Previous seminars

The efficient redistribution and control of flow is essential in many biological and engineered systems, from our cardiovascular system to plants and soft robotics. Inspired by nature, microfluidic devices with passive valves have been developed to perform a variety of tasks, from cell manipulation to fluid mixing and reaction control, giving rise to the field of soft hydraulics. In this talk, we present two prototypes of passive valves: one harnessing buckling in slender beams and another leveraging snapping in spherical shells, where fluid-induced elastic instabilities are tamed to achieve function. Through a combination of precision desktop-scale experiments, numerical simulations, and theoretical analysis, we rationalize these behaviors and provide design rules based on scaling regimes of material, geometric, and fluid parameters.

Earthquake, landslide, and glacier instabilities are multi-scale processes that cannot be predicted. When a rock is subjected to large stress, it can develop fractures that grow and connect to form a network. This network of fractures spans the entire rock right before it fails catastrophically. Despite extensive research on rock damage, researchers are still far from being able to predict when a rock will fail. Here, I will show how to use a multi-view convolutional neural network model to identify characteristics of a failing rock. By training a neural network model on images of rock samples exposed to different levels of stress, one can predict how close a rock is to failure. The neural network model outperforms traditional estimates based on fracture density. More importantly, the model provides fundamental insight that may offer precursory information on impending material failure. For instance, according to the model, the angle of the fracture plane relative to the principal loading direction becomes a key factor contributing to failure.

Swimming microorganisms and artificial microswimmers use environmental cues to influence their dynamics, helping them to improve transport, reach specific flow regions, or avoid harmful conditions. Although plankton navigate turbulent flows in nature, it is poorly understood how they do so efficiently. By combining Reinforcement Learning with analytical perturbation theory, we investigate how microswimmers can sense environmental signals and adjust a few control parameters to navigate turbulent flows. This approach helps identify effective navigation strategies for various tasks. We illustrate this with two key tasks: avoiding high-strain regions and navigating against gravity, both essential for plankton in the ocean.
 

AI has made enormous progress along different domains in the last decades, from playing complex games, to analyzing and generating images and text. However, we have so far not seen the same impressive capabilities in AI for robotics. Why is that? In this presentation, I will talk about a few of the problems that makes AI for robotics a unique challenge, and what we at the group for Robotics and Intelligent Systems (ROBIN) are doing to address those challenges.

We consider several situations where drops can be captured by fibrous materials, from sprays on textiles (where the droplets are much larger than the typical fiber size) to meshes placed in a flow of fog (where the droplets are smaller than the typical fiber size). This last system, often encountered in aerosol filtration, exhibits a wealth of phenomena coupling capillarity and aerodynamics. In particular, we characterize experimentally and theoretically the deposition of the mist droplets on the fibre and the flow through and around the porous mesh, that both strongly depend on the mesh porosity and fine structure (i.e. the arrangements of the fibers). We further consider the dynamics of large drops sitting on fibers when exposed to a cross-flow, in particular their interactions with their unsteady wakes.

A liquid droplet placed on a rigid, planar surface has a captivating simplicity: its surface is a spherical cap and remains in equilibrium. In this talk, I’ll show that the addition of a deformable boundary leads to a range of new phenomena: evaporating droplets with an elastic skin may develop flat tops, but even without a complex rheology, reaching equilibrium can happen slowly, with the droplet’s contact ageing. I will discuss two specific examples, presenting a combination of theory and experiment for each.

The theory of flexible plates can be applied to models of floating solar panels. When wet, the structure vibrates with lower frequencies than when in vacuo.  Our study is concerned with thin rectangular porous plates with free edge conditions in three dimensions exposed to incoming waves. We develop a complete hydrodynamical theory of the wave-flexible-structure interaction including the coupled radiation-diffraction problem. The formulation leads to a set of integral equations for the potentials on the wetted side of the plate. The Green function in three dimensions is implemented along the floating geometry. We use the beam method for the displacement of the rectangular free plate. A variational equation is minimized for the solutions of the interaction problem

During this cold winter in Oslo, we certainly all have experienced the unsteady nature of friction and the sudden loss of stress bearing capacity that initiates catastrophic sliding. A similar kind of frictional rupture arises at the onset of a wide variety of natural disasters that includes earthquakes, landslides, as well as snow avalanches. In this talk, I will focus on the incipient stage of these catastrophic events that is characterized by the local nucleation of failure and its rapid propagation towards intact regions of the material. I will discuss how the analogy to fracture mechanics can be exploited to describe the dynamics of these rupture fronts and develop quantitative models to characterize the onset of failure in geomaterials.

Bone stress injuries affect athletic populations who undertake activities in which bones are repeatedly loaded. In order to understand and reduce the risk of bone stress injuries, we need to quantify the loading experienced by the bones during activities such as running. Bone loading is difficult to quantify as the magnitudes of stress are influenced to a large extent by the magnitude of muscular forces acting on the bone. Musculoskeletal modelling, ranging from very simple to very complex approaches, can be used to estimate the internal loading experienced by the bone during running. This has allowed us to explore factors such as speed, slope and step length and their influence on bone loading during running. However, in order to truly understand risk of stress injuries this needs to be taken out of the lab and in-field. This talk will consider what we know and the limitations to current understanding.

