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Flame-wall interaction (FWI) in premixed turbulent combustion has been analysed based on a counter-flow like configuration at the statistically stationary state. For the present configuration, the two FWI sub-zones, i.e the influence zone and the quenching zone, can be identified from the DNS results. Detailed analysis of the important quantities, such as the flame temperature, flame-wall distance, wall heat flux, flame curvature and dilatation (including the flame normal and tangential strain rates), and some orientation relations between the flame normal and the principal strain rate directions, have been reported, together with the physical explanations. All these statistical results are determined by the relative strengths of the wall heat flux, thermal expansion and the flame-turbulence interaction. [ABSTRACT FROM AUTHOR]

The local and non-local statistics of vorticity ... (i.e. curl of velocity vector ...) and pseudo-vorticity ... (i.e. curl of density-weighted velocity vector ...) have been investigated in detail based on a direct numerical simulation (DNS) database of freely propagating statistically planar flames in different regimes of premixed turbulent combustion. It has been found that the relative orientation of ... can be significantly different to that of ... for small values of the Karlovitz number and large values of the Reynolds number. The vorticity ... shows, depending on the case, considerable alignment with either the most extensive or compressive principal strain rate. In contrast, ... has been found to align with the intermediate principal strain rate for all cases. The transport statistics of Reynolds averaged enstrophy (i.e. ...) and pseudo-enstrophy (i.e. ...) have been investigated in detail. The non-local vorticity statistics in turbulent premixed combustion have been investigated based on the vorticity line segment (VLS) structure, which is defined with respect to each grid point as the part of the (pseudo) vorticity line bounded by the two adjacent extremal points of the (pseudo) vorticitymagnitude. VLS can be characterised by the segment length and the (pseudo) vorticity magnitude difference across the segment, i.e. (Δω*) Δω. Strong dilatation effects tend to increase VLS length, whereas turbulent convection disturbs the flow field resulting in shorter VLS. The joint PDFs between the segment length and (Δω) Δω* are found to be symmetric, whereas the joint PDFs between the segment length and the pseudo-vorticity magnitude difference across the VLS in the flame normal direction show significant asymmetry, which is primarily determined by density variation across the flame front and turbulence intensity. [ABSTRACT FROM AUTHOR]

Because in the Lagrangian frame the time scale separation has a stronger Reynolds number dependence than the length scale case in the Eulerian frame, it is more difficult to reveal inertial range scaling laws, as predicted from dimensional arguments. The present work introduces a newly defined trajectory segment structure to tentatively understand Lagrangian statistics. When a fluid particle evolves in space, its Lagrangian trajectory encounters regions of different dynamics, which can be characterized by the magnitude of material acceleration, i.e., |...|, in certain time span. The extrema of |...| are considered as the representative markers along the Lagrangian trajectories. A trajectory segment is defined as the part bounded by two adjacent extrema of |...|. The time difference and magnitude of the velocity difference at the two ends of each segment are chosen as the characteristic parameters. It shows that such structure reveals interesting turbulence physics, such as the scaling of the structure function and the quantitative description of the time scale. The corresponding explanation and analysis of flow physics are provided as well to improve the understanding of some remaining challenging issues [ABSTRACT FROM AUTHOR]

The interaction of flame and surrounding fluid motion is of central importance in the fundamental understanding of turbulent combustion. It is demonstrated here that this interaction can be represented using streamline segment analysis, which was previously applied in nonreactive turbulence. The present work focuses on the effects of the global Lewis number (Le) on streamline segment statistics in premixed flames in the thin-reaction-zones regime. A direct numerical simulation database of freely propagating thin-reaction-zones regime flames with Le ranging from 0.34 to 1.2 is used to demonstrate that Le has significant influences on the characteristic features of the streamline segment, such as the curve length, the difference in the velocity magnitude at two extremal points, and their correlations with the local flame curvature. The strengthenings of the dilatation rate, flame normal acceleration, and flame-generated turbulence with decreasing Le are principally responsible for these observed effects. An expression for the probability density function (pdf) of the streamline segment length, originally developed for nonreacting turbulent flows, captures the qualitative behavior for turbulent premixed flames in the thin-reaction-zones regime for a wide range of Le values. The joint pdfs between the streamline length and the difference in the velocity magnitude at two extremal points for both unweighted and density-weighted velocity vectors are analyzed and compared. Detailed explanations are provided for the observed differences in the topological behaviors of the streamline segment in response to the global Le. [ABSTRACT FROM AUTHOR]

