Efficient Numerical Schemes for Multidimensional Population Balance Models

TL;DR

This paper introduces a computationally efficient finite difference scheme for multidimensional population balance models that leverages operator splitting, special meshes, and variable transformations to achieve high accuracy with minimal computational cost.

Contribution

The paper presents a novel finite difference scheme that reduces computational expense while maintaining accuracy for complex multidimensional PBMs using operator splitting and operator commutativity.

Findings

Achieves zero discretization error for certain PBMs

Demonstrates low computational cost through case studies

Effective for models with multiple intrinsic variables

Abstract

Multidimensional population balance models (PBMs) describe chemical and biological processes having a distribution over two or more intrinsic properties (such as size and age, or two independent spatial variables). The incorporation of additional intrinsic variables into a PBM improves its descriptive capability and can be necessary to capture specific features of interest. As most PBMs of interest cannot be solved analytically, computationally expensive high-order finite difference or finite volume methods are frequently used to obtain an accurate numerical solution. We propose a finite difference scheme based on operator splitting and solving each sub-problem at the limit of numerical stability that achieves a discretization error that is zero for certain classes of PBMs and low enough to be acceptable for other classes. In conjunction to employing specially constructed meshes and variable transformations, the scheme exploits the commutative property of the differential operators present in many classes of PBMs. The scheme has very low computational cost -- potentially as low as just memory reallocation. Multiple case studies demonstrate the performance of the proposed scheme.