Meshing and Adaptive Mesh Refinement: Information on meshing is given in “Initial Meshing”. For information on the AMR process, see “Using Adaptive Mesh Refinement (AMR)”. Adaptive mesh refinement is performed on the basis of the variability of the potential distribution, with regions of higher variability relative to the local element size being more refined. The process is considered converged when the step-to-step change in conductor charge falls below the target value. This typically produces reasonable refined meshes over a sequence of several AMR steps. In some cases it may be useful to seed the mesh with more elements in certain areas (by applying mesh constraints), or globally using the element size parameters on the initial mesh simulation properties panel. This can lead to higher quality solutions early in the AMR process, possibly reducing the number of steps required for solution convergence.
Resource Usage: As with other solution types, the computer resource requirements are a strong function of the problem. As compared to other solvers, ES3p generally takes less time on a given mesh than would a frequency-domain solver or a magnetostatic solver, because of the simplicity of the materials. On a given problem, solution time is affected by the choice of linear solver, and also the tolerances that were chosen. The default linear solver is the conjugate gradient iterative method, and this is generally the most efficient solver for electrostatics. For an iterative linear solver the solution time will generally increase as the Convergence Tolerance is reduced.