Discrete Vortex Method-Based Model for Ground-Effect Studies

A discrete vortex method-based model has been proposed for two-dimensional/three-dimensional ground-effect prediction. The model merely requires two-dimensional sectional aerodynamics in free flight. This free-flight data can be obtained either from experiments or a high-fidelity computational fluid dynamics solver. The first step of this two-step model involves a constrained optimization procedure that modifies the vortex distribution on the camber line as obtained from a discrete vortex method to match the free-flight data from experiments/computational fluid dynamics. In the second step, the vortex distribution thus obtained is further modified to account for the presence of the ground plane within a discrete vortex method-based framework. Whereas the predictability of the lift appears as a natural extension, the drag predictability within a potential flow framework is achieved through the introduction of what are referred to as drag panels. The need for the use of the generalized Kutta-Joukowski theorem is emphasized. The extension of the model to three dimensions is by the way of using the numerical lifting-line theory that allows for wing sweep. The model is extensively validated for both two-dimensional and three-dimensional ground-effect studies. The work also demonstrates the ability of the model to predict lift and drag coefficients of a high-lift wing in ground effect to about 2 and 8% accuracy, respectively, as compared to the results obtained using a Reynolds-averaged Navier-Stokes solver involving grids with several million volumes. The model shows a lot of promise in design, particularly during the early phase.

Optimal power allocation for protective jamming in wireless networks: A flow based model

We address the problem of passive eavesdroppers in multi-hop wireless networks using the technique of friendly jamming. The network is assumed to employ Decode and Forward (DF) relaying. Assuming the availability of perfect channel state information (CSI) of legitimate nodes and eavesdroppers, we consider a scheduling and power allocation (PA) problem for a multiple-source multiple-sink scenario so that eavesdroppers are jammed, and source-destination throughput targets are met while minimizing the overall transmitted power. We propose activation sets (AS-es) for scheduling, and formulate an optimization problem for PA. Several methods for finding AS-es are discussed and compared. We present an approximate linear program for the original nonlinear, non-convex PA optimization problem, and argue that under certain conditions, both the formulations produce identical results. In the absence of eavesdroppers' CSI, we utilize the notion of Vulnerability Region (VR), and formulate an optimization problem with the objective of minimizing the VR. Our results show that the proposed solution can achieve power-efficient operation while defeating eavesdroppers and achieving desired source-destination throughputs simultaneously. (C) 2015 Elsevier B.V. All rights reserved.

Performance evaluation of mobile agent based resource management protocol for MANETs

The nodes with dynamicity, and management without administrator are key features of mobile ad hoc networks (1VIANETs). Increasing resource requirements of nodes running different applications, scarcity of resources, and node mobility in MANETs are the important issues to be considered in allocation of resources. Moreover, management of limited resources for optimal allocation is a crucial task. In our proposed work we discuss a design of resource allocation protocol and its performance evaluation. The proposed protocol uses both static and mobile agents. The protocol does the distribution and parallelization of message propagation (mobile agent with information) in an efficient way to achieve scalability and speed up message delivery to the nodes in the sectors of the zones of a MANET. The protocol functionality has been simulated using Java Agent Development Environment (JADE) Framework for agent generation, migration and communication. A mobile agent migrates from central resource rich node with message and navigate autonomously in the zone of network until the boundary node. With the performance evaluation, it has been concluded that the proposed protocol consumes much less time to allocate the required resources to the nodes under requirement, utilize less network resources and increase the network scalability. (C) 2015 Elsevier B.V. All rights reserved.

A fuzzy logic based dynamic wave model inversion algorithm for canal regulation

A fuzzy logic based centralized control algorithm for irrigation canals is presented. Purpose of the algorithm is to control downstream discharge and water level of pools in the canal, by adjusting discharge release from the upstream end and gates settings. The algorithm is based on the dynamic wave model (Saint-Venant equations) inversion in space, wherein the momentum equation is replaced by a fuzzy rule based model, while retaining the continuity equation in its complete form. The fuzzy rule based model is developed on fuzzification of a new mathematical model for wave velocity, the derivational details of which are given. The advantages of the fuzzy control algorithm, over other conventional control algorithms, are described. It is transparent and intuitive, and no linearizations of the governing equations are involved. Timing of the algorithm and method of computation are explained. It is shown that the tuning is easy and the computations are straightforward. The algorithm provides stable, realistic and robust outputs. The disadvantage of the algorithm is reduced precision in its outputs due to the approximation inherent in the fuzzy logic. Feed back control logic is adopted to eliminate error caused by the system disturbances as well as error caused by the reduced precision in the outputs. The algorithm is tested by applying it to water level control problem in a fictitious canal with a single pool and also in a real canal with a series of pools. It is found that results obtained from the algorithm are comparable to those obtained from conventional control algorithms.

