https://doi.org/10.1140/epjp/s13360-023-03895-5
Regular Article
A novel dimensionless number characterizing flow regimes based on smoothed dissipative particle dynamics (SDPD)
1
Department of Physics, Changzhi University, 046011, Changzhi, China
2
Research Center of Shanxi Province for Solar Energy Engineering and Technology, North University of China, 030051, Taiyuan, China
3
BIC-ESAT, College of Engineering, Peking University, 100871, Beijing, China
4
State Key Laboratory for Turbulence and Complex Systems, Peking University, 100871, Beijing, China
Received:
5
October
2022
Accepted:
15
March
2023
Published online:
30
March
2023
Smooth dissipative particle dynamics (SDPD) is a Lagrangian formulation of the continuum equations by the adding fluctuations, so it naturally possesses continuity and stochastic property. However, there is no clear rule to quantitatively characterize continuity and randomness. Therefore, it is crucial to propose judgment criteria for different flow regimes, so that efficient algorithms can be chosen to improve computational efficiency. Firstly, the randomness and continuity of SDPD were verified by some numerical examples. Then, three dimensionless numbers characterizing the thermal fluctuations were derived by normalizing the Navier–Stokes equations and their effects were analyzed. For isothermal flows, a dimensionless number, Macro-Mesoscopic Critical Reynolds Number (MMCR), was proposed by integrating the three dimensionless numbers mentioned above, which can determine different flow regimes. SDPD was applied to flows including Poiseuille flow, Couette flow, and flow through a square lattice of cylinders. Statistical information such as the Mathematical expectation, Variance, Kurtosis, and Autocorrelation coefficient was analyzed. Finally, we simulated the wetting using different algorithms in different regions according to the MMCR (multi-region algorithm), and compared the computational results with the SDPD method. The results indicated that MMCR can classify the different flow regimes, which greatly reduces the computational time of SDPD and helps to promote the application of the method.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
