Ultrasonic absorption in polymer gel dosimeters

Ultrasonic absorption in polymer gel dosimeters was investigated. An ultrasonic interferometer was used to study the frequency (f) dependence of the absorption coefficient (alpha) in a polyacrylamide gel dosimeter (PAG) in the frequency range 5-20 MHz. The frequency dependence of ultrasonic absorption deviated from that of an ideal viscous fluid. The presence of relaxation mechanisms was evidenced by the frequency dependence of alpha/f(2) and the dispersion in ultrasonic velocity. It was concluded that absorption in polymer gel dosimeters is due to a number of relaxation processes which may include polymer-solvent interactions as well as relaxation due to motion of polymer side groups. The dependence of ultrasonic absorption on absorbed dose and formulation was also investigated in polymer gel dosimeters as a function of pH and chemical composition. Changes in dosimeter pH and chemical composition resulted in a variation in ultrasonic dose response curves. The observed dependence on pH was considered to be due to pH induced modifications in the radiation yield while changes in chemical composition resulted in differences in polymerisation kinetics. (C) 2003 Elsevier B.V. All rights reserved.

Spatial variability of particle size fractions of an Oxisol cultivated with conilon coffee

Information on the spatial distribution of particle size fractions is essential for use planning and management of soils. The aim of this work to was to study the spatial variability of particle size fractions of a Typic Hapludox cultivated with conilon coffee. The soil samples were collected at depths of 0-0.20 and 0.20-0.40 m in the coffee canopy projection, totaling 109 georeferentiated points. At the depth of 0.2-0.4 m the clay fraction showed average value significantly higher, while the sand fraction showed was higher in the depth of 0-0.20 m. The silt showed no significant difference between the two depths. The particle size fractions showed medium and high spatial variability. The levels of total sand and clay have positive and negative correlation, respectively, with the altitude of the sampling points, indicating the influence of landscape configuration.

Determination of reactivity rates of silicate particle-size fractions

The efficiency of sources used for soil acidity correction depends on reactivity rate (RR) and neutralization power (NP), indicated by effective calcium carbonate (ECC). Few studies establish relative efficiency of reactivity (RER) for silicate particle-size fractions, therefore, the RER applied for lime are used. This study aimed to evaluate the reactivity of silicate materials affected by particle size throughout incubation periods in comparison to lime, and to calculate the RER for silicate particle-size fractions. Six correction sources were evaluated: three slags from distinct origins, dolomitic and calcitic lime separated into four particle-size fractions (2, 0.84, 0.30 and <0.30-mm sieves), and wollastonite, as an additional treatment. The treatments were applied to three soils with different texture classes. The dose of neutralizing material (calcium and magnesium oxides) was applied at equal quantities, and the only variation was the particle-size material. After a 90-day incubation period, the RER was calculated for each particle-size fraction, as well as the RR and ECC of each source. The neutralization of soil acidity of the same particle-size fraction for different sources showed distinct solubility and a distinct reaction between silicates and lime. The RER for slag were higher than the limits established by Brazilian legislation, indicating that the method used for limes should not be used for the slags studied here.

Probing the scalar-pseudoscalar mixing in the 125 GeV Higgs particle with current data

LHC has found hints for a Higgs particle of 125 GeV. We investigate the possibility that such a particle is a mixture of scalar and pseudoscalar states. For definiteness, we concentrate on a two-Higgs doublet model with explicit CP violation and soft Z(2) violation. Including all Higgs production mechanisms, we determine the current constraints obtained by comparing h -> yy with h -> VV*, and comment on the information which can be gained by measurements of h -> b (b) over bar. We find bounds vertical bar s(2)vertical bar less than or similar to 0.83 at one sigma, where vertical bar s(2)vertical bar = 0 (vertical bar s(2)vertical bar = 1) corresponds to a pure scalar (pure pseudoscalar) state.

Re-entrant phase behaviour of network fluids: A patchy particle model with temperature-dependent valence

We study a model consisting of particles with dissimilar bonding sites ("patches"), which exhibits self-assembly into chains connected by Y-junctions, and investigate its phase behaviour by both simulations and theory. We show that, as the energy cost epsilon(j) of forming Y-junctions increases, the extent of the liquid-vapour coexistence region at lower temperatures and densities is reduced. The phase diagram thus acquires a characteristic "pinched" shape in which the liquid branch density decreases as the temperature is lowered. To our knowledge, this is the first model in which the predicted topological phase transition between a fluid composed of short chains and a fluid rich in Y-junctions is actually observed. Above a certain threshold for epsilon(j), condensation ceases to exist because the entropy gain of forming Y-junctions can no longer offset their energy cost. We also show that the properties of these phase diagrams can be understood in terms of a temperature-dependent effective valence of the patchy particles. (C) 2011 American Institute of Physics. [doi: 10.1063/1.3605703]