A fuzzy dynamic flood routing model for natural channels

A fuzzy dynamic flood routing model (FDFRM) for natural channels is presented, wherein the flood wave can be approximated to a monoclinal wave. This study is based on modification of an earlier published work by the same authors, where the nature of the wave was of gravity type. Momentum equation of the dynamic wave model is replaced by a fuzzy rule based model, while retaining the continuity equation in its complete form. Hence, the FDFRM gets rid of the assumptions associated with the momentum equation. Also, it overcomes the necessity of calculating friction slope (S-f) in flood routing and hence the associated uncertainties are eliminated. The fuzzy rule based model is developed on an equation for wave velocity, which is obtained in terms of discontinuities in the gradient of flow parameters. The channel reach is divided into a number of approximately uniform sub-reaches. Training set required for development of the fuzzy rule based model for each sub-reach is obtained from discharge-area relationship at its mean section. For highly heterogeneous sub-reaches, optimized fuzzy rule based models are obtained by means of a neuro-fuzzy algorithm. For demonstration, the FDFRM is applied to flood routing problems in a fictitious channel with single uniform reach, in a fictitious channel with two uniform sub-reaches and also in a natural channel with a number of approximately uniform sub-reaches. It is observed that in cases of the fictitious channels, the FDFRM outputs match well with those of an implicit numerical model (INM), which solves the dynamic wave equations using an implicit numerical scheme. For the natural channel, the FDFRM Outputs are comparable to those of the HEC-RAS model.

Structure-Based Design of DevR Inhibitor Active against Nonreplicating Mycobacterium tuberculosis

Antitubercular treatment is directed against actively replicating organisms. There is an urgent need to develop drugs targeting persistent subpopulations of Mycobacterium tuberculosis. The DevR response regulator is believed to play a key role in bacterial dormancy adaptation during hypoxia. We developed a homology-based model of DevR and used it for the rational design of inhibitors. A phenylcoumarin derivative (compound 10) identified by in silico pharmacophore-based screening of 2.5 million compounds employing protocols with some novel features including a water-based pharmacophore query, was characterized further. Compound 10 inhibited DevR binding to target DNA, down-regulated dormancy genes transcription, and drastically reduced survival of hypoxic but not nutrient-starved dormant bacteria or actively growing organ ` isms. Our findings suggest that compound 10 ``locks'' DevR in an inactive conformation that is unable to bind cognate DNA and induce the dormancy regulon. These results provide proof-of-concept for DevR as a novel target to develop molecules with sterilizing activity against tubercle bacilli.

Unified large and small signal non-quasi-static model for long channel symmetric DG MOSFET

We propose a unified model for large signal and small signal non-quasi-static analysis of long channel symmetric double gate MOSFET. The model is physics based and relies only on the very basic approximation needed for a charge-based model. It is based on the EKV formalism Enz C, Vittoz EA. Charge based MOS transistor modeling. Wiley; 2006] and is valid in all regions of operation and thus suitable for RF circuit design. Proposed model is verified with professional numerical device simulator and excellent agreement is found. (C) 2010 Elsevier Ltd. All rights reserved.

