Morphing fixed wing MAV modeling using VAM

The design and implementation of a morphing Micro Air Vehicle (MAV) wing using a smart composite is attempted in this research work. Control surfaces actuated by traditional servos are difficult to instrument and fabricate on thin composite-wings of MAVs. Piezoelectric Fiber Reinforced Composites (PFRCs) are the chosen smart structural materials in the current work for incorporation onto fixed-wing MAVs to simultaneously perform the dual functions of structural load-bearing and actuation of flexure, torsion and/or extension for morphing. Further, PFRC use can be extended towards shape control of a “fixed” wing MAV to meet changing performance requirements. Wings that can warp into desired shapes and/or have variable camber are well-known to exhibit improved efficiency in aerodynamic control. During an entire flight cycle, there are multiple optimal configurations, each of which suits a particular phase of the flight regime. Widely proposed methods of wing morphing include changes in camber, twist, sweep and span. However, camber change during flight is already established, in terms of its potential, as a major factor in improving the aerofoil efficiency and flow separation behavior. Hence, for this work, morphing with camber change is adopted with the goal to better tailor aerodynamic properties.

Evaluation of the accuracy of an impact load cell using lumped parameter modeling and analysis

The objective of the current study is to evaluate the fidelity of load cell reading during impact testing in a drop-weight impactor using lumped parameter modeling. For the most common configuration of a moving impactor-load cell system in which dynamic load is transferred from the impactor head to the load cell, a quantitative assessment is made of the possible discrepancy that can result in load cell response. A 3-DOF (degrees-of-freedom) LPM (lumped parameter model) is considered to represent a given impact testing set-up. In this model, a test specimen in the form of a steel hat section similar to front rails of cars is represented by a nonlinear spring while the load cell is assumed to behave in a linear manner due to its high stiffness. Assuming a given load-displacement response obtained in an actual test as the true behavior of the specimen, the numerical solution of the governing differential equations following an implicit time integration scheme is shown to yield an excellent reproduction of the mechanical behavior of the specimen thereby confirming the accuracy of the numerical approach. The spring representing the load cell, however,predicts a response that qualitatively matches the assumed load-displacement response of the test specimen with a perceptibly lower magnitude of load.

An enthalpy method for modeling eutectic solidification

This paper presents a new micro-scale model for solidification of eutectic alloys. The model is based on the enthalpy method and simulates the growth of adjacent alpha and beta phases from a melt of eutectic composition in a two-dimensional Eulerian framework. The evolution of the two phases is obtained from the solution of volume averaged energy and species transport equations which are formulated using the nodal enthalpy and concentration potential values. The three phases are tracked using the beta-phase fraction and the liquid fraction values in all the computational nodes. Solutal convection flow field in the domain is obtained from the solution of volume-averaged momentum and continuity equations. The governing equations are solved using a coupled explicit-implicit scheme. The model is qualitatively validated with Jackson-Hunt theory. Results show expected eutectic growth pattern and proper species transfer and diffusion field ahead of the interface. Capabilities of the model such as lamella width selection, division of lamella into thinner lamellae and the presence of solutal convection are successfully demonstrated. The present model can potentially be incorporated into the existing framework of enthalpy based micro-scale dendritic solidification models thus leading to an efficient generalized microstructure evolution model. (C) 2014 Elsevier Inc. All rights reserved.

EESM-Based Link Adaptation in Point-to-Point and Multi-Cell OFDM Systems: Modeling and Analysis

In contemporary wideband orthogonal frequency division multiplexing (OFDM) systems, such as Long Term Evolution (LTE) and WiMAX, different subcarriers over which a codeword is transmitted may experience different signal-to-noise-ratios (SNRs). Thus, adaptive modulation and coding (AMC) in these systems is driven by a vector of subcarrier SNRs experienced by the codeword, and is more involved. Exponential effective SNR mapping (EESM) simplifies the problem by mapping this vector into a single equivalent fiat-fading SNR. Analysis of AMC using EESM is challenging owing to its non-linear nature and its dependence on the modulation and coding scheme. We first propose a novel statistical model for the EESM, which is based on the Beta distribution. It is motivated by the central limit approximation for random variables with a finite support. It is simpler and as accurate as the more involved ad hoc models proposed earlier. Using it, we develop novel expressions for the throughput of a point-to-point OFDM link with multi-antenna diversity that uses EESM for AMC. We then analyze a general, multi-cell OFDM deployment with co-channel interference for various frequency-domain schedulers. Extensive results based on LTE and WiMAX are presented to verify the model and analysis, and gain new insights.

