On the generation of metal clusters with the electrochemical scanning tunneling microscope

In the present work we consider two aspects of the deposition of metal clusters on an electrode surface. The formation of such clusters with the tip of a scanning tunneling microscope is simulated by atom dynamics. Subsequently the stability of these clusters is investigated by Monte Carlo simulations in a grand-canonical ensemble. In particular, the following systems were considered explicitly: Pd clusters on Au(111), Cu on Au(111), Ag on Au(111), Pb on Au(111) and Cu on Ag(111). The analysis of the results obtained for the different systems leads to the conclusion that optimal systems for nanostructuring are those where the metals participating have similar cohesive energies and negative heats of alloy formation. In this respect, the system Cu-Pd(111) is predicted as a good candidate for the formation of stable clusters. (c) 2005 Elsevier B.V. All rights reserved.

Lie algebra methods for the applications to the statistical theory of turbulence

Approximate Lie symmetries of the Navier-Stokes equations are used for the applications to scaling phenomenon arising in turbulence. In particular, we show that the Lie symmetries of the Euler equations are inherited by the Navier-Stokes equations in the form of approximate symmetries that allows to involve the Reynolds number dependence into scaling laws. Moreover, the optimal systems of all finite-dimensional Lie subalgebras of the approximate symmetry transformations of the Navier-Stokes are constructed. We show how the scaling groups obtained can be used to introduce the Reynolds number dependence into scaling laws explicitly for stationary parallel turbulent shear flows. This is demonstrated in the framework of a new approach to derive scaling laws based on symmetry analysis [11]-[13].

Projeto E análise de uma rede neural para resolver problemas de programação dinâmica

Systems based on artificial neural networks have high computational rates due to the use of a massive number of simple processing elements and the high degree of connectivity between these elements. Neural networks with feedback connections provide a computing model capable of solving a large class of optimization problems. This paper presents a novel approach for solving dynamic programming problems using artificial neural networks. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points which represent solutions (not necessarily optimal) for the dynamic programming problem. Simulated examples are presented and compared with other neural networks. The results demonstrate that proposed method gives a significant improvement.

Robust control to parametric uncertainties in smart structures using linear matrix inequalities

The study of algorithms for active vibrations control in flexible structures became an area of enormous interest, mainly due to the countless demands of an optimal performance of mechanical systems as aircraft and aerospace structures. Smart structures, formed by a structure base, coupled with piezoelectric actuators and sensor are capable to guarantee the conditions demanded through the application of several types of controllers. This article shows some steps that should be followed in the design of a smart structure. It is discussed: the optimal placement of actuators, the model reduction and the controller design through techniques involving linear matrix inequalities (LMI). It is considered as constraints in LMI: the decay rate, voltage input limitation in the actuators and bounded output peak (output energy). Two controllers robust to parametric variation are designed: the first one considers the actuator in non-optimal location and the second one the actuator is put in an optimal placement. The performance are compared and discussed. The simulations to illustrate the methodology are made with a cantilever beam with bonded piezoelectric actuators.

Response surface methodology applied to the evaluation of the SO 2 sorption process in two Brazilian limestones

This paper proposes a response surface methodology to evaluate the influence of the particle size and temperature as variables and their interaction on the sulfation process using two Brazilian limestones, a calcite (ICB) and a dolomite (DP). Experiments were performed according to an experimental design [central composite rotatable design (CCRD)] carried out on a thermogravimetric balance and a nitrogen adsorption porosimeter. In the SO 2 sorption process, DP was shown to be more efficient than ICB. The best results for both limestones in relation to conversion and Brunauer-Emmett-Teller (BET) surface area were obtained under central point conditions (545 μm and 850 C for DP and 274 μm and 815 C for ICB). The optimal values for conversion were 52% for DP and 37% for ICB. For BET surface area, the optimal values were 35 m2 g-1 for DP and 45 m2 g-1 for ICB. A relationship between conversion and pore size distribution has been established. The experiments that showed higher conversions also exhibited more pores in the region between 20 and 150 Å and larger BET surface area, indicating that the amount of smaller pores may be an important factor in the reactivity of limestones. © 2013 American Chemical Society.

