Scaling of Fracture Strength in Disordered Quasi-Brittle Materials

This paper presents two main results. The first result indicates that in materials with broadly distributed microscopic heterogeneities, the fracture strength distribution corresponding to the peak load of the material response does not follow the commonly used Weibull and (modified) Gumbel distributions. Instead, a {\it lognormal} distribution describes more adequately the fracture strengths corresponding to the peak load of the response. Lognormal distribution arises naturally as a consequence of multiplicative nature of large number of random distributions representing the stress scale factors necessary to break the subsequent "primary" bond (by definition, an increase in applied stress is required to break a "primary" bond) leading up to the peak load. Numerical simulations based on two-dimensional triangular and diamond lattice topologies with increasing system sizes substantiate that a {\it lognormal} distribution represents an excellent fit for the fracture strength distribution at the peak load. The second significant result of the present study is that, in materials with broadly distributed microscopic heterogeneities, the mean fracture strength of the lattice system behaves as $\mu_f = \frac{\mu_f^\star}{(Log L)^\psi} + \frac{c}{L}$, and scales as $\mu_f \approx \frac{1}{(Log L)^\psi}$ as the lattice system size, $L$, approaches infinity.Comment: 24 pages including 11 figure

A Compact 1:4 Lossless T-Junction Power Divider Using Open Complementary Split Ring Resonator

This paper presents the size miniaturized and harmonic suppressed lossless 1:4 T-junction unequal power divider using an open complementary split ring resonator (OCSRR). By embedding the OCSRR structure in the microstrip transmission line, slow wave effect is introduced and thereby size reduction is achieved. The dimensions of OCSRR are optimized to reduce the length of high impedance and low impedance quarter-wavelength transmission lines. In our design high impedance line length is reduced to 58.6%, and low impedance line length is reduced to 12% when compared to the conventional quarter wavelength lines. The proposed power divider is having small dimensions of 0.18 λg × 0.33 λg and is 51.94% smaller than the conventional unequal power divider

The importance of wind turbulence and coherence to the loads on a wind turbine blade

The wind simulation method proposed by Paul S. Veers from Sandia National Laboratories has been checked for the measured wind velocities at 40m and 80m height at FINO1 platform. The Sandia method applied as it is, gives relatively higher standard deviation (turbulence) than the actual but works better when random phases are used. A code for the generation of wind field based on Sandia method has been developed from a time series of wind speed at hub center. Various wind fields based on difference in wind shear and turbulence intensity have been projected on to the reference wind turbine to find root bending moments of the blade. The bending moments are calculated based on 2D Beam Element Momentum theory. The mean bending moment values are affected by wind shear while the maximum bending moments by turbulent intensity. An increase in turbulence in the wind field will increase the maximum and standard deviation of flap-wise bending moment drastically.Master's Thesis in EnergyENERGI399MAMN-ENER

Energy efficient processor operation and vibration based energy harvesting schemes for wireless sensor nodes

A wireless Sensor Network (WSN) is a network of spatially distributed autonomous sensors deployed in the environment in order to cooperatively monitor physical or environmental conditions such as temperature, sound, pressure, motion or pollutants at different locations. Each node in a sensor network is equipped with a radio transceiver, a microprocessor and an energy source such as a battery which should be replaced periodically. To increase the lifetime of the network keeping the small size in mind, methods should be put in place to reduce the power consumption of the sensor node or increase the node life and/or to supply power to the battery from external sources. In this thesis, the first paper presents an energy-efficient frequency adaptation based approach to minimize the power consumption of the microprocessor in an attempt to increase the lifetime of the sensor node...The second paper, on the other hand, presents an energy harvesting circuitry to charge the battery of the sensor node so that the time to replacement can be extended --Abstract, page iv

Management in the culture and manufacture of tea in India.

Thesis (M.B.A.)--Boston Universit

Global stability of a commensal- host ecological model with limited resources and both are harvesting at a constant rate

In this paper we establish the global stability of a commensalism-host ecological model  surviving with limited resources and both are harvesting at a constant rate, by constructing a suitable Liapunov’s function in case of co-existent equilibrium state

Densification of selected agricultural crop residues as feedstock for the biofuel industry

