Optimization of the Temperature-Time Curve for the Curing Process of Thermoset Matrix Composites

An intelligent optimization model aiming at off-line or pre-series optimization of the thermal curing cycle of polymer matrix composites is proposed and discussed. The computational procedure is based on the coupling of a finite element thermochemical process model, dynamic artificial neural networks and genetic algorithms. Objective of the optimization routine is the maximization of the composite degree of cure by the definition of the autoclave temperature. Obtained outcomes evidenced the capability of the method as well as its efficiency with respect to hard computing or experimental procedures.     

The dependence of the photocatalytic activity of TiO2/carbon nanotubes nanocomposites on the modification of the carbon nanotubes

Nanostructural TiO₂/modified multi-wall carbon nanotubes photocatalysts were prepared by hydrolysis of Ti(iso-OC₃H₇)₄ providing chemical bonding of anatase TiO₂ nanoparticles onto oxidized- or amino-functionalized multi-wall carbon nanotubes (MWCNT). The processes of functionalization of the MWCNT and the deposition of TiO₂ influence the photocatalytic activity of the synthesized nanocomposites. The phase composition, crystallite size, and the structural and surface properties of the obtained TiO₂/modified-MWCNT nanocomposite were analyzed from XRD, FEG-SEM, TEM/HRTEM and FTIR data, as well low temperature N₂ adsorption. In the photocatalytic study, the TiO₂/oxidized-MWCNT catalyst showed the highest and the TiO₂/amino functionalized-MWCNT catalysts somewhat lower degradation rates, indicating that the enhancement of photocatalysis was supported by the more effective electron transfer properties of the oxygen- than amino-containing functional groups, which support the efficient charge transportation and separation of the photogenerated electron-hole pairs.     

Synthesis and characterization of monetite and hydroxyapatite whiskers obtained by a hydrothermal method

High temperature hydrothermal syntheses, using calcium nitrate tetrahydrate, sodium dihydrogen phosphate and urea as precursors, and characterization of hydroxyapatite (HAp) whiskers are reported herein. The morphology and chemical composition of the crystals from a monetite to a hydroxyapatite phase were controlled by varying the starting concentrations of the precursors and the solution pH through the amount of urea that is decomposed during heating. X-ray diffraction (XRD) analysis, infrared spectroscopy (IR), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), transmission electron microscopy (TEM) and high-resolution TEM (HRTEM) were used to investigate the products of the syntheses in order to find the optimum reaction conditions for obtaining the desired morphology and phase composition. Different morphologies ranging from single crystals of monetite through rods and plates of hydroxyapatite with different size distribution to whisker-like single hydroxyapatite crystal were achieved by simply varying the starting concentration of urea. Structural refinement of the hydroxyapatite whiskers confirmed a strong preferential orientation along the c-axis direction of the hexagonal crystal structure, which was significantly different from the usually observed random crystal orientation. TEM and SEM analysis of the apatite whiskers confirmed single crystal structure with the a c-axis orientation parallel to the long axis of the whiskers, with sizes up to 150 μm in length, 10 μm in width and with a thickness of about 300 nm, that grew from the same centre of nucleation, forming flaky-like particles.     

Exploiting symmetries for scaling loopy belief propagation and relational training

Judging by the increasing impact of machine learning on large-scale data analysis in the last decade, one can anticipate a substantial growth in diversity of the machine learning applications for “big data” over the next decade. This exciting new opportunity, however, also raises many challenges. One of them is scaling inference within and training of graphical models. Typical ways to address this scaling issue are inference by approximate message passing, stochastic gradients, and MapReduce, among others. Often, we encounter inference and training problems with symmetries and redundancies in the graph structure. A prominent example are relational models that capture complexity. Exploiting these symmetries, however, has not been considered for scaling yet. In this paper, we show that inference and training can indeed benefit from exploiting symmetries. Specifically, we show that (loopy) belief propagation (BP) can be lifted. That is, a model is compressed by grouping nodes together that send and receive identical messages so that a modified BP running on the lifted graph yields the same marginals as BP on the original one, but often in a fraction of time. By establishing a link between lifting and radix sort, we show that lifting is MapReduce-able. Still, in many if not most situations training relational models will not benefit from this (scalable) lifting: symmetries within models easily break since variables become correlated by virtue of depending asymmetrically on evidence. An appealing idea for such situations is to train and recombine local models. This breaks long-range dependencies and allows to exploit lifting within and across the local training tasks. Moreover, it naturally paves the way for the first scalable lifted training approaches based on stochastic gradients, both in an online and a MapReduced fashion. On several datasets, the online training, for instance, converges to the same quality solution over an order of magnitude faster, simply because it starts optimizing long before having seen the entire mega-example even once.     

