Experiments and zero D modeling studies using specific Wiebe coefficients for producer gas as fuel in spark-ignited engines

The paper addresses experiments and modeling studies on the use of producer gas, a bio-derived low energy content fuel in a spark-ignited engine. Producer gas, generated in situ, has thermo-physical properties different from those of fossil fuel(s). Experiments on naturally aspirated and turbo-charged engine operation and subsequent analysis of the cylinder pressure traces reveal significant differences in the heat release pattern within the cylinder compared with a typical fossil fuel. The heat release patterns for gasoline and producer gas compare well in the initial 50% but beyond this, producer gas combustion tends to be sluggish leading to an overall increase in the combustion duration. This is rather unexpected considering that producer gas with nearly 20% hydrogen has higher flame speeds than gasoline. The influence of hydrogen on the initial flame kernel development period and the combustion duration and hence on the overall heat release pattern is addressed. The significant deviations in the heat release profiles between conventional fuels and producer gas necessitates the estimation of producer gas-specific Wiebe coefficients. The experimental heat release profiles are used for estimating the Wiebe coefficients. Experimental evidence of lower fuel conversion efficiency based on the chemical and thermal analysis of the engine exhaust gas is used to arrive at the Wiebe coefficients. The efficiency factor a is found to be 2.4 while the shape factor m is estimated at 0.7 for 2% to 90% burn duration. The standard Wiebe coefficients for conventional fuels and fuel-specific coefficients for producer gas are used in a zero D model to predict the performance of a 6-cylinder gas engine under naturally aspirated and turbo-charged conditions. While simulation results with standard Wiebe coefficients result in excessive deviations from the experimental results, excellent match is observed when producer gas-specific coefficients are used. Predictions using the same coefficients on a 3-cylinder gas engine having different geometry and compression ratio(s) indicate close match with the experimental traces highlighting the versatility of the coefficients.

Process control of cantilever deflection for sensor application based on optical waveguides

This paper reports on the fabrication of cantilever silicon-on-insulator (SOI) optical waveguides and presents solutions to the challenges of using a very thin 260-nm active silicon layer in the SOI structure to enable single-transverse-mode operation of the waveguide with minimal optical transmission losses. In particular, to ameliorate the anchor effect caused by the mean stress difference between the active silicon layer and buried oxide layer, a cantilever flattening process based on Ar plasma treatment is developed and presented. Vertical deflections of 0.5 mu m for 70-mu m-long cantilevers are mitigated to within few nanometers. Experimental investigations of cantilever mechanical resonance characteristics confirm the absence of significant detrimental side effects. Optical and mechanical modeling is extensively used to supplement experimental observations. This approach can satisfy the requirements for on-chip simultaneous readout of many integrated cantilever sensors in which the displacement or resonant frequency changes induced by analyte absorption are measured using an optical-waveguide-based division multiplexed system.

Exploring the color of transition metal ions in irregular coordination geometries: new colored inorganic oxides based on the spiroffite structure, Zn2-xMxTe3O8 (M = Co, Ni, Cu)

We describe the synthesis, crystal structures, and optical absorption spectra of transition metal substituted spiroffite derivatives, Zn2-xMxTe3O8 (M-II = Co, Ni, Cu; 0 < x <= 1.0). The oxides are readily synthesized by solid state reaction of stoichiometric mixtures of the constituent binaries at 620 degrees C. Reitveld refinement of the crystal structures from powder X-ray diffraction (XRD) data shows that the Zn/MO6 octahedra are strongly distorted, as in the parent Zn2Te3O8 structure, consisting of five relatively short Zn/M-II-O bonds (1.898-2.236 angstrom) and one longer Zn/M-II-O bond (2.356-2.519 angstrom). We have interpreted the unique colors and the optical absorption/diffuse reflectance spectra of Zn2-xMxTe3O8 in the visible, in terms of the observed/irregular coordination geometry of the Zn/M-II-O chromophores. We could not however prepare the fully substituted M2Te3O8 (M-II = Co, Ni, Cu) by the direct solid state reaction method. Density Functional Theory (DFT) modeling of the electronic structure of both the parent and the transition metal substituted derivatives provides new insights into the bonding and the role of transition metals toward the origin of color in these materials. We believe that transition metal substituted spiroffites Zn2-xMxTe3O8 reported here suggest new directions for the development of colored inorganic materials/pigments featuring irregular/distorted oxygen coordination polyhedra around transition metal ions.

