On the basis of the inverse heat transfer methodology, the quantitative recognition of heat source parameters (location and intensity) in granary is studied using the finite element method and the quantum-behaved particle swarm optimization (QPSO) method. For the quantitative recognition problem of heat source parameters, a recognition system based on QPSO is established: the fitness function of QPSO is constructed according to the offsets between the measuring temperatures and calculating temperatures in granary, and the heat source parameters are obtained by optimizing the fitness function. The influence of heat source intensity, number of measuring points, measurement error, and initial guess value on the recognition results is studied through experiments, and compared with Levenberg–Marquardt method. The results show that the QPSO method in this paper can obviously weaken the influence of the number of measurement points and the initial guess on the recognition results and is able to ameliorate the anti-interference ability to the measurement errors.


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    Title :

    Quantitative Recognition of Granary Heat Source Using Quantum-Behaved Particle Swarm Optimization Method


    Contributors:
    Wang, Kun (author) / Liu, Juan (author) / Kong, Songtao (author) / Cai, Ping (author) / Xu, Hongmei (author) / Wan, Shibin (author)

    Published in:

    Publication date :

    2020-07-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English