Abstract
An adaptive Monte Carlo method for nuclear data evaluation is presented. A fast evaluation method based on the linearization of the nuclear model guides the adaptation of the sampling distribution towards the posterior distribution. The method is suited for parallel computation and provides detailed uncertainty information about nuclear model parameters. Especially, the posterior distribution of the model parameters is not restricted to be multivariate normal. The method is demonstrated in an evaluation of the 181 Ta total cross section for incident neutrons. Future applications are as an efficient sampling scheme in the Total Monte Carlo method, and the restriction of parameter uncertainties in nuclear models by both differential and integral data.
Highlights
Computer simulations guide the development of nuclear facilities, such as fusion reactors and accelerator-driven systems for nuclear waste incineration
We introduce an adaptive Monte Carlo scheme that incorporates a fast evaluation scheme based on the linearization of nuclear models to adapt the sampling distribution towards the posterior distribution
We demonstrate the adaptive Monte Carlo method in an evaluation of the 181Ta total cross section for incident neutrons with energies between 5 and 100 MeV
Summary
Computer simulations guide the development of nuclear facilities, such as fusion reactors and accelerator-driven systems for nuclear waste incineration. An important prerequisite to perform a simulation is evaluated nuclear data in the form of an ENDF file If this ENDF file contains besides cross sections and related quantities the associated uncertainties as covariance matrices, the latter can be propagated through the simulation by means of perturbation theory. The propagation of covariance matrices using perturbation theory, exhibits a fundamental shortcoming Both the uncertainties of evaluated nuclear data and the uncertainties of simulation results are restricted to be of Gaussian shape. It is of utmost importance to use an efficient sampling scheme in order to accelerate convergence and, related, to reduce execution time For this reason, we introduce an adaptive Monte Carlo scheme that incorporates a fast evaluation scheme based on the linearization of nuclear models to adapt the sampling distribution towards the posterior distribution. This measure can significantly speed up convergence, as will be demonstrated in an evaluation of the 181Ta total cross section for incident neutrons
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