Accelerated gradient based diffuse optical tomographic image reconstruction

Purpose: Fast reconstruction of interior optical parameter distribution using a new approach called Broyden-based model iterative image reconstruction (BMOBIIR) and adjoint Broyden-based MOBIIR (ABMOBIIR) of a tissue and a tissue mimicking phantom from boundary measurement data in diffuse optical tomography (DOT). Methods: DOT is a nonlinear and ill-posed inverse problem. Newton-based MOBIIR algorithm, which is generally used, requires repeated evaluation of the Jacobian which consumes bulk of the computation time for reconstruction. In this study, we propose a Broyden approach-based accelerated scheme for Jacobian computation and it is combined with conjugate gradient scheme (CGS) for fast reconstruction. The method makes explicit use of secant and adjoint information that can be obtained from forward solution of the diffusion equation. This approach reduces the computational time many fold by approximating the system Jacobian successively through low-rank updates. Results: Simulation studies have been carried out with single as well as multiple inhomogeneities. Algorithms are validated using an experimental study carried out on a pork tissue with fat acting as an inhomogeneity. The results obtained through the proposed BMOBIIR and ABMOBIIR approaches are compared with those of Newton-based MOBIIR algorithm. The mean squared error and execution time are used as metrics for comparing the results of reconstruction. Conclusions: We have shown through experimental and simulation studies that Broyden-based MOBIIR and adjoint Broyden-based methods are capable of reconstructing single as well as multiple inhomogeneities in tissue and a tissue-mimicking phantom. Broyden MOBIIR and adjoint Broyden MOBIIR methods are computationally simple and they result in much faster implementations because they avoid direct evaluation of Jacobian. The image reconstructions have been carried out with different initial values using Newton, Broyden, and adjoint Broyden approaches. These algorithms work well when the initial guess is close to the true solution. However, when initial guess is far away from true solution, Newton-based MOBIIR gives better reconstructed images. The proposed methods are found to be stable with noisy measurement data. (C) 2011 American Association of Physicists in Medicine. DOI: 10.1118/1.3531572]

Analysis of shear bands in slow granular flows using a frictional Cosserat model

The slow flow of granular materials is often marked by the existence of narrow shear layers, adjacent to large regions that suffer little or no deformation. This behaviour, in the regime where shear stress is generated primarily by the frictional interactions between grains, has so far eluded theoretical description. In this paper, we present a rigid-plastic frictional Cosserat model that captures thin shear layers by incorporating a microscopic length scale. We treat the granular medium as a Cosserat continuum, which allows the existence of localised couple stresses and, therefore, the possibility of an asymmetric stress tensor. In addition, the local rotation is an independent field variable and is not necessarily equal to the vorticity. The angular momentum balance, which is implicitly satisfied for a classical continuum, must now be solved in conjunction with the linear momentum balances. We extend the critical state model, used in soil plasticity, for a Cosserat continuum and obtain predictions for flow in plane and cylindrical Couette devices. The velocity profile predicted by our model is in qualitative agreement with available experimental data. In addition, our model can predict scaling laws for the shear layer thickness as a function of the Couette gap, which must be verified in future experiments. Most significantly, our model can determine the velocity field in viscometric flows, which classical plasticity-based model cannot.

Performance Modeling based on Multidimensional Surface Learning for Performance Predictions of Parallel Applications in Non-Dedicated Environments

Modeling the performance behavior of parallel applications to predict the execution times of the applications for larger problem sizes and number of processors has been an active area of research for several years. The existing curve fitting strategies for performance modeling utilize data from experiments that are conducted under uniform loading conditions. Hence the accuracy of these models degrade when the load conditions on the machines and network change. In this paper, we analyze a curve fitting model that attempts to predict execution times for any load conditions that may exist on the systems during application execution. Based on the experiments conducted with the model for a parallel eigenvalue problem, we propose a multi-dimensional curve-fitting model based on rational polynomials for performance predictions of parallel applications in non-dedicated environments. We used the rational polynomial based model to predict execution times for 2 other parallel applications on systems with large load dynamics. In all the cases, the model gave good predictions of execution times with average percentage prediction errors of less than 20%

A finite population model of mlecular evolution: theory and computation

This article is concerned with the evolution of haploid organisms that reproduce asexually. In a seminal piece of work, Eigen and coauthors proposed the quasispecies model in an attempt to understand such an evolutionary process. Their work has impacted antiviral treatment and vaccine design strategies. Yet, predictions of the quasispecies model are at best viewed as a guideline, primarily because it assumes an infinite population size, whereas realistic population sizes can be quite small. In this paper we consider a population genetics-based model aimed at understanding the evolution of such organisms with finite population sizes and present a rigorous study of the convergence and computational issues that arise therein. Our first result is structural and shows that, at any time during the evolution, as the population size tends to infinity, the distribution of genomes predicted by our model converges to that predicted by the quasispecies model. This justifies the continued use of the quasispecies model to derive guidelines for intervention. While the stationary state in the quasispecies model is readily obtained, due to the explosion of the state space in our model, exact computations are prohibitive. Our second set of results are computational in nature and address this issue. We derive conditions on the parameters of evolution under which our stochastic model mixes rapidly. Further, for a class of widely used fitness landscapes we give a fast deterministic algorithm which computes the stationary distribution of our model. These computational tools are expected to serve as a framework for the modeling of strategies for the deployment of mutagenic drugs.

A model of coupled thermal, mechanical, and electrostatic field effects in III-N thin film heterostructures

A one-dimensional coupled multi-physics based model has been developed to accurately compute the effects of electrostatic, mechanical, and thermal field interactions on the electronic energy band structure in group III-nitrides thin film heterostructures. Earlier models reported in published literature assumes electro-mechanical field with uniform temperature thus neglecting self-heating. Also, the effects of diffused interface on the energy band structure were not studied. We include these effects in a self-consistent manner wherein the transport equation is introduced along with the electro-mechanical models, and the lattice structural variation as observed in experiments are introduced at the interface. Due to these effects, the electrostatic potential distribution in the heterostructure is altered. The electron and hole ground state energies decrease by 5% and 9%, respectively, at a relative temperature of 700 K, when compared with the results obtained from the previously reported electro-mechanical model assuming constant and uniform temperature distribution. A diffused interface decreases the ground state energy of electrons and holes by about 11% and 9%, respectively, at a relative temperature of 700 K when compared with the predictions based on uniform temperature based electro-mechanical model. (C) 2013 AIP Publishing LLC.

Computational framework to integrate the effect of antigen recognition on disease epidemiology outcome: multi-scale approach

Human Leukocyte Antigen (HLA) plays an important role, in presenting foreign pathogens to our immune system, there by eliciting early immune responses. HLA genes are highly polymorphic, giving rise to diverse antigen presentation capability. An important factor contributing to enormous variations in individual responses to diseases is differences in their HLA profiles. The heterogeneity in allele specific disease responses decides the overall disease epidemiological outcome. Here we propose an agent based computational framework, capable of incorporating allele specific information, to analyze disease epidemiology. This framework assumes a SIR model to estimate average disease transmission and recovery rate. Using epitope prediction tool, it performs sequence based epitope detection for a given the pathogenic genome and derives an allele specific disease susceptibility index depending on the epitope detection efficiency. The allele specific disease transmission rate, that follows, is then fed to the agent based epidemiology model, to analyze the disease outcome. The methodology presented here has a potential use in understanding how a disease spreads and effective measures to control the disease.

High-Performance Sensors Based on Resistance Fluctuations of Single-Layer-Graphene Transistors

One of the most interesting predicted applications of graphenemonolayer-based devices is as high-quality sensors. In this article, we show, through systematic experiments, a chemical vapor sensor based on the measurement of lowfrequency resistance fluctuations of single-layer-graphene field-effect-transistor devices. The sensor has extremely high sensitivity, very high specificity, high fidelity, and fast response times. The performance of the device using this scheme of measurement (which uses resistance fluctuations as the detection parameter) is more than 2 orders of magnitude better than a detection scheme in which changes in the average value of the resistance is monitored. We propose a number-densityfluctuation-based model to explain the superior characteristics of a noisemeasurement-based detection scheme presented in this article.

Ultrasonic Guided Wave Based Damage Detection Using Low-dimensional Data from Laser Doppler Scan

In the current state of the art, it remains an open problem to detect damage with partial ultrasonic scan data and with measurements at coarser spatial scale when the location of damage is not known. In the present paper, a recent development of finite element based model reduction scheme in frequency domain that employs master degrees of freedom covering the surface scan region of interests is reported in context of non-contact ultrasonic guided wave based inspection. The surface scan region of interest is grouped into master and slave degrees of freedom. A finite element wise damage factor is derived which represents damage state over distributed areas or sharp condition of inter-element boundaries (for crack). Laser Doppler Vibrometer (LDV) scan data obtained from plate type structure with inaccessible surface line crack are considered along with the developed reduced order damage model to analyze the extent of scan data dimensional reduction. The proposed technique has useful application in problems where non-contact monitoring of complex structural parts are extremely important and at the same time LDV scan has to be done on accessible surfaces only.