Stabilized updated Lagrangian corrected SPH for explicit dynamic problems

genetic algorithm based on explicit memory for solving dynamic problems

nowadays, it is common to find optimal point of the dynamic problem; dynamic problems whose optimal point changes over time require algorithms which dynamically adapt the search space instability. in the most of them, the exploitation of some information from the past allows to quickly adapt after an environmental change (some optimal points change). this is the idea underlining the use of memo...

Explicit stabilized integration of stiff determinisitic or stochastic problems

Explicit stabilized methods for stiff ordinary differential equations have a long history. Proposed in the early 1960s and developed during 40 years for the integration of stiff ordinary differential equations, these methods have recently been extended to implicit-explicit or partitioned type methods for advection-diffusion-reaction problems, and to efficient explicit solvers for stiff mean-squ...

Explicit Stabilized Runge-Kutta Methods

A stabilized explicit Lagrange multiplier based domain decomposition method for parabolic problems

A fully explicit, stabilized domain decomposition method for solving moderately stiff parabolic partial differential equations (PDEs) is presented. Writing the semi-discretized equations as a differential-algebraic equation (DAE) system where the interface continuity constraints between subdomains are enforced by Lagrange multipliers, the method uses the Runge–Kutta–Chebyshev projection scheme ...

Lagrangian-Lagrangian simulation of solid-liquid flows by the DEM-SPH method

In this study, we present the DEM-SPM method, a Lagrangian-Lagrangian coupled algorithm, for simulating solid-liquid flows involving free surfaces. The DEM solid phase and the SPH liquid phase are connected using the local averaging technique, where the governing equations are modeled taking account of the local mean voidage. Conservative forms of momentum balance are derived via a variational ...