Adaptive neuro-fuzzy wheel slip control

Due to complex and nonlinear dynamics of a braking process and complexity in the tire–road interaction, the control of automotive braking systems performance simultaneously with the wheel slip represents a challenging problem. The non-optimal wheel slip level during braking, causing inability to achieve the desired tire–road friction force strongly influences the braking distance. In addition, steerability and maneuverability of the vehicle could be disturbed. In this paper, an active neuro-fuzzy approach has been developed for improving the wheel slip control in the longitudinal direction of the commercial vehicle. The dynamic neural network has been used for prediction and an adaptive control of the brake actuation pressure, during each braking cycle, according to the identified maximum adhesion coefficient between the wheel and road surface. The brake actuation pressure was dynamically adjusted on the level that provides the optimal level of the longitudinal wheel slip vs. the brake pressure selected by driver, the current vehicle speed, the brake interface temperature, vehicle load conditions, and the current value of longitudinal wheel slip. Thus the dynamic neural network model operates (learn, generalize and predict) on-line during each braking cycle, fuzzy logic has been integrated with the neural model as a support to the neural controller control actions in the case when prediction error of the dynamic neural model reached the predefined value. The hybrid control approach presented here provided intelligent dynamic model – based control of the brake actuation pressure in order to keep the longitudinal wheel slip on the optimum level during a braking cycle.     

Water saturation limit of transformer oils - [feature article]

The stable functioning of power transformers is closely related to the state of their insulation systems [1]. Most power transformers at present employ paper/oil insulation [2]. This type of insulation has many advantages, but deteriorates under the combined influence of a number of interrelated factors. The insulation properties of the paper/oil system depend strongly on the degree of aging, moisture accumulation in the oil and the paper, and the migration of moisture between them.     

An interval method for finding all operating points of non-linear resistive circuits

A new method for finding the set of all d.c. operating points of non-linear electronic circuits is suggested. it is applicable in the case where the circuit equations are written in the hybrid-representation form. In the general case, the non-linear elements involved may be described by non-monotone continuous characteristics. The method suggested is based on interval analysis techniques. Unlike other non-interval methods, this approach guarantees that all operating points will be found within prescribed accuracy in a finite number of steps. The computational efficiency of the present method is illustrated by a numerical example.     

Modeling of transport and chemistry in channel flows of automotive catalytic converters

A novel comprehensive numerical study is presented for a better understanding of mass transfer in channel flows with catalytically active walls at moderate temperatures and surface reaction rates. Altogether, 18 different numerical models are compared, which represent mass transfer in single channels of a honeycomb-type automotive catalytic converter operated under direct oxidation conditions. Three different channel geometries have been investigated: circular cross-section, square cross-section, and square cross-section with rounded corners (fillets). 1D plug-flow, 2D boundary-layer and Navier–Stokes, and 3D Navier–Stokes equations are applied to model the reactor geometry. The diffusion limitation within the porous washcoat has been modeled by a simplified zero-dimensional effectiveness factor model as well as multidimensional reaction–diffusion models. Furthermore, simulations are also carried out for cases with instantaneous diffusion within the washcoat. All numerical models account for the coupled interactions of mass-transfer and heterogeneous chemistry within the channels. The chemical conversion of the pollutants on the platinum catalyst is described by an elementary-step-like heterogeneous reaction mechanism consisting of 74 reactions among 11 gas-phase species and 22 adsorbed surface species. The results of numerical simulations are compared with experimental data.