Nonlinear preconditioning for efficient and accurate interface capturing in simulation of multicomponent compressible flows

Single fluid schemes that rely on an interface function for phase identification in multicomponent compressible flows are widely used to study hydrodynamic flow phenomena in several diverse applications. Simulations based on standard numerical implementation of these schemes suffer from an artificial increase in the width of the interface function owing to the numerical dissipation introduced by an upwind discretization of the governing equations. In addition, monotonicity requirements which ensure that the sharp interface function remains bounded at all times necessitate use of low-order accurate discretization strategies. This results in a significant reduction in accuracy along with a loss of intricate flow features. In this paper we develop a nonlinear transformation based interface capturing method which achieves superior accuracy without compromising the simplicity, computational efficiency and robustness of the original flow solver. A nonlinear map from the signed distance function to the sigmoid type interface function is used to effectively couple a standard single fluid shock and interface capturing scheme with a high-order accurate constrained level set reinitialization method in a way that allows for oscillation-free transport of the sharp material interface. Imposition of a maximum principle, which ensures that the multidimensional preconditioned interface capturing method does not produce new maxima or minima even in the extreme events of interface merger or breakup, allows for an explicit determination of the interface thickness in terms of the grid spacing. A narrow band method is formulated in order to localize computations pertinent to the preconditioned interface capturing method. Numerical tests in one dimension reveal a significant improvement in accuracy and convergence; in stark contrast to the conventional scheme, the proposed method retains its accuracy and convergence characteristics in a shifted reference frame. Results from the test cases in two dimensions show that the nonlinear transformation based interface capturing method outperforms both the conventional method and an interface capturing method without nonlinear transformation in resolving intricate flow features such as sheet jetting in the shock-induced cavity collapse. The ability of the proposed method in accounting for the gravitational and surface tension forces besides compressibility is demonstrated through a model fully three-dimensional problem concerning droplet splash and formation of a crownlike feature. (C) 2014 Elsevier Inc. All rights reserved.

Diffusion on a rugged energy landscape with spatial correlations

Rugged energy landscapes find wide applications in diverse fields ranging from astrophysics to protein folding. We study the dependence of diffusion coefficient (D) of a Brownian particle on the distribution width (epsilon) of randomness in a Gaussian random landscape by simulations and theoretical analysis. We first show that the elegant expression of Zwanzig Proc. Natl. Acad. Sci. U.S.A. 85, 2029 (1988)] for D(epsilon) can be reproduced exactly by using the Rosenfeld diffusion-entropy scaling relation. Our simulations show that Zwanzig's expression overestimates D in an uncorrelated Gaussian random lattice - differing by almost an order of magnitude at moderately high ruggedness. The disparity originates from the presence of ``three-site traps'' (TST) on the landscape - which are formed by the presence of deep minima flanked by high barriers on either side. Using mean first passage time formalism, we derive a general expression for the effective diffusion coefficient in the presence of TST, that quantitatively reproduces the simulation results and which reduces to Zwanzig's form only in the limit of infinite spatial correlation. We construct a continuous Gaussian field with inherent correlation to establish the effect of spatial correlation on random walk. The presence of TSTs at large ruggedness (epsilon >> k(B)T) gives rise to an apparent breakdown of ergodicity of the type often encountered in glassy liquids. (C) 2014 AIP Publishing LLC.

Spectrum sensing using distributed sequential detection via noisy reporting MAC

This paper considers cooperative spectrum sensing algorithms for Cognitive Radios which focus on reducing the number of samples to make a reliable detection. We propose algorithms based on decentralized sequential hypothesis testing in which the Cognitive Radios sequentially collect the observations, make local decisions and send them to the fusion center for further processing to make a final decision on spectrum usage. The reporting channel between the Cognitive Radios and the fusion center is assumed more realistically as a Multiple Access Channel (MAC) with receiver noise. Furthermore the communication for reporting is limited, thereby reducing the communication cost. We start with an algorithm where the fusion center uses an SPRT-like (Sequential Probability Ratio Test) procedure and theoretically analyze its performance. Asymptotically, its performance is close to the optimal centralized test without fusion center noise. We further modify this algorithm to improve its performance at practical operating points. Later we generalize these algorithms to handle uncertainties in SNR and fading. (C) 2014 Elsevier B.V. All rights reserved.