The two main sources of biomass for energy generation are purpose-grown energy crops and waste materials. Energy crops, such as Miscanthus and short rotation woody crops (coppice), are cultivated mainly for energy purposes and are associated with the food vs. fuels debate, which is concerned with whether land should be used for fuel rather than food production. The use of residues from agriculture, such as barley, canola, oat and wheat straw, for energy generation circumvents the food vs. fuel dilemma and adds value to existing crops. In fact, these residues represent an abundant, inexpensive and readily available source of renewable lignocellulosic biomass. In order to reduce industry’s operational cost as well as to meet the requirement of raw material for biofuel production, biomass must be processed and handled in an efficient manner. Due to its high moisture content, irregular shape and size, and low bulk density, biomass is very difficult to handle, transport, store, and utilize in its original form. Densification of biomass into durable compacts is an effective solution to these problems and it can reduce material waste. Upon densification, many agricultural biomass materials, especially those from straw and stover, result in a poorly formed pellets or compacts that are more often dusty, difficult to handle and costly to manufacture. This is caused by lack of complete understanding on the natural binding characteristics of the components that make up biomass. An integrated approach to postharvest processing (chopping, grinding and steam explosion), and feasibility study on lab-scale and pilot scale densification of non-treated and steam exploded barley, canola, oat and wheat straw was successfully established to develop baseline data and correlations, that assisted in performing overall specific energy analysis. A new procedure was developed to rapidly characterize the lignocellulosic composition of agricultural biomass using the Fourier Transform Infrared (FTIR) spectroscopy. In addition, baseline knowledge was created to determine the physical and frictional properties of non-treated and steam exploded agricultural biomass grinds. Particle size reduction of agricultural biomass was performed to increase the total surface area, pore size of the material and the number of contact points for inter-particle bonding in the compaction process. Predictive regression equations having higher R2 values were developed that could be used by biorefineries to perform economic feasibility of establishing a processing plant. Specific energy required by a hammer mill to grind non-treated and steam exploded barley, canola, oat and wheat straw showed a negative power correlation with hammer mill screen sizes. Rapid and cost effective quantification of lignocellulosic components (cellulose, hemicelluloses and lignin) of agricultural biomass (barley, canola, oat and wheat) is essential to determine the effect of various pre-treatments (such as steam explosion) on biomass used as feedstock for the biofuel industry. A novel procedure to quantitatively predict lignocellulosic components of non-treated and steam exploded barley, canola, oat and wheat straw was developed using Fourier Transformed Infrared (FTIR) spectroscopy. Regression equations having R2 values of 0.89, 0.99 and 0.98 were developed to predict the cellulose, hemicelluloses and lignin compounds of biomass, respectively. The average absolute difference in predicted and measured cellulose, hemicellulose and lignin in agricultural biomass was 7.5%, 2.5%, and 3.8%, respectively. Application of steam explosion pre-treatment on agricultural straw significantly altered the physical and frictional properties, which has direct significance on designing new and modifying existing bins, hoppers and feeders for handling and storage of straw for biofuel industry. As a result, regression equations were developed to enhance process efficiency by eliminating the need for experimental procedure while designing and manufacturing of new handling equipment. Compaction of low bulk density agricultural biomass is a critical and desirable operation for sustainable and economic availability of feedstock for the biofuel industry. A comprehensive study of the compression characteristics (density of pellet and total specific energy required for compression) of ground non-treated and steam exploded barley, canola, oat and wheat straw obtained from three hammer mill screen sizes of 6.4, 3.2 and 1.6 mm at 10% moisture content (wb) was conducted. Four preset pressures of 31.6, 63.2, 94.7 and 138.9 MPa, were applied using an Instron testing machine to compress samples in a cylindrical die. It was determined that the applied pressure (60.4%) was the most significant factor affecting pellet density followed by the application of steam explosion pre-treatment (39.4%). Similarly, the type of biomass (47.1%) is the most significant factor affecting durability followed by the application of pre-treatment (38.2%) and grind size (14.6%). Also, the applied pressure (58.3%) was the most significant factor affecting specific energy required to manufacture pellets followed by the biomass (15.3%), pre-treatment (13.3%) and grind size (13.2%), which had lower but similar effect affect on specific energy. In addition, correlations for pellet density and specific energy with applied pressure and hammer mill screen sizes having highest R2 values were developed. Higher grind sizes and lower applied pressures resulted in higher relaxations (lower pellet densities) during storage of pellets. Three compression models, namely: Jones model, Cooper-Eaton model, and Kawakita-Ludde model were considered to determine the pressure-volume and pressure-density relationship of non-treated and steam exploded straws. Kawakita-Ludde model provided the best fit to the experimental data having R2 values of 0.99 for non-treated straw and 1.00 for steam exploded biomass samples. The steam exploded straw had higher porosity than non-treated straw. In addition, the steam exploded straw was easier to compress since it had lower yield strength or failure stress values compared to non-treated straw. Pilot scale pelleting experiments were performed on non-treated, steam exploded and customized (adding steam exploded straw grinds in increments of 25% to non-treated straw) barley, canola, oat and wheat straw grinds obtained from 6.4, 3.2, 1.6 and 0.8 mm hammer mill screen sizes at 10% moisture content (wb). The pilot scale pellet mill produced pellets from ground non-treated straw at hammer mill screen sizes of 0.8 and 1.6 mm and customized samples having 25% steam exploded straw at 0.8 mm. It was observed that the pellet bulk density and particle density are positively correlated. The density and durability of agricultural straw pellets significantly increased with a decrease in hammer mill screen size from 1.6 mm to 0.8 mm. Interestingly, customization of agricultural straw by adding 25% of steam exploded straw by weight resulted in higher durability (> 80%) pellets but did not improve durability compared to non-treated straw pellets. In addition, durability of pellets was negatively correlated to pellet mill throughput and was positively correlated to specific energy consumption. Total specific energy required to form pellets increased with a decrease in hammer mill screen size from 1.6 to 0.8 mm and also the total specific energy significantly increased with customization of straw at 0.8 mm screen size. It has been determined that the net specific energy available for production of biofuel is a significant portion of original agricultural biomass energy (89-94%) for all agricultural biomass

