A computational fluid-structure interaction study of inflation cycles of a tension cone decelerator

Inflatable aerodynamic decelerators present potential advantages for planetary entry in missions of robotic and human exploration. The design of these structures face many engineering challenges, including complex deformable geometries, anisotropic material response, and coupled shockturbulence interactions. In this paper, we describe a comprehensive computational fluid-structure interaction study of an inflation cycle of a tension cone decelerator in supersonic flow and compare the simulations with earlier published experimental results. The aeroshell design and flow conditions closely match recent experiments conducted at Mach 2.5. The structural model is a 16-sided polygonal tension cone with seams between each segment. The computational model utilizes adaptive mesh refinement, large-eddy simulation, and shell mechanics with self-contact modeling to represent the flow and structure interaction. This study focuses on the dynamics of the structure as the inflation pressure varies gradually, and the behavior of forces experienced by the flexible and rigid (the payload capsule) structures. © 2011 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

A flame surface density approach to large-eddy simulation of premixed turbulent combustion

The flame surface density approach to the modeling of premixed turbulent combustion is well established in the context of Reynolds-averaged simulations. For the future, it is necessary to consider large-eddy simulation (LES), which is likely to offer major advantages in terms of physical accuracy, particularly for unsteady combustion problems. LES relies on spatial filtering for the removal of unresolved phenomena whose characteristic length scales are smaller than the computational grid scale. Thus, there is a need for soundly based physical modeling at the subgrid scales. The aim of this paper is to explore the usefulness of the flame surface density concept as a basis for LES modeling of premixed turbulent combustion. A transport equation for the filtered flame surface density is presented, and models are proposed for unclosed terms. Comparison with Reynolds-averaged modeling is shown to reveal some interesting similarities and differences. These were exploited together with known physics and statistical results from experiment and from direct numerical stimulation in order to gain insight and refine the modeling. The model has been implemented in a combustion LES code together with standard models for scalar and momentum transport. Computational results were obtained for a simple three-dimensional flame propagation test problem, and the relative importance of contributing terms in the modeled equation for flame surface density was assessed. Straining and curvature are shown to have a major influence at both the resolved and subgrid levels.

Wetness loss prediction for a low pressure steam turbine using computational fluid dynamics

Two-phase computational fluid dynamics modelling is used to investigate the magnitude of different contributions to the wet steam losses in a three-stage model low pressure steam turbine. The thermodynamic losses (due to irreversible heat transfer across a finite temperature difference) and the kinematic relaxation losses (due to the frictional drag of the drops) are evaluated directly from the computational fluid dynamics simulation using a concept based on entropy production rates. The braking losses (due to the impact of large drops on the rotor) are investigated by a separate numerical prediction. The simulations show that in the present case, the dominant effect is the thermodynamic loss that accounts for over 90% of the wetness losses and that both the thermodynamic and the kinematic relaxation losses depend on the droplet diameter. The numerical results are brought into context with the well-known Baumann correlation, and a comparison with available measurement data in the literature is given. The ability of the numerical approach to predict the main wetness losses is confirmed, which permits the use of computational fluid dynamics for further studies on wetness loss correlations. © IMechE 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Operational Optimization of GaN Thin Film Growth Employing Numerical Simulation in a Showerhead MOCVD Reactor

A detailed reaction-tran sport model was studied in a showerhead reactor for metal organic chemical vapor deposition of GaN film by using computational fluid dynamics simulation. It was found that flat flow lines without swirl are crucial to improve the uniformity of the film growth, and thin temperature gradient above the suscptor can increase the film deposition rate. By above-mentioned research, we can employ higher h (the distance from the susceptor to the inlet), P (operational pressure) and the rate of susceptor rotation to improve the film growth.

