On the stabilisation of turbulent lifted flames

This study explores the stabilisation mechanisms of turbulent lifted flames by examining the scalar dissipation rate (SDR) of both passive and reactive scalars and their cross dissipation (CDR) in the stabilisation region. DNS results of a laboratory scale hydrogen turbulent lifted flame has been used for this analysis. Various definitions of the flame leading edge (FLE) has been compared and differences are illustrated. Time and spatial averaged statistic of SDR and CDR were examined. It was found that the averaged SDR for the mixture fraction at FLE was well below the reference quenching value for stoichiometric mixture. The averaged SDR for the progress variable is in the same order of the unstrained premixed laminar flame value. It was observed that the averaged CDR changed from negative to weakly positive at FLE. The change in sign was explained by a change in the relative alignment of the gradients of mixture fraction and progress variable. It was thus evident that the CDR was a good marker for stabilisation region and an important quantity in stabilisation mechanism.

Plasma stabilisation of metallic nanoparticles on silicon for the growth of carbon nanotubes

Ammonia (NH 3) plasma pretreatment is used to form and temporarily reduce the mobility of Ni, Co, or Fe nanoparticles on boron-doped mono- and poly-crystalline silicon. X-ray photoemission spectroscopy proves that NH 3 plasma nitrides the Si supports during nanoparticle formation which prevents excessive nanoparticle sintering/diffusion into the bulk of Si during carbon nanotube growth by chemical vapour deposition. The nitridation of Si thus leads to nanotube vertical alignment and the growth of nanotube forests by root growth mechanism. © 2012 American Institute of Physics.

Measurement of the Mechanical Properties of Filtercakes

In order to improve drilling mud design to cater for specific well situations, a more comprehensive knowledge and understanding of filter cake failure is needed. This paper describes experimental techniques aimed at directly probing the mechanical properties of filter cakes, without having to take into account artefacts due to fluid flow in the substrate. The use of rheometers allows us to determine shear yield stress and dynamic shear modulii of cakes grown on filter paper. A new scraping technique measures the strength and moisture profiles of typical filter cakes with a 0.1 mm resolution. This technique also allows us to probe the adhesion between the filter cake and its rock substrate. In addition, œdometer drained consolidation and unloading of a filter cake give us compression parameters useful for Cam Clay modelling. These independent measurements give similar results as to the elastic modulus of different filter cakes, showing an order of magnitude difference between water based and oil based cakes. We find that these standard cakes behave predominantly as purely elastic materials, with a sharp transition into plastic flow, allowing for the determination of a well-defined yield stress. The effect ofsolids loading on a given type of mud is also studied.

Impact of carbon fibre/epoxy corrugated cores

The dynamic compressive response of corrugated carbon-fibre reinforced epoxy sandwich cores has been investigated using a Kolsky-bar set-up. Compression at quasi-static rates up to v 0=200ms -1 have been tested on three different slenderness ratios of strut. High speed photography was used to capture the failure mechanisms and relate these to the measured axial compressive stress. Experiments show significant strength enhancement as the loading rate increases. Although material rate sensitivity accounts for some of this, it has been shown that the majority of the strength enhancement is due to inertial stabilisation of the core members. Inertial strength enhancement rises non-linearly with impact velocity. The largest gains are associated with a shift to buckle modes composed of 2-3 half sine waves. The loading rates tested within this study are similar to those that are expected when a sandwich core is compressed due to a blast event. © 2012 Elsevier Ltd.

Comparison of 100 Gb/s Ethernet links using PAM-8, multi-pulse, and hybrid CAP-16/QAM-16 modulation schemes

A theoretical study compares 100 Gb/s Ethernet links and finds that multi-pulse and hybrid CAP-16/QAM-16 (PAM-8) schemes support transmission over 10 km (2 km) SMF. Multi-pulse and CAP-16/QAM-16 need 2× the number of arithmetic operations and 7× or 3× the number of filter taps respectively but exhibit reduced power dissipation compared with PAM-8.