Nature of the effective interaction between dendrimers

We have performed fully atomistic classical molecular dynamics simulations to calculate the effective interaction between two polyamidoamine dendrimers. Using the umbrella sampling technique, we have obtained the potential of mean force (PMF) between the dendrimers and investigated the effects of protonation level and dendrimer size on the PMF. Our results show that the interaction between the dendrimers can be tuned from purely repulsive to partly attractive by changing the protonation level. The PMF profiles are well-fitted by the sum of an exponential and a Gaussian function with the weight of the exponential function dominating over that of the Gaussian function. This observation is in disagreement with the results obtained in previous analytic C. Likos, M. Schmidt, H. Lowen, M. Ballauff, D. Potschke, and P. Lindner, Macromolecules 34, 2914 (2001)] and coarse-grained simulation I. Gotze, H. Harreis, and C. Likos, J. Chem. Phys. 120, 7761 (2004)] studies which predicted the effective interaction to be Gaussian. (C) 2014 AIP Publishing LLC.

Newton-based stochastic optimization using q-Gaussian smoothed functional algorithms

We present the first q-Gaussian smoothed functional (SF) estimator of the Hessian and the first Newton-based stochastic optimization algorithm that estimates both the Hessian and the gradient of the objective function using q-Gaussian perturbations. Our algorithm requires only two system simulations (regardless of the parameter dimension) and estimates both the gradient and the Hessian at each update epoch using these. We also present a proof of convergence of the proposed algorithm. In a related recent work (Ghoshdastidar, Dukkipati, & Bhatnagar, 2014), we presented gradient SF algorithms based on the q-Gaussian perturbations. Our work extends prior work on SF algorithms by generalizing the class of perturbation distributions as most distributions reported in the literature for which SF algorithms are known to work turn out to be special cases of the q-Gaussian distribution. Besides studying the convergence properties of our algorithm analytically, we also show the results of numerical simulations on a model of a queuing network, that illustrate the significance of the proposed method. In particular, we observe that our algorithm performs better in most cases, over a wide range of q-values, in comparison to Newton SF algorithms with the Gaussian and Cauchy perturbations, as well as the gradient q-Gaussian SF algorithms. (C) 2014 Elsevier Ltd. All rights reserved.

Investigation of selective sensing of a diamine for aldehyde by experimental and simulation studies

An organic molecule-o-phenylene diamine (OPD)-is selected as an aldehyde sensing material. It is studied for selectivity to aldehyde vapours both by experiment and simulation. A chemiresistor based sensor for detection of aldehyde vapours is fabricated. An o-phenylene diamine-carbon black composite is used as the sensing element. The amine groups in the OPD would interact with the carbonyl groups of the aldehydes. The selectivity and cross-sensitivity of the OPD-CB sensor to VOCs aldehyde, ketone and alcohol-are studied. The sensor shows good response to aldehydes compared to other VOCs. The higher response for aldehydes is attributed to the interaction of the carbonyl oxygen of aldehydes with-NH2 groups of OPD. The surface morphology of the sensing element is studied by scanning electron microscopy. The OPD-CB sensor is responsive to 10 ppm of formaldehyde. The interaction of the VOCs with the OPD-CB nanocomposite is investigated by molecular dynamics studies. The interaction energies of the analyte with the OPD-CB nanocomposite were calculated. It is observed that the interaction energies for aldehydes are higher than those for other analytes. Thus the OPD-CB sensor shows selectivity to aldehydes. The simulated radial distribution function is calculated for the O-H pair of analyte and OPD which further supports the finding that the amine groups are involved in the interaction. These results suggest that it is important and easy to identify appropriate sensing materials based on the understanding of analyte interaction properties.

MULTI-PITCH TRACKING USING GAUSSIAN MIXTURE MODEL WITH TIME VARYING PARAMETERS AND GRATING COMPRESSION TRANSFORM

Grating Compression Transform (GCT) is a two-dimensional analysis of speech signal which has been shown to be effective in multi-pitch tracking in speech mixtures. Multi-pitch tracking methods using GCT apply Kalman filter framework to obtain pitch tracks which requires training of the filter parameters using true pitch tracks. We propose an unsupervised method for obtaining multiple pitch tracks. In the proposed method, multiple pitch tracks are modeled using time-varying means of a Gaussian mixture model (GMM), referred to as TVGMM. The TVGMM parameters are estimated using multiple pitch values at each frame in a given utterance obtained from different patches of the spectrogram using GCT. We evaluate the performance of the proposed method on all voiced speech mixtures as well as random speech mixtures having well separated and close pitch tracks. TVGMM achieves multi-pitch tracking with 51% and 53% multi-pitch estimates having error <= 20% for random mixtures and all-voiced mixtures respectively. TVGMM also results in lower root mean squared error in pitch track estimation compared to that by Kalman filtering.

