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Validity and scalability of an asymptotically reduced single-channel model for full-size catalytic monolith converters

Author

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  • Sharma, A.K.
  • Birgersson, E.

Abstract

A catalytic monolith converter usually comprise several hundred or thousands of channels. Mathematical modeling that seek to resolve the coupled transport phenomena – mass, momentum, species and heat – on a discrete-channel scale is a computationally-challenging task. In this context, we present an efficient approach to overcome the difficulties in the modeling of a monolith converter. In short, we establish the condition for validity of a fast and efficient reduced single monolith channel model for modeling multiple channels. The reduced model is then verified for an assembly of two channels with the full set of equations; good agreement is found for typical monolith material and operating conditions indicating the ability of the reduced model to capture conjugate heat transfer across channels. We then study the computational efficiency of the reduced model for monoliths comprising O(104) channels. The computational penalty for reduced model is much less as compared to the full model, making it a possible candidate for detailed monolith simulations.

Suggested Citation

  • Sharma, A.K. & Birgersson, E., 2016. "Validity and scalability of an asymptotically reduced single-channel model for full-size catalytic monolith converters," Applied Mathematics and Computation, Elsevier, vol. 281(C), pages 186-198.
    • Handle: RePEc:eee:apmaco:v:281:y:2016:i:c:p:186-198
    DOI: 10.1016/j.amc.2016.01.053
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    References listed on IDEAS

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    1. Sharma, A.K. & Birgersson, E. & Vynnycky, M., 2015. "Towards computationally-efficient modeling of transport phenomena in three-dimensional monolithic channels," Applied Mathematics and Computation, Elsevier, vol. 254(C), pages 392-407.
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    Cited by:

    1. Kannan, Vishvak & Xue, Hansong & Raman, K. Ashoke & Chen, Jiasheng & Fisher, Adrian & Birgersson, Erik, 2020. "Quantifying operating uncertainties of a PEMFC – Monte Carlo-machine learning based approach," Renewable Energy, Elsevier, vol. 158(C), pages 343-359.

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