Aeolian transport layer

We investigate the airborne transport of particles on a granular surface by the saltation mechanism through numerical simulation of particle motion coupled with turbulent flow. We determine the saturated flux $q_{s}$ and show that its behavior is consistent with a classical empirical relation obtained from wind tunnel measurements. Our results also allow to propose a new relation valid for small fluxes, namely, $q_{s}=a(u_{*}-u_{t})^{\alpha}$, where $u_{*}$ and $u_{t}$ are the shear and threshold velocities of the wind, respectively, and the scaling exponent is $\alpha \approx 2$. We obtain an expression for the velocity profile of the wind distorted by the particle motion and present a dynamical scaling relation. We also find a novel expression for the dependence of the height of the saltation layer as function of the wind velocity.Comment: 4 pages, 4 figure

Fast model predictive control scheme for attitude control systems of rigid-flexible satellite

In recent years, the applications related to artificial satellites have considerably grown in several areas such as telecommunications, astronomy and meteorology. An important point that must be taken into account to place a satellite in orbit is the design of Attitude Control System (ACS) to control the angular position according to a fixed reference frame. Most satellites have in their structure the presence of flexible appendices such as solar panels, sails, or even antennas that may produce undesirable oscillations during satellite maneuvers and this can excite the whole system’s structure. Therefore, it is important to develop an ACS that limits the excursion of the flexible structure and meet the control requirements for attitude stabilization. In this paper, a Model Predictive Control (MPC) scheme is proposed for ACS of a Rigid-Flexible Satellite. MPC handles structurally the system’s constraints in problem formulation by solving at each sampling instant an optimization problem that express the control objectives. As a result, MPC is able to track efficiently the references for attitude control by keeping the displacement of flexible structure within predetermined limits reducing vibration of the system. Moreover, MPC also deals with constraints on control inputs since actuators are physically bounded by its maximum allowable value. Another important feature of the proposed control strategy is the parameterization of MPC which reduces considerably the complexity of the optimization problem enabling short computation times. Simulation results are shown to emphasize the efficiency of the parameterized MPC strategy and a comparison with a Linear Quadratic Regulator (LQR) is also performed

Resistividade em solos: efeito dos índices físicos e condições de análise / Soil resistivity: phisical indexes and analysis conditions influence

A resistividade elétrica é um parâmetro amplamente utilizado na avaliação da corrosividade dos solos. Entretanto, algumas metodologias de análise em laboratório têm divergência nos procedimentos e resultados obtidos, o que pode impactar na correta avaliação da propriedade e sua consequente correlação com a resistência à corrosão. Neste estudo foi realizado uma revisão dos ensaios de resistividade de solo propostos pelos procedimentos adotados pelas normas ABNT NBR 16254-1:2014 (Anexo C), ASTM G-187-15 e ASTM G-187-15 modificada pela adoção de cálculos dos índices físicos para obtenção do grau de saturação da amostra de solo. A avaliação dos resultados indicou que o os procedimentos realizados pela norma ASTM-G187-15 e pelo mesmo procedimento modificado, adotando os índices físicos foram compatíveis. Já o ensaio realizado pela norma ABNT NBR 16254-1:2014 (Anexo C) não teve resultados satisfatórios

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio