Abstract
The test particle Monte Carlo method for solving the linearized Boltzmann equation is considered. This method is used for simulation of a gas mixture flow when the concentration of one of the component is low. We study the errors of the method for three main macroparameters (density, velocity, and temperature). The new approach to construction of asymptotic confidence intervals for the estimates of velocity and temperature is proposed. The expressions for optimal selection of the number of grid nodes and the sample size which guarantee attaining a specified level of the error are proposed on the basis of the theory of functional Monte Carlo algorithms. The proposed approaches are examined on the examples of the classical problem of heat transfer between two parallel plates and the two-dimensional problem of a transversal supersonic flow of a rarefied binary gas mixture around a plate.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.