Fractal tracer distributions in turbulent field theories

We study the motion of passive tracers in a two-dimensional turbulent velocity field generated by the Kuramoto-Sivashinsky equation. By varying the direction of the velocity-vector with respect to the field-gradient we can continuously vary the two Lyapunov exponents for the particle motion and thereby find a regime in which the particle distribution is a strange attractor. We compare the Lyapunov dimension to the information dimension of actual particle distributions and show that there is good agreement with the Kaplan-Yorke conjecture. Similar phenomena have been observed experimentally.Comment: 17 pages, 7 figures, elsart.sty, psfig.sty, LaTe

From Grain to Feed

Horse beans are used as an example of grain, which have potentials to substitute fish meal in fish feed. After several grinding and fractionation steps it was possible to obtain a protein-rich horse bean fraction with a yield of 19 %. This fraction contained 55.9 % protein. The high protein content together with the amino acid profile makes the fraction very suitable as a substitute to fish meal in fish feed. The protein-rich fraction was used as a successful additive in extruded fish pellets

Hash Embeddings for Efficient Word Representations

We present hash embeddings, an efficient method for representing words in a continuous vector form. A hash embedding may be seen as an interpolation between a standard word embedding and a word embedding created using a random hash function (the hashing trick). In hash embeddings each token is represented by $k$ $d$-dimensional embeddings vectors and one $k$ dimensional weight vector. The final $d$ dimensional representation of the token is the product of the two. Rather than fitting the embedding vectors for each token these are selected by the hashing trick from a shared pool of $B$ embedding vectors. Our experiments show that hash embeddings can easily deal with huge vocabularies consisting of millions of tokens. When using a hash embedding there is no need to create a dictionary before training nor to perform any kind of vocabulary pruning after training. We show that models trained using hash embeddings exhibit at least the same level of performance as models trained using regular embeddings across a wide range of tasks. Furthermore, the number of parameters needed by such an embedding is only a fraction of what is required by a regular embedding. Since standard embeddings and embeddings constructed using the hashing trick are actually just special cases of a hash embedding, hash embeddings can be considered an extension and improvement over the existing regular embedding types

Fremstilling af økologisk fiskefoder

Poteinindholdet i råvare afgrøderne var ca. 22%, hvilket er langt under niveauet i fiskemel (70-72% protein). Der har derfor været udført forsøg m.h.p. at opkoncentrere proteinindholdet i økologisk raps, ærter, hestebønner og lupin. Der blev benyttet mekaniske metoder (afskalning, formaling og lufttørring. Følgende proteinindhold blev opnået: Raps 29% protein, ærter: 52% protein, Hestebønner: 56% protein og Lupin: 56% protein. Baseret på dissse proteinkoncentrater blev der fremstillet forsøgsdiæter, hvor fiskemel var delvist erstattet med disse koncentrater

Power Flow Optimization with Graph Neural Networks

Power flow analysis is an important tool in power engineering for planning and operating power systems. The standard power flow problem consists of a set of non-linear equations, which are traditionally solved using numerical optimization techniques, such as the Newton-Raphson method. However, these methods can become computationally expensive for larger systems, and convergence to the global optimum is usually not guaranteed. In recent years, several methods using Graph Neural Networks (GNNs) have been proposed to speed up the computation of the power flow solutions, without making large sacrifices in terms of accuracy. This class of models can learn localized features that are independent from a global graph structure. Therefore, by representing power systems as graphs these methods can, in principle, generalize to systems of different size and topology. However, most of the current approaches have only been applied to systems with a fixed topology and none of them were trained simultaneously on systems of different topology. Hence, these models are not fully shown to generalize to widely different systems or even to small perturbations of a given system. In this thesis, several supervised GNN models are proposed to solve the power flow problem, using established GNN blocks from the literature. These GNNs are trained on a set of different tasks, where the goal is to study the generalizability to both perturbations and completely different systems, as well as comparing performance to standard Multi-Layered Perceptron (MLP) models. The experimental results show that the GNNs are comparatively successful at generalizing to widely different topologies seen during training, but do not manage to generalize to unseen topologies and are not able to outperform an MLP on slight perturbations of the same energy system. The study presented in this thesis allowed to draw important insights about the applicability of GNN as power flow solvers. In the conclusion, several possible ways for improving the GNN-based solvers are discussed

A general extrudate bulk density model for both twin-screw and single-screw extruder extrusion cooking processes

Effects of extrusion parameters and raw materials on extrudate expansion are respectively investigated in a twin-screw extruder and a single-screw extruder extrusion cooking experiments for fish feed, wheat, and oat & wheat mixture processing. A new phenomenological model is proposed to correlated extrudate bulk density, extrusion parameters and raw material changes based on the experimental results. The average absolute deviation (AAD) of the correlation is 2.2% for fish feed extrusion in the twin-screw extrusion process. For the single-screw extrusion process, the correlation AAD is respectively 3.03%, 5.14% for wheat and oat & wheat mixture extrusion; and the correlation AAD is 6.6% for raw material change effects. The correlation results demonstrate that the proposed equation can be used to calculate extrudate bulk density for both the twin-screw extruder and the single-screw extruder extrusion cooking processes

Can Local Stress Enhancement Induce Stability in Fracture Processes? Part I: Apparent Stability

By comparing the evolution of the local and equal load sharing fiber bundle models, we point out the paradoxical result that stresses seem to make the local load sharing model stable when the equal load sharing model is not. We explain this behavior by demonstrating that it is only an apparent stability in the local load sharing model, which originates from a statistical effect due to sample averaging. Even though we use the fiber bundle model to demonstrate the apparent stability, we argue that it is a more general feature of fracture processes.Comment: 7 pages, 8 figure