51st Fluid Dynamics Conference / the 37th Aerospace Numerical Simulation Symposium (July 1-3, 2019. International Conference Center, Waseda University), Shinjuku-ku, Tokyo, Japan
抄録（英）
The machine learning approach for exploring the approximate analytical description for the collision term in the Boltzmann equation is presented in this paper. Such approximate model is expected to significantly accelerate the numerical simulation of rarefied gas flows without severe degradation in the accuracy. The combination of a genetic programming (GP) method and a nonlinear least square method was adopted for the exploring. Training data was a solution of Direct Simulation Monte Carlo (DSMC) method, which is a stochastic method for rarefied gas dynamics. Considering the separation of the collision term from the translational term in the Boltzmann equation, we only solved the molecular collision by the DSMC method. The numerical solution of the obtained approximate model exhibited almost precise model. But there was still unphysical behavior in the profile of the velocity distribution function. It is suggested that GP may produce the equation including terms with numerical instability and that some restriction should be applied to assure the numerical stability as well as the physical accuracy at the exploring by GP.
内容記述
形態: カラー図版あり
内容記述（英）
Physical characteristics: Original contains color illustrations
遺伝的プログラミングを利用したボルツマン方程式衝突項の近似モデル探索
AA1930011011.pdf
(942KB)
