UV durable colour pigment doped SmA liquid crystal composites for outdoor trans-reflective bi-stable displays

High brightness trans-reflective bi-stable displays based on smectic A (SmA) liquid crystals (LCs) can have nearly perfect transparency in the clear state and very high reflection in the scattered state. Because the LC material in use is stable under UV radiation, this kind of displays can stand for strong day-light and therefore be ideal for outdoor applications from e-books to public signage and advertisement. However, the colour application has been limited because the traditional colourants in use are conventional dyes which are lack of UV stability and that their colours are easily photo bleached. Here we present a colour SmA display demonstrator using pigments as colourant. Mixing pigments with SmA LCs and maintain the desirable optical switching performance is not straightforward. We show here how it can be done, including how to obtain fine sized pigment nano-particles, the effects of particle size and size distribution on the display performance. Our optimized pigments/SmA compositions can be driven by a low frequency waveform (∼101Hz) to a scattered state to exhibit colour while by a high frequency waveform (∼103Hz) to a cleared state showing no colour. Finally, we will present its excellent UV life-time (at least >7.2 years) in comparison with that of dye composition (∼2.4 years). The complex interaction of pigment nano-particles with LC molecules and the resulting effects on the LC electro-optical performances are still to be fully understood. We hope this work will not only demonstrate a new and practical approach for outdoor reflective colour displays but also provide a new material system for fundamental liquid crystal colloid research work. © 2012 SPIE.

Investigation of DyBa2Cu3O7-d superconducting domains grown by the infiltration technique starting with small size Dy-211 particles

An infiltration and growth process is here used as an alternative to the classical top-seeded melt-textured growth process for the production of Dy-123 single-domains with finely dispersed small size Dy-211 particles. The starting materials are the 211-particles and a barium and copper rich liquid phase precursor. The infiltration and growth process allows for controlling both the spatial and size distribution of the 211-particles in the final superconducting 123-single-domain. The main parameters (set-ups, maximum processing temperature with respect to the peritectic temperature, nature of reactant, porosity of the 211-preform) of the infiltration and growth process are discussed. Moreover, different processes of chimie douce are shown in order to produce Dy-211 particles with controlled shape and size, particles that can be used as precursors for the infiltration and growth process. © 2005 IOP Publishing Ltd.

Complex chemistry DNS of n-heptane spray autoignition at high pressure and intermediate temperature conditions

Direct Numerical Simulations (DNS) of turbulent n-heptane sprays autoigniting at high pressure (P=24bar) and intermediate air temperature (Tair=1000K) have been performed to investigate the physical mechanisms present under conditions where low-temperature chemistry is expected to be important. The initial turbulence in the carrier gas, the global equivalence ratio in the spray region, and the initial droplet size distribution of the spray were varied. Results show that spray ignition exhibits a spotty nature, with several kernels developing independently in those regions where the mixture fraction is close to its most reactive value ξMR (as determined from homogeneous reactor calculations) and the scalar dissipation rate is low. Turbulence reduces the ignition delay time as it promotes mixing between air and the fuel vapor, eventually resulting in lower values of scalar dissipation. High values of the global equivalence ratio are responsible for a larger number of ignition kernels, due to the higher probability of finding regions where ξ=ξMR. Spray polydispersity results in the occurrence of ignition over a wider range of mixture fraction values. This is a consequence of the inhomogeneities in the mixing field that characterize these sprays, where poorly mixed rich spots are seen to alternate with leaner ones which are well-mixed. The DNS simulations presented in this work have also been used to assess the applicability of the Conditional Moment Closure (CMC) method to the simulation of spray combustion. CMC is found to be a valid method for capturing spray autoignition, although care should be taken in the modelling of the unclosed terms appearing in the CMC equations. © 2013 The Combustion Institute.

Flow field measurements of pulverized coal combustion using optical diagnostic techniques

This paper demonstrates the application of laser Doppler velocimetry (LDV) and particle image velocimetry (PIV) techniques to a particle-laden reacting flow of pulverized coal. A laboratory-scale open-type annular burner is utilized to generate velocity profiles of coal particles and micrometric alumina particles. Pair-wise two-component LDV measurements and high-speed stereo PIV measurements provide three-dimensional velocity components of the flow field. A detailed comparison of velocities for alumina and coal particle seeding revealed differences attributed to the wide size distribution of coal particles. In addition, the non-spherical shape and high flame luminosity associated with coal particle combustion introduces noise to the Mie scatter images. The comparison of mean and RMS velocities measured by LDV and PIV techniques showed that PIV measurements are affected by the wide size distribution of coal particles, whereas LDV measurements become biased toward the velocity of small particles, as signals from large particles are rejected. This small-particle bias is also reflected in the spectral characteristics for both techniques, which are in good agreement within the range of frequencies accessible. PIV measurements showed an expected lack of response of large coal particles to the turbulence fluctuations. The overall good agreement between LDV and PIV measurements demonstrates the applicability of the high-speed PIV technique to a particle-laden, high luminosity coal flame while highlighting some of its limitations. © 2013 Springer-Verlag Berlin Heidelberg.

Preliminary model development for predicting strength and stiffness of cement-stabilized soils using artificial neural networks

This paper presents ongoing work on data collection and collation from a large number of laboratory cement-stabilization projects worldwide. The aim is to employ Artificial Neural Networks (ANN) to establish relationships between variables, which define the properties of cement-stabilized soils, and the two parameters determined by the Unconfined Compression Test, the Unconfined Compressive Strength (UCS), and stiffness, using E50 calculated from UCS results. Bayesian predictive neural network models are developed to predict the UCS values of cement-stabilized inorganic clays/silts, as well as sands as a function of selected soil mix variables, such as grain size distribution, water content, cement content and curing time. A model which can predict the stiffness values of cement-stabilized clays/silts is also developed and compared to the UCS model. The UCS model results emulate known trends better and provide more accurate estimates than the results from the E50 stiffness model. © 2013 American Society of Civil Engineers.

Development of predictive models for cement stabilized soils

Factors that affect the engineering properties of cement stabilized soils such as strength are discussed in this paper using data on these factors. The selected factors studied in this paper are initial soil water content, grain size distribution, organic matter content, binder dosage, age and curing temperature, which has been collated from a number of international deep mixing projects. Some resulting correlations from this data are discussed and presented. The concept of Artificial Neural Networks and its applicability in developing predictive models for deep mixed soils is presented and discussed using a subset of the collated data. The results from the neural network model were found to emulate the known trends and reasonable estimates of strength as a function of the selected variables were obtained. © 2012 American Society of Civil Engineers.