A Fast Multipole Method for High Order Boundary Elements. Rio de Janeiro, 2018. 64p. Tese de doutorado–Departamento de Engenharia Civil e Ambiental, Pontifícia Universidade Católica do Rio de Janeiro.

The Fast Multipole Method (FMM) has been used since the 1990s with the Boundary Elements Method (BEM) for the simulation of large-scale problems. This method relies on Taylor series expansions of the underlying fundamental solutions to cluster the nodes on the discretised boundary of a domain, aiming to reduce the computational time required to carry out the simulation. It has become an important tool for the BEMs, as they present matrices that are full and nonsymmetric, so that the improvement of storage allocation and execution time is not a simple task. The application of the FMM to the BEM ends up with a very intricate code, and usually changing from one problem’s fundamental solution to another is not a simple matter. This work presents a kernel-independent formulation of the FMM, here called the General Fast Multipole Method (GFMM), which is also able to deal with high order, curved boundary elements in a straightforward manner. This is an important feature, as the fast multipole implementations reported in the literature only apply to constant elements. All necessary aspects of this method are presented, starting with the mathematical basics of both FMM and BEM, carrying out some numerical assessments, and ending up with the solution of large potential problems. The application of the GFMM to both potential and elasticity problems is discussed and validated in the context of BEM. Furthermore, the formulation of the GFMM with the Simplified Hybrid Boundary Elements Method (SHBEM) is presented. Several numerical assessments show that the GFMM is highly efficient and may be as accurate as arbitrarily required, for problems with up to many millions of degrees of freedom. The literature proposes that the FMM is capable of reducing the time complexity of the BEM algorithms from O (N2) to O (N), where N is the number of degrees of freedom. In fact, it is shown that the GFMM is able to arrive at such time reduction without resorting to techniques of computational optimisation.