We present a method to compute the delay-constrained multicast routing tree by employing chaotic neural networks. The experimental result shows that the noisy chaotic neural network (NCNN) provides an optimal solution more often compared to the transiently chaotic neural network (TCNN) and the Hopfield neural network (HNN). Furthermore, compared with the bounded shortest multicast algorithm (BSMA), the NCNN is able to find multicast trees with lower cost. [ABSTRACT FROM AUTHOR]

Abstract-In this paper, we argue that for a C-class classification problem, C 2-class classifiers, each of which discriminating one class from the other classes and having a characteristic input feature subset, should in general outperform, or at least match the performance of, a C-class classifier with one single input feature subset. For each class, we select a desirable feature subset, which leads to the lowest classification error rate for this class using a classifier for a given feature subset search algorithm. To fairly compare all models, we propose a weight method for the class-dependent classifier, i.e., assigning a weight to each model's output before the comparison is carried out. The method's performance is evaluated on two artificial data sets and several real-world benchmark data sets, with the support vector machine (SVM) as the classifier, and with the RELIEF, class separability, and minimal-redundancy-maximal-relevancy (mRMR) as attribute importance measures. Our results indicate that the class-dependent feature subsets found by our approach can effectively remove irrelevant or redundant features, while maintaining or improving (sometimes substantially) the classification accuracy, in comparison with other feature selection methods. [ABSTRACT FROM AUTHOR]

Dissipation element analysis is a new approach to study turbulent scalar fields. Gradient trajectories starting from each material point in a fluctuating scalar field [lowercase_phi_synonym]′(xvector ,t) in ascending and descending directions will inevitably reach a maximal and a minimal point. The ensemble of material points sharing the same pair ending points is named a dissipation element. Dissipation elements can be parametrized by the length scale l and the scalar difference Δ[lowercase_phi_synonym]′, which are defined as the straight line connecting the two extremal points and the scalar difference at these points, respectively. The decomposition of a turbulent field into dissipation elements is space filling. This allows us to reconstruct certain statistical quantities of fine scale turbulence which cannot be obtained otherwise. The marginal probability density function (PDF) of the length scale distribution had been modeled in the previous work based on a Poisson random cutting-reconnection process and had been compared to data from direct numerical simulation (DNS). The joint PDF of l and Δ[lowercase_phi_synonym]′ contains the important information that is needed for the modeling of scalar mixing in turbulence, such as the marginal PDF of the length of elements and conditional moments, as well as their scaling exponents. In order to be able to predict these quantities, there is a need to model the joint PDF. A compensation-defect model is put forward in this work and the agreement between the model prediction and DNS results is satisfactory. [ABSTRACT FROM AUTHOR]

The article examines the noisy chaotic neural networks with variable thresholds (NCNN-VT) approach in solving the frequency assignment problem in satellite communications. According to the study, the NCNN-VT approach obtains better solutions compared with gradual neural network on the benchmark examples. Compared to simulated annealing, the NCNN-VT finds optimal or suboptimal solutions with less computation costs. It also achieves better solutions with fewer iterations compared to transiently chaotic neural networks and noisy chaotic neural networks.