Numerical simulation of a bubble rising in shear-thinning fluids

The motion of a single bubble rising freely in quiescent non-Newtonian viscous fluids was investigated experimentally and computationally. The non-Newtonian effects in the flow of viscous inelastic fluids are modeled by the Carreau theological model. An improved level set approach for computing the incompressible two-phase flow with deformable free interface is used. The control volume formulation with the SIMPLEC algorithm incorporated is used to solve the governing equations on a staggered Eulerian grid. The simulation results demonstrate that the algorithm is robust for shear-thinning liquids with large density (rho(1)/rho(g) up to 10(3)) and high viscosity (eta(1)/eta(g) up to 10(4)). The comparison of the experimental measurements of terminal bubble shape and velocity with the computational results is satisfactory. It is shown that the local change in viscosity around a bubble greatly depends on the bubble shape and the zero-shear viscosity of non-Newtonian shear-thinning liquids. The shear-rate distribution and velocity fields are used to elucidate the formation of a region of large viscosity at the rear of a bubble as a result of the rather stagnant flow behind the bubble. The numerical results provide the basis for further investigations, such as the numerical simulation of viscoelastic fluids. (C) 2010 Elsevier B.V. All rights reserved.

Mechanical Behavior of Nanometer Ni by MD Simulation

The indention simulation of the crystal Ni is carried out by molecular dynamics technique (MD) to study the mechanical behavior at nanometer scales, the indenter tips with sphere shape is used. Some defects such as dislocations, point defects are observed. It is found that defects (dislocations, amorphous) nucleated is from local region near the pin tip or the sample surface. The temperature distribution of local region is analyzed and it can explain our MD simulation result.

Large-eddy simulation of flows past a flapping airfoil using immersed boundary method

The numerical simulation of flows past flapping foils at moderate Reynolds numbers presents two challenges to computational fluid dynamics: turbulent flows and moving boundaries. The direct forcing immersed boundary (IB) method has been developed to simulate laminar flows. However, its performance in simulating turbulent flows and transitional flows with moving boundaries has not been fully evaluated. In the present work, we use the IB method to simulate fully developed turbulent channel flows and transitional flows past a stationary/plunging SD7003 airfoil. To suppress the non-physical force oscillations in the plunging case, we use the smoothed discrete delta function for interpolation in the IB method. The results of the present work demonstrate that the IB method can be used to simulate turbulent flows and transitional flows with moving boundaries.

Quench Protection Design and Simulation of a 7 Tesla Superconducting Magnet

A 7 Tesla superconducting magnet with a clear warm bore of 156 mm in diameter has been developed for Lanzhou Penning Trap at the Institute of Modern Physics for high precision mass measurement. The magnet is comprised of 9 solenoid coils and operates in persistent mode with a total energy of 2.3 MJ. Due to the considerable amount of energy stored during persistent mode operation, the quench protection system is very important when designing and operating the magnet. A passive protection system based on a subdivided scheme is adopted to protect the superconducting magnet from damage caused by quenching. Cold diodes and resistors are put across the subdivision to reduce both the voltage and temperature hot spots. Computational simulations have been carried in Opera-quench. The designed quench protection circuit and the finite element method model for quench simulations are described; the time changing of temperature, voltage and current decay during the quench process is also analysed.

Simulation of characteristic electrochemical behavior of azobenzene derivative self-assembled monolayers

The characteristic electrochemical mechanics of azobenzene derivative self-assembled monolayers is discussed in present paper. It is presented that the structure inhibition is one of the most important factors in the increase of electrochemical reactive energy. A corresponding mathematical model was established based on Levich and Marcus's theory. Moreover, computational program was written to simulate the decrease of apparent rate constant (k(app)) of electron transfer with increasing surface concentration.

Numerical simulation of principal tidal constituents in the South China Sea, Gulf of Tonkin and Gulf of Thailand