DMT of parallel-path and layered networks under the half-duplex constraint

In this paper, we study the diversity-multiplexing-gain tradeoff (DMT) of wireless relay networks under the half-duplex constraint. It is often unclear what penalty if any, is imposed by the half-duplex constraint on the DMT of such networks. We study two classes of networks; the first class, called KPP(I) networks, is the class of networks with the relays organized in K parallel paths between the source and the destination. While we assume that there is no direct source-destination path, the K relaying paths can interfere with each other. The second class, termed as layered networks, is comprised of relays organized in layers, where links exist only between adjacent layers. We present a communication scheme based on static schedules and amplify-and-forward relaying for these networks. We also show that for KPP(I) networks with K >= 3, the proposed schemes can achieve full-duplex DMT performance, thus demonstrating that there is no performance hit on the DMT due to the half-duplex constraint. We also show that, for layered networks, a linear DMT of d(max)(1 - r)(+) between the maximum diversity d(max) and the maximum MG, r(max) = 1 is achievable. We adapt existing DMT optimal coding schemes to these networks, thus specifying the end-to-end communication strategy explicitly.

Modeling of temperature and field-dependent electron mobility in a single-layer graphene sheet

In this paper, we address a physics-based analytical model of electric-field-dependent electron mobility (mu) in a single-layer graphene sheet using the formulation of Landauer and Mc Kelvey's carrier flux approach under finite temperature and quasi-ballistic regime. The energy-dependent, near-elastic scattering rate of in-plane and out-of-plane (flexural) phonons with the electrons are considered to estimate mu over a wide range of temperature. We also demonstrate the variation of mu with carrier concentration as well as the longitudinal electric field. We find that at high electric field (>10(6) Vm(-1)), the mobility falls sharply, exhibiting the scattering between the electrons and flexural phonons. We also note here that under quasi-ballistic transport, the mobility tends to a constant value at low temperature, rather than in between T-2 and T-1 in strongly diffusive regime. Our analytical results agree well with the available experimental data, while the methodologies are put forward to estimate the other carrier-transmission-dependent transport properties.

Improved modeling of groundwater recharge in agricultural watersheds using a combination of crop model and remote sensing

For improved water management and efficiency of use in agriculture, studies dealing with coupled crop-surface water-groundwater models are needed. Such integrated models of crop and hydrology can provide accurate quantification of spatio-temporal variations of water balance parameters such as soil moisture store, evapotranspiration and recharge in a catchment. Performance of a coupled crop-hydrology model would depend on the availability of a calibrated crop model for various irrigated/rainfed crops and also on an accurate knowledge of soil hydraulic parameters in the catchment at relevant scale. Moreover, such a coupled model should be designed so as to enable the use/assimilation of recent satellite remote sensing products (optical and microwave) in order to model the processes at catchment scales. In this study we present a framework to couple a crop model with a groundwater model for applications to irrigated groundwater agricultural systems. We discuss the calibration of the STICS crop model and present a methodology to estimate the soil hydraulic parameters by inversion of crop model using both ground and satellite based data. Using this methodology we demonstrate the feasibility of estimation of potential recharge due to spatially varying soil/crop matrix.

