Dynamic modeling of physical phenomena for probabilistic risk assessments using artificial neural networks

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In most probabilistic risk assessments, there is a subset of accident scenarios that involves physical challenges to the system, such as high heat rates and/or accelerations. The system`s responses to these challenges may be complicated, and their prediction may require the use of long-running computer codes. To deal with the many scenarios demanded by a risk assessment, the authors have been investigating the use of artificial neural networks (ANNs) as a fast-running estimation tool. They have developed a multivariate linear spline algorithm by extending previous ANN methods that use radial basis functions. They have applied the algorithm to problems involving ... continued below

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11 p.

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Benjamin, A.S.; Paez, T.L. & Brown, N.N. January 1, 1998.

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  • Sandia National Laboratories
    Publisher Info: Sandia National Labs., Albuquerque, NM (United States)
    Place of Publication: Albuquerque, New Mexico

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Description

In most probabilistic risk assessments, there is a subset of accident scenarios that involves physical challenges to the system, such as high heat rates and/or accelerations. The system`s responses to these challenges may be complicated, and their prediction may require the use of long-running computer codes. To deal with the many scenarios demanded by a risk assessment, the authors have been investigating the use of artificial neural networks (ANNs) as a fast-running estimation tool. They have developed a multivariate linear spline algorithm by extending previous ANN methods that use radial basis functions. They have applied the algorithm to problems involving fires, shocks, and vibrations. They have found that within the parameter range for which it is trained, the algorithm can simulate the nonlinear responses of complex systems with high accuracy. Running times per case are less than one second.

Physical Description

11 p.

Notes

INIS; OSTI as DE98004217

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  • ESREL`98: European safety and reliability conference, Trondheim (Norway), 16-19 Jun 1998

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  • Other: DE98004217
  • Report No.: SAND--98-0216C
  • Report No.: CONF-980621--
  • Grant Number: AC04-94AL85000
  • Office of Scientific & Technical Information Report Number: 654104
  • Archival Resource Key: ark:/67531/metadc704732

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  • January 1, 1998

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  • Sept. 12, 2015, 6:31 a.m.

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  • April 13, 2016, 1:36 p.m.

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Benjamin, A.S.; Paez, T.L. & Brown, N.N. Dynamic modeling of physical phenomena for probabilistic risk assessments using artificial neural networks, article, January 1, 1998; Albuquerque, New Mexico. (digital.library.unt.edu/ark:/67531/metadc704732/: accessed September 23, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.