Deterministic adjoint functions for biasing Monte Carlo reactor cavity dosimetry calculations

Reactor cavity dosimetry is performed to benchmark models for pressure vessel fluence calculations. These calculations are used to estimate reactor pressure vessel integrity and provide a basis for plant-life extension; therefore, their accuracy is of great importance. Since the Monte Carlo method is considered to be the most accurate method available for solving radiation transport problems, it is only natural to attempt to use it for this application. However, due to its nature of simulating individual particles and inferring the average behavior of the particles in the system (using the Central Limit Theorem) from the average behavior of the individually simulated particles, it is extremely computationally expensive. In fact, for many reactor applications, as well as medical and nuclear-well logging applications, the computer time required by the Monte Carlo method is considered prohibitive and/or impractical. Therefore, for difficult problems such as the cavity dosimeter calculation, where the natural probability that a particle will contribute to the detector of interest is small, some effective means of variance reduction or biasing must be used.