An efficient adaptive frequency sampling scheme for large-scale transient boundary element analysis

Abstract

The frequency-domain approach (FDA) to transient analysis of the boundary element method, although is appealing for engineering applications, is computationally expensive. This paper proposes a novel adaptive frequency sampling (AFS) algorithm to reduce the computational time of the FDA by effectively reducing the number N c of sampling frequencies. The AFS starts with a few initial frequencies and automatically determines the subsequent sampling frequencies. It can reduce N c by more than 2 times while still preserving good accuracy. In a porous solid model with around 0.3 million unknowns, 4 times reduction of N c and the total computational time is successfully achieved.

A Data-Driven Adaptive Sampling Method Based on Edge Computing

“…By detecting the variation of the signal spectrum through the change detection test (CDT), the purpose is to extract the variation of the signal. Thus, the sampling frequency can be adjusted adaptively; Jinyou Xiao proposed a method of using transient boundary element analysis to realize adaptive sampling frequency in the frequency domain [20]. This method used the vector fitting method [21] to fit the frequency response function of the rational function and then solved it.…”