Automated variance reduction of Monte Carlo shielding calculations using the discrete ordinates adjoint function
- Pennsylvania State Univ., University Park, PA (United States). Dept. of Nuclear Engineering
Although the Monte Carlo method is considered to be the most accurate method available for solving radiation transport problems, its applicability is limited by its computational expense. Thus, biasing techniques, which require intuition, guesswork, and iterations involving manual adjustments, are employed to make reactor shielding calculations feasible. To overcome this difficulty, the authors have developed a method for using the S{sub N} adjoint function for automated variance reduction of Monte Carlo calculations through source biasing and consistent transport biasing with the weight window technique. They describe the implementation of this method into the standard production Monte Carlo code MCNP and its application to a realistic calculation, namely, the reactor cavity dosimetry calculation. The computational effectiveness of the method, as demonstrated through the increase in calculational efficiency, is demonstrated and quantified. Important issues associated with this method and its efficient use are addressed and analyzed. Additional benefits in terms of the reduction in time and effort required of the user are difficult to quantify but are possibly as important as the computational efficiency. In general, the automated variance reduction method presented is capable of increases in computational performance on the order of thousands, while at the same time significantly reducing the current requirements for user experience, time, and effort. Therefore, this method can substantially increase the applicability and reliability of Monte Carlo for large, real-world shielding applications.
- Sponsoring Organization:
- USDOE
- OSTI ID:
- 616159
- Journal Information:
- Nuclear Science and Engineering, Vol. 128, Issue 2; Other Information: PBD: Feb 1998
- Country of Publication:
- United States
- Language:
- English
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