Ignition delay studies on hydrocarbon fuel with and without additives

Single pulse shock tube facility has been developed in the High Temperature Chemical Kinetics Lab, Aerospace Engineering Department, to carry out ignition delay studies and spectroscopic investigations of hydrocarbon fuels. Our main emphasis is on measuring ignition delay through pressure rise and by monitoring CH emission for various jet fuels and finding suitable additives for reducing the delay. Initially the shock tube was tested and calibrated by measuring the ignition delay of C2H6-O2 mixture. The results are in good agreement with earlier published works. Ignition times of exo-tetrahdyrodicyclopentadiene (C10H16), which is a leading candidate fuel for scramjet propulsion has been studied in the reflected shock region in the temperature range 1250 - 1750 K with and without adding Triethylamine (TEA). Addition of TEA results in substantial reduction of ignition delay of C10H16.

Location of concentrators in a computer communication network: a stochastic automation search method

The following problem is considered. Given the locations of the Central Processing Unit (ar;the terminals which have to communicate with it, to determine the number and locations of the concentrators and to assign the terminals to the concentrators in such a way that the total cost is minimized. There is alao a fixed cost associated with each concentrator. There is ail upper limit to the number of terminals which can be connected to a concentrator. The terminals can be connected directly to the CPU also In this paper it is assumed that the concentrators can bo located anywhere in the area A containing the CPU and the terminals. Then this becomes a multimodal optimization problem. In the proposed algorithm a stochastic automaton is used as a search device to locate the minimum of the multimodal cost function . The proposed algorithm involves the following. The area A containing the CPU and the terminals is divided into an arbitrary number of regions (say K). An approximate value for the number of concentrators is assumed (say m). The optimum number is determined by iteration later The m concentrators can be assigned to the K regions in (mk) ways (m > K) or (km) ways (K>m).(All possible assignments are feasible, i.e. a region can contain 0,1,…, to concentrators). Each possible assignment is assumed to represent a state of the stochastic variable structure automaton. To start with, all the states are assigned equal probabilities. At each stage of the search the automaton visits a state according to the current probability distribution. At each visit the automaton selects a 'point' inside that state with uniform probability. The cost associated with that point is calculated and the average cost of that state is updated. Then the probabilities of all the states are updated. The probabilities are taken to bo inversely proportional to the average cost of the states After a certain number of searches the search probabilities become stationary and the automaton visits a particular state again and again. Then the automaton is said to have converged to that state Then by conducting a local gradient search within that state the exact locations of the concentrators are determined This algorithm was applied to a set of test problems and the results were compared with those given by Cooper's (1964, 1967) EAC algorithm and on the average it was found that the proposed algorithm performs better.

Delay characteristics of a simple triggered vacuum gap

Firing delays of a simple triggered vacuum gap are reported in this paper. The effects of insulating materials in the auxiliary gap, auxiliary gap current, main gap current and electrode separation on the delay have been investigated. The presence of insulating material in the auxiliary gap having low auxiliary gap resistance appears to exhibit large delay. Delay decreases considerably with increase of current in the auxiliary and the main gaps, but it increases with increase of electrode separation. The scatter in the delay is less than 25 ps and 500 ps with supramica (Mycalex Corporation of America) and silicon carbide respectively at lower values of auxiliary gap current and it becomes negligible for supramica at auxiliary gap currents greater than 6A. This investigation appears to indicate that the simple device can be used as a fast switch.

Low-voltage and high-current delay characteristics of a simple triggered vacuum gap

Low-voltage and high-current switching delay characteristics of a simple triggered vacuum gap (TVG) are described using lead zirconate titanate as the dielectric material in the auxiliary gap. This TVG has superior performance at high currents (up to 14 kA was studied) with regard to delay, reliable firing and extended life as compared to the one using either supramica or silicon carbide. The total delay consists of three intervals: to break down the auxiliary gap, to propagate the trigger plasma and to break down the main gap. The data on the influence of the various parameters like the trigger voltage, current, energy and the main circuit energy are given. It has been found that the delay due to the first two intervals is small compared to the third.

Carbon-Supported Pt-TiO2 as a Methanol-Tolerant Oxygen-Reduction Catalyst for DMFCs

Carbon-supported Pt-TiO2 (Pt-TiO2/C) catalysts with varying at. wt ratios of Pt to Ti, namely, 1:1, 2:1, and 3:1, are prepared by the sol-gel method. The electrocatalytic activity of the catalysts toward oxygen reduction reaction (ORR), both in the presence and absence of methanol, is evaluated for application in direct methanol fuel cells (DMFCs). The optimum at. wt ratio of Pt to Ti in Pt-TiO2/C is established by fuel cell polarization, linear sweep voltammetry, and cyclic voltammetry studies. Pt-TiO2/C heattreated at 750 degrees C with Pt and Ti in an at. wt ratio of 2:1 shows enhanced methanol tolerance, while maintaining high catalytic activity toward ORR. The DMFC with a Pt-TiO2/C cathode catalyst exhibits an enhanced peak power density of 180 mW/cm(2) in contrast to the 80 mW/cm(2) achieved from the DMFC with carbon-supported Pt catalyst while operating under identical conditions. Complementary data on the influence of TiO2 on the crystallinity of Pt, surface morphology, and particle size, surface oxidation states of individual constituents, and bulk and surface compositions are also obtained by powder X-ray diffraction, scanning and transmission electron microscopy, X-ray photoelectron spectroscopy, energy dispersive analysis by X-ray, and inductively coupled plasm optical emission spectrometry.