Engineering Plasmonic Nanostructures for Light Management and Sensing

The two major global problems are to provide health safety and to meet energy demands for ever growing population on a large scale. The study of light interaction with nanostructures has shown a promising solution in improving the fields of bio-sensor and solar energy devices which addresses above mentioned two major global problems. Nanostructures have tunable physicochemical properties such as light absorption, electrical and thermal properties unlike bulk materials, which gives an advantage in applications like bio-sensing and energy harvesting devices. The development of nanofabrication techniques along with the discovery of Surface Enhanced Raman Scattering (SERS) and Plasmon Enhanced Fluorescence (PEF), led to the development of Point of Care (POC) sensing devices. The fundamental understanding of light path in a nanostructured material led to the improvement in solar energy harvesting performance. For both of these applications, engineering nanostructures is the key to improving performance. In this work, different plasmonic nanostructures were designed, fabricated and analyzed for biosensor and light management applications. A new fabrication route, which combines nanosphere lithography with silicon-based clean-room microfabrication processes, has been developed to produce large-area long-range ordered gold nanoring array patterns in a controllable fashion. The developed nanoring structure has SERS enhancement of 2*109 and is used for miRNA detection. A novel pyramid array on gold film 3D plasmonic nanostructure is designed to convert plasmonic light scattering to confined light absorption. This structure generates a cavity mode by hybridization of fundamental modes, which creates a strong electric and magnetic field with a large mode volume. Due to its unique properties pyramids coupled film structure is used for both solar light management device and in Metal Enhanced Fluorescence (MEF). The fabricated structure is used to demonstrate plexiton (plasmon – exciton coupling) generation and is very effective in light trapping in the gap mode. In MEF, the sandwich nanostructure is used for Metal Organic Framework (MOF) fluorescence enhancement and the enhancement factor is around 5*102. With the plasmonic metal nanostructure optimization, the performance of a specific application is improved. However, the metals used for plasmonic applications are noble metals like gold and silver to support strong localized surface plasmon resonance (LSPR), which are expensive. Two-dimensional semiconductor materials have shown plasmon resonance in the visible region, having a lot of applications in sensing and photonics. Heavily doped semiconductors could replace expensive metals without compromising the performance. LSPR in metals is tuned by shape, size and refractive index of surroundings. This restricts plasmon resonance tuning over a narrow wavelength range and need to choose a different metal to exceed the rage of application. In contrast, LSPR in plasmonic semiconductors can be tuned with parameters like carrier density, annealing temperature and doping. This gives an advantage of tuning the plasmon peak over a broad range including visible, Near Infrared (NIR) and Infrared(IR) regions. This is because, for semiconductor materials, the carrier concentration can be varied over a large range. Herein, the molybdenum oxide thin films were directly deposited and nitrogen annealed which showed a tunable localized surface plasmon resonance (LSPR). A chip based 2D semiconductor material is fabricated to study the structural and size dependent plasmon resonance. This work establishes a way to fabricate chip based ordered semiconductor nanostructures, which helps in a systematic study of plasmon properties on nanostructures

