Nuclear Techniques ›› 2014, Vol. 37 ›› Issue (04): 40503-040503.doi: 10.11889/j.0253-3219.2014.hjs.37.040503

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Study on fitness functions of genetic algorithm for dynamically correcting nuclide atmospheric diffusion model

JI Zhilong MA Yuanwei WANG Dezhong   

  1. (School of Nuclear Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)
  • Received:2014-01-14 Revised:2014-02-17 Online:2014-04-10 Published:2014-04-11
  • Supported by:

    Natural Science Foundation of China


Background: In radioactive nuclides atmospheric diffusion models, the empirical dispersion coefficients were deduced under certain experiment conditions, whose difference with nuclear accident conditions is a source of deviation. A better estimation of the radioactive nuclide’s actual dispersion process could be done by correcting dispersion coefficients with observation data, and Genetic Algorithm (GA) is an appropriate method for this correction procedure. Purpose: This study is to analyze the fitness functions’ influence on the correction procedure and the forecast ability of diffusion model. Methods: GA, coupled with Lagrange dispersion model, was used in a numerical simulation to compare 4 fitness functions’ impact on the correction result. Results: In the numerical simulation, the fitness function with observation deviation taken into consideration stands out when significant deviation exists in the observed data. After performing the correction procedure on the Kincaid experiment data, a significant boost was observed in the diffusion model’s forecast ability. Conclusion: As the result shows, in order to improve dispersion models’ forecast ability using GA, observation data should be given different weight in the fitness function corresponding to their error.

Key words: Atmospheric diffusion model, Genetic algorithm, Fitness function, Numerical simulation, Evaluation of nuclear accidents&rsquo, consequence