Escoamentos viscosos não são, em geral, discutidos de maneira detalhada em cursos de física geral e básica. Isso se deve, em parte, ao fato da equação de Navier-Stokes admitir solução analítica apenas para alguns poucos casos restritos, enquanto problemas mais sofisticados só podem ser resolvidos por meio de métodos numéricos. Neste trabalho, apresentamos um túnel de vento simulado, i.e., apresentamos um conjunto de programas de resolver a equação de Navier-Stokes para um objeto de formato arbitrário inserido em um túnel de vento. O túnel nos possibilita visualizar a formação de vórtices atrás do objeto, os conhecidos vórtices de von Kármán. É possível também calcular a força de arrasto sobre o objeto. Acreditamos que este túnel de vento numérico possa subsidiar o professor e permitir que uma discussão mais elaborada de escoamentos viscosos seja realizada. As potencialidades do túnel são exemplificadas por meio do estudo do comportamento da força de arrasto sobre um modelo simplificado de asa cujo ângulo de ataque pode ser controlado. Um link para o download dos programas que compõem o túnel é apresentado ao final do texto.

Flow of viscous fluids are not usually discussed in detail in general and basic courses of physics. This is due in part to the fact that the Navier-Stokes equation has analytical solution only for a few restricted cases, while more sophisticated problems can only be solved by numerical methods. In this text, we present a computer simulation of wind tunnel, i.e., we present a set of programs to solve the Navier-Stokes equation for an arbitrary object inserted in a wind tunnel. The tunnel enables us to visualize the formation of vortices behind object, the so-called von Kármán vortices, and calculate the drag force on the object. We believe that this numerical wind tunnel can support the teacher and allow a more elaborate discussion of viscous flow. The potential of the tunnel is exemplified by the study of the drag on a simplified model of wing whose angle of attack can be controlled. A link to download the programs that make up the tunnel appears at the end.

As flutuações do meio ambiente, bem como flutuações aleatórias no interior de um organismo biológico participam ativamente da sua vida ou evolução. O citado ''organismo'' pode ser uma célula, um orgão multicelular, um indivíduo, uma população ou um grupo de populações, etc. Pode-se compreender muitos aspectos gerais da influência destas flutuações na vida ou na evolução destes organismos através de modelos simplificados, em particular equações dinâmicas do tipo Langevin.

Environment fluctuations as well as random fluctuations within a biological organism take part of its life or evolution. The quoted ''organism'' maybe a cell, a multicellular organ, an individual, a population or a set of populations, etc. It is possible to understand many general aspects of these fluctuations' influence on the life or evolution of these organisms through simplified models, in particular dynamic equations like that of Langevin.

The Broad Histogram Method (BHM) allows one to determine the energy degeneracy g(E), i.e. the energy spectrum of a given system, from the knowledge of the microcanonical averages < Nup (E) > and < Ndn (E) > of two macroscopic quantities Nup and Ndn defined within the method. The fundamental BHM equation relating g(E) to the quoted averages is exact and completely general for any conceivable system. Thus, the only possible source of numerical inaccuracies resides on the measurement of the averages themselves. In this text, we introduce a Monte Carlo recipe to measure microcanonical averages. In order to test its performance, we applied it to the Ising ferromagnet on a 32 × 32 square lattice. The exact values of g(E) are known up to this lattice size, thus it is a good standard to compare our numerical results with. Measuring the deviations relative to the exactly known values, we verified a decay proportional to 1/<img src="http:/img/fbpe/bjp/v30n4/24eq01.gif" border="0">, by increasing the counter (counts) of averaged samples over at least 6 decades. That is why we believe this microcanonical simulator presents no bias besides the normal statistical fluctuations. For counts ~ 10(10), we measured relative deviations near 10(5) for both g(E) and the specific heat peak, obtained through BHM relation.

The Broad Histogram is a method allowing the direct calculation of the energy degeneracy g(E). This quantity is independent of thermodynamic concepts such as thermal equilibrium. It only depends on the distribution of allowed (micro) states along the energy axis, but not on the energy changes between the system and its environment. Once one has obtained g(E), no further effort is needed in order to consider different environment conditions, for instance, different temperatures, for the same system. The method is based on the exact relation between g(E) and the microcanonical averages of certain macroscopic quantities Nup and Ndn . For an application to a particular problem, one needs to choose an adequate instrument in order to determine the averages < Nup (E) > and < Ndn(E) >, as functions of energy. Replacing the usual fixed-temperature canonical by the fixed-energy microcanonical ensemble, new subtle concepts emerge. The temperature, for instance, is no longer an external parameter controlled by the user. Instead, the microcanonical temperature Tm(E) is a function of energy defined from g(E) itself, being thus an internal (environment independent) characteristic of the system. Accordingly, all microcanonical averages are functions of E. The present text is an overview of the method. Some features of the microcanonical ensemble are also discussed, as well as some clues towards the deffinition of efficient Monte Carlo microcanonical sampling rules.