Algorithmic styles for nonlinear function generation in lumped-parameter systems

Abstract

Within the simulation community, it is often believed that integration is the limiting factor to program performance and run-time efficiency when simulating large-scale continuous feedback systems. Much attention is given to selection of the best approximation technique (e.g., Euler, Runge-Kutta, Adams-Bashforth). However, performance is shown to be up to six times more sensitive to impact on nonlinear function generation due to computational complexities associated with one- and two-dimensional table-look-up algorithms. Such algorithms utilize linear interpolation and sequential search that when combined, produce a number of approaches to algorithm construction. Algorithms and associated data structures have been formulated in contrasting general-purpose languages to illustrate stylistic differences and for direct application in nonlinear simulation of lumped-parameter systems.