UKACM2025 School

The UKACM 2025 School will focus on Advanced Computational Methods in Engineering, including fluid mechanics, coupled multi-physics problems and data-driven methods.

Professor Angela Busse

Angela Busse is Professor of Numerical Fluid Dynamics in the Institute of Fluid Dynamics and Technical Acoustics at the Technical University Berlin. Focus of her research is the investigation of problems in fluid dynamics using numerical, experimental, and analytical methods. In her scientific career to date, she has investigated a wide range of flows that occur in technical applications and in the natural environment. This includes wall-bounded turbulent flows with focus on flows over complex surfaces, such as rough and superhydrophobic surfaces, wind-plant interaction, the fundamental properties of Navier-Stokes and magnetohydrodynamic turbulence, flow past bluff bodies, and two-phase flows. Furthermore, she pursues the development of numerical methods and models for computational fluid dynamics in the context of high-performance computing, and the development of experimental methods in interdisciplinary applications of fluid dynamics.

Course Description

Topic: From surface scan to the equivalent sand grain roughness – using DNS as a tool to study the fluid dynamic properties of rough surfaces

Roughness is encountered in many different contexts in engineering and geophysics. This is because of the many different processes that can cause roughness, for example, fouling, erosion, or imperfections in the manufacturing. While obtaining high resolution surface scans has become a matter of routine with modern measurement equipment, to date, there is no simple way to accurately predict the fluid dynamic properties of a surface solely based on its topography. Therefore, experiments or high-resolution simulations are required to obtain key parameters that capture fluid dynamic roughness effects such as the equivalent sand grain roughness.

In this lecture, we will cover the practical aspects of using direct numerical simulations as a numerical wind tunnel to investigate the fluid dynamic properties of a given surface topography, e.g., based on a surface scan. First, an introduction to the topographical characterisation of rough surfaces will be given and then key considerations in the simulation design will be explained using examples. Finally, we will discuss how we can use methods from tribology to generate realistic surfaces that allow us to explore the fluid dynamic effects of key topographical parameters in a systematic manner.

Dr Tim Hageman

Tim Hageman is a departmental lecturer and an 1851 research fellow in at the University of Oxford, working on developing novel finite element schemes for a wide range of multi-physics and multi-scale problems (including hydraulic fracturing processes within ice sheets, hydrogen embrittlement and failure of metals, and corrosion processes). Prior to this, he was a research associate at Imperial College London performing research related to similar topics. Tim obtained his PhD at the university of Sheffield under the supervision of prof. René de Borst, developing multi-scale models for poroelasticity. His PhD thesis has earned the UKACM 2022 Roger Owen prize for best thesis in the area of computational mechanics in the UK, and the ECCOMAS PhD award for one of the two best theses related to computational modelling in Europe.

Course Description

Topic: Considerations when expanding from single-physics to multi-physics simulations

Abstract: Stability issues and the methods to prevent them from occurring within specific fields are well-known, e.g. mesh locking in solid mechanics and inf-sup/checkerboarding type instabilities in fluid mechanics. While preventing these issues when considering multi-physical systems is also paramount, the coupling between different fields also introduces new modelling choices, which can result in instabilities themselves. Here, we will discuss considerations to make when you have a well-working simulation for a single type of physics, and want to expand this to include additional mechanisms. Through some examples (chemical diffusion within solids, fluid flow through porous materials, and electric-field driven diffusion in fluids), these modelling choices will be explained, parallels between other applications will be considered, and their impact on the overall efficiency, accuracy, and stability of the model will be discussed.

Dr Burigede Liu

Burgiede Liu is Granta Design Assistant Professor at the University of Cambridge. He received his Ph.D. in Engineering at University of Cambridge in 2019. He was a postdoc in Department of Mechanical and Process Engineering at ETH Zürich (2019) and a postdoctoral fellow in Mechanical and Civil Engineering at California Institute of Technology (2019-2021).

Course Description

Topic: Operator learning in Mechanics and Materials

Mechanics and materials are gradually becoming data-rich due to rapid advances in experimental science and high-performance multiscale computing. There has been a growing interest in the field of solid mechanics for developing data-driven and learning-based methods to characterize, understand, model, and design material/structural systems. With data-driven approaches, it is possible to remove/relax the need for ad hoc constitutive models for describing the material behavior, to achieve fast multi-scale computation for structures as well as to generate optimal designs. This module will introduce a wide spectrum of operator learning based methods that have been developed and used in mechanics and materials, with an emphasis on developing a working understanding of how to apply these methods in practice.

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