Two meshless (or mesh-free) methods that are widely employed in hydrodynamic modeling of problems with steep gradients and large deformation are smoothed particle hydrodynamics (SPH) and moving particle simulation (MPS) that are classified into two categories of incompressible and weakly compressible fluids (Gotoh & Khayyer, 2016). Although SPH was initially introduced for astrophysical applications (Gingold & Monaghan, 1977), in the scope of hydrodynamics, SPH and MPS were developed by Monaghan (1992) and Koshizuka and Oka (1996), respectively, for solving the free-surface incompressible flow using Navier-Stokes equations. Gotoh et al. ( 2001) presented a sub-particle scale closure model for closing large eddy simulation (LES) to simulate turbulence fluctuations in the MPS method. The theory of a weakly compressible flow was adopted by Monaghan (1994) in SPH and Shakibaeinia and Jin (2010) in MPS using a thermodynamic equation of state for calculating the pressure instead of resorting to the Poisson equation.The main difference between SPH and MPS arises from different differential operators employed in the spatial integration of the partial differential equations. In SPH, the differential operators are obtained from the differentiation of a weighted average function, the so-called kernel, without directly applying differencing schemes for flow variables. The superposition of the kernels then computes flow variables' derivatives (Liu & Liu, 2010). In contrast, MPS is the extension of the finite volume method to a mesh-free approach in which the spatial discretization is obtained based on the differentiation of flow variables. The weighted average of physical quantities derivatives of neighboring particles is applied to the target particle. However, unlike the finite volume method in which the velocity divergence is used for pressure calculation, the particle number density is adopted in MPS. The particle number density indicating the number of particles surrounding a particle is a key variable in MPS that is straightforward to fulfill flow incompressibility (Koshizuka et al., 2018).