The mean and turbulence structure simulation of the monsoon trough boundary layer using a one-dimensional model with e-l and e-epsilon closures

An attempt has been made here to study the sensitivity of the mean and the turbulence structure of the monsoon trough boundary layer to the choice of the constants in the dissipation equation for two stations Delhi and Calcutta, using one-dimensional atmospheric boundary layer model with e-epsilon turbulence closure. An analytical discussion of the problems associated with the constants of the dissipation equation is presented. It is shown here that the choice of the constants in the dissipation equation is quite crucial and the turbulence structure is very sensitive to these constants. The modification of the dissipation equation adopted by earlier studies, that is, approximating the Tke generation (due to shear and buoyancy production) in the epsilon-equation by max (shear production, shear + buoyancy production), can be avoided by a suitable choice of the constants suggested here. The observed turbulence structure is better simulated with these constants. The turbulence structure simulation with the constants recommended by Aupoix et al (1989) (which are interactive in time) for the monsoon region is shown to be qualitatively similar to the simulation obtained with the constants suggested here, thus implying that no universal constants exist to regulate dissipation rate. Simulations of the mean structure show little sensitivity to the type of the closure parameterization between e-l and e-epsilon closures. However the turbulence structure simulation with e-epsilon closure is far better compared to the e-l model simulations. The model simulations of temperature profiles compare quite well with the observations whenever the boundary layer is well mixed (neutral) or unstable. However the models are not able to simulate the nocturnal boundary layer (stable) temperature profiles. Moisture profiles are simulated reasonably better. With one-dimensional models, capturing observed wind variations is not up to the mark.

Stability of stochastic Leipholz column with stochastic loading

The Leipholz column which is having the Young modulus and mass per unit length as stochastic processes and also the distributed tangential follower load behaving stochastically is considered. The non self-adjoint differential equation and boundary conditions are considered to have random field coefficients. The standard perturbation method is employed. The non self-adjoint operators are used within the regularity domain. Full covariance structure of the free vibration eigenvalues and critical loads is derived in terms of second order properties of input random fields characterizing the system parameter fluctuations. The mean value of critical load is calculated using the averaged problem and the corresponding eigenvalue statistics are sought. Through the frequency equation a transformation is done to yield load parameter statistics. A numerical study incorporating commonly observed correlation models is reported which illustrates the full potentials of the derived expressions.

A New Class of Exactly Solvable Interacting Fermion Models in One Dimension

We investigate a model containing two species of one-dimensional fermions interacting via a gauge field determined by the positions of all particles of the opposite species. The model can be salved exactly via a simple unitary transformation. Nevertheless, correlation functions exhibit nontrivial interaction-dependent exponents. A similar model defined on a lattice is introduced and solved. Various generalizations, e.g., to the case of internal symmetries of the fermions, are discussed. The present treatment also clarifies certain aspects of Luttinger's original solution of the "Luttinger model."

Relative performances of textural models in estimating soil moisture characteristic

The soil moisture characteristic (SMC) forms an important input to mathematical models of water and solute transport in the unsaturated-soil zone. Owing to their simplicity and ease of use, texture-based regression models are commonly used to estimate the SMC from basic soil properties. In this study, the performances of six such regression models were evaluated on three soils. Moisture characteristics generated by the regression models were statistically compared with the characteristics developed independently from laboratory and in-situ retention data of the soil profiles. Results of the statistical performance evaluation, while providing useful information on the errors involved in estimating the SMC, also highlighted the importance of the nature of the data set underlying the regression models. Among the models evaluated, the one possessing an underlying data set of in-situ measurements was found to be the best estimator of the in-situ SMC for all the soils. Considerable errors arose when a textural model based on laboratory data was used to estimate the field retention characteristics of unsaturated soils.

