Efficient numerical methods for simulating surface tension of multi-component mixtures with the gradient theory of fluid interfaces
KAUST DepartmentComputational Transport Phenomena Lab
Earth Science and Engineering Program
Environmental Science and Engineering Program
Physical Science and Engineering (PSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/564196
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AbstractSurface tension significantly impacts subsurface flow and transport, and it is the main cause of capillary effect, a major immiscible two-phase flow mechanism for systems with a strong wettability preference. In this paper, we consider the numerical simulation of the surface tension of multi-component mixtures with the gradient theory of fluid interfaces. Major numerical challenges include that the system of the Euler-Lagrange equations is solved on the infinite interval and the coefficient matrix is not positive definite. We construct a linear transformation to reduce the Euler-Lagrange equations, and naturally introduce a path function, which is proven to be a monotonic function of the spatial coordinate variable. By using the linear transformation and the path function, we overcome the above difficulties and develop the efficient methods for calculating the interface and its interior compositions. Moreover, the computation of the surface tension is also simplified. The proposed methods do not need to solve the differential equation system, and they are easy to be implemented in practical applications. Numerical examples are tested to verify the efficiency of the proposed methods. © 2014 Elsevier B.V.
CitationKou, J., Sun, S., & Wang, X. (2015). Efficient numerical methods for simulating surface tension of multi-component mixtures with the gradient theory of fluid interfaces. Computer Methods in Applied Mechanics and Engineering, 292, 92–106. doi:10.1016/j.cma.2014.10.023
SponsorsThis work is supported by National Natural Science Foundation of China (No. 11301163), the Key Project of Chinese Ministry of Education (No. 212109) and the KAUST faculty research fund (No. PID7000000058).