Use of Exocentric and Egocentric Representations in the Concurrent Planning of Sequential Saccades

The concurrent planning of sequential saccades offers a simple model to study the nature of visuomotor transformations since the second saccade vector needs to be remapped to foveate the second target following the first saccade. Remapping is thought to occur through egocentric mechanisms involving an efference copy of the first saccade that is available around the time of its onset. In contrast, an exocentric representation of the second target relative to the first target, if available, can be used to directly code the second saccade vector. While human volunteers performed a modified double-step task, we examined the role of exocentric encoding in concurrent saccade planning by shifting the first target location well before the efference copy could be used by the oculomotor system. The impact of the first target shift on concurrent processing was tested by examining the end-points of second saccades following a shift of the second target during the first saccade. The frequency of second saccades to the old versus new location of the second target, as well as the propagation of first saccade localization errors, both indices of concurrent processing, were found to be significantly reduced in trials with the first target shift compared to those without it. A similar decrease in concurrent processing was obtained when we shifted the first target but kept constant the second saccade vector. Overall, these results suggest that the brain can use relatively stable visual landmarks, independent of efference copy-based egocentric mechanisms, for concurrent planning of sequential saccades.

Simulation study of a two-stage adsorber system

The present study simulates a two-stage silica gel + water adsorption desalination (AD) and chiller system. The adsorber system thermally compresses the low pressure steam generated in the evaporator to the condenser pressure in two stages. Unlike a standalone adsorption chiller unit which operates in a closed cycle the present system is an open cycle wherein the condensed desalinated water is not fed back to the evaporator. The mathematical relations formulated in the current study are based on conservation of mass and energy along with isotherm relation and kinetics for RD-type silica gel + water pair. Various constitutive relations for each component namely the evaporator, adsorber and condenser are integrated in the model. The dynamics of heat exchanger are modeled using LMTD method, and LDF model is used to predict the dynamic characteristic of the adsorber bed. The system performance indicators namely, specific cooling capacity (SCC), specific daily water production (SDWP) and coefficient of performance (COP) are used as objective functions to optimize the system. The novelty of the present work is in introduction of inter-stage pressure as a new parameter for optimizing the two-stage operation of AD chiller system. (C) 2014 Elsevier Ltd. All rights reserved.

Double Gaussian distribution of barrier height observed in densely packed GaN nanorods over Si (111) heterostructures

GaN nanorods were grown by plasma assisted molecular beam epitaxy on intrinsic Si (111) substrates which were characterized by powder X-ray diffraction, field emission scanning electron microscopy, and photoluminescence. The current-voltage characteristics of the GaN nanorods on Si (111) heterojunction were obtained from 138 to 493K which showed the inverted rectification behavior. The I-V characteristics were analyzed in terms of thermionic emission model. The temperature variation of the apparent barrier height and ideality factor along with the non-linearity of the activation energy plot indicated the presence of lateral inhomogeneities in the barrier height. The observed two temperature regimes in Richardson's plot could be well explained by assuming two separate Gaussian distribution of the barrier heights. (C) 2014 AIP Publishing LLC.

Simultaneous Perturbation Newton Algorithms for Simulation Optimization

We present a new Hessian estimator based on the simultaneous perturbation procedure, that requires three system simulations regardless of the parameter dimension. We then present two Newton-based simulation optimization algorithms that incorporate this Hessian estimator. The two algorithms differ primarily in the manner in which the Hessian estimate is used. Both our algorithms do not compute the inverse Hessian explicitly, thereby saving on computational effort. While our first algorithm directly obtains the product of the inverse Hessian with the gradient of the objective, our second algorithm makes use of the Sherman-Morrison matrix inversion lemma to recursively estimate the inverse Hessian. We provide proofs of convergence for both our algorithms. Next, we consider an interesting application of our algorithms on a problem of road traffic control. Our algorithms are seen to exhibit better performance than two Newton algorithms from a recent prior work.

