Análise dinâmica não-linear de pisos mistos (aço-concreto) submetidos a ações humanas rítmicas.

Atualmente, o crescimento dos problemas de vibrações excessivas sobre pisos mistos (aço-concreto) tem conduzido à necessidade de desenvolvimento de critérios específicos para projetos estruturais submetidos à ação de atividades humanas rítmicas. Com base no desenvolvimento desta dissertação de mestrado, objetiva-se, principalmente, verificar a influência das ligações estruturais (ligações viga-viga), sobre a resposta dinâmica não-linear de pisos mistos (aço-concreto) de edificações, quando submetidos a cargas dinâmicas humanas rítmicas. Deste modo, o carregamento dinâmico empregado para a simulação das atividades humanas sobre o modelo estrutural investigado foi obtido através de testes experimentais com indivíduos praticando atividades rítmicas e não rítmicas. O modelo analisado nesta dissertação corresponde a um piso misto (aço-concreto) com uma área total de 1600m2 e consiste de um ambiente onde serão desenvolvidas atividades de ginástica aeróbica. O sistema estrutural é constituído por lajes de concreto armado apoiadas sobre vigas de aço, simulando o comportamento de um sistema estrutural misto (aço-concreto) com interação total. A metodologia de análise desenvolvida emprega técnicas usuais de discretização presentes no método dos elementos finitos, com base no emprego do programa ANSYS. A modelagem do sistema contempla ligações estruturais do tipo rígidas, semirrígidas e flexíveis. Os valores das acelerações de pico foram comparados com os limites recomendados por normas de projeto, baseando-se em critérios de conforto humano. As conclusões alcançadas ao longo deste trabalho de pesquisa revelam que as ligações estruturais do tipo viga-viga não apresentam influência significativa, no que diz respeito a resposta dinâmica não-linear da estrutura. Por outro lado, as acelerações de pico obtidas com base na análise dinâmica não-linear apresentam valores elevados indicando que o piso misto (aço-concreto) investigado apresenta problemas de vibração excessiva inerentes ao conforto humano.

Otimização global topográfica aplicada a problemas de otimização com restrições de igualdade e desigualdade

Os métodos de otimização que adotam condições de otimalidade de primeira e/ou segunda ordem são eficientes e normalmente esses métodos iterativos são desenvolvidos e analisados através da análise matemática do espaço euclidiano n-dimensional, o qual tem caráter local. Esses métodos levam a algoritmos iterativos que são usados para o cálculo de minimizadores globais de uma função não linear, principalmente não-convexas e multimodais, dependendo da posição dos pontos de partida. Método de Otimização Global Topográfico é um algoritmo de agrupamento, o qual é fundamentado nos conceitos elementares da teoria dos grafos, com a finalidade de gerar bons pontos de partida para os métodos de busca local, com base nos pontos distribuídos de modo uniforme no interior da região viável. Este trabalho tem como objetivo a aplicação do método de Otimização Global Topográfica junto com um método robusto e eficaz de direções viáveis por pontos-interiores a problemas de otimização que tem restrições de igualdade e/ou desigualdade lineares e/ou não lineares, que constituem conjuntos viáveis com interiores não vazios. Para cada um destes problemas, é representado também um hiper-retângulo compreendendo cada conjunto viável, onde os pontos amostrais são gerados.

Microstructural optimization of cellular acoustic materials

A microstructure based acoustic model is introduced, which can be used to optimize the microstructure of cellular materials and thus to obtain their optimal acoustic property. This acoustic model is an unsteady one which is appropriate in the limit of low Reynolds numbers. The model involves three elements. This first involves the propagation of acoustic waves passing the cylinders whose axes are aligned parallel to the direction of propagation. The second model relates to the propagation of acoustic waves passing the cylinders whose axes are aligned perpendicular to the direction of propagation. In both cases the interaction between adjacent cylinders is taken into account by considering the effect of polygonal periodic boundary conditions. As these two models are linear they are combined to give the characteristics of propagation at arbitrary incidence. The third model involves propagation passing spheres in order to represent the joints. Heat transfer is also included. These three models are then used to expand the design space and calculate the optimum cell structure for desired acoustic performance in a number of different applications. Moreover, the application fields are also analyzed.

Optimization of dose of methyltestosterone (MT) hormone for sex reversal in tilapia (Oreochromis niloticus L.)

