On the convergence of Monte Carlo -generalized Polynomial Chaos for uncertainty propagation in k eff computations for neutronics

Key Points

• Uncertainty propagation is improved in k eff computations with Monte Carlo methods, and generalized polynomial chaos enhances accuracy.
• Significant findings demonstrate an efficiency increase, with Monte Carlo observably reducing uncertainty in k eff calculations.
• This approach uses Monte Carlo and polynomial chaos to effectively model uncertainties, demonstrating superior performance than conventional methods.
• Implications suggest that adopting these techniques may revolutionize computational approaches in neutronics while ensuring accuracy.