Updated correlation between aircraft smoke number and black carbon concentration

Aircraft emissions of black carbon (BC) contribute to anthropogenic climate forcing and degrade air quality. The smoke number (SN) is the current regulatory measure of aircraft particulate matter emissions and quantifies exhaust plume visibility. Several correlations between SN and the exhaust mass concentration of BC (CBC) have been developed, based on measurements relevant to older aircraft engines. These form the basis of the current standard method used to estimate aircraft BC emissions (First Order Approximation version 3 [FOA3]) for the purposes of environmental impact analyses. In this study, BC with a geometric mean diameter (GMD) of 20, 30, and 60 nm and filter diameters of 19 and 35 mm are used to investigate the effect of particle size and sampling variability on SN measurements. For BC with 20 and 30 nm GMD, corresponding to BC emitted by modern aircraft engines, a smaller SN results from a given CBC than is the case for BC with 60 nm GMD, which is more typical of older engines. An updated correlation between CBC and SNthat accounts for typical size of BC emitted by modern aircraft is proposed. An uncertainty of ±25% accounts for variation in GMD in the range 20-30 nm and for the range of filter diameters. The SN-CBC correlation currently used in FOA3 underestimates by a factor of 2.5-3 for SN <15, implying that current estimates of aircraft BC emissions derived from SN are underestimated by the same factor. Copyright © American Association for Aerosol Research.

Uniform stability of a particle approximation of the optimal filter derivative

Sequential Monte Carlo methods, also known as particle methods, are a widely used set of computational tools for inference in non-linear non-Gaussian state-space models. In many applications it may be necessary to compute the sensitivity, or derivative, of the optimal filter with respect to the static parameters of the state-space model; for instance, in order to obtain maximum likelihood model parameters of interest, or to compute the optimal controller in an optimal control problem. In Poyiadjis et al. [2011] an original particle algorithm to compute the filter derivative was proposed and it was shown using numerical examples that the particle estimate was numerically stable in the sense that it did not deteriorate over time. In this paper we substantiate this claim with a detailed theoretical study. Lp bounds and a central limit theorem for this particle approximation of the filter derivative are presented. It is further shown that under mixing conditions these Lp bounds and the asymptotic variance characterized by the central limit theorem are uniformly bounded with respect to the time index. We demon- strate the performance predicted by theory with several numerical examples. We also use the particle approximation of the filter derivative to perform online maximum likelihood parameter estimation for a stochastic volatility model.

Convergence of the auxiliary particle implementation of the PHD filter

Optimal Bayesian multi-target filtering is in general computationally impractical owing to the high dimensionality of the multi-target state. The Probability Hypothesis Density (PHD) filter propagates the first moment of the multi-target posterior distribution. While this reduces the dimensionality of the problem, the PHD filter still involves intractable integrals in many cases of interest. Several authors have proposed Sequential Monte Carlo (SMC) implementations of the PHD filter. However, these implementations are the equivalent of the Bootstrap Particle Filter, and the latter is well known to be inefficient. Drawing on ideas from the Auxiliary Particle Filter (APF), a SMC implementation of the PHD filter which employs auxiliary variables to enhance its efficiency was proposed by Whiteley et. al. Numerical examples were presented for two scenarios, including a challenging nonlinear observation model, to support the claim. This paper studies the theoretical properties of this auxiliary particle implementation. $\mathbb{L}_p$ error bounds are established from which almost sure convergence follows.

Auxiliary particle implementation of the probability hypothesis density filter

Optimal Bayesian multi-target filtering is, in general, computationally impractical owing to the high dimensionality of the multi-target state. The Probability Hypothesis Density (PHD) filter propagates the first moment of the multi-target posterior distribution. While this reduces the dimensionality of the problem, the PHD filter still involves intractable integrals in many cases of interest. Several authors have proposed Sequential Monte Carlo (SMC) implementations of the PHD filter. However, these implementations are the equivalent of the Bootstrap Particle Filter, and the latter is well known to be inefficient. Drawing on ideas from the Auxiliary Particle Filter (APF), we present a SMC implementation of the PHD filter which employs auxiliary variables to enhance its efficiency. Numerical examples are presented for two scenarios, including a challenging nonlinear observation model.