Graphene single-electron transistor as a spin sensor for magnetic adsorbates

We study single-electron transport through a graphene quantum dot with magnetic adsorbates. We focus on the relation between the spin order of the adsorbates and the linear conductance of the device. The electronic structure of the graphene dot with magnetic adsorbates is modeled through numerical diagonalization of a tight-binding model with an exchange potential. We consider several mechanisms by which the adsorbate magnetic state can influence transport in a single-electron transistor: tuning the addition energy, changing the tunneling rate, and in the case of spin-polarized electrodes, through magnetoresistive effects. Whereas the first mechanism is always present, the others require that the electrode has to have either an energy- or spin-dependent density of states. We find that graphene dots are optimal systems to detect the spin state of a few magnetic centers.

Optimal allocation of a cross-connection and sectionalizers in distribution systems

In this paper, the placement of sectionalizers, as well as, a cross-connection is optimally determined so that the objective function is minimized. The objective function employed in this paper consists of two main parts, the switch cost and the reliability cost. The switch cost is composed of the cost of sectionalizers and cross-connection and the reliability cost is assumed to be proportional to a reliability index, SAIDI. To optimize the allocation of sectionalizers and cross-connection problem realistically, the cost related to each element is considered as discrete. In consequence of binary variables for the availability of sectionalizers, the problem is extremely discrete. Therefore, the probability of local minimum risk is high and a heuristic-based optimization method is needed. A Discrete Particle Swarm Optimization (DPSO) is employed in this paper to deal with this discrete problem. Finally, a testing distribution system is used to validate the proposed method.

On the optimal capacity expansion of electric power systems

The analysis of investment in the electric power has been the subject of intensive research for many years. The efficient generation and distribution of electrical energy is a difficult task involving the operation of a complex network of facilities, often located over very large geographical regions. Electric power utilities have made use of an enormous range of mathematical models. Some models address time spans which last for a fraction of a second, such as those that deal with lightning strikes on transmission lines while at the other end of the scale there are models which address time horizons consisting of ten or twenty years; these usually involve long range planning issues. This thesis addresses the optimal long term capacity expansion of an interconnected power system. The aim of this study has been to derive a new, long term planning model which recognises the regional differences which exist for energy demand and which are present in the construction and operation of power plant and transmission line equipment. Perhaps the most innovative feature of the new model is the direct inclusion of regional energy demand curves in the nonlinear form. This results in a nonlinear capacity expansion model. After review of the relevant literature, the thesis first develops a model for the optimal operation of a power grid. This model directly incorporates regional demand curves. The model is a nonlinear programming problem containing both integer and continuous variables. A solution algorithm is developed which is based upon a resource decomposition scheme that separates the integer variables from the continuous ones. The decompostion of the operating problem leads to an interactive scheme which employs a mixed integer programming problem, known as the master, to generate trial operating configurations. The optimum operating conditions of each trial configuration is found using a smooth nonlinear programming model. The dual vector recovered from this model is subsequently used by the master to generate the next trial configuration. The solution algorithm progresses until lower and upper bounds converge. A range of numerical experiments are conducted and these experiments are included in the discussion. Using the operating model as a basis, a regional capacity expansion model is then developed. It determines the type, location and capacity of additional power plants and transmission lines, which are required to meet predicted electicity demands. A generalised resource decompostion scheme, similar to that used to solve the operating problem, is employed. The solution algorithm is used to solve a range of test problems and the results of these numerical experiments are reported. Finally, the expansion problem is applied to the Queensland electricity grid in Australia.