Field-Effect and Frequency Dependent Transport in Semiconductor-Enriched Single-Wall Carbon Nanotube Network Device

The electrical and optical response of a field-effect device comprising a network of semiconductor-enriched single-wall carbon nanotubes, gated with sodium chloride solution is investigated. Field-effect is demonstrated in a device that uses facile fabrication techniques along with a small-ion as the gate electrolyte-and this is accomplished as a result of the semiconductor enhancement of the tubes. The optical transparency and electrical resistance of the device are modulated with gate voltage. A time-response study of the modulation of optical transparency and electrical resistance upon application of gate voltage suggests the percolative charge transport in the network. Also the ac response in the network is investigated as a function of frequency and temperature down to 5 K. An empirical relation between onset frequency and temperature is determined.

Firing delay scheme for 3-Ï thyristor converters

A simple firing delay circuit for 3-φ fully controlled bridge using a phase locked loop is described. The circuit uses very few components and is an improved scheme over the existing methods. The use of this circuit in three-phase thyristor converters and 'circulating current free' mode dual converters is described.

The non-linear response of a gyrostabilized platform: Effects of correction torque time delay and of random inputs

First, the non-linear response of a gyrostabilized platform to a small constant input torque is analyzed in respect to the effect of the time delay (inherent or deliberately introduced) in the correction torque supplied by the servomotor, which itself may be non-linear to a certain extent. The equation of motion of the platform system is a third order nonlinear non-homogeneous differential equation. An approximate analytical method of solution of this equation is utilized. The value of the delay at which the platform response becomes unstable has been calculated by using this approximate analytical method. The procedure is illustrated by means of a numerical example. Second, the non-linear response of the platform to a random input has been obtained. The effects of several types of non-linearity on reducing the level of the mean square response have been investigated, by applying the technique of equivalent linearization and solving the resulting integral equations by using laguerre or Gaussian integration techniques. The mean square responses to white noise and band limited white noise, for various values of the non-linear parameter and for different types of non-linearity function, have been obtained. For positive values of the non-linear parameter the levels of the non-linear mean square responses to both white noise and band-limited white noise are low as compared to the linear mean square response. For negative values of the non-linear parameter the level of the non-linear mean square response at first increases slowly with increasing values of the non-linear parameter and then suddenly jumps to a high level, at a certain value of the non-linearity parameter.

A Team of Continuous-Action Learning Automata for Noise-Tolerant Learning of Half-Spaces

Learning automata are adaptive decision making devices that are found useful in a variety of machine learning and pattern recognition applications. Although most learning automata methods deal with the case of finitely many actions for the automaton, there are also models of continuous-action-set learning automata (CALA). A team of such CALA can be useful in stochastic optimization problems where one has access only to noise-corrupted values of the objective function. In this paper, we present a novel formulation for noise-tolerant learning of linear classifiers using a CALA team. We consider the general case of nonuniform noise, where the probability that the class label of an example is wrong may be a function of the feature vector of the example. The objective is to learn the underlying separating hyperplane given only such noisy examples. We present an algorithm employing a team of CALA and prove, under some conditions on the class conditional densities, that the algorithm achieves noise-tolerant learning as long as the probability of wrong label for any example is less than 0.5. We also present some empirical results to illustrate the effectiveness of the algorithm.

Strategies for efficient disruption of metabolism in Mycobacterium tuberculosis from network analysis

Tuberculosis continues to be a major health challenge, warranting the need for newer strategies for therapeutic intervention and newer approaches to discover them. Here, we report the identification of efficient metabolism disruption strategies by analysis of a reactome network. Protein-protein dependencies at a genome scale are derived from the curated metabolic network, from which insights into the nature and extent of inter-protein and inter-pathway dependencies have been obtained. A functional distance matrix and a subsequent nearness index derived from this information, helps in understanding how the influence of a given protein can pervade to the metabolic network. Thus, the nearness index can be viewed as a metabolic disruptability index, which suggests possible strategies for achieving maximal metabolic disruption by inhibition of the least number of proteins. A greedy approach has been used to identify the most influential singleton, and its combination with the other most pervasive proteins to obtain highly influential pairs, triplets and quadruplets. The effect of deletion of these combinations on cellular metabolism has been studied by flux balance analysis. An obvious outcome of this study is a rational identification of drug targets, to efficiently bring down mycobacterial metabolism.

