skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Application of Artificial Neural Networks to Reliable Nuclear Data for Nonproliferation Modeling and Simulation

Journal Article · · International Journal of Monitoring and Surveillance Technologies Research
 [1];  [2];  [3];  [1]
  1. Purdue Univ., West Lafayette, IN (United States)
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  3. Purdue Univ., West Lafayette, IN (United States); Univ. of Utah, Salt Lake City, UT (United States)
+ Show Author Affiliations

Detection and identification of special nuclear materials (SNMs) are an essential part of the US nonproliferation effort. Modern cutting-edge SNM detection methodologies rely more and more on modeling and simulation techniques. Experiments with radiological samples in realistic configurations, is the ultimate tool that establishes the minimum detection limits of SNMs in a host of different geometries. Modern modeling and simulation approaches have the potential to significantly reduce the number of experiments with radioactive sources needed to determine these detection limits and reduce the financial barrier to SNM detection. Unreliable nuclear data is one of the principal causes of uncertainty in modeling and simulating nuclear systems. In particular, nuclear cross sections introduce a significant uncertainty in the nuclear data. The goal of this research is to develop a methodology that will autonomously extract the correct nuclear resonance characteristics of experimental data in a reliable way, a task previously left to expert judgement. Accurate nuclear data will in turn allow contemporary modeling and simulation to become far more reliable, de-escalating the extent of experimental testing. Modeling and simulation techniques reduce the use and distribution of radiological sources, while at the same time increase the reliability of the currently used methods for the detection and identification of SNMs.

Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE
DOE Contract Number:
AC05-00OR22725
OSTI ID:
1362228
Journal Information:
International Journal of Monitoring and Surveillance Technologies Research, Vol. 4, Issue 4; ISSN 2166-7241
Country of Publication:
United States
Language:
English

References (18)

Fundamentals of Neural Networks book March 2001
PWR system simulation and parameter estimation with neural networks journal November 2002
Creating artificial neural networks that generalize journal January 1991
Neuro-SVM Anticipatory System for Online Monitoring of Radiation and Abrupt Change Detection journal April 2013
Backpropagation Neural Network for Interval Prediction of Three-Phase Ampacity Level in Power Systems journal July 2016
ANN based Sensor Faults Detection, Isolation, and Reading Estimates – SFDIRE: Applied in a nuclear process journal November 2012
Dynamic simulation of a steam generator by neural networks journal February 1999
Prediction of U-Mo dispersion nuclear fuels with Al-Si alloy using artificial neural network
  • Susmikanti, Mike; Sulistyo, Jos
  • 4TH INTERNATIONAL CONFERENCE ON ADVANCES IN NUCLEAR SCIENCE AND ENGINEERING (ICANSE 2013), AIP Conference Proceedings https://doi.org/10.1063/1.4895881
conference January 2014
Neutron total cross sections and resonance parameters of Cu 29 63 and Cu 29 65 . I journal February 1977
Bayesian Interpolation journal May 1992
Representing Nuclear Criticality Excursion Experiment Data by an Artificial Neural Network journal March 2015
Nuclear reactor monitoring with the combination of neural network and expert system journal September 2002
Fault detection and analysis in nuclear research facility using artificial intelligence methods
  • Ghazali, Abu Bakar; Ibrahim, Maslina Mohd
  • ADVANCING NUCLEAR SCIENCE AND ENGINEERING FOR SUSTAINABLE NUCLEAR ENERGY INFRASTRUCTURE: Proceeding of the International Nuclear Science, Technology and Engineering Conference 2015 (iNuSTEC2015), AIP Conference Proceedings https://doi.org/10.1063/1.4940079
conference January 2016
Hybrid Methods for Nonlinear Least Squares journal January 1987
Assessment of Fuzzy Logic Radioisotopic Pattern Identifier on Gamma-Ray Signals with Application to Security journal January 2014
Design and optimization of fuzzy-PID controller for the nuclear reactor power control journal November 2009
A Conceptual Model for Integrative Monitoring of Nuclear Power Plants Operational Activities Based on Historical Nuclear Incidents and Accidents journal January 2013
Application of artificial intelligence techniques in modeling and control of a nuclear power plant pressurizer system journal March 2013

Similar Records

Application of Artificial Neural Networks to Reliable Nuclear Data for Nonproliferation Modeling and Simulation
Journal Article · Sat Oct 01 00:00:00 EDT 2016 · International Journal of Monitoring and Surveillance Technologies Research · OSTI ID:1362228
+1 more
Assessment of Impact of Monoenergetic Photon Sources on Prioritized Nonproliferation Applications: Simulation Study Report
Technical Report · Fri Dec 30 00:00:00 EST 2016 · OSTI ID:1362228
+8 more
Atoms for peace and the nonproliferation treaty: unintended consequences
Journal Article · Thu Jan 01 00:00:00 EST 2009 · Nonproliferation Review · OSTI ID:1362228

Related Subjects

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL PROTECTION