Constant Time Causalization using Resizable Arrays

Abstract

Equation-based modeling that utilizes reusable componentsto represent real-world systems can result in excessivelylarge models. This, in turn, significantly increasescompilation time and code size, even when employingstate-of-the-art scalarization and causalizationtechniques. This paper presents an algorithm that leveragesrepeating patterns and uniform causalization to enablearray-size-independent constant time processing. Allowingstructural parameters that govern array sizes to remainresizable during and after the causalization processenables the formulation of an integer-valued nonlinearoptimization problem. This approach identifies the minimalmodel configuration that preserves the required structuralintegrity, which can subsequently be resized as needed forsimulation. The proposed method has been implemented inOpenModelica and builds upon preliminary work aimed atpreserving array structures during causalization, whilestill resolving the underlying problem in a scalarizedmanner.