Meshless $h$-adaptive Solution for non-Newtonian Natural Convection in a Differentially Heated Cavity

TL;DR

This paper presents a meshless, h-adaptive numerical method for simulating non-Newtonian natural convection in a heated cavity, improving efficiency through adaptive node refinement.

Contribution

It introduces an adaptive discretisation approach for meshless methods applied to non-Newtonian fluid flow, focusing on shear-thinning behavior and boundary layer refinement.

Findings

Adaptive node refinement improves computational efficiency.

Refinement parameters significantly influence solution accuracy.

The method effectively captures sharp flow structures in boundary layers.

Abstract

One of the main challenges in numerically solving partial differential equations is finding a discretisation for the computational domain that balances the accurate representation of the underlying field with computational efficiency. Meshless methods approximate differential operators based on the values of the field in computational nodes, offering a natural approach to adaptivity. The density of computational nodes can either be increased to enhance accuracy or decreased to reduce the number of numerical operations, depending on the properties of the intermediate solution. In this paper, we utilise an adaptive discretisation approach for the numerical simulation of natural convection in non-Newtonian fluid flow. The shear-thinning behaviour is interesting both due to its numerous occurrences in nature, blood being a prime example, and due to its properties, as the decreasing viscosity with increasing shear rate results in sharper flow structures. We focus on the de Vahl Davis test case, a natural convection driven flow in a differentially heated rectangular cavity. The thin boundary layer flow along the vertical boundaries makes this an ideal test case for refinement. We demonstrate that adaptively refining the node density enhances computational efficiency and examine how the parameters for adaptive refinement affect the solution.