Numerical and experimental investigations of three-dimensional container filling with Newtonian viscous fluids

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A two-fluid model for refrigerant flow through adiabatic capillary tubes

This work presents a numerical model to simulate refrigerant flow through capillary tubes, commonly used as expansion devices in refrigeration systems. The flow is divided in a single-phase region, where the refrigerant is in the subcooled liquid state, and a region of two-phase flow. The capillary tube is considered straight and horizontal. The flow is taken as one-dimensional and adiabatic. Steady-state condition is also assumed and the metastable flow phenomena are neglected. The two-fluid model, considering the hydrodynamic and thermal non-equilibrium between the liquid and vapor phases, is applied to the two-phase flow region. Comparisons are made with experimental measurements of the mass flow rate and pressure distribution along two capillary tubes working with refrigerant R-134a in different operating conditions. The results indicate that the present model provides a better estimation than the commonly employed homogeneous model. Some computational results referring to the quality, void fraction, velocities, and temperatures of each phase are presented and discussed.

Combined active and semi-supervised learning using particle walking temporal dynamicscombined active and semi-supervised learning using particle walking temporal dynamics

Both Semi-Supervised Leaning and Active Learning are techniques used when unlabeled data is abundant, but the process of labeling them is expensive and/or time consuming. In this paper, those two machine learning techniques are combined into a single nature-inspired method. It features particles walking on a network built from the data set, using a unique random-greedy rule to select neighbors to visit. The particles, which have both competitive and cooperative behavior, are created on the network as the result of label queries. They may be created as the algorithm executes and only nodes affected by the new particles have to be updated. Therefore, it saves execution time compared to traditional active learning frameworks, in which the learning algorithm has to be executed several times. The data items to be queried are select based on information extracted from the nodes and particles temporal dynamics. Two different rules for queries are explored in this paper, one of them is based on querying by uncertainty approaches and the other is based on data and labeled nodes distribution. Each of them may perform better than the other according to some data sets peculiarities. Experimental results on some real-world data sets are provided, and the proposed method outperforms the semi-supervised learning method, from which it is derived, in all of them.

Semi-supervised learning with concept drift using particle dynamics applied to network intrusion detection data

Concept drift, which refers to non stationary learning problems over time, has increasing importance in machine learning and data mining. Many concept drift applications require fast response, which means an algorithm must always be (re)trained with the latest available data. But the process of data labeling is usually expensive and/or time consuming when compared to acquisition of unlabeled data, thus usually only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are based on assumptions that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenging task in machine learning. Recently, a particle competition and cooperation approach has been developed to realize graph-based semi-supervised learning from static data. We have extend that approach to handle data streams and concept drift. The result is a passive algorithm which uses a single classifier approach, naturally adapted to concept changes without any explicit drift detection mechanism. It has built-in mechanisms that provide a natural way of learning from new data, gradually "forgetting" older knowledge as older data items are no longer useful for the classification of newer data items. The proposed algorithm is applied to the KDD Cup 1999 Data of network intrusion, showing its effectiveness.

Análise dinâmica não-linear de um sistema não-ideal, utilizando amortecedor magneto-reológico

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)