Modeling Time-Varying Aggregate Interference in Cognitive Radio Systems, and Application to Primary Exclusive Zone Design

Accurately characterizing the time-varying interference caused to the primary users is essential in ensuring a successful deployment of cognitive radios (CR). We show that the aggregate interference at the primary receiver (PU-Rx) from multiple, randomly located cognitive users (CUs) is well modeled as a shifted lognormal random process, which is more accurate than the lognormal and the Gaussian process models considered in the literature, even for a relatively dense deployment of CUs. It also compares favorably with the asymptotically exact stable and symmetric truncated stable distribution models, except at high CU densities. Our model accounts for the effect of imperfect spectrum sensing, which depends on path-loss, shadowing, and small-scale fading of the link from the primary transmitter to the CU; the interweave and underlay modes or CR operation, which determine the transmit powers of the CUs; and time-correlated shadowing and fading of the links from the CUs to the PU-Rx. It leads to expressions for the probability distribution function, level crossing rate, and average exceedance duration. The impact of cooperative spectrum sensing is also characterized. We validate the model by applying it to redesign the primary exclusive zone to account for the time-varying nature of interference.

Copula-Based Modeling of TMI Brightness Temperature With Rainfall Type

Overland rain retrieval using spaceborne microwave radiometer offers a myriad of complications as land presents itself as a radiometrically warm and highly variable background. Hence, land rainfall algorithms of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) have traditionally incorporated empirical relations of microwave brightness temperature (Tb) with rain rate, rather than relying on physically based radiative transfer modeling of rainfall (as implemented in the TMI ocean algorithm). In this paper, sensitivity analysis is conducted using the Spearman rank correlation coefficient as benchmark, to estimate the best combination of TMI low-frequency channels that are highly sensitive to the near surface rainfall rate from the TRMM Precipitation Radar (PR). Results indicate that the TMI channel combinations not only contain information about rainfall wherein liquid water drops are the dominant hydrometeors but also aid in surface noise reduction over a predominantly vegetative land surface background. Furthermore, the variations of rainfall signature in these channel combinations are not understood properly due to their inherent uncertainties and highly nonlinear relationship with rainfall. Copula theory is a powerful tool to characterize the dependence between complex hydrological variables as well as aid in uncertainty modeling by ensemble generation. Hence, this paper proposes a regional model using Archimedean copulas, to study the dependence of TMI channel combinations with respect to precipitation, over the land regions of Mahanadi basin, India, using version 7 orbital data from the passive and active sensors on board TRMM, namely, TMI and PR. Studies conducted for different rainfall regimes over the study area show the suitability of Clayton and Gumbel copulas for modeling convective and stratiform rainfall types for the majority of the intraseasonal months. Furthermore, large ensembles of TMI Tb (from the most sensitive TMI channel combination) were generated conditional on various quantiles (25th, 50th, 75th, and 95th) of the convective and the stratiform rainfall. Comparatively greater ambiguity was observed to model extreme values of the convective rain type. Finally, the efficiency of the proposed model was tested by comparing the results with traditionally employed linear and quadratic models. Results reveal the superior performance of the proposed copula-based technique.

Cybernetic modeling of adaptive prediction of environmental changes by microorganisms

Microorganisms exhibit varied regulatory strategies such as direct regulation, symmetric anticipatory regulation, asymmetric anticipatory regulation, etc. Current mathematical modeling frameworks for the growth of microorganisms either do not incorporate regulation or assume that the microorganisms utilize the direct regulation strategy. In the present study, we extend the cybernetic modeling framework to account for asymmetric anticipatory regulation strategy. The extended model accurately captures various experimental observations. We use the developed model to explore the fitness advantage provided by the asymmetric anticipatory regulation strategy and observe that the optimal extent of asymmetric regulation depends on the selective pressure that the microorganisms experience. We also explore the importance of timing the response in anticipatory regulation and find that there is an optimal time, dependent on the extent of asymmetric regulation, at which microorganisms should respond anticipatorily to maximize their fitness. We then discuss the advantages offered by the cybernetic modeling framework over other modeling frameworks in modeling the asymmetric anticipatory regulation strategy. (C) 2013 Published by Elsevier Inc.