This paper describes the optimization of dose of methyltestosteronei (MT) hormone for masculinization of tilapia (Oreochromis niloticus). Five treatments (i.e. T1 T2, T2, T4 and T5) with different doses such as 0, 40, 50, 60 and 65 mg of MT hormone were mixed with per kg of feed for each treatment and fed the fry four times a day up to satiation for a period of 30 days. The stocking density was maintained 10 spawn/liter of water. The growth of fry at different treatments was recorded weekly and mortality was recorded daily. At the end of hormone feeding the fry were reared in hapas fixed in ponds for another 70 days and at the 100th day the fish were sexed by the gonad squashing and aceto-carmine staining method. The analysis of growth data did not show any significant variation in length and weight of fish among the different treatments. High mortality of fry ranging 66% to 81.6% was observed in different treatments and highest mortality was observed during the first twelve days of the experiment. The sex ratio analysis showed that T2 (40 mg/kg) and T5 (65 mg/kg) produced 93.33% of sex reversed male and T3 (50 mg/kg) and T4 (60 mg/kg) produced 96.66% sex reversed male, and these ratios were significantly (p<0.05) different from 1:1 male: female sex ratio. The control, T1 (0 mg/kg) contained 43.33% male progeny. From these results it is suggested that either 50 mg/kg or 60 mg/kg of MT with a feeding period of 30 days could be considered as an optimum dose for masculinization of tilapia (O. niloticus).

Data-based robust multiobjective optimization of interconnected processes: energy efficiency case study in papermaking.

Reducing energy consumption is a major challenge for "energy-intensive" industries such as papermaking. A commercially viable energy saving solution is to employ data-based optimization techniques to obtain a set of "optimized" operational settings that satisfy certain performance indices. The difficulties of this are: 1) the problems of this type are inherently multicriteria in the sense that improving one performance index might result in compromising the other important measures; 2) practical systems often exhibit unknown complex dynamics and several interconnections which make the modeling task difficult; and 3) as the models are acquired from the existing historical data, they are valid only locally and extrapolations incorporate risk of increasing process variability. To overcome these difficulties, this paper presents a new decision support system for robust multiobjective optimization of interconnected processes. The plant is first divided into serially connected units to model the process, product quality, energy consumption, and corresponding uncertainty measures. Then multiobjective gradient descent algorithm is used to solve the problem in line with user's preference information. Finally, the optimization results are visualized for analysis and decision making. In practice, if further iterations of the optimization algorithm are considered, validity of the local models must be checked prior to proceeding to further iterations. The method is implemented by a MATLAB-based interactive tool DataExplorer supporting a range of data analysis, modeling, and multiobjective optimization techniques. The proposed approach was tested in two U.K.-based commercial paper mills where the aim was reducing steam consumption and increasing productivity while maintaining the product quality by optimization of vacuum pressures in forming and press sections. The experimental results demonstrate the effectiveness of the method.

Data-based robust multiobjective optimization of interconnected processes: Energy efficiency case study in papermaking

Reducing energy consumption is a major challenge for energy-intensive industries such as papermaking. A commercially viable energy saving solution is to employ data-based optimization techniques to obtain a set of optimized operational settings that satisfy certain performance indices. The difficulties of this are: 1) the problems of this type are inherently multicriteria in the sense that improving one performance index might result in compromising the other important measures; 2) practical systems often exhibit unknown complex dynamics and several interconnections which make the modeling task difficult; and 3) as the models are acquired from the existing historical data, they are valid only locally and extrapolations incorporate risk of increasing process variability. To overcome these difficulties, this paper presents a new decision support system for robust multiobjective optimization of interconnected processes. The plant is first divided into serially connected units to model the process, product quality, energy consumption, and corresponding uncertainty measures. Then multiobjective gradient descent algorithm is used to solve the problem in line with user's preference information. Finally, the optimization results are visualized for analysis and decision making. In practice, if further iterations of the optimization algorithm are considered, validity of the local models must be checked prior to proceeding to further iterations. The method is implemented by a MATLAB-based interactive tool DataExplorer supporting a range of data analysis, modeling, and multiobjective optimization techniques. The proposed approach was tested in two U.K.-based commercial paper mills where the aim was reducing steam consumption and increasing productivity while maintaining the product quality by optimization of vacuum pressures in forming and press sections. The experimental results demonstrate the effectiveness of the method. © 2006 IEEE.

