Aalto-Tampere miniseminar on Complexity in Materials, November 20, 2019
Tampere University, Hervanta Campus, Kampusklubi Workshop Centre (Kampusareena building, 5th floor)
Everyone is welcome to attend, but please register by email (lasse.laurson@tuni.fi) before Friday November 15, so that we’ll be able to estimate the amount of coffee/tea needed!
Program:
11:45 – 12:05
Jonatan Mac Intyre (Aalto University)
Predicting the time dependent dynamics of local yielding in dry foams
Abstract: The yielding of dry foams is enabled by small elementary yield events on the bubble scale, called “T1″‘s. We study the large scale detection of these in an expanding 2D flow geometry using artificial intelligence (AI) and nearest neighbour analysis. Using a score parameter, we find that the AI reaches a good level of accuracy using only a single frame.
12:05 – 12:25
Henri Salmenjoki (Aalto University)
Machine learning plastic deformation of crystals
Abstract: Crystal plasticity occurs by deformation bursts due to collective, avalanche-like motion of dislocations causing sample-to-sample variations on micron-scale. Here we present our results of applying machine learning to predict the stress-strain curves of dislocation structures simulated with discrete dislocation dynamics model, and discuss how the avalanche behaviour affects the predictability.
12:25 – 12:45
Tero Mäkinen (Aalto University)
Deformation band dynamics in the Portevin-Le Chatelier effect
Abstract: During deformation some metal alloys under certain conditions exhibit collective dislocation dynamics called the Portevin-Le Chatelier effect which can be observed as serrations in the stress-strain curve and visible deformation bands moving on the sample. This effect in terms of the dynamics of these deformation bands has been studied in high-resolution experiments and some of the results are presented in this talk.
12:45 – 13:05
Audun Skaugen (Tampere University)
Depinning dynamics of magnetic domain walls
Abstract: Magnetic domain walls moving in quenched disorder is an important realization of the depinning transition, leading to power-law noise in the magnetization dynamics known as Barkhausen noise. However, in contrast to simple elastic-line models of avalanches, the magnetic domain wall contains additional features such as internal degrees of freedom and nonlocal interactions. I will present an efficient model for exploring the effect of such differences on the resulting depinning dynamics.