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Computational source term estimation of the Gaussian puff dispersion.
- Source :
-
Soft Computing - A Fusion of Foundations, Methodologies & Applications . Jan2019, Vol. 23 Issue 1, p59-75. 17p. - Publication Year :
- 2019
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14327643
- Volume :
- 23
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Soft Computing - A Fusion of Foundations, Methodologies & Applications
- Publication Type :
- Academic Journal
- Accession number :
- 134079144
- Full Text :
- https://doi.org/10.1007/s00500-018-3440-2