MCNP-A General Monte Carlo N-Particle Transport Code. Monte Carlo N-Particle Transport Code (MCNP) is a software package for simulating nuclear processes. It is developed by Los Alamos National Laboratory since at least 1957 with several further major improvements. It is distributed within the United States by the Radiation Safety Information Computational Center in Oak Ridge, TN and internationally by the Nuclear Energy Agency in Paris, France. It is used primarily for the simulation of nuclear processes, such as fission, but has the capability to simulate particle interactions involving neutrons, photons, and electrons. ”Specific areas of application include, but are not limited to, radiation protection and dosimetry, radiation shielding, radiography, medical physics, nuclear criticality safety, detector design and analysis, nuclear oil well logging, accelerator target design, fission and fusion reactor design, decontamination and decommissioning.”

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  1. Favetti, R.; Chiovaro, P.; Di Maio, P. A.; Spagnuolo, G. A.: Validation of multi-physics integrated procedure for the HCPB breeding blanket (2020)
  2. Valentin Niess, Anne Barnoud, Cristina Cârloganu, Olivier Martineau-Huynh: TURTLE: A C library for an optimistic stepping through a topography (2019) arXiv
  3. Pandya, Tara M.; Johnson, Seth R.; Evans, Thomas M.; Davidson, Gregory G.; Hamilton, Steven P.; Godfrey, Andrew T.: Implementation, capabilities, and benchmarking of shift, a massively parallel Monte Carlo radiation transport code (2016)
  4. Choi, Sooyoung; Smith, Kord; Lee, Hyun Chul; Lee, Deokjung: Impact of inflow transport approximation on light water reactor analysis (2015)
  5. Mohamed, Nader M. A.: Efficient algorithm for generating Maxwell random variables (2011)
  6. McClarren, Ryan G.; Urbatsch, Todd J.: A modified implicit Monte Carlo method for time-dependent radiative transfer with adaptive material coupling (2009)
  7. Korobeynikov, A.; Ginkin, V.: Computing analysis and optimization of neutron beam for tumor therapy (2008)
  8. Evans, T. M.; Urbatsch, T. J.; Lichtenstein, H.; Morel, J. E.: A residual Monte Carlo method for discrete thermal radiative diffusion (2003)
  9. Hammond, Kevin (ed.); Michaelson, Greg (ed.): Research directions in parallel functional programming (1999)
  10. Kollman, Craig; Baggerly, Keith; Cox, Dennis; Picard, Rick: Adapative importance sampling on discrete Markov chains (1999)