Monte Carlo Techniques for Analyzing Deep-Penetration Problems

S. N. Cramer, J. Gonnord, J. S. Hendricks
1986 Nuclear science and engineering  
A review of current methods and difficulties in Monte Carlo deep-penetration calculations is presented. Statistical uncertainty is discussed, and recent adjoint optimization of splitting, Russian roulette, and exponential-transformation biasing is reviewed. Other aspects of the random walk and estimation processes are covered, including the relatively new DXANG angular biasing technique. Specific items summarized ai*e albedo scattering, Monte Carlo coupling techniques with discrete ordinates
more » ... other methods, adjoint solutions, and multi-group Monte Carlo. The topic of code-generated biasing parameters is presented, including the creation of adjoint importance functions from forward calculations. Finally, current and future work in the area of computer learning and artificial intelligence is discussed in connection with Monte Carlo applications.
doi:10.13182/nse86-a18177 fatcat:zwymx3246jcold2fyoaebumtcm