Numerical and experimental investigation of turbulent dissipation

Turbulent energy dissipation is presented in the theoretical context of the famous Kolmogorov theory, formulated in 1941. Some remarks and comments about this theory help the reader understand the approach to turbulence study, as well as give some basic insights to the problem. A clear distinction is made amongst dissipation, pseudo-dissipation and dissipation surrogates. Dissipation regulates how turbulent kinetic energy in a flow gets transformed into internal energy, which makes this quantity a fundamental characteristic to investigate in order to enhance our understanding of turbulence. The dissertation focuses on experimental investigation of the pseudo-dissipation. Indeed this quantity is difficult to measure as it requires the knowledge of all the possible derivatives of the three dimensional velocity field. Once considering an hot-wire technique to measure dissipation we need to deal with surrogates of dissipation, since not all the terms can be measured. The analysis of surrogates is the main topic of this work. In particular two flows, the turbulent channel and the turbulent jet, are considered. These canonic flows, introduced in a brief fashion, are often used as a benchmark for CFD solvers and experimental equipment due to their simple structure. Observations made in the canonic flows are often transferable to more complicated and interesting cases, with many industrial applications. The main tools of investigation are DNS simulations and experimental measures. DNS data are used as a benchmark for the experimental results since all the components of dissipation are known within the numerical simulation. The results of some DNS were already available at the start of this thesis, so the main work consisted in reading and processing the data. Experiments were carried out by means of hot-wire anemometry, described in detail on a theoretical and practical level. The study of DNS data of a turbulent channel at Re=298 reveals that the traditional surrogate can be improved Consequently two new surrogates are proposed and analysed, based on terms of the velocity gradient that are easy to measure experimentally. We manage to find a formulation that improves the accuracy of surrogates by an order of magnitude. For the jet flow results from a DNS at Re=1600 of a temporal jet, and results from our experimental facility CAT at Re=70000, are compared to validate the experiment. It is found that the ratio between components of the dissipation differs between DNS and experimental data. Possible errors in both sets of data are discussed, and some ways to improve the data are proposed.

Deep learning for computer vision: a comparison between convolutional neural networks and hierarchical temporal memories on object recognition tasks

In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general. However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of "intelligence". The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them. CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems. HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain. In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.