Background: Since the single nucleotide polymorphisms (SNPs) are genetic variations which determine the difference between any two unrelated individuals, the SNPs can be used to identify the correct source population of an individual. For efficient population identification with the HapMap genotype data, as few informative SNPs as possible are required from the original 4 million SNPs. Recently, Park et al. (2006) adopted the nearest shrunken centroid method to classify the three populations, i.e., Utah residents with ancestry from Northern and Western Europe (CEU), Yoruba in Ibadan, Nigeria in West Africa (YRI), and Han Chinese in Beijing together with Japanese in Tokyo (CHB+JPT), from which 100,736 SNPs were obtained and the top 82 SNPs could completely classify the three populations. Results: In this paper, we propose to first rank each feature (SNP) using a ranking measure, i.e., a modified t-test or F-statistics. Then from the ranking list, we form different feature subsets by sequentially choosing different numbers of features (e.g., 1, 2, 3, ..., 100.) with top ranking values, train and test them by a classifier, e.g., the support vector machine (SVM), thereby finding one subset which has the highest classification accuracy. Compared to the classification method of Park et al., we obtain a better result, i.e., good classification of the 3 populations using on average 64 SNPs. Conclusion: Experimental results show that the both of the modified t-test and F-statistics method are very effective in ranking SNPs about their classification capabilities. Combined with the SVM classifier, a desirable feature subset (with the minimum size and most informativeness) can be quickly found in the greedy manner after ranking all SNPs. Our method is able to identify a very small number of important SNPs that can determine the populations of individuals. [ABSTRACT FROM AUTHOR]

In this paper, we propose a gradual noisy chaotic neural network (G-NCNN) to solve the NP-complete broadcast scheduling problem (BSP) in packet radio networks. The objective of the BSP is to design an optimal time-division multiple-access (TDMA) frame structure with minimal TDMA frame length and maximal channel utilization. A two-phase optimization is adopted to achieve the two objectives with two different energy functions, so that the G-NCNN not only finds the minimum TDMA frame length but also maximizes the total node transmissions. In the first phase, we propose a G-NCNN which combines the noisy chaotic neural network (NCNN) and the gradual expansion scheme to find a minimal TDMA frame length. In the second phase, the NCNN is used to find maximal node transmissions in the TOMA frame obtained in the first phase. The performance is evaluated through several benchmark examples and 600 randomly generated instances. The results show that the G-NCNN outperforms previous approaches, such as mean field annealing, a hybrid Hopfield network-genetic algorithm, the sequential vertex coloring algorithm, and the gradual neural network. [ABSTRACT FROM AUTHOR]

Recently Chen and Aihara have demonstrated both experimentally and. mathematically that their chaotic simulated annealing (CSA) has better search ability for solving combinatorial optimization problems compared to both the Hopfield-Tank approach and stochastic simulated annealing (SSA). However, CSA may not find a globally optimal solution no matter how slowly annealing is carried out, because the chaotic dynamics are completely deterministic. In contrast, SSA tends to settle down to a global optimum if the temperature is reduced sufficiently slowly. Here we combine the best features of both SSA and CSA, thereby proposing a new approach for solving optimization problems, i.e., stochastic chaotic simulated annealing, by using a noisy chaotic neural network. We show the effectiveness of this new approach with two difficult combinatorial optimization problems, i.e., a traveling salesman problem and a channel assignment problem for cellular mobile communications. [ABSTRACT FROM AUTHOR]

Presents a study which carried out data dimensionality reduction to improve classification performance and to reduce the number of attributes as well as the complexity of a radial basis function (RBF) neural network. Separability-correlation measure for the ranking importance of attributes; Construction of the modified RBF neural network classifier; Experimental results and conclusions.