Automatic speech segmentation using probabilistic latent component modeling

Latent variable methods, such as PLCA (Probabilistic Latent Component Analysis) have been successfully used for analysis of non-negative signal representations. In this paper, we formulate PLCS (Probabilistic Latent Component Segmentation), which models each time frame of a spectrogram as a spectral distribution. Given the signal spectrogram, the segmentation boundaries are estimated using a maximum-likelihood approach. For an efficient solution, the algorithm imposes a hard constraint that each segment is modelled by a single latent component. The hard constraint facilitates the solution of ML boundary estimation using dynamic programming. The PLCS framework does not impose a parametric assumption unlike earlier ML segmentation techniques. PLCS can be naturally extended to model coarticulation between successive phones. Experiments on the TIMIT corpus show that the proposed technique is promising compared to most state of the art speech segmentation algorithms.

Modeling the vulnerability of an urban groundwater system due to the combined impacts of climate change and management scenarios

Climate change impact on a groundwater-dependent small urban town has been investigated in the semiarid hard rock aquifer in southern India. A distributed groundwater model was used to simulate the groundwater levels in the study region for the projected future rainfall (2012-32) obtained from a general circulation model (GCM) to estimate the impacts of climate change and management practices on groundwater system. Management practices were based on the human-induced changes on the urban infrastructure such as reduced recharge from the lakes, reduced recharge from water and wastewater utility due to an operational and functioning underground drainage system, and additional water extracted by the water utility for domestic purposes. An assessment of impacts on the groundwater levels was carried out by calibrating a groundwater model using comprehensive data gathered during the period 2008-11 and then simulating the future groundwater level changes using rainfall from six GCMs Institute of Numerical Mathematics Coupled Model, version 3.0 (INM-CM. 3.0); L'Institut Pierre-Simon Laplace Coupled Model, version 4 (IPSL-CM4); Model for Interdisciplinary Research on Climate, version 3.2 (MIROC3.2); ECHAM and the global Hamburg Ocean Primitive Equation (ECHO-G); Hadley Centre Coupled Model, version 3 (HadCM3); and Hadley Centre Global Environment Model, version 1 (HadGEM1)] that were found to show good correlation to the historical rainfall in the study area. The model results for the present condition indicate that the annual average discharge (sum of pumping and natural groundwater outflow) was marginally or moderately higher at various locations than the recharge and further the recharge is aided from the recharge from the lakes. Model simulations showed that groundwater levels were vulnerable to the GCM rainfall and a scenario of moderate reduction in recharge from lakes. Hence, it is important to sustain the induced recharge from lakes by ensuring that sufficient runoff water flows to these lakes.

MODELING AND SIMULATION OF HYDRODYNAMIC INTERACTION OF DNA IN A MICRO-FLUIDIC CHANNEL

The motion of DNA (in the bulk solution) and the non-Newtonian effective fluid behavior are considered separately and self-consistently with the fluid motion satisfying the no-slip boundary condition on the surface of the confining geometry in the presence of channel pressure gradients. A different approach has been developed to model DNA in the micro-channel. In this study the DNA is assumed as an elastic chain with its characteristic Young's modulus, Poisson's ratio and density. The force which results from the fluid dynamic pressure, viscous forces and electromotive forces is applied to the elastic chain in a coupled manner. The velocity fields in the micro-channel are influenced by the transport properties. Simulations are carried out for the DNAs attached to the micro-fluidic wall. Numerical solutions based on a coupled multiphysics finite element scheme are presented. The modeling scheme is derived based on mass conservation including biomolecular mass, momentum balance including stress due to Coulomb force field and DNA-fluid interaction, and charge transport associated to DNA and other ionic complexes in the fluid. Variation in the velocity field for the non-Newtonian flow and the deformation of the DNA strand which results from the fluid-structure interaction are first studied considering a single DNA strand. Motion of the effective center of mass is analyzed considering various straight and coil geometries. Effects of DNA statistical parameters (geometry and spatial distribution of DNAs along the channel) on the effective flow behavior are analyzed. In particular, the dynamics of different DNA physical properties such as radius of gyration, end-to-end length etc. which are obtained from various different models (Kratky-Porod, Gaussian bead-spring etc.) are correlated to the nature of interaction and physical properties under the same background fluid environment.