Future robots need to be robust and adaptable, and new design approaches are needed for new production methods. I will talk about my research in using evolutionary algorithms and biologically inspired methods with the aim of having more intelligent, robust, and adaptive behavior in robots. I will give a short introduction to some of the algorithms and show how we apply them in our robotic platforms for exploring automatic design and adaptation.

Engineering principles to develop advanced biomaterials and scaffolds. This study focuses on utilising engineering principles to facilitate new bone growth, specifically in designing and fabricating bone scaffolds. The core of our investigation lies in applying titanium dioxide (TiO2) scaffolds, which have emerged as promising candidates due to their osteoconductive properties and the potential for enhancing bone tissue engineering.

Our research aims to bridge the gap between engineering and biology by harnessing fluid mechanics and material science to create scaffolds that mimic the natural bone environment. By comparing TiO2 scaffolds with commercial porous calcium phosphate biomaterials under simulated perfusion culture conditions, we delve into the mechanical and fluidic stimuli that are crucial for bone regeneration. The study emphasises the role of fluid dynamics to make better “spare-parts” for the human body, highlighting the importance of permeability, mechanical load transfer and wall shear stress with a porous ceramic.

Furthermore, we validate our fluid mechanic simulations with the impact of dynamic seeding techniques. Our findings demonstrate the superiority of dynamic culture conditions in enhancing the expression of bone-related proteins and genes, thereby facilitating more effective bone regeneration. In addition, the study presents a clinical trial that shows that these porous bioceramic functions well in patients. The trial's outcomes confirm the potential of TiO2 scaffolds for in vivo bone formation and showcase the successful integration of engineering principles in the development of biomaterials for tissue engineering.

This research underscores the pivotal role of engineering in advancing tissue engineering and biomaterials science. By leveraging engineering principles to optimise scaffold design and functionality, we pave the way for innovative strategies in bone regeneration, offering new hope for patients requiring bone tissue reconstruction.

Controlling the spontaneous ruptures of nanoscale liquid thin films is crucial to various applications such as solar cell manufacturing. Over the past few decades, theoretical work based on the long-wave theory of thin liquid films has successfully identified a critical film height, below which the surface nanowaves become linearly unstable, leading to spontaneous rupture. This dewetting in the ‘spinodal regime’ has been repeatedly confirmed in experiments using atomic force microscopy on polymer films. However, ruptures are also observed for thicker films (linearly stable) in a different manner. It is believed that the random (Brownian) movement of particles is the cause of dewetting in this ‘thermal regime’ but the theoretical framework predicting the rupture is missing. In this talk, we present a theory to account for the rupture of a two dimensional linearly stable thin film by utilizing fluctuating hydrodynamics and rare events theory. By modelling the film dynamics with the stochastic thin-film equation (STF) and solving it numerically, we observe rupture in the linearly stable thermal regime and record the average waiting time for rupture. We show that the STF can be rearranged into the form of a gradient flow, which allows us to apply Kramer’s law from the rare events theory to obtain a theoretical prediction of the average waiting time. Molecular dynamics (MD) simulations are also performed and we find good agreements between the numerics, the prediction, and the MD.

A peculiarity of nonlinear hyperbolic problems is that they must be interpreted as limits of second-order equations with vanishing viscosity. Despite not explicitly being present in the hyperbolic case, diffusion is needed, e. g., at discontinuities or to avoid the occurrence of nonphysical states. In the case of gas dynamics, for instance, dissipation corresponds to the production of thermodynamic entropy. To solve hyperbolic problems numerically, one needs to adapt these ideas to the discrete setting. Standard high-order methods, however, do not incorporate the appropriate amounts of artificial viscosity because these need to be chosen adaptively based on the solution. Among the high-resolution schemes capable of doing so are the recently proposed monolithic convex limiting (MCL) techniques [1] to be discussed in this talk. They offer a way to enforce physical admissibility, entropy stability, and discrete maximum principles for conservation laws. These methods can also be generalized to systems of balance laws in a well-balanced manner [2]. In addition to second-order finite element methods, extensions to high-order discontinuous Galerkin (DG) schemes shall also be presented [3]. Numerical examples for the so-called KPP problem, the nonconservative shallow water system, and the compressible Euler equations will be shown. An overview of MCL and other property-preserving methods can be found in our recently published book [4].

We combine a pressure correction scheme with interior penalty discontinuous Galerkin (dG) discretisation to solve the time-dependent Navier–Stokes equations. We prove unconditional energy stability and a priori error estimates for the velocity. With duality arguments, optimal L2 error rates are obtained. Convergence of the discrete pressure is also established.  Further, we propose a splitting scheme,  integrating the pressure correction approach, for the Cahn–Hilliard–Navier–Stokes system  The numerical analysis of dG combined with this scheme is discussed. Namely, we show well--posedness, stability, and error estimates. Numerical results with manufactured solutions display our theoretical findings, and a spinodal decomposition example portrays the robustness of our approach.