Future Prospects

This chapter presents a broad series of topics that will likely draw significant interest in the future. The lattice Boltzmann method is a promising approach which is derived from the Lattice Gas Automata method, a cellular automaton molecular dynamics model developed in the late 1980s. The Arbitrary Lagrangian-Eulerian (ALE) technique is another advantageous approach, that is extremely promising for coastal Computational fluid dynamics application. The ALE cycle for hydrodynamic calculations is divided into three phases. Phase I is a standard Lagrangian calculation. Phase II is the rezoning phase, in which rezone velocities are specified to reduce distortions in the mesh. Phase III performs all the advective flux calculations; these are necessary if the mesh is not purely Lagrangian. Machine learning is a series of techniques that allow computers to perform tasks without being explicitly programmed to do so, by “learning” insights from input datasets.