Computational Analysis of Flow through a Multiple Nozzle Driven Laser Cavity and Diffuser

Computations have been carried out for simulating supersonic flow through a set of converging-diverging nozzles with their expanding jets forming a laser cavity and flow patterns through diffusers, past the cavity. A thorough numerical investigation with 3-D RANS code is carried out to capture the flow distribution which comprises of shock patterns and multiple supersonic jet interactions. The analysis of pressure recovery characteristics during the flow through the diffusers is an important parameter of the simulation and is critical for the performance of the laser device. The results of the computation have shown a close agreement with the experimentally measured parameters as well as other established results indicating that the flow analysis done is found to be satisfactory.

Experimental and computational studies on a gasifier based stove

The work reported here is concerned with a detailed thermochemical evaluation of the flaming mode behaviour of a gasifier based stove. Determination of the gas composition over the fuel bed, surface and gas temperatures in the gasification process constitute principal experimental features. A simple atomic balance for the gasification reaction combined with the gas composition from the experiments is used to determine the CH(4) equivalent of higher hydrocarbons and the gasification efficiency (eta g). The components of utilization efficiency, namely, gasification-combustion and heat transfer are explored. Reactive flow computational studies using the measured gas composition over the fuel bed are used to simulate the thermochemical flow field and heat transfer to the vessel; hither-to-ignored vessel size effects in the extraction of heat from the stove are established clearly. The overall flaming mode efficiency of the stove is 50-54%; the convective and radiative components of heat transfer are established to be 45-47 and 5-7% respectively. The efficiency estimates from reacting computational fluid dynamics (RCFD) compare well with experiments. (C) 2011 Elsevier Ltd. All rights reserved.

On the solution of bivariate population balance equations for aggregation: X-discretization of space for expansion and contraction of computational domain

A new structured discretization of 2D space, named X-discretization, is proposed to solve bivariate population balance equations using the framework of minimal internal consistency of discretization of Chakraborty and Kumar [2007, A new framework for solution of multidimensional population balance equations. Chem. Eng. Sci. 62, 4112-4125] for breakup and aggregation of particles. The 2D space of particle constituents (internal attributes) is discretized into bins by using arbitrarily spaced constant composition radial lines and constant mass lines of slope -1. The quadrilaterals are triangulated by using straight lines pointing towards the mean composition line. The monotonicity of the new discretization makes is quite easy to implement, like a rectangular grid but with significantly reduced numerical dispersion. We use the new discretization of space to automate the expansion and contraction of the computational domain for the aggregation process, corresponding to the formation of larger particles and the disappearance of smaller particles by adding and removing the constant mass lines at the boundaries. The results show that the predictions of particle size distribution on fixed X-grid are in better agreement with the analytical solution than those obtained with the earlier techniques. The simulations carried out with expansion and/or contraction of the computational domain as population evolves show that the proposed strategy of evolving the computational domain with the aggregation process brings down the computational effort quite substantially; larger the extent of evolution, greater is the reduction in computational effort. (C) 2011 Elsevier Ltd. All rights reserved.

Computational Biology and Language

Current scientific research is characterized by increasing specialization, accumulating knowledge at a high speed due to parallel advances in a multitude of sub-disciplines. Recent estimates suggest that human knowledge doubles every two to three years – and with the advances in information and communication technologies, this wide body of scientific knowledge is available to anyone, anywhere, anytime. This may also be referred to as ambient intelligence – an environment characterized by plentiful and available knowledge. The bottleneck in utilizing this knowledge for specific applications is not accessing but assimilating the information and transforming it to suit the needs for a specific application. The increasingly specialized areas of scientific research often have the common goal of converting data into insight allowing the identification of solutions to scientific problems. Due to this common goal, there are strong parallels between different areas of applications that can be exploited and used to cross-fertilize different disciplines. For example, the same fundamental statistical methods are used extensively in speech and language processing, in materials science applications, in visual processing and in biomedicine. Each sub-discipline has found its own specialized methodologies making these statistical methods successful to the given application. The unification of specialized areas is possible because many different problems can share strong analogies, making the theories developed for one problem applicable to other areas of research. It is the goal of this paper to demonstrate the utility of merging two disparate areas of applications to advance scientific research. The merging process requires cross-disciplinary collaboration to allow maximal exploitation of advances in one sub-discipline for that of another. We will demonstrate this general concept with the specific example of merging language technologies and computational biology.

DMT of Multihop Networks: End Points and Computational Tools

In this paper, the diversity-multiplexing gain tradeoff (DMT) of single-source, single-sink (ss-ss), multihop relay networks having slow-fading links is studied. In particular, the two end-points of the DMT of ss-ss full-duplex networks are determined, by showing that the maximum achievable diversity gain is equal to the min-cut and that the maximum multiplexing gain is equal to the min-cut rank, the latter by using an operational connection to a deterministic network. Also included in the paper, are several results that aid in the computation of the DMT of networks operating under amplify-and-forward (AF) protocols. In particular, it is shown that the colored noise encountered in amplify-and-forward protocols can be treated as white for the purpose of DMT computation, lower bounds on the DMT of lower-triangular channel matrices are derived and the DMT of parallel MIMO channels is computed. All protocols appearing in the paper are explicit and rely only upon AF relaying. Half-duplex networks and explicit coding schemes are studied in a companion paper.