Assessment of conditional moment closure models of turbulent autoignition using DNS data

A systematic assessment of the submodels of conditional moment closure (CMC) formalism for the autoignition problem is carried out using direct numerical simulation (DNS) data. An initially non-premixed, n-heptane/air system, subjected to a three-dimensional, homogeneous, isotropic, and decaying turbulence, is considered. Two kinetic schemes, (1) a one-step and (2) a reduced four-step reaction mechanism, are considered for chemistry An alternative formulation is developed for closure of the mean chemical source term , based on the condition that the instantaneous fluctuation of excess temperature is small. With this model, it is shown that the CMC equations describe the autoignition process all the way up to near the equilibrium limit. The effect of second-order terms (namely, conditional variance of temperature excess sigma(2) and conditional correlations of species q(ij)) in modeling is examined. Comparison with DNS data shows that sigma(2) has little effect on the predicted conditional mean temperature evolution, if the average conditional scalar dissipation rate is properly modeled. Using DNS data, a correction factor is introduced in the modeling of nonlinear terms to include the effect of species fluctuations. Computations including such a correction factor show that the species conditional correlations q(ij) have little effect on model predictions with a one-step reaction, but those q(ij) involving intermediate species are found to be crucial when four-step reduced kinetics is considered. The "most reactive mixture fraction" is found to vary with time when a four-step kinetics is considered. First-order CMC results are found to be qualitatively wrong if the conditional mean scalar dissipation rate is not modeled properly. The autoignition delay time predicted by the CMC model compares excellently with DNS results and shows a trend similar to experimental data over a range of initial temperatures.

Experimental validation of analytical performance models for IEEE 802.11 networks

We consider the simplest IEEE 802.11 WLAN networks for which analytical models are available and seek to provide an experimental validation of these models. Our experiments include the following cases: (i) two nodes with saturated queues, sending fixed-length UDP packets to each other, and (ii) a TCP-controlled transfer between two nodes. Our experiments are based entirely on Aruba AP-70 access points operating under Linux. We report our observations on certain non-standard behavior of the devices. In cases where the devices adhere to the standards, we find that the results from the analytical models estimate the experimental data with a mean error of 3-5%.

The orientational relaxation of bipolar active regions

In the mean, bipolar active regions are oriented nearly toroidally, according to Hale's polarity law, with a latitude-dependent tilt known as Joy's Law. The tilt angles of individual active regions deviate from this mean behavior and change over time. It has been found that on average the change is toward the mean angle at a rate characteristic of 4.37 days (Howard, 1996). We show that this orientational relaxation is consistent with the standard model of flux tube emergence from a deep dynamo layer. Under this scenario Joy's law results from the Coriolis effect on the rising flux tube (D'Silva and Choudhuri, 1993), and departures from it result from turbulent buffeting of the tubes (Longcope and Fisher, 1996). We show that relaxation toward Joy's angle occurs because the turbulent perturbations relax on shorter time scales than the perturbations from the Coriolis force. The turbulent perturbations relax more rapidly because they are localized to the topmost portion of the convection zone while the Coriolis perturbations are more widely distributed. If a fully-developed active region remains connected to the strong toroidal magnetic field at the base of the convection zone, its tilt will eventually disappear, leaving it aligned perfectly toroidally. On the other hand, if the flux becomes disconnected from the toroidal field the bipole will assume a tilt indicative of the location of disconnection. We compare models which are connected and disconnected from the toroidal field. Only those disconnected at points very deep in the convection zone a-re consistent with observed time scale of orientational relaxation.

Voltage and Temperature Scalable Standard Cell Leakage Models Based on Stacks for Statistical Leakage Characterization

With extensive use of dynamic voltage scaling (DVS) there is increasing need for voltage scalable models. Similarly, leakage being very sensitive to temperature motivates the need for a temperature scalable model as well. We characterize standard cell libraries for statistical leakage analysis based on models for transistor stacks. Modeling stacks has the advantage of using a single model across many gates there by reducing the number of models that need to be characterized. Our experiments on 15 different gates show that we needed only 23 models to predict the leakage across 126 input vector combinations. We investigate the use of neural networks for the combined PVT model, for the stacks, which can capture the effect of inter die, intra gate variations, supply voltage(0.6-1.2 V) and temperature (0 - 100degC) on leakage. Results show that neural network based stack models can predict the PDF of leakage current across supply voltage and temperature accurately with the average error in mean being less than 2% and that in standard deviation being less than 5% across a range of voltage, temperature.

