Preparation and characterization of proton‐exchange hybrid membranes

Two types of composite were prepared, based on a thermoplastic polymer, polyvinylidene fluoride (PVDF), and an elastomer, ethylene‐propylene‐diene terpolymer (EPDM), respectively. We obtained both series by addition of an inorganic proton‐conducting antimonic acid derivative (HSb) and polystyrene crosslinked with a small percentage of divinylbenzene (PS‐co‐DVB). From these composites, membranes were obtained and subjected to a heterogeneous‐phase sulfonation reaction with chlorosulfonic acid. All experimental materials were characterized from a morphological and electrical point of view, by means of techniques such as differential scanning calorimetry (DSC), dynamic mechanical analysis (DMA), non‐isothermal crystallization and complex impedance analysis. Copyright © 2005 Society of Chemical Industry     

Critical Shear Stress of Bimodal Sediment in Sand-Gravel Rivers

A new model for the critical shear stress and the transport of graded sediment is presented. The model is based on the size distribution of the bed surface and can be used to compute sediment transport rates in numerical simulations with an active layer model. This model makes a distinction between unimodal and bimodal sediments. It is assumed that all size fractions of unimodal sediments have the same critical shear stress while there is selective transport for the gravel fractions of bimodal sediments. A recently published laboratory transport data set is used to calibrate our model.     

Aide in decision-making: contribution to uncertainties in three-dimensional measurement

The authorities of the standards organization International Organization of Standardization (ISO) advocate mastering any uncertainties in all parts of the industrialization process. In the three-dimensional (3D) measurement process, uncertainty is usually obtained at the end of a battery of tests. It is defined as a whole because it includes several types of errors, known systematic components, unknown systematic components and random components. Automated calculations of uncertainty can be made based on statistics. This method is based on statistical concepts, which are in accordance with “The Guide to the expression of the uncertainty in measurement” (GUM). It also enables us to generate uncertainties on the verification of ISO specifications (or specs in the ISO directives). In the course of this article, a usage will be presented that takes the knowledge of uncertainties into account: this usage will help the operator to take a decision on the conformance of a mechanical part in reference to its conformance to geometric tolerance.     

A non-linear forecasting system for the Ebro River at Zaragoza,Spain

This paper addresses the problem of modelling and forecasting river flows and levels based on flood routing type models. Though this is generally considered as a non-linear problem, very often it is treated by linear models. A forecasting system is built for the level and flow measurements registered in the Ebro River at the station of Zaragoza (Spain), with the main purpose of preventing floods in an early stage of development. The model takes advantage of the wealth of data available at the Ebro Hydrographical Confederation and is non-linear in essence. The system is obtained by application of system identification tools, starting from a linear specification and relating the parameters of the model estimated to some transformation of the input in the system. Such transformation requires the application of a Kalman Filter in a particular set up and the full estimation algorithm involves an iterative procedure. The model is fully developed on a data set and is thoroughly validated on a different span of data.     

Crude glycerine inclusion in Limousin bull diets: Animal performance,carcass characteristics and meat quality

Three hundred and six Limousin young bulls (7 ± 1 months of age, initial body weight 273 ± 43 kg) were used to evaluate the effect of crude glycerine supplementation on animal performance, carcass characteristics and meat quality. Animals were assigned to three different diets: Control (0% of crude glycerine), G2 and G4 (2 and 4% of crude glycerine, respectively). The diets were administrated ad libitum for 240 days (final body weight 644 ± 24 kg). Average daily weight gain, average daily feed intake, the gain:feed ratio, ultrasound measures in vivo, carcass characteristics, pH, water holding capacity, drip losses, and cooking losses were not affected (P > 0.05) by diets. Diet decreased C16:0 (P < 0.01) and C16:1 (P < 0.05) contents in meat. The G4 meat showed lower C12:0, C14:0, C17:0, C18:0, C18:1, C18:2, C18:3, c9,t11-c18:2, C20:0 and C20:4 levels (P < 0.05) than control. Glycerine increased desirable fatty acid percentages (P < 0.05) in intramuscular fat.     

Numerical modeling and design of supercritical CO2 pre-cooler for fusion nuclear reactors

One of the key issues of fusion technology is the efficient recovery of the fusion power extracted by heat transfer fluids in the breeding blanket. The Spanish National Program TECNO_FUS is exploring a dual-coolant breeding blanket design concept and its plant auxiliary systems for a future power reactor (DEMO), with liquid lead–lithium as main primary nuclear power recovering fluid. Supercritical CO₂ is chosen for the secondary circuit, since its high efficiency at significantly lower required temperatures than for the Brayton helium cycle, due to low compression work near the critical point and also because its additional major benefits in terms of tritium control. Use of printed circuit heat exchangers (PCHE) is suggested in literature due to its highly compact design and robustness for the high pressures found. This work deals with the heat exchanger devoted to release the thermal energy of the power cycle to the thermal sink. The aim of this work is analyzing how the nearness of the CO₂ to its critical point affects the performance of the heat exchanger. Computer Fluid Dynamics (CFD) simulations that include the complex thermal behavior of CO₂ properties at supercritical conditions are used in order to achieve an accurate approach to the design of this heat exchanger. These results are compared with others obtained through correlations found in the open literature. The behavior of CO₂ close to its critical point results in an inefficient use of the exchange area, giving a temperature profile in CO₂ which remembers a condensation process and an overall heat transfer coefficient 1.4 times higher than the one achieved with literature correlations design.     

When is the Naive Bayes approximation not so naive?

The Naive Bayes approximation (NBA) and associated classifier are widely used and offer robust performance across a large spectrum of problem domains. As it depends on a very strong assumption—independence among features—this has been somewhat puzzling. Various hypotheses have been put forward to explain its success and many generalizations have been proposed. In this paper we propose a set of “local” error measures—associated with the likelihood functions for subsets of attributes and for each class—and show explicitly how these local errors combine to give a “global” error associated to the full attribute set. By so doing we formulate a framework within which the phenomenon of error cancelation, or augmentation, can be quantified and its impact on classifier performance estimated and predicted a priori. These diagnostics allow us to develop a deeper and more quantitative understanding of why the NBA is so robust and under what circumstances one expects it to break down. We show how these diagnostics can be used to select which features to combine and use them in a simple generalization of the NBA, applying the resulting classifier to a set of real world data sets.     

Neuro-evolutionary approach applied for optimizing the PEMFC performance

A multi-objective optimization strategy, based on stacked neural network–genetic algorithm (SNN–GA) hybrid approach, was applied to study the C/PBI content on a high temperature PEMFC performance. The operating conditions of PEMFC were correlated with power density and electrochemical active surface area for electrodes. The structure of the stack was determined in an optimal form related to the contribution of individual neural networks, after applying an interpolation based procedure. Multi-objective optimization using SNN as model and GA as solving procedure provides optimal working conditions which lead to a high PEMFC performance. Simulation results were in agreement with experimental data, both for model validation and system optimization (the C/PBI content in the range of 17–21%).