Rack Level Modeling of Air Flow Through Perforated Tile in a Data Center

Effective air flow distribution through perforated tiles is required to efficiently cool servers in a raised floor data center. We present detailed computational fluid dynamics (CFD) modeling of air flow through a perforated tile and its entrance to the adjacent server rack. The realistic geometrical details of the perforated tile, as well as of the rack are included in the model. Generally, models for air flow through perforated tiles specify a step pressure loss across the tile surface, or porous jump model based on the tile porosity. An improvement to this includes a momentum source specification above the tile to simulate the acceleration of the air flow through the pores, or body force model. In both of these models, geometrical details of tile such as pore locations and shapes are not included. More details increase the grid size as well as the computational time. However, the grid refinement can be controlled to achieve balance between the accuracy and computational time. We compared the results from CFD using geometrical resolution with the porous jump and body force model solution as well as with the measured flow field using particle image velocimetry (PIV) experiments. We observe that including tile geometrical details gives better results as compared to elimination of tile geometrical details and specifying physical models across and above the tile surface. A modification to the body force model is also suggested and improved results were achieved.

Validation based sparse Gaussian processes for ordinal regression

This paper proposes a sparse modeling approach to solve ordinal regression problems using Gaussian processes (GP). Designing a sparse GP model is important from training time and inference time viewpoints. We first propose a variant of the Gaussian process ordinal regression (GPOR) approach, leave-one-out GPOR (LOO-GPOR). It performs model selection using the leave-one-out cross-validation (LOO-CV) technique. We then provide an approach to design a sparse model for GPOR. The sparse GPOR model reduces computational time and storage requirements. Further, it provides faster inference. We compare the proposed approaches with the state-of-the-art GPOR approach on some benchmark data sets. Experimental results show that the proposed approaches are competitive.

Group testing based spectrum hole search using a simple sub-nyquist sampling scheme

In this paper, we consider the problem of finding a spectrum hole of a specified bandwidth in a given wide band of interest. We propose a new, simple and easily implementable sub-Nyquist sampling scheme for signal acquisition and a spectrum hole search algorithm that exploits sparsity in the primary spectral occupancy in the frequency domain by testing a group of adjacent subbands in a single test. The sampling scheme deliberately introduces aliasing during signal acquisition, resulting in a signal that is the sum of signals from adjacent sub-bands. Energy-based hypothesis tests are used to provide an occupancy decision over the group of subbands, and this forms the basis of the proposed algorithm to find contiguous spectrum holes. We extend this framework to a multi-stage sensing algorithm that can be employed in a variety of spectrum sensing scenarios, including non-contiguous spectrum hole search. Further, we provide the analytical means to optimize the hypothesis tests with respect to the detection thresholds, number of samples and group size to minimize the detection delay under a given error rate constraint. Depending on the sparsity and SNR, the proposed algorithms can lead to significantly lower detection delays compared to a conventional bin-by-bin energy detection scheme; the latter is in fact a special case of the group test when the group size is set to 1. We validate our analytical results via Monte Carlo simulations.

Examining the Effectiveness of Discriminant Function Analysis and Cluster Analysis in Species Identification of Male Field Crickets Based on Their Calling Songs