Voltage and Temperature Scalable Gate Delay and Slew Models Including Intra-Gate Variations

We investigate the feasibility of developing a comprehensive gate delay and slew models which incorporates output load, input edge slew, supply voltage, temperature, global process variations and local process variations all in the same model. We find that the standard polynomial models cannot handle such a large heterogeneous set of input variables. We instead use neural networks, which are well known for their ability to approximate any arbitrary continuous function. Our initial experiments with a small subset of standard cell gates of an industrial 65 nm library show promising results with error in mean less than 1%, error in standard deviation less than 3% and maximum error less than 11% as compared to SPICE for models covering 0.9- 1.1 V of supply, -40degC to 125degC of temperature, load, slew and global and local process parameters. Enhancing the conventional libraries to be voltage and temperature scalable with similar accuracy requires on an average 4x more SPICE characterization runs.

Voltage and Temperature Scalable Gate Delay and Slew Models Including Intra-Gate Variations

We investigate the feasibility of developing a comprehensive gate delay and slew models which incorporates output load, input edge slew, supply voltage, temperature, global process variations and local process variations all in the same model. We find that the standard polynomial models cannot handle such a large heterogeneous set of input variables. We instead use neural networks, which are well known for their ability to approximate any arbitrary continuous function. Our initial experiments with a small subset of standard cell gates of an industrial 65 nm library show promising results with error in mean less than 1%, error in standard deviation less than 3% and maximum error less than 11% as compared to SPICE for models covering 0.9- 1.1 V of supply, -40degC to 125degC of temperature, load, slew and global and local process parameters. Enhancing the conventional libraries to be voltage and temperature scalable with similar accuracy requires on an average 4x more SPICE characterization runs.

A comparative study of ordinary kriging and support vector machine models for the spatial variability of rock depth in Bangalore

In this paper, reduced level of rock at Bangalore, India is arrived from the 652 boreholes data in the area covering 220 sq.km. In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth, ordinary kriging and Support Vector Machine (SVM) models have been developed. In ordinary kriging, the knowledge of the semivariogram of the reduced level of rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of Bangalore, where field measurements are not available. A cross validation (Q1 and Q2) analysis is also done for the developed ordinary kriging model. The SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing e-insensitive loss function has been used to predict the reduced level of rock from a large set of data. A comparison between ordinary kriging and SVM model demonstrates that the SVM is superior to ordinary kriging in predicting rock depth.

Field emission properties of carbon nanotube arrays with defects and impurities

Field emission from carbon nanotubes (CNTs) in the form of arrays or thin films give rise to several strongly correlated process of electromechanical interaction and degradation. Such processes are mainly due to (1) electron-phonon interaction (2) electromechanical force field leading to stretching of CNTs (3) ballistic transport induced thermal spikes, coupled with high dynamic stress, leading to degradation of emission performance at the device scale. Fairly detailed physics based models of CNTs considering the aspects (1) and (2) above have already been developed by these authors, and numerical results indicate good agreement with experimental results. What is missing in such a system level modeling approach is the incorporation of structural defects and vacancies or charge impurities. This is a practical and important problem due to the fact that degradation of field emission performance is indeed observed in experimental I-V curves. What is not clear from these experiments is whether such degradation in the I-V response is due to dynamic reorientation of the CNTs or due to the defects or due to both of these effects combined. Non-equilibrium Green’s function based simulations using a tight-binding Hamiltonian for single CNT segment show up the localization of carrier density at various locations of the CNTs. About 11% decrease in the drive current with steady difference in the drain current in the range of 0.2-0.4V of the gate voltage was reported in literature when negative charge impurity was introduced at various locations of the CNT over a length of ~20nm. In the context of field emission from CNT tips, a simplistic estimate of defects have been introduced by a correction factor in the Fowler-Nordheim formulae. However, a more detailed physics based treatment is required, while at the same time the device-scale simulation is necessary. The novelty of our present approach is the following. We employ a concept of effective stiffness degradation for segments of CNTs, which is due to structural defects, and subsequently, we incorporate the vacancy defects and charge impurity effects in the Green’s function based approach. Field emission induced current-voltage characteristics of a vertically aligned CNT array on a Cu-Cr substrate is then simulated using a detailed nonlinear mechanistic model of CNTs coupled with quantum hydrodynamics. An array of 10 vertically aligned and each 12 m long CNTs is considered for the device scale analysis. Defect regions are introduced randomly over the CNT length. The result shows the decrease in the longitudinal strain due to defects. Contrary to the expected influence of purely mechanical degradation, this result indicates that the charge impurity and hence weaker transport can lead to a different electromechanical force field, which ultimately can reduce the strain. However, there could be significant fluctuation in such strain field due to electron-phonon coupling. The effect of such fluctuations (with defects) is clearly evident in the field emission current history. The average current also decreases significantly due to such defects.

