Integrated planning of forest exploration infrastructures in an amazonian sustainable forest management area.

Planning the allocation of infrastructure exploration in native forests plays an important role in reducing costs and environmental damage. Traditionally, companies manually plan the infrastructure for exploration, which requires a lot of time and effort and implies low planning precision. Additionally, it makes it impossible for decision-makers to explore different scenarios and plan such structures in an integrated way. This research aims to evaluate two strategies that combine computational techniques for allocating the necessary exploration infrastructures in native forests. The study area was a native forest under a sustainable management regime located in the Brazilian Amazon. Three instances were formulated for resolution. Both employed strategies use exact and approximate methods for allocating infrastructures. The results indicate that the location of yards directly influences the optimization of road allocation and skid trails. However, it is essential that the manager evaluates several scenarios considering different numbers of yards to make the decision. It was also concluded that integrated planning makes it possible to obtain better results, as it allows for the choice of planning based on the best global solution by combining the set of infrastructures

Análise Estatística das Topologias Virtuais para Redes Ópticas em Anéis Hierárquicos

O problema do projeto de topologias virtuais consiste em encontrar uma topologia na camada óptica para o roteamento de tráfego em uma rede óptica de forma que alguma métrica de avaliação de desempenho de rede seja otimizado, em nosso caso, o congestionamento. Este problema é classicamente modelado como um problema de programação linear inteira mista e é classificado como NP-Hard, isto é, a busca pela solução ótima é intratável a medida que o número de nós aumenta. Com isso, desde 1996, quando foi proposto o problema, busca-se aplicar métodos heurísticos que encontrem uma boa solução (n˜ão necessariamente a soluçã˜o ótima). Tais métodos, em geral, possuem elevado custo computacional e normalmente n˜ão é estudado o t˜ão quanto é boa a solução encontrada. Esta dissertação estuda a dificuldade de encontrar uma solução satisfatória para o caso de redes com arquitetura em anéis hierárquicos. Optou-se o estudo em anéis hierárquicos devido suas vantagens de tolerˆância a falhas e facilidades de implementa ção computacional. Para realizar as análises utilizamos teoria básica de estatística, descrevendo a distribuição do conjunto das soluç˜ões e avaliando o valor mínimo encontrado. Concluímos que, fazendo uma análise exaustiva com pequenas amostras aleatórias simples encontram-se soluções que pertencem ao seleto grupo dos 2% melhores

Machine-learnt topology of complex tip geometries in gas turbine rotors

The work presents a data driven based strategy to develop a new statistical model of complex tip shape for high-pressure turbine stages exploiting an existing dataset of optimized squealer-like rotor tips. Using the exploratory data analysis (EDA), a set of statistical methods were used to improve the quality of previous CFD-based optimization dataset, as an aid in reducing outliers, data skewness and avoiding the presence of redundant information. The pre-processed dataset was analyzed by unsupervised learning method in order to gain insight on the correlation between tip geometry and single stage axial turbine performance. Utilizing the Principal Component Analysis (PCA), we developed a new continuous, dimensionality-reduced parametrization which allows overcoming the limitations of discrete topology approaches. The novel statistical shape model, coupled with genetic operators into a NSGA-II optimization strategy, was used to explore the design space of optimal solutions generating new designs to enrich the available Pareto front in terms of aero-thermodynamic performance metrics. Two metamodels for performance prediction, respectively based on Artificial Neural Network (ANN) and Gradient Boosting Regressor (GBR) have been developed in order to guide the Pareto front exploration avoiding the use of computationally intensive CFD simulations. New tip designs were carried out to spread the previous optimal front and, successively, aiming to design individuals able to reduce macroscopic not uniformity of the flow keeping optimal aerodynamic performance