Computational investigations on covalent dimerization/oligomerization of polyacenes: Is it relevant to soot formation?

We have postulated a novel pathway that could assist in the nucleation of soot particles through covalent dimerization and oligomerizations of a variety of PAHs. DFT calculations were performed with the objective of obtaining the relative thermal stabilities and formation probabilities of oligomeric species that exploit the facile dimerization that is known to occur in linear oligoacenes. We propose that the presence of small stretches of linear oligoacence (tetracene or longer) in extended PAH, either embedded or tethered, would be adequate for enabling the formation of such dimeric and oligomeric adducts; these could then serve as nuclei for the growth of soot particles. Our studies also reveal the importance of p-stacking interactions between extended aromatic frameworks in governing the relative stabilities of the oligomeric species that are formed. (c) 2012 Wiley Periodicals, Inc.

On maximum entropy and minimum KL-divergence optimization by Grobner basis methods

In this paper we study constrained maximum entropy and minimum divergence optimization problems, in the cases where integer valued sufficient statistics exists, using tools from computational commutative algebra. We show that the estimation of parametric statistical models in this case can be transformed to solving a system of polynomial equations. We give an implicit description of maximum entropy models by embedding them in algebraic varieties for which we give a Grobner basis method to compute it. In the cases of minimum KL-divergence models we show that implicitization preserves specialization of prior distribution. This result leads us to a Grobner basis method to embed minimum KL-divergence models in algebraic varieties. (C) 2012 Elsevier Inc. All rights reserved.

Mesh Simplification Based on Edge Collapsing Could Improve Computational Efficiency in Near Infrared Optical Tomographic Imaging

The diffusion equation-based modeling of near infrared light propagation in tissue is achieved by using finite-element mesh for imaging real-tissue types, such as breast and brain. The finite-element mesh size (number of nodes) dictates the parameter space in the optical tomographic imaging. Most commonly used finite-element meshing algorithms do not provide the flexibility of distinct nodal spacing in different regions of imaging domain to take the sensitivity of the problem into consideration. This study aims to present a computationally efficient mesh simplification method that can be used as a preprocessing step to iterative image reconstruction, where the finite-element mesh is simplified by using an edge collapsing algorithm to reduce the parameter space at regions where the sensitivity of the problem is relatively low. It is shown, using simulations and experimental phantom data for simple meshes/domains, that a significant reduction in parameter space could be achieved without compromising on the reconstructed image quality. The maximum errors observed by using the simplified meshes were less than 0.27% in the forward problem and 5% for inverse problem.

Computational algorithms inspired by biological processes and evolution

In recent times computational algorithms inspired by biological processes and evolution are gaining much popularity for solving science and engineering problems. These algorithms are broadly classified into evolutionary computation and swarm intelligence algorithms, which are derived based on the analogy of natural evolution and biological activities. These include genetic algorithms, genetic programming, differential evolution, particle swarm optimization, ant colony optimization, artificial neural networks, etc. The algorithms being random-search techniques, use some heuristics to guide the search towards optimal solution and speed-up the convergence to obtain the global optimal solutions. The bio-inspired methods have several attractive features and advantages compared to conventional optimization solvers. They also facilitate the advantage of simulation and optimization environment simultaneously to solve hard-to-define (in simple expressions), real-world problems. These biologically inspired methods have provided novel ways of problem-solving for practical problems in traffic routing, networking, games, industry, robotics, economics, mechanical, chemical, electrical, civil, water resources and others fields. This article discusses the key features and development of bio-inspired computational algorithms, and their scope for application in science and engineering fields.

Bromenium-Catalysed Tandem Ring Opening/Cyclisation of Vinylcyclopropanes and Vinylcyclobutanes: A Metal-Free 3+2+1]/4+2+1] Cascade for the Synthesis of Chiral Amidines and Computational Investigation

We present a detailed study of a 3+2+1] cascade cyclisation of vinylcyclopropanes (VCP) catalysed by a bromenium species (Brd+?Xd-) generated in situ, which results in the synthesis of chiral bicyclic amidines in a tandem one-pot operation. The formation of amidines involves the ring-opening of VCPs with Br?X, followed by a Ritter-type reaction with chloramine-T and a tandem cyclisation. The reaction has been further extended to vinylcyclobutane systems and involves a 4+2+1] cascade cyclisation with the same reagents. The versatility of the methodology has been demonstrated by careful choice of VCPs and VCBs to yield bicyclo4.3.0]-, -4.3.1]- and -4.4.0]amidines in enantiomerically pure form. On the basis of the experimental observations and DFT calculations, a reasonable mechanism has been put forth to account for the formation of the products and the observed stereoselectivity. We propose the existence of a p-stabilised homoallylic carbocation at the cyclopropane carbon as the reason for high stereoselectivity. DFT studies at B3LYP/6-311+G** and M06-2X/6-31+G* levels of theory in gas-phase calculations suggest the ring-opening of VCP is initiated at the p-complex stage (between the double bond and Br?X). This can be clearly perceived from the solution-phase (acetonitrile) calculations using the polarisable continuum model (PCM) solvation model, from which the extent of the ring opening of VCP was found to be noticeably high. Studies also show that the formation of zero-bridge bicyclic amidines is favoured over other bridged bicyclic amidines. The energetics of competing reaction pathways is compared to explain the product selectivity.