Relative electrical distance concept for evaluation of network reactive power and loss contributions in a deregulated system

With the liberalisation of electricity market it has become very important to determine the participants making use of the transmission network.Transmission line usage computation requires information of generator to load contributions and the path used by various generators to meet loads and losses. In this study relative electrical distance (RED) concept is used to compute reactive power contributions from various sources like generators, switchable volt-amperes reactive(VAR) sources and line charging susceptances that are scattered throughout the network, to meet the system demands. The transmission line charge susceptances contribution to the system reactive flows and its aid extended in reducing the reactive generation at the generator buses are discussed in this paper. Reactive power transmission cost evaluation is carried out in this study. The proposed approach is also compared with other approaches viz.,proportional sharing and modified Y-bus.Detailed case studies with base case and optimised results are carried out on a sample 8-bus system. IEEE 39-bus system and a practical 72-bus system, an equivalent of Indian Southern grid are also considered for illustration and results are discussed.

Inferring neuronal network connectivity from spike data: A temporal data mining approach

Understanding the functioning of a neural system in terms of its underlying circuitry is an important problem in neuroscience. Recent d evelopments in electrophysiology and imaging allow one to simultaneously record activities of hundreds of neurons. Inferring the underlying neuronal connectivity patterns from such multi-neuronal spike train data streams is a challenging statistical and computational problem. This task involves finding significant temporal patterns from vast amounts of symbolic time series data. In this paper we show that the frequent episode mining methods from the field of temporal data mining can be very useful in this context. In the frequent episode discovery framework, the data is viewed as a sequence of events, each of which is characterized by an event type and its time of occurrence and episodes are certain types of temporal patterns in such data. Here we show that, using the set of discovered frequent episodes from multi-neuronal data, one can infer different types of connectivity patterns in the neural system that generated it. For this purpose, we introduce the notion of mining for frequent episodes under certain temporal constraints; the structure of these temporal constraints is motivated by the application. We present algorithms for discovering serial and parallel episodes under these temporal constraints. Through extensive simulation studies we demonstrate that these methods are useful for unearthing patterns of neuronal network connectivity.

Modelling heterogeneity of concrete using 2D lattice network for concrete fracture and comparison with AE study

In this paper, numerical modelling of fracture in concrete using two-dimensional lattice model is presented and also a few issues related to lattice modelling technique applicable to concrete fracture are reviewed. A comparison is made with acoustic emission (AE) events with the number of fractured elements. To implement the heterogeneity of the plain concrete, two methods namely, by generating grain structure of the concrete using Fuller's distribution and the concrete material properties are randomly distributed following Gaussian distribution are used. In the first method, the modelling of the concrete at meso level is carried out following the existing methods available in literature. The shape of the aggregates present in the concrete are assumed as perfect spheres and shape of the same in two-dimensional lattice network is circular. A three-point bend (TPB) specimen is tested in the experiment under crack mouth opening displacement (CMOD) control at a rate of 0.0004 mm/sec and the fracture process in the same TPB specimen is modelled using regular triangular 2D lattice network. Load versus crack mouth opening isplacement (CMOD) plots thus obtained by using both the methods are compared with experimental results. It was observed that the number of fractured elements increases near the peak load and beyond the peak load. That is once the crack starts to propagate. AE hits also increase rapidly beyond the peak load. It is compulsory here to mention that although the lattice modelling of concrete fracture used in this present study is very similar to those already available in literature, the present work brings out certain finer details which are not available explicitly in the earlier works.

Random network behaviour of protein structures

Geometric and structural constraints greatly restrict the selection of folds adapted by protein backbones, and yet, folded proteins show an astounding diversity in functionality. For structure to have any bearing on function, it is thus imperative that, apart from the protein backbone, other tunable degrees of freedom be accountable. Here, we focus on side-chain interactions, which non-covalently link amino acids in folded proteins to form a network structure. At a coarse-grained level, we show that the network conforms remarkably well to realizations of random graphs and displays associated percolation behavior. Thus, within the rigid framework of the protein backbone that restricts the structure space, the side-chain interactions exhibit an element of randomness, which account for the functional flexibility and diversity shown by proteins. However, at a finer level, the network exhibits deviations from these random graphs which, as we demonstrate for a few specific examples, reflect the intrinsic uniqueness in the structure and stability, and perhaps specificity in the functioning of biological proteins.

A Methanol-Tolerant Carbon-Supported Pt-Au Alloy Cathode Catalyst for Direct Methanol Fuel Cells and Its Evaluation by DFT

A Pt-Au alloy catalyst of varying compositions is prepared by codeposition of Pt and Au nanoparticles onto a carbon support to evaluate its electrocatalytic activity toward an oxygen reduction reaction (ORR) with methanol tolerance in direct methanol fuel cells. The optimum atomic weight ratio of Pt to Au in the carbon-supported Pt-Au alloy (Pt-Au/C) as established by cell polarization, linear-sweep voltammetry (LSV), and cyclic voltammetry (CV) studies is determined to be 2:1. A direct methanol fuel cell (DMFC) comprising a carbon-supported Pt-Au (2:1) alloy as the cathode catalyst delivers a peak power density of 120 mW/cm2 at 70 °C in contrast to the peak power density value of 80 mW/cm2 delivered by the DMFC with carbon-supported Pt catalyst operating under identical conditions. Density functional theory (DFT) calculations on a small model cluster reflect electron transfer from Pt to Au within the alloy to be responsible for the synergistic promotion of the oxygen-reduction reaction on a Pt-Au electrode.