Welcome! I am a lecturer at the University of Sheffield and part of the Dynamics group. I mostly develop code and mathematical models for waves (like sound and radio) and more broadly solid mechanics. For an overview see my projects, publications or CV.
PhD in applied mathematics, 2015
NUI Galway
MSc in applied mathematics, 2011
State University of Campinas
BSc in applied mathematics, 2009
State University of Campinas
Software for wave propagation and scattering.
Surface waves and wrinkles can be used to characterise the underlying soft solid.
Use AI concepts, such as entropy maximisation, to play games.
The aim is to understand how waves (like sound, radio, light, and vibrations) behave in materials with random microstructure.
Combining maths, data and code to solve commercial and industrial problems.
How do we mathematically describe the stresses and deformation of anisotropic soft solids.
How do you take a reliable measurement of a material whose microstructure is random? When using wave scattering, the answer is often to take an ensemble average (average over time or space). By ensemble averaging we can calculate the average scattered wave and the effective wavenumber. To date, the literature has focused on calculating the effective wavenumber for a plate filled with particles. One clear unanswered question was how to extend this approach to a material of any geometry and for any source. For example, does the effective wavenumber depend on only the microstructure, or also on the material geometry?
Measuring stress levels in loaded structures is crucial to assess and monitor structure health and to predict the length of remaining structural life. Many ultrasonic methods are able to accurately predict in-plane stresses inside a controlled laboratory environment but struggle to be robust outside, in a real-world setting. This paper presents an ultrasonic method to evaluate the in-plane stress in situ directly, without knowing any material constants. The method is simple in principle, as it only requires measuring the speed of two angled shear waves.
Materials comprising randomly distributed particles, or inclusions, occur frequently in the world around us. Common examples include composites, emulsions, suspensions, complex gases, etc… This paper is the first to clearly prove that, contrary to the belief of most in the community, multiple effective waves propagate in particulate materials (in an ensemble-average sense).
We show that in general there is not only one effective wave, but there is a series of effective waves in an ensemble averaged particulate material. By particulate material we mean a medium filled with randomly placed particles and consider waves governed by the scalar wave equation. Although most of these waves decay rapidly, they make a significant contribution to reflection and transmission beyond the low-frequency regime. In two spatial dimensions, we develop an efficient method to calculate all these waves, which gives highly accurate predictions when compared to a finite-difference scheme. This method is, to the authors knowledge, the first of its kind to give accurate predictions across a broad frequency range and particle volume fraction.
To what extent can particulate random media be characterised using direct wave backscattering from a single receiver/source? Here, in a two-dimensional setting, we show using a machine learning approach that both the particle radius and concentration can be accurately measured when the boundary condition on the particles is of Dirichlet type. Although the methods we introduce could be applied to any particle type. In general backscattering is challenging to interpret for a wide range of particle concentrations, because multiple scattering cannot be ignored, except in the very dilute range. Across the concentration range from 1% to 20% we find that the mean backscattered wave field is sufficient to accurately determine the concentration of particles. However, to accurately determine the particle radius, the second moment, or average intensity, of the backscattering is necessary. We are also able to determine what is the ideal frequency range to measure a broad range of particles sizes. To get rigorous results with supervised machine learning requires a large, highly precise, dataset of backscattered waves from an infinite half-space filled with particles. We are able to create this dataset by introducing a numerical approach which accurately approximates the backscattering from an infinite half-space.