Early Warning Signals of Ecological Transitions: Methods for Spatial Patterns

A number of ecosystems can exhibit abrupt shifts between alternative stable states. Because of their important ecological and economic consequences, recent research has focused on devising early warning signals for anticipating such abrupt ecological transitions. In particular, theoretical studies show that changes in spatial characteristics of the system could provide early warnings of approaching transitions. However, the empirical validation of these indicators lag behind their theoretical developments. Here, we summarize a range of currently available spatial early warning signals, suggest potential null models to interpret their trends, and apply them to three simulated spatial data sets of systems undergoing an abrupt transition. In addition to providing a step-by-step methodology for applying these signals to spatial data sets, we propose a statistical toolbox that may be used to help detect approaching transitions in a wide range of spatial data. We hope that our methodology together with the computer codes will stimulate the application and testing of spatial early warning signals on real spatial data.

A Communication-Theoretic Framework for 2-DMR Channel Modeling: Performance Evaluation of Coding and Signal Processing Methods

We develop a communication theoretic framework for modeling 2-D magnetic recording channels. Using the model, we define the signal-to-noise ratio (SNR) for the channel considering several physical parameters, such as the channel bit density, code rate, bit aspect ratio, and noise parameters. We analyze the problem of optimizing the bit aspect ratio for maximizing SNR. The read channel architecture comprises a novel 2-D joint self-iterating equalizer and detection system with noise prediction capability. We evaluate the system performance based on our channel model through simulations. The coded performance with the 2-D equalizer detector indicates similar to 5.5 dB of SNR gain over uncoded data.

Modeling Equitable Distribution of Water: Dynamic Inversion-Based Controller Approach

It is a well-known fact that most of the developing countries have intermittent water supply and the quantity of water supplied from the source is also not distributed equitably among the consumers. Aged pipelines, pump failures, and improper management of water resources are some of the main reasons for it. This study presents the application of a nonlinear control technique to overcome this problem in different zones in the city of Bangalore. The water is pumped to the city from a large distance of approximately 100km over a very high elevation of approximately 400m. The city has large undulating terrain among different zones, which leads to unequal distribution of water. The Bangalore, inflow water-distribution system (WDS) has been modeled. A dynamic inversion (DI) nonlinear controller with proportional integral derivative (PID) features (DI-PID) is used for valve throttling to achieve the target flows to different zones of the city. This novel approach of equitable water distribution using DI-PID controllers that can be used as a decision support system is discussed in this paper.

Density Functional Theoretical Modeling, Electrostatic Surface Potential and Surface Enhanced Raman Spectroscopic Studies on Biosynthesized Silver Nanoparticles: Observation of 400 pM Sensitivity to Explosives

Interaction of adsorbate on charged surfaces, orientation of the analyte on the surface, and surface enhancement aspects have been studied. These aspects have been explored in details to explain the surface-enhanced Raman spectroscopic (SERS) spectra of 2,4,6,8,10,12-hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane (HNIW or CL-20), a well-known explosive, and 2,4,6-trinitrotoluene (TNT) using one-pot synthesis of silver nanoparticles via biosynthetic route using natural precursor extracts of clove and pepper. The biosynthesized silver nanoparticles (bio Ag Nps) have been characterized using UV-vis spectroscopy, scanning electron microscopy and atomic force microscopy. SERS studies conducted using bio Ag Nps on different water insoluble analytes, such as CL-20 and TNT, lead to SERS signals at concentration levels of 400 pM. The experimental findings have been corroborated with density functional computational results, electrostatic surface potential calculations, Fukui functions and potential measurements.

Mechanical and thermal modeling of In-Cu composites for thermal interface materials applications

In-Cu composite solders have been proposed as an effective thermal interface material. Here, finite element analysis and theoretical treatment of their mechanical and thermal behavior is presented. It was determined that the stresses and the strains were concentrated in the narrow and wider In channels, respectively. Furthermore, it is suggested that an In-Cu composite with disk-shaped Cu inclusions may not only further improve the thermal conductivity but may also reduce the stiffness of In-Cu composites in shear.

Modeling of Bubble-Size Distribution in Water and Freon Co-Blown Free Rise Polyurethane Foams

A model has been developed to simulate the foam characteristics obtained, when chemical (water) and physical (Freon) blowing agents are used together for the formation of polyurethane foams. The model considers the rate of reaction, the consequent rise in temperature of the reaction mixture, nucleation of bubbles, and mass transfer of CO2 and Freon to them till the time of gelation. The model is able to explain the experimental results available in literature. It further predicts that the nucleation period gets reduced with increase in water (at constant Freon content), whereas with increase in Freon (at constant water) concentration nucleation period decreases marginally leading to narrower bubble-size distribution. By the use of uniform sized nuclei added initially, the model predicts that the bubble-size distribution can be made independent of the rate of homogeneous nucleation and can, thus, offer an extra parameter for its control. (C) 2014 Wiley Periodicals, Inc.