In this lecture, we'll cover the following:

• How to generate a contour (heat map) plot of vertical slab displacements
• Reshaping displacement data to align with the computational grid
• Enhancing visualisation by overlaying nodal points and mesh elements
• Interpolating results onto a finer grid using SciPy’s griddata
• Encapsulating plotting functionality into a reusable utility function

In this lecture, we focus on visualising the computed vertical displacements of a slab using contour plots. We begin by reshaping the displacement vector so it aligns with the spatial grid, enabling us to produce a filled contour (heat map) plot. We explore how plotting choices such as colour maps, contour levels, and aspect ratio affect interpretation, and we reinforce the importance of visual checks for qualitative validation. By overlaying nodal points and mesh elements, we make clear where values are computed versus interpolated, improving transparency in the results.

We then extend this visualisation by interpolating displacement values onto a finer grid using SciPy’s griddata function. Although optional, this step produces a smoother contour plot and demonstrates how interpolation can refine presentation without altering underlying results. Finally, we consolidate the plotting workflow into a reusable function, allowing flexible inclusion of nodes, elements, labels, and titles. We conclude by confirming correctness through testing and preview the next stage of post-processing, which will focus on bending moments and shear forces.

Next up

In the next lecture, we will build an evaluation grid that enables the computation of bending moments and shear forces across the plate.