Inverse models to analyze the spatiotemporal variations of chemical weathering fluxes in a granito-gneissic watershed: Mule Hole, South India

Water-rock reactions are driven by the influx of water, which are out of equilibrium with the mineral assemblage in the rock. Here a mass balance approach is adopted to quantify these reactions. Based on field experiments carried out in a granito-gneissic small experimental watershed (SEW), Mule Hole SEW (similar to 4.5 km(2)), quartz, oligoclase, sericite, epidote and chlorite are identified as the basic primary minerals while kaolinite, goethite and smectite are identified as the secondary minerals. Observed groundwater chemistry is used to determine the weathering rates, in terms of `Mass Transfer Coefficients' (MTCs), of both primary and secondary minerals. Weathering rates for primary and secondary minerals are quantified in two steps. In the first step, top red soil is analyzed considering precipitation chemistry as initial phase and water chemistry of seepage flow as final phase. In the second step, minerals present in the saprolite layer are analyzed considering groundwater chemistry as the output phase. Weathering rates thus obtained are converted into weathering fluxes (Q(weathering)) using the recharge quantity. Spatial variability in the mineralogy observed among the thirteen wells of Mule Hole SEW is observed to be reflected in the MTC results and thus in the weathering fluxes. Weathering rates of the minerals in this silicate system varied from few 10 mu mol/L (in case of biotite) to 1000 s of micromoles per liter (calcite). Similarly, fluxes of biotite are observed to be least (7 +/- 5 mol/ha/yr) while those of calcite are highest (1265 791 mol/ha/yr). Further, the fluxes determined annually for all the minerals are observed to be within the bandwidth of the standard deviation of these fluxes. Variations in these annual fluxes are indicating the variations in the precipitation. Hence, the standard deviation indicated the temporal variations in the fluxes, which might be due to the variations in the annual rainfall. Thus, the methodology adopted defines an inverse way of determining weathering fluxes, which mainly contribute to the groundwater concentration. (C) 2011 Elsevier B.V. All rights reserved.

Low-Complexity Detection in Large-Dimension MIMO-ISI Channels Using Graphical Models

In this paper, we deal with low-complexity near-optimal detection/equalization in large-dimension multiple-input multiple-output inter-symbol interference (MIMO-ISI) channels using message passing on graphical models. A key contribution in the paper is the demonstration that near-optimal performance in MIMO-ISI channels with large dimensions can be achieved at low complexities through simple yet effective simplifications/approximations, although the graphical models that represent MIMO-ISI channels are fully/densely connected (loopy graphs). These include 1) use of Markov random field (MRF)-based graphical model with pairwise interaction, in conjunction with message damping, and 2) use of factor graph (FG)-based graphical model with Gaussian approximation of interference (GAI). The per-symbol complexities are O(K(2)n(t)(2)) and O(Kn(t)) for the MRF and the FG with GAI approaches, respectively, where K and n(t) denote the number of channel uses per frame, and number of transmit antennas, respectively. These low-complexities are quite attractive for large dimensions, i.e., for large Kn(t). From a performance perspective, these algorithms are even more interesting in large-dimensions since they achieve increasingly closer to optimum detection performance for increasing Kn(t). Also, we show that these message passing algorithms can be used in an iterative manner with local neighborhood search algorithms to improve the reliability/performance of M-QAM symbol detection.

Queuing Network Models for Aircraft Availability and Spares Management

Interactions of major activities involved in airfleet operations, maintenance, and logistics are investigated in the framework of closed queuing networks with finite number of customers. The system is viewed at three levels, namely: operations at the flying-base, maintenance at the repair-depot, and logistics for subsystems and their interactions in achieving the system objectives. Several performance measures (eg, availability of aircraft at the flying-base, mean number of aircraft on ground at different stages of repair, use of repair facilities, and mean time an aircraft spends in various stages of repair) can easily be computed in this framework. At the subsystem level the quantities of interest are the unavailability (probability of stockout) of a spare and the duration of its unavailability. The repair-depot capability is affected by the unavailability of a spare which in turn, adversely affects the availability of aircraft at the flying-base level. Examples illustrate the utility of the proposed models.