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  • Open Access

    ARTICLE

    Evolutionary Algorithms Applied to Estimation of Thermal Property by Inverse Problem

    V.C. Mariani1, V. J. Neckel2, L. S. Coelho3

    CMES-Computer Modeling in Engineering & Sciences, Vol.68, No.2, pp. 167-184, 2010, DOI:10.3970/cmes.2010.068.167

    Abstract In this study an inverse heat conduction problem using two optimization methods to estimate apparent thermal diffusivity at different drying temperatures is solved. Temperature and moisture versus time were obtained numerically using heat and mass transfer equations with drying temperatures in the range between 20°C to 70°C. The solution of the partial differential equation is made with a finite difference method coupled to optimization techniques of Differential Evolution (DE) and Particle Swarm Optimization (PSO) used in inverse problem. Statistical analysis shows no significant differences between reported and estimated curves, and no remarkable differences between results More >

  • Open Access

    ARTICLE

    Research on Activated Carbon Supercapacitors Electrochemical Properties Based on Improved PSO-BP Neural Network

    Xiaoyi Liang1, Zhen Yang1,2, Xingsheng Gu3, Licheng Ling1

    CMC-Computers, Materials & Continua, Vol.13, No.2, pp. 135-152, 2009, DOI:10.3970/cmc.2009.013.135

    Abstract Supercapacitors, also called electrical double-layer capacitors (EDLCs), occupy a region between batteries and dielectric capacitors on the Ragone plot describing the relation between energy and power. BET specific surface area and specific capacitance are two important electrochemical property parameters for activated carbon EDLCs, which are usually tested by experimental method. However, it is misspent time to repeat lots of experiments for EDLCs' studies. In this investigation, we developed one theoretical model based on improved particle swarm optimization algorithm back propagation (PSO-BP) neural network (NN) to simulate and optimize BET specific surface area and specific capacitance. More >

  • Open Access

    ARTICLE

    Aircraft Structural Integrity Assessment through Computational Intelligence Techniques

    RamanaM. Pidaparti1

    Structural Durability & Health Monitoring, Vol.2, No.3, pp. 131-148, 2006, DOI:10.3970/sdhm.2006.002.131

    Abstract This paper provides an overview of the computational intelligence methods developed for the structural integrity assessment of aging aircraft structures. Computational intelligence techniques reviewed include artificial neural networks, inverse neural network mapping, wavelet based image processing methods, genetic algorithms, spectral element methods, and particle swarm optimization. Multi-site damage, corrosion, and corrosion-fatigue damage in aging aircraft is specifically discussed. Results obtained from selected computational intelligence methods are presented and compared to the existing alternate solutions and experimental data. The results presented illustrate the applicability of computational intelligence methods for assessing the structural integrity of aging aircraft More >

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