@Article{CiCP-36-1,
author = {Shing-Ian, Huang and Wu, Sheng, Chang and Yang, Tsung-Yu and Ting, Chi-Heng and Yi-Jhen, Wu and Yang-Yao, Niu},
title = {Development of a Less Dissipative Interface Variable Reconstruction to Solve the Euler Equations by Q Learning Method},
journal = {Communications in Computational Physics},
year = {2024},
volume = {36},
number = {1},
pages = {160--199},
abstract = {<p style="text-align: justify;">In this study, we propose a blend of the average of THINC-EM and MUSCL
(ATM) methods based on the AUSMD scheme for solving detonation wave problems.
It is well known that the simulation of the detonation problems can produce incorrect
shock information or strong spurious due to the stiff source term. Accurate simulation
of detonation problems plays a crucial role in the design of detonation engines. The
proposed ATM method combines the MUSCL and THINC-EM methods with different
weighting functions, the optimized parameters of which are determined by the Q-learning method in order to accurately capture detonation waves, shock waves, and
expansion fans. To validate the proposed numerical method, one and two-dimensional
shock tube and the detonation tube and nozzles are chosen as benchmark test cases.
Our numerical results show that the proposed the ATM type AUSMD scheme has
great potential for handling more complex detonation problems and pulse detonation
engine flow problems.</p>},
issn = {1991-7120},
doi = {https://doi.org/10.4208/cicp.OA-2022-0224},
url = {https://global-sci.com/article/90903/development-of-a-less-dissipative-interface-variable-reconstruction-to-solve-the-euler-equations-by-q-learning-method}
}