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    Sparsity-enhanced optimization for ejector performance prediction

    241811.pdf (528.6Kb)
    Access Status
    Open access
    Authors
    Li, Fenglei
    Wu, Changzhi
    Wang, Xiangyu
    Tian, Q.
    Teo, Kok Lay
    Date
    2016
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Li, F. and Wu, C. and Wang, X. and Tian, Q. and Teo, K.L. 2016. Sparsity-enhanced optimization for ejector performance prediction. Energy. 113: pp. 25-34.
    Source Title
    Energy
    DOI
    10.1016/j.energy.2016.07.041
    ISSN
    0360-5442
    School
    Department of Construction Management
    URI
    http://hdl.handle.net/20.500.11937/46929
    Collection
    • Curtin Research Publications
    Abstract

    Within a model of the ejector performance prediction, the influence of ejector component efficiencies is critical in the prediction accuracy of the model. In this paper, a unified method is developed based on sparsity-enhanced optimization to determine correlation equations of ejector component efficiencies in order to improve the prediction accuracy of the ejector performance. An ensemble algorithm that combines simulated annealing and gradient descent algorithm is proposed to obtain its global solution for the proposed optimization problem. The ejector performance prediction of a 1-D model in the literature is used as an example to illustrate and validate the proposed method. Tests results reveal that the maximum and average absolute errors for the ejector performance prediction are reduced much more when compared with existing results under the same experimental condition. Furthermore, the results indicate that the ratio of geometric parameters to operating parameters is a key factor affecting the ejector performance.

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