Particle swarm optimization based approaches to vehicle-to-grid scheduling

This paper addresses the problem of energy resources management using modern metaheuristics approaches, namely Particle Swarm Optimization (PSO), New Particle Swarm Optimization (NPSO) and Evolutionary Particle Swarm Optimization (EPSO). The addressed problem in this research paper is intended for aggregators’ use operating in a smart grid context, dealing with Distributed Generation (DG), and gridable vehicles intelligently managed on a multi-period basis according to its users’ profiles and requirements. The aggregator can also purchase additional energy from external suppliers. The paper includes a case study considering a 30 kV distribution network with one substation, 180 buses and 90 load points. The distribution network in the case study considers intense penetration of DG, including 116 units from several technologies, and one external supplier. A scenario of 6000 EVs for the given network is simulated during 24 periods, corresponding to one day. The results of the application of the PSO approaches to this case study are discussed deep in the paper.

A decision-support system based on particle swarm optimization for multiperiod hedging in electricity markets

This paper proposes a particle swarm optimization (PSO) approach to support electricity producers for multiperiod optimal contract allocation. The producer risk preference is stated by a utility function (U) expressing the tradeoff between the expectation and variance of the return. Variance estimation and expected return are based on a forecasted scenario interval determined by a price range forecasting model developed by the authors. A certain confidence level is associated to each forecasted scenario interval. The proposed model makes use of contracts with physical (spot and forward) and financial (options) settlement. PSO performance was evaluated by comparing it with a genetic algorithm-based approach. This model can be used by producers in deregulated electricity markets but can easily be adapted to load serving entities and retailers. Moreover, it can easily be adapted to the use of other type of contracts.

Distributed energy resource short-term scheduling using signaled particle swarm optimization

Distributed Energy Resources (DER) scheduling in smart grids presents a new challenge to system operators. The increase of new resources, such as storage systems and demand response programs, results in additional computational efforts for optimization problems. On the other hand, since natural resources, such as wind and sun, can only be precisely forecasted with small anticipation, short-term scheduling is especially relevant requiring a very good performance on large dimension problems. Traditional techniques such as Mixed-Integer Non-Linear Programming (MINLP) do not cope well with large scale problems. This type of problems can be appropriately addressed by metaheuristics approaches. This paper proposes a new methodology called Signaled Particle Swarm Optimization (SiPSO) to address the energy resources management problem in the scope of smart grids, with intensive use of DER. The proposed methodology’s performance is illustrated by a case study with 99 distributed generators, 208 loads, and 27 storage units. The results are compared with those obtained in other methodologies, namely MINLP, Genetic Algorithm, original Particle Swarm Optimization (PSO), Evolutionary PSO, and New PSO. SiPSO performance is superior to the other tested PSO variants, demonstrating its adequacy to solve large dimension problems which require a decision in a short period of time.

A short-term risk management tool applied to OMEL electricity market using particle swarm optimization

Short-term risk management is highly dependent on long-term contractual decisions previously established; risk aversion factor of the agent and short-term price forecast accuracy. Trying to give answers to that problem, this paper provides a different approach for short-term risk management on electricity markets. Based on long-term contractual decisions and making use of a price range forecast method developed by the authors, the short-term risk management tool presented here has as main concern to find the optimal spot market strategies that a producer should have for a specific day in function of his risk aversion factor, with the objective to maximize the profits and simultaneously to practice the hedge against price market volatility. Due to the complexity of the optimization problem, the authors make use of Particle Swarm Optimization (PSO) to find the optimal solution. Results from realistic data, namely from OMEL electricity market, are presented and discussed in detail.

Particle swarm optimization applied to integrated demand response resources scheduling

The concept of demand response has a growing importance in the context of the future power systems. Demand response can be seen as a resource like distributed generation, storage, electric vehicles, etc. All these resources require the existence of an infrastructure able to give players the means to operate and use them in an efficient way. This infrastructure implements in practice the smart grid concept, and should accommodate a large number of diverse types of players in the context of a competitive business environment. In this paper, demand response is optimally scheduled jointly with other resources such as distributed generation units and the energy provided by the electricity market, minimizing the operation costs from the point of view of a virtual power player, who manages these resources and supplies the aggregated consumers. The optimal schedule is obtained using two approaches based on particle swarm optimization (with and without mutation) which are compared with a deterministic approach that is used as a reference methodology. A case study with two scenarios implemented in DemSi, a demand Response simulator developed by the authors, evidences the advantages of the use of the proposed particle swarm approaches.

Computing the phase diagram of binary mixtures: A patchy particle case study

We investigate the phase behaviour of 2D mixtures of bi-functional and three-functional patchy particles and 3D mixtures of bi-functional and tetra-functional patchy particles by means of Monte Carlo simulations and Wertheim theory. We start by computing the critical points of the pure systems and then we investigate how the critical parameters change upon lowering the temperature. We extend the successive umbrella sampling method to mixtures to make it possible to extract information about the phase behaviour of the system at a fixed temperature for the whole range of densities and compositions of interest. (C) 2013 AIP Publishing LLC.