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Computational source term estimation of the Gaussian puff dispersion

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Abstract

The hazardous or toxic chemical releases have a detrimental impact on public safety. Estimating source parameters is of particular importance in aiding emergency response and post-assessment. Source term estimation from sensor measurements with a given Gaussian puff dispersion model is a typical inverse problem, which can be transformed into an optimization problem. In this paper, we employed the particle swarm optimization, the Nelder–Mead method, and their hybrid method to solve the optimization problem. Furthermore, we proposed a three-dimensional neighborhood topology which considerably improves performance of the particle swarm optimization. We implemented all these algorithms in JAVA on an embedded system to make a preliminary estimation of the accidental puff release. Numerical experiments with synthetic datasets show that the particle swarm optimization maintains a balance between computation time, accuracy, robustness, and implementation complexity. In contrast, the hybrid algorithm has an advantage in computation time at the expense of more sophisticated implementation.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under grants of the general technical foundation research joint fund (Project No. U1636208). And this project was also supported by the Ministry of Science and Technology of China under grants of the national key technology R&D program (Project No. 2015BAK39B02).

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Authors and Affiliations

  1. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China

    Hui Li

  2. Fluid Mechanics and Heat Transfer Laboratory, Beijing University of Chemical Technology, Beijing, 100029, China

    Jianwen Zhang

  3. Beijing Information Science and Technology University, Beijing, 100101, China

    Junkai Yi

Authors
  1. Hui Li
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  2. Jianwen Zhang
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  3. Junkai Yi
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Correspondence to Hui Li.

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Li, H., Zhang, J. & Yi, J. Computational source term estimation of the Gaussian puff dispersion. Soft Comput 23, 59–75 (2019). https://doi.org/10.1007/s00500-018-3440-2

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