The multi-scale numerical procedure proposed in our previous work based on smoothed dissipative particle dynamics (SDPD) is employed, and a new multi-phase interaction model based on the inter-particle force (IPF) that includes a consistent repulsion force is presented and verified. A comparative investigation utilizing smoothed particle hydrodynamics (SPH), SDPD, and our multi-scale methods is then carried out and the computational efficiency, droplet morphology, wetting flow field, and advantages of the multi-scale method are demonstrated. In addition, the droplet thickness is derived from the Navier–Stokes equations with a stochastic force, demonstrating the effect of thermal fluctuations on the mesoscopic scale. Finally, the wetting states are simulated at different surface roughness values, and the transition of states and some new mechanisms are clarified. When the roughness scale is smaller than the interaction range between particles, the wetting state may change significantly, and this effect becomes weaker when the roughness scale exceeds the interaction range. The results also show that the horizontal roughness (direction of droplet spreading) is more decisive than vertical one (perpendicular to the direction of droplet spreading), usually leading to a transition of the wetting states, while the vertical roughness usually plays a reinforcing role.
The interval model, which requires less prior information than probabilistic and fuzzy models, is used to describe the uncertainty of the material and geometric parameters of soft biological tissues. A quadtree scaled boundary finite element method (quadtree SBFEM) and an optimization-based numerical algorithm are developed for the interval damage analysis of soft biological tissues. The material is hyperelastic, and the damage behavior is described by a gradient-enhanced damage model without mesh dependence. The deterministic problem is solved by the image-based quadtree SBFEM, and the interval problem is solved via an optimization based bounds estimation, which is reliable and insensitive to the scale of the intervals. A Legendre polynomial surrogate (LPS) is constructed to approximate the SBFEM-based deterministic solutions to reduce the computational cost of the optimization process. Numerical examples are presented to illustrate the effectiveness of the proposed approaches, and the uncertain behavior of the Cauchy stress and damage function.