Traditional taxonomy based on morphology has often failed in accurate species identification owing to the occurrence of cryptic species, which are reproductively isolated but morphologically identical. Molecular data have thus been used to complement morphology in species identification. The sexual advertisement calls in several groups of acoustically communicating animals are species-specific and can thus complement molecular data as non-invasive tools for identification. Several statistical tools and automated identifier algorithms have been used to investigate the efficiency of acoustic signals in species identification. Despite a plethora of such methods, there is a general lack of knowledge regarding the appropriate usage of these methods in specific taxa. In this study, we investigated the performance of two commonly used statistical methods, discriminant function analysis (DFA) and cluster analysis, in identification and classification based on acoustic signals of field cricket species belonging to the subfamily Gryllinae. Using a comparative approach we evaluated the optimal number of species and calling song characteristics for both the methods that lead to most accurate classification and identification. The accuracy of classification using DFA was high and was not affected by the number of taxa used. However, a constraint in using discriminant function analysis is the need for a priori classification of songs. Accuracy of classification using cluster analysis, which does not require a priori knowledge, was maximum for 6-7 taxa and decreased significantly when more than ten taxa were analysed together. We also investigated the efficacy of two novel derived acoustic features in improving the accuracy of identification. Our results show that DFA is a reliable statistical tool for species identification using acoustic signals. Our results also show that cluster analysis of acoustic signals in crickets works effectively for species classification and identification.

Pattern-growth based frequent serial episode discovery

Frequent episode discovery is a popular framework for pattern discovery from sequential data. It has found many applications in domains like alarm management in telecommunication networks, fault analysis in the manufacturing plants, predicting user behavior in web click streams and so on. In this paper, we address the discovery of serial episodes. In the episodes context, there have been multiple ways to quantify the frequency of an episode. Most of the current algorithms for episode discovery under various frequencies are apriori-based level-wise methods. These methods essentially perform a breadth-first search of the pattern space. However currently there are no depth-first based methods of pattern discovery in the frequent episode framework under many of the frequency definitions. In this paper, we try to bridge this gap. We provide new depth-first based algorithms for serial episode discovery under non-overlapped and total frequencies. Under non-overlapped frequency, we present algorithms that can take care of span constraint and gap constraint on episode occurrences. Under total frequency we present an algorithm that can handle span constraint. We provide proofs of correctness for the proposed algorithms. We demonstrate the effectiveness of the proposed algorithms by extensive simulations. We also give detailed run-time comparisons with the existing apriori-based methods and illustrate scenarios under which the proposed pattern-growth algorithms perform better than their apriori counterparts. (C) 2013 Elsevier B.V. All rights reserved.

Structural Insights into Saccharomyces cerevisiae Msh4-Msh5 Complex Function Using Homology Modeling

The Msh4-Msh5 protein complex in eukaryotes is involved in stabilizing Holliday junctions and its progenitors to facilitate crossing over during Meiosis I. These functions of the Msh4-Msh5 complex are essential for proper chromosomal segregation during the first meiotic division. The Msh4/5 proteins are homologous to the bacterial mismatch repair protein MutS and other MutS homologs (Msh2, Msh3, Msh6). Saccharomyces cerevisiae msh4/5 point mutants were identified recently that show two fold reduction in crossing over, compared to wild-type without affecting chromosome segregation. Three distinct classes of msh4/5 point mutations could be sorted based on their meiotic phenotypes. These include msh4/5 mutations that have a) crossover and viability defects similar to msh4/5 null mutants; b) intermediate defects in crossing over and viability and c) defects only in crossing over. The absence of a crystal structure for the Msh4-Msh5 complex has hindered an understanding of the structural aspects of Msh4-Msh5 function as well as molecular explanation for the meiotic defects observed in msh4/5 mutations. To address this problem, we generated a structural model of the S. cerevisiae Msh4-Msh5 complex using homology modeling. Further, structural analysis tailored with evolutionary information is used to predict sites with potentially critical roles in Msh4-Msh5 complex formation, DNA binding and to explain asymmetry within the Msh4-Msh5 complex. We also provide a structural rationale for the meiotic defects observed in the msh4/5 point mutations. The mutations are likely to affect stability of the Msh4/5 proteins and/or interactions with DNA. The Msh4-Msh5 model will facilitate the design and interpretation of new mutational data as well as structural studies of this important complex involved in meiotic chromosome segregation.