On the optimal capacity expansion of electric power systems

The analysis of investment in the electric power has been the subject of intensive research for many years. The efficient generation and distribution of electrical energy is a difficult task involving the operation of a complex network of facilities, often located over very large geographical regions. Electric power utilities have made use of an enormous range of mathematical models. Some models address time spans which last for a fraction of a second, such as those that deal with lightning strikes on transmission lines while at the other end of the scale there are models which address time horizons consisting of ten or twenty years; these usually involve long range planning issues. This thesis addresses the optimal long term capacity expansion of an interconnected power system. The aim of this study has been to derive a new, long term planning model which recognises the regional differences which exist for energy demand and which are present in the construction and operation of power plant and transmission line equipment. Perhaps the most innovative feature of the new model is the direct inclusion of regional energy demand curves in the nonlinear form. This results in a nonlinear capacity expansion model. After review of the relevant literature, the thesis first develops a model for the optimal operation of a power grid. This model directly incorporates regional demand curves. The model is a nonlinear programming problem containing both integer and continuous variables. A solution algorithm is developed which is based upon a resource decomposition scheme that separates the integer variables from the continuous ones. The decompostion of the operating problem leads to an interactive scheme which employs a mixed integer programming problem, known as the master, to generate trial operating configurations. The optimum operating conditions of each trial configuration is found using a smooth nonlinear programming model. The dual vector recovered from this model is subsequently used by the master to generate the next trial configuration. The solution algorithm progresses until lower and upper bounds converge. A range of numerical experiments are conducted and these experiments are included in the discussion. Using the operating model as a basis, a regional capacity expansion model is then developed. It determines the type, location and capacity of additional power plants and transmission lines, which are required to meet predicted electicity demands. A generalised resource decompostion scheme, similar to that used to solve the operating problem, is employed. The solution algorithm is used to solve a range of test problems and the results of these numerical experiments are reported. Finally, the expansion problem is applied to the Queensland electricity grid in Australia

Optimal allocation of water in village irrigation systems of Sri Lanka

This PhD study examines whether water allocation becomes more productive when it is re-allocated from 'low' to 'high' efficient alternative uses in village irrigation systems (VISs) in Sri Lanka. Reservoir-based agriculture is a collective farming economic activity, which inter-sectoral allocation of water is assumed to be inefficient due to market imperfections and weak user rights. Furthermore, the available literature shows that a „head-tail syndrome. is the most common issue for intra-sectoral water management in „irrigation. agriculture. This research analyses the issue of water allocation by using primary data collected from two surveys of 460 rice farmers and 325 fish farming groups in two administrative districts in Sri Lanka. Technical efficiency estimates are undertaken for both rice farming and culture-based fisheries (CBF) production. The equi-marginal principle is applied for inter and intra-sectoral allocation of water. Welfare benefits of water re-allocation are measured through consumer surplus estimation. Based on these analyses, the overall findings of the thesis can be summarised as follows. The estimated mean technical efficiency (MTE) for rice farming is 73%. For CBF production, the estimated MTE is 33%. The technical efficiency distribution is skewed to the left for rice farming, while it skewed to the right for CBF production. The results show that technical efficiency of rice farming can be improved by formalising transferability of land ownership and, therefore, water user rights by enhancing the institutional capacity of Farmer Organisations (FOs). Other effective tools for improving technical efficiency of CBF production are strengthening group stability of CBF farmers, improving the accessibility of official consultation, and attracting independent investments. Inter-sectoral optimal allocation shows that the estimated inefficient volume of water in rice farming, which can be re-allocated for CBF production, is 32%. With the application of successive policy instruments (e.g., a community transferable quota system and promoting CBF activities), there is potential for a threefold increase in marginal value product (MVP) of total reservoir water in VISs. The existing intra-sectoral inefficient volume of water use in tail-end fields and head-end fields can potentially be removed by reducing water use by 10% and 23% respectively and re-allocating this to middle fields. This re-allocation may enable a twofold increase in MVP of water used in rice farming without reducing the existing rice output, but will require developing irrigation practices to facilitate this re-allocation. Finally, the total productivity of reservoir water can be increased by responsible village level institutions and primary level stakeholders (i.e., co-management) sharing responsibility of water management, while allowing market forces to guide the efficient re-allocation decisions. This PhD has demonstrated that instead of farmers allocating water between uses haphazardly, they can now base their decisions on efficient water use with a view to increasing water productivity. Such an approach, no doubt will enhance farmer incomes and community welfare.

Optimal coordination of overcurrent relays in distribution systems with distributed generators based on differential evolution algorithm

Distributed generators (DGs) are defined as generators that are connected to a distribution network. The direction of the power flow and short-circuit current in a network could be changed compared with one without DGs. The conventional protective relay scheme does not meet the requirement in this emerging situation. As the number and capacity of DGs in the distribution network increase, the problem of coordinating protective relays becomes more challenging. Given this background, the protective relay coordination problem in distribution systems is investigated, with directional overcurrent relays taken as an example, and formulated as a mixed integer nonlinear programming problem. A mathematical model describing this problem is first developed, and the well-developed differential evolution algorithm is then used to solve it. Finally, a sample system is used to demonstrate the feasiblity and efficiency of the developed method.