Multiple-Target Tracking in Complex Scenarios

In this dissertation, we develop computationally efficient algorithms for multiple-target tracking: MTT) in complex scenarios. For each of these scenarios, we develop measurement and state-space models, and then exploit the structure in these models to propose efficient tracking algorithms. In addition, we address design issues such as sensor selection and resource allocation. First, we consider MTT when the targets themselves are moving in a time-varying multipath environment. We develop a sparse-measurement model that allows us to exploit the inherent joint delay-Doppler diversity offered by the environment. We then reformulate the problem of MTT as a block-support recovery problem using the sparse measurement model. We exploit the structure of the dictionary matrix to develop a computationally efficient block support recovery algorithm: and thereby a multiple-target tracking algorithm) under the assumption that the channel state describing the time-varying multipath environment is known. Further, we also derive an upper bound on the overall error probability of wrongly identifying the support of the sparse signal. We then relax the assumption that the channel state is known. We develop a new particle filter called the Multiple Rao-Blackwellized Particle Filter: MRBPF) to jointly estimate both the target and the channel states. We also compute the posterior Cramér-Rao bound: PCRB) on the estimates of the target and the channel states and use the PCRB to find a suitable subset of antennas to be used for transmission in each tracking interval, as well as the power transmitted by these antennas. Second, we consider the problem of tracking an unknown number and types of targets using a multi-modal sensor network. In a multi-modal sensor network, different quantities associated with the same state are measured using sensors of different kinds. Hence, an efficient method that can suitably combine the diverse information measured by each sensor is required. We first develop a Hierarchical Particle Filter: HPF) to estimate the unknown state from the multi-modal measurements for a special class of problems which can be modeled hierarchically. We then model our problem of tracking using a hierarchical model and then use the proposed HPF for joint initiation, termination and tracking of multiple targets. The multi-modal data consists of the measurements collected from a radar, an infrared camera and a human scout. We also propose a unified framework for multi-modal sensor management that comprises sensor selection: SS), resource allocation: RA) and data fusion: DF). Our approach is inspired by the trading behavior of economic agents in commercial markets. We model the sensors and the sensor manager as economic agents, and the interaction among them as a double sided market with both consumers and producers. We propose an iterative double auction mechanism for computing the equilibrium of such a market. We relate the equilibrium point to the solutions of SS, RA and DF. Third, we address MTT problem in the presence of data association ambiguity that arises due to clutter. Data association corresponds to the problem of assigning a measurement to each target. We treat the data association and state estimation as separate subproblems. We develop a game-theoretic framework to solve the data association, in which we model each tracker as a player and the set of measurements as strategies. We develop utility functions for each player, and then use a regret-based learning algorithm to find the correlated equilibrium of this game. The game-theoretic approach allows us to associate measurements to all the targets simultaneously. We then use particle filtering on the reduced dimensional state of each target, independently

Computational Thermal Fluid Dynamic Analysis of Cooling Systems for Fusion Reactor Components

In a fusion reactor during plasma operation, the heat loads on plasma facing components can be as high as 5 MW/m2 [1], which should be removed by a proper mechanism to prevent the damage of reactor components. In order to handle such high heat fluxes a suitable heat sink with proper thermal hydraulics is required. In the recent past several heat sinks have been proposed; among which the Hypervapotron heat sink, operating in the highly subcooled boiling regime, is considered as one of the potential candidates. In order to accurately predict the performance of the system, a thermal hydraulic analysis is required. This thesis employs a Computational Fluid Dynamic (CFD) approach to do the thermal hydraulic analysis of the subcooled flow boiling inside the Hypervapotron channel. For this purpose four boiling models are tested using two commercial CFD codes. The four boiling models tested are Rensselaer Polytechnic Institute (RPI) boiling model [2] available in ANSYS-FLUENT 13, Bergles-Rohsenow (BR) model [3] implemented as an external User Defined Function (UDF) in the FLUENT code, the Rohsenow boiling model [4] extended with the capability of transition to film boiling for high heat fluxes available in STAR-CCM+ 7.02, and finally Transition boiling model [4] available in STAR-CCM+ 7.02. These models are used to test the thermal performance of Hypervapotron using the experimental data (showing the variation of temperature with heat flux) obtained from the experiments conducted at Efremov Institute Russia and Joint European Torus United Kingdom. Simulations were conducted using the above mentioned boiling models, the obtained results were compared against the experimental data and also different boiling models are compared with each other whenever possible to test their applicability. From the simulations conducted on the Hypervapotron geometries it is found that the Transition boiling model can capture the thermal performance (in terms of tracing the experimental data) better than any other model both quantitatively and qualitatively, covering the different boiling regimes shown by the experiments ( that is no boiling, nucleate boiling and hard boiling regimes), than the other models