In recent years several local extrema-based methodologies have been proposed to investigate either the nonlinear or the nonstationary time series for scaling analysis. In the present work, we study systematically the distribution of the local extrema for both synthesized scaling processes and turbulent velocity data from experiments. The results show that for the fractional Brownian motion (fBm) without intermittency correction the measured extremal-point-density (EPD) agrees well with a theoretical prediction. For a multifractal random walk (MRW) with the lognormal statistics, the measured EPD is independent of the intermittency parameter μ, suggesting that the intermittency correction does not change the distribution of extremal points but changes the amplitude. By introducing a coarse-grained operator, the power-law behavior of these scaling processes is then revealed via the measured EPD for different scales. For fBm the scaling exponent ξ(H) is found to be ξ(H)=H, where H is Hurst number, while for MRW ξ(μ) shows a linear relation with the intermittency parameter μ. Such EPD approach is further applied to the turbulent velocity data obtained from a wind tunnel flow experiment with the Taylor scale λ-based Reynolds number Reλ=720, and a turbulent boundary layer with the momentum thickness θ based Reynolds number Reθ=810. A scaling exponent ξ≃0.37 is retrieved for the former case. For the latter one, the measured EPD shows clearly four regimes, which agrees well with the corresponding sublayer structures inside the turbulent boundary layer. [ABSTRACT FROM AUTHOR]

For jet flow emanating from noncircular orifices, an unbalanced surface tension force leads to capillary instability, which is independent of influence from the ambient air in the Rayleigh regime. In the present article, the dynamic behavior of incompressible elliptical jets in the Rayleigh regime is investigated. Theoretically, with the consideration of the fluid viscosity, the solution of the Cosserat equation consists of a particular solution and a complementary solution. For the complementary solution the wave number of disturbance modes has two complex conjugate roots, which are responsible for the jet breakup. To match the nonzero particular solution, a spatial wave needs to be introduced, which is independent of external perturbations. Physically, such a spatial wave is interpreted as the axis-switching phenomenon. The predicted features of the axis-switching wavelength and the damping effect from the fluid viscosity have been successfully verified by experimental results. Moreover, the dispersion relations from the present theory suggest that the growth rate of spatial instability is influenced by orifice eccentricity, the Weber number, and the Ohnesorge number. [ABSTRACT FROM AUTHOR]

The active interaction between the bacteria and fluid generates turbulent structures even at zero Reynolds number. The velocity of such a flow obtained experimentally has been quantitatively investigated based on streamline segment analysis. There is a clear transition at about 16 times the organism body length separating two different scale regimes, which may be attributed to the different influence of the viscous effect. Surprisingly the scaling extracted from the streamline segment indicates the existence of scale similarity even at the zero Reynolds number limit. Moreover, the multifractal feature can be quantitatively described via a lognormal formula with the Hurst number H=0.76 and the intermittency parameter μ=0.20, which is coincidentally in agreement with the three-dimensional hydrodynamic turbulence result. The direction of cascade is measured via the filter-space technique. An inverse energy cascade is confirmed. For the enstrophy, a forward cascade is observed when r/R≤3, and an inverse one is observed when r/R>3, where r and R are the separation distance and the bacteria body size, respectively. Additionally, the lognormal statistics is verified for the coarse-grained energy dissipation and enstrophy, which supports the lognormal formula to fit the measured scaling exponent. [ABSTRACT FROM AUTHOR]

Differing from the conventional no-slip wall boundary condition, the moving surface may have strong influences on the flow structures and the flow physics. Such effects are potentially important and useful for flow control. In this paper we analyze the two-dimensional flow over a roller array with different spacing and rotating speeds at the low Reynolds numbers. The numerical results indicate that the pressure drag and the friction drag of the rollers are strongly dependent on flowing and geometric parameters. Physically, surface motion can induce the viscous traction stream, which leads to very important effects, such as the so called impingement block and traction stream flush. These interesting findings may help to better understand the fluid dynamics for the general moving boundary cases. [ABSTRACT FROM AUTHOR]

An introduction to the journal is presented in which the editor discusses various reports published within the issue including one by Xiao-Lei Xia and colleagues on multiclass support vector machine (SVM) algorithm, one by Guanhua Chen and colleagues on partitioning-based algorithms, and one by Chunqiao Tan on the generalized fuzzy geometric aggregation operator.