by two orders of magnitude or more with desired numerical accuracy, while capturing both the average properties and the variability of the dependent variables in the 3D counterparts. The information provided can also be applied to other kinds of plate-type fuel cells whose flow fields consist of parallel plain channels separated by solid ribs. These fast and efficient models allow statistical sensitivity analysis for a sample size in the order of 1000 without prohibitive computational cost to be performed to investigate not only the individual, but also the simultaneous effects of a group of varying geometrical, material, and operational parameters. This provides important information for cell/stack design, and to illustrate this, Monte Carlo simulation of the reduced P-SOFC model is conducted at both the single-cell and stack levels.

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