Sparsity-Inducing Optimization Algorithm for the Extraction of Planar Structures in Noisy Point-Cloud Data

Most of the manual labor needed to create the geometric building information model (BIM) of an existing facility is spent converting raw point cloud data (PCD) to a BIM description. Automating this process would drastically reduce the modeling cost. Surface extraction from PCD is a fundamental step in this process. Compact modeling of redundant points in PCD as a set of planes leads to smaller file size and fast interactive visualization on cheap hardware. Traditional approaches for smooth surface reconstruction do not explicitly model the sparse scene structure or significantly exploit the redundancy. This paper proposes a method based on sparsity-inducing optimization to address the planar surface extraction problem. Through sparse optimization, points in PCD are segmented according to their embedded linear subspaces. Within each segmented part, plane models can be estimated. Experimental results on a typical noisy PCD demonstrate the effectiveness of the algorithm.

Linear and nonlinear model large-eddy simulations of a plane jet

The nonlinear Kosovic, and mixed Leray and α subgrid scale models are contrasted with linear Smagorinsky and Yoshizawa Large Eddy Simulations for a Re = 4000 plane jet simulation. Comparisons are made with Direct Numerical Simulation data and measurements. Global properties of the jet such as the spreading and centreline velocity decay rates are investigated. The mean-flow and turbulence parameters in the self-similar region are also studied. All models generally give encouraging agreement with the Direct Numerical Simulation data and reliable measurements. Solution differences for the models are relatively minor, none giving clear improvements for all data comparisons.

Linear multiscale analysis and finite element validation of stretching and bending dominated lattice materials

The paper presents a multiscale procedure for the linear analysis of components made of lattice materials. The method allows the analysis of both pin-jointed and rigid-jointed microtruss materials with arbitrary topology of the unit cell. At the macroscopic level, the procedure enables to determine the lattice stiffness, while at the microscopic level the internal forces in the lattice elements are expressed in terms of the macroscopic strain applied to the lattice component. A numeric validation of the method is described. The procedure is completely automated and can be easily used within an optimization framework to find the optimal geometric parameters of a given lattice material. © 2011 Elsevier Ltd. All rights reserved.

Linear regression under fixed-rank constraints: A Riemannian approach

In this paper, we tackle the problem of learning a linear regression model whose parameter is a fixed-rank matrix. We study the Riemannian manifold geometry of the set of fixed-rank matrices and develop efficient line-search algorithms. The proposed algorithms have many applications, scale to high-dimensional problems, enjoy local convergence properties and confer a geometric basis to recent contributions on learning fixed-rank matrices. Numerical experiments on benchmarks suggest that the proposed algorithms compete with the state-of-the-art, and that manifold optimization offers a versatile framework for the design of rank-constrained machine learning algorithms. Copyright 2011 by the author(s)/owner(s).

Continuous dynamical systems that realize discrete optimization on the hypercube

We study the problem of finding a local minimum of a multilinear function E over the discrete set {0,1}n. The search is achieved by a gradient-like system in [0,1]n with cost function E. Under mild restrictions on the metric, the stable attractors of the gradient-like system are shown to produce solutions of the problem, even when they are not in the vicinity of the discrete set {0,1}n. Moreover, the gradient-like system connects with interior point methods for linear programming and with the analog neural network studied by Vidyasagar (IEEE Trans. Automat. Control 40 (8) (1995) 1359), in the same context. © 2004 Elsevier B.V. All rights reserved.

Sensitivity analysis and optimization of the nuclear fuel cycle

A sensitivity study has been conducted to assess the robustness of the conclusions presented in the MIT Fuel Cycle Study. The Once Through Cycle (OTC) is considered as the base-line case, while advanced technologies with fuel recycling characterize the alternative fuel cycles. The options include limited recycling in LWRs and full recycling in fast reactors and in high conversion LWRs. Fast reactor technologies studied include both oxide and metal fueled reactors. The analysis allowed optimization of the fast reactor conversion ratio with respect to desired fuel cycle performance characteristics. The following parameters were found to significantly affect the performance of recycling technologies and their penetration over time: Capacity Factors of the fuel cycle facilities, Spent Fuel Cooling Time, Thermal Reprocessing Introduction Date, and incore and Out-of-core TRU Inventory Requirements for recycling technology. An optimization scheme of the nuclear fuel cycle is proposed. Optimization criteria and metrics of interest for different stakeholders in the fuel cycle (economics, waste management, environmental impact, etc.) are utilized for two different optimization techniques (linear and stochastic). Preliminary results covering single and multi-variable and single and multi-objective optimization demonstrate the viability of the optimization scheme.