Despite the growing sophistication of building modeling tools, errors can arise from approximations that are made by a practitioner during model creation.
Nevertheless, it is difficult to estimate the inherent errors of the continuous approach arising from the approximation process in evaluating natural frequencies, especially higher frequencies, of the combined system.
The error arising from the approximation is evaluated by calculating a pair of vortex rings impinging to themselves.
In this note, we focus our efforts on specific applications and, more precisely, on large linear systems arising from the approximation of the weighted Laplacian with various boundary conditions.
Daubechies family of wavelets combined to the Incomplete Lower Upper (ILU) factorization are considered as preconditioners for a block sparse linear system arising from the approximation of the time harmonic elastic wave equations by the Partition of Unity Finite Element Method (PUFEM).
However, in typical cases,, and any difference arising from this approximation would be inconsequential.
It is shown that the uncertainties arising from the approximations and from the non-thermodynamic considerations do not impair the basic principle of the procedure, and that the results will become more and more reliable as the experimental techniques are improved.
This is due to the less accurate results arising from the approximations and/or simplifications when initial frequency offset values become large.
Next, a combined optimal robust control scheme is employed to minimize the worst-case effect arising from adaptive fuzzy approximation error and external disturbance to improve the tracking performance of missile.
We consider fast solvers for large linear systems arising from the Galerkin approximation based on B-splines of classical d-dimensional elliptic problems, d≥1, in the context of isogeometric analysis.
This bias, arising from the numerical approximations, has to be taken into account when later testing which model generated the data, forcing us to use a finer and computationally more expensive grid size.