Probabilistic stress variation studies on composite single lap joint using Monte Carlo simulation

The work presented in this paper involves the stochastic finite element analysis of composite-epoxy adhesive lap joints using Monte Carlo simulation. A set of composite adhesive lap joints were prepared and loaded till failure to obtain their strength. The peel and shear strain in the bond line region at different levels of load were obtained using digital image correlation (DIC). The corresponding stresses were computed assuming a plane strain condition. The finite element model was verified by comparing the numerical and experimental stresses. The stresses exhibited a similar behavior and a good correlation was obtained. Further, the finite element model was used to perform the stochastic analysis using Monte Carlo simulation. The parameters influencing stress distribution were provided as a random input variable and the resulting probabilistic variation of maximum peel and shear stresses were studied. It was found that the adhesive modulus and bond line thickness had significant influence on the maximum stress variation. While the adherend thickness had a major influence, the effect of variation in longitudinal and shear modulus on the stresses was found to be little. (C) 2014 Elsevier Ltd. All rights reserved.

SIMULATION STUDY OF THE ADSORPTION DYNAMICS OF CYLINDRICAL SILICA GEL PARTICLES

The paper presents a simulation study of loose cylindrically shaped particles packed within a copper plate and aluminum fins. The model presented solves coupled heat and mass transfer equations using the finite volume method based on ANSY S FLUENT medium. Three different arrangements of cylindrical particles are considered. The model is validated with experimental data. It is found that the arrangements which represented monolayer configurations are only marginally better in heat transfer and uptake efficiency than the tri-layer configuration in the presence of fins. However, there is an appreciable difference in the uptake curve between monoand tri-layer configurations in the absence of fins. Finally, it is found that the fin pitch also plays an important role in determining the time constant for the adsorber design.

A Gaussian network model study suggests that structural fluctuations are higher for inactive states than active states of protein kinases

We performed Gaussian network model based normal mode analysis of 3-dimensional structures of multiple active and inactive forms of protein kinases. In 14 different kinases, a more number of residues (1095) show higher structural fluctuations in inactive states than those in active states (525), suggesting that, in general, mobility of inactive states is higher than active states. This statistically significant difference is consistent with higher crystallographic B-factors and conformational energies for inactive than active states, suggesting lower stability of inactive forms. Only a small number of inactive conformations with the DFG motif in the ``in'' state were found to have fluctuation magnitudes comparable to the active conformation. Therefore our study reports for the first time, intrinsic higher structural fluctuation for almost all inactive conformations compared to the active forms. Regions with higher fluctuations in the inactive states are often localized to the aC-helix, aG-helix and activation loop which are involved in the regulation and/or in structural transitions between active and inactive states. Further analysis of 476 kinase structures involved in interactions with another domain/protein showed that many of the regions with higher inactive-state fluctuation correspond to contact interfaces. We also performed extensive GNM analysis of (i) insulin receptor kinase bound to another protein and (ii) holo and apo forms of active and inactive conformations followed by multi-factor analysis of variance. We conclude that binding of small molecules or other domains/proteins reduce the extent of fluctuation irrespective of active or inactive forms. Finally, we show that the perceived fluctuations serve as a useful input to predict the functional state of a kinase.

Simulation studies on the performance of thermoacoustic prime movers and refrigerator

The prime movers and refrigerators based on thermoacoustics have gained considerable importance toward practical applications in view of the absence of moving components, reasonable efficiency, use of environmental friendly working fluids, etc. Devices such as twin Standing Wave ThermoAcoustic Prime Mover (SWTAPM), Traveling Wave ThermoAcoustic Prime Mover (TWTAPM) and thermoacoustically driven Standing Wave ThermoAcoustic Refrigerator (SWTAR) have been studied by researchers. The numerical modeling and simulation play a vital role in their development. In our efforts to build the above thermoacoustic systems, we have carried out numerical analysis using the procedures of CFD on the above systems. The results of the analysis are compared with those of DeltaEC (freeware from LANL, USA) simulations and the experimental results wherever possible. For the CFD analysis commercial code Fluent 6.3.26 has been used along with the necessary boundary conditions for different working fluids at various average pressures. The results of simulation indicate that choice of the working fluid and the average pressure are critical to the performance of the above thermoacoustic devices. Also it is observed that the predictions through the CFD analysis are closer to the experimental results in most cases, compared to those of DeltaEC simulations. (C) 2015 Elsevier Ltd. All rights reserved.