Mechanical properties and dimensional effects of ZnO- and SnO(2)-based varistors

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Distribution transformers modeling with angular displacement: actual values and per unit analysis

In this work a detailed modeling of three-phase distribution transformers aimed at complementing well-known approaches is presented. Thus, incidence of angular displacement and tapping is taken into account in the proposed models, considering both actual values and per unit. The analysis is based on minimal data requirement: solely short-circuit admittance is needed since three-phase transformers are treated as non-magnetically-coupled single-phase transformers. In order to support the proposed methodology, results obtained through laboratory tests are presented.

Molecular modeling and small angle X-ray scattering studies of Hoplosternum littorale cathodic haemoglobin

Considerable interest is currently focused on fish haemoglobins in order to identify the structural basis for their diversity of functional behavior. Hoplosternum littorale is a catfish that presents bimodal gill (water)/gut (air) -breathing, which allows this species to survive in waters with low oxygen content. The hemolysate of this fish showed the presence of two main haemoglobins, cathodic and anodic. This work describes structural features analyzed here by integration of molecular modeling with small angle X-ray scattering. Here is described a molecular model for the cathodic haemoglobin in the unliganded and liganded states. The models were determined by molecular modeling based on the high-resolution crystal structure of fish haemoglobins. The structural models for both forms of H. littorale haemoglobin were compared to human haemoglobin. (C) 2004 Elsevier B.V. All rights reserved.

Modeling thermal conductivity, specific heat, and density of milk: A neural network approach

The accurate determination of thermophysical properties of milk is very important for design, simulation, optimization, and control of food processing such as evaporation, heat exchanging, spray drying, and so forth. Generally, polynomial methods are used for prediction of these properties based on empirical correlation to experimental data. Artificial neural networks are better Suited for processing noisy and extensive knowledge indexing. This article proposed the application of neural networks for prediction of specific heat, thermal conductivity, and density of milk with temperature ranged from 2.0 to 71.0degreesC, 72.0 to 92.0% of water content (w/w), and 1.350 to 7.822% of fat content (w/w). Artificial neural networks presented a better prediction capability of specific heat, thermal conductivity, and density of milk than polynomial modeling. It showed a reasonable alternative to empirical modeling for thermophysical properties of foods.

Modeling and simulation of cephalosporin C production in a fed-batch tower-type bioreactor

Immobilized cell utilization in tower-type bioreactor is one of the main alternatives being studied to improve the industrial bioprocess. Other alternatives for the production of beta -lactam antibiotics, such as a cephalosporin C fed-batch p recess in an aerated stirred-tank bioreactor with free cells of Cepha-losporium acremonium or a tower-type bioreactor with immobilized cells of this fungus, have proven to be more efficient than the batch profess. In the fed-batch process, it is possible to minimize the catabolite repression exerted by the rapidly utilization of carbon sources (such as glucose) in the synthesis of antibiotics by utilizing a suitable flow rate of supplementary medium. In this study, several runs for cephalosporin C production, each lasting 200 h, were conducted in a fed-batch tower-type bioreactor using different hydrolyzed sucrose concentrations, For this study's model, modifications were introduced to take into account the influence of supplementary medium flow rate. The balance equations considered the effect of oxygen limitation inside the bioparticles. In the Monod-type rate equations, eel concentrations, substrate concentrations, and dissolved oxygen were included as reactants affecting the bioreaction rate. The set of differential equations was solved by the numerical method, and the values of the parameters were estimated by the classic nonlinear regression method following Marquardt's procedure with a 95% confidence interval. The simulation results showed that the proposed model fit well with the experimental data,and based on the experimental data and the mathematical model an optimal mass flow rate to maximize the bioprocess productivity could be proposed.

A CASE tool for the modeling of methods and information systems

MODSI is a multi-models tool for information systems modeling. A modeling process in MODSI can be driven according to three different approaches: informal, semi-formal and formal. The MODSI tool is therefore based on the linked usage of these three modeling approaches. It can be employed at two different levels: the meta-modeling of a method and the modeling of an information system.In this paper we start presenting different types of modeling by making an analysis of their particular features. Then, we introduce the meta-model defined in our tool, as well as the tool functional architecture. Finally, we describe and illustrate the various usage levels of this tool.

Dynamic modeling and simulation of a flexible robotic manipulator

This work focuses on the dynamic modeling of a flexible robotic manipulator with two flexible links and two revolute joints, which rotates in the horizontal plane. The dynamic equations are derived using the Newton-Euler formulation and the finite element method, based on elementary beam theory. Computer simulation results are presented to illustrate this study. The dynamic model becomes necessary for use in future design and control applications.

Modeling hourly diffuse solar-radiation in the city of São Paulo using a neural-network technique

dIn this work, a perceptron neural-network technique is applied to estimate hourly values of the diffuse solar-radiation at the surface in São Paulo City, Brazil, using as input the global solar-radiation and other meteorological parameters measured from 1998 to 2001. The neural-network verification was performed using the hourly measurements of diffuse solar-radiation obtained during the year 2002. The neural network was developed based on both feature determination and pattern selection techniques. It was found that the inclusion of the atmospheric long-wave radiation as input improves the neural-network performance. on the other hand traditional meteorological parameters, like air temperature and atmospheric pressure, are not as important as long-wave radiation which acts as a surrogate for cloud-cover information on the regional scale. An objective evaluation has shown that the diffuse solar-radiation is better reproduced by neural network synthetic series than by a correlation model. (C) 2004 Elsevier Ltd. All rights reserved.

Agent-based model to rural-urban migration analysis

In this paper, we analyze the rural-urban migration phenomenon as it is usually observed in economies which are in the early stages of industrialization. The analysis is conducted by means of a statistical mechanics approach which builds a computational agent-based model. Agents are placed on a lattice and the connections among them are described via an Ising-like model. Simulations on this computational model show some emergent properties that are common in developing economies, such as a transitional dynamics characterized by continuous growth of urban population, followed by the equalization of expected wages between rural and urban sectors (Harris-Todaro equilibrium condition), urban concentration and increasing of per capita income. (c) 2005 Elsevier B.V. All rights reserved.

Analog Modeling of Human Cognitive Functions with Tripartite Synapses

Searching for an understanding of how the brain supports conscious processes, cognitive scientists have proposed two main classes of theory: Global Workspace and Information Integration theories. These theories seem to be complementary, but both still lack grounding in terms of brain mechanisms responsible for the production of coherent and unitary conscious states. Here we propose following James Robertson's "Astrocentric Hypothesis" - that conscious processing is based on analog computing in astrocytes. The "hardware" for these computations is calcium waves mediated by adenosine triphosphate signaling. Besides presenting our version of this hypothesis, we also review recent findings on astrocyte morphology that lend support to their functioning as Local Hubs (composed of protoplasmic astrocytes) that integrate synaptic activity, and as a Master Hub (composed, in the human brain, by a combination of interlaminar, fibrous, polarized and varicose projection astrocytes) that integrates whole-brain activity.

Artificial neural networks for machining processes surface roughness modeling

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Host-guest complexation of a nitroheterocyclic compound with cyclodextrins: a spectrofluorimetric and molecular modeling study

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Unbalanced Three-Phase Induction Motors Starting and Arc Welding Machines Short-Circuit Modeling

Three-Phase Induction Motors (TIM) and Arc Welding Machines (AWM) are loads of special behavior widely used in industrial and commercial installations, and therefore may contribute significantly to the deterioration of the quality of energy supplied by utilities. This paper proposes a modeling in constant power of the unbalanced TIM starting using Genetic Algorithm (GA) and AWM short-circuit based on their statics characteristics curves. The proposed models are compared with the conventional models in the literature. The results showed the good performance of the proposed models, allowing a more precise analysis of the real requests of these loads.

Challenges of modeling depth-integrated marine primary productivity over multiple decades: A case study at BATS and HOT

The performance of 36 models (22 ocean color models and 14 biogeochemical ocean circulation models (BOGCMs)) that estimate depth-integrated marine net primary productivity (NPP) was assessed by comparing their output to in situ (14)C data at the Bermuda Atlantic Time series Study (BATS) and the Hawaii Ocean Time series (HOT) over nearly two decades. Specifically, skill was assessed based on the models' ability to estimate the observed mean, variability, and trends of NPP. At both sites, more than 90% of the models underestimated mean NPP, with the average bias of the BOGCMs being nearly twice that of the ocean color models. However, the difference in overall skill between the best BOGCM and the best ocean color model at each site was not significant. Between 1989 and 2007, in situ NPP at BATS and HOT increased by an average of nearly 2% per year and was positively correlated to the North Pacific Gyre Oscillation index. The majority of ocean color models produced in situ NPP trends that were closer to the observed trends when chlorophyll-alpha was derived from high-performance liquid chromatography (HPLC), rather than fluorometric or SeaWiFS data. However, this was a function of time such that average trend magnitude was more accurately estimated over longer time periods. Among BOGCMs, only two individual models successfully produced an increasing NPP trend (one model at each site). We caution against the use of models to assess multiannual changes in NPP over short time periods. Ocean color model estimates of NPP trends could improve if more high quality HPLC chlorophyll-alpha time series were available.

Object oriented modeling of piston engines

This work reports a conception phase of a piston engine global model. The model objective is forecast the motor performance (power, torque and specific consumption as a function of rotation and environmental conditions). Global model or Zero-dimensional is based on flux balance through each engine component. The resulting differential equations represents a compressive unsteady flow, in which, all dimensional variables are areas or volumes. A review is presented first. The ordinary differential equation system is presented and a Runge-Kutta method is proposed to solve it numerically. The model includes the momentum conservation equation to link the gas dynamics with the engine moving parts rigid body mechanics. As an oriented to objects model the documentation follows the UML standard. A discussion about the class diagrams is presented, relating the classes with physical model related. The OOP approach allows evolution from simple models to most complex ones without total code rewrite. Copyright © 2001 Society of Automotive Engineers, Inc.