The principal tidal constituents M-2, S-2, K-1 and O-1 in the South China Sea, Gulf of Tonkin and Gulf of Thailand are simulated simultaneously using the numerical scheme of Kwok et al. (1995 Proceedings of the 1st Asian Computational Fluid Dynamics Conference, pp. 16-19). The average differences between the computed and observed harmonic constants are mostly within 5 cm and 10 degrees for amplitudes and phase-lags, respectively. The simulated tidal regimes in the present model are believed to be more accurate than the previous numerical results. Our studies confirm that a clockwise rotating M-2 amphidromic system lies in the southeast of the Gulf of Thailand and an S-2 amphidromic system at the near-shore area of the northeast South China Sea. The linear tidal energy equation developed by Garrett (1975 Deep-Sea Research 22, 23-35) is generalized to the nonlinear case. Based on the numerical results, the energy budgets in the South China Sea and its subareas, namely the Taiwan Strait, the Gulf of Tonkin, the Gulf of Thailand and the remaining area are investigated. The tidal motion in the Taiwan Strait is maintained mainly by the energy fluxes from the East China Sea for both semidiurnal and diurnal species and partially from the Luzon Strait for semidiurnal species. For the other parts of the South China Sea, the tidal motion is mainly maintained by the energy fluxes through the Luzon Strait. The energy inputs from the tide-generating force are negative for semidiurnal species and positive for diurnal species. (C) 1999 Elsevier Science Ltd. All rights reserved.

KInNeSS: A Modular Framework for Computational Neuroscience

Making use of very detailed neurophysiological, anatomical, and behavioral data to build biological-realistic computational models of animal behavior is often a difficult task. Until recently, many software packages have tried to resolve this mismatched granularity with different approaches. This paper presents KInNeSS, the KDE Integrated NeuroSimulation Software environment, as an alternative solution to bridge the gap between data and model behavior. This open source neural simulation software package provides an expandable framework incorporating features such as ease of use, scalabiltiy, an XML based schema, and multiple levels of granularity within a modern object oriented programming design. KInNeSS is best suited to simulate networks of hundreds to thousands of branched multu-compartmental neurons with biophysical properties such as membrane potential, voltage-gated and ligand-gated channels, the presence of gap junctions of ionic diffusion, neuromodulation channel gating, the mechanism for habituative or depressive synapses, axonal delays, and synaptic plasticity. KInNeSS outputs include compartment membrane voltage, spikes, local-field potentials, and current source densities, as well as visualization of the behavior of a simulated agent. An explanation of the modeling philosophy and plug-in development is also presented. Further developement of KInNeSS is ongoing with the ultimate goal of creating a modular framework that will help researchers across different disciplines to effecitively collaborate using a modern neural simulation platform.

KInNeSS: A Modular Framework for Computational Neuroscience

Making use of very detailed neurophysiological, anatomical, and behavioral data to build biologically-realistic computational models of animal behavior is often a difficult task. Until recently, many software packages have tried to resolve this mismatched granularity with different approaches. This paper presents KInNeSS, the KDE Integrated NeuroSimulation Software environment, as an alternative solution to bridge the gap between data and model behavior. This open source neural simulation software package provides an expandable framework incorporating features such as ease of use, scalability, an XML based schema, and multiple levels of granularity within a modern object oriented programming design. KInNeSS is best suited to simulate networks of hundreds to thousands of branched multi-compartmental neurons with biophysical properties such as membrane potential, voltage-gated and ligand-gated channels, the presence of gap junctions or ionic diffusion, neuromodulation channel gating, the mechanism for habituative or depressive synapses, axonal delays, and synaptic plasticity. KInNeSS outputs include compartment membrane voltage, spikes, local-field potentials, and current source densities, as well as visualization of the behavior of a simulated agent. An explanation of the modeling philosophy and plug-in development is also presented. Further development of KInNeSS is ongoing with the ultimate goal of creating a modular framework that will help researchers across different disciplines to effectively collaborate using a modern neural simulation platform.

Computational inference of neural information flow networks.

Determining how information flows along anatomical brain pathways is a fundamental requirement for understanding how animals perceive their environments, learn, and behave. Attempts to reveal such neural information flow have been made using linear computational methods, but neural interactions are known to be nonlinear. Here, we demonstrate that a dynamic Bayesian network (DBN) inference algorithm we originally developed to infer nonlinear transcriptional regulatory networks from gene expression data collected with microarrays is also successful at inferring nonlinear neural information flow networks from electrophysiology data collected with microelectrode arrays. The inferred networks we recover from the songbird auditory pathway are correctly restricted to a subset of known anatomical paths, are consistent with timing of the system, and reveal both the importance of reciprocal feedback in auditory processing and greater information flow to higher-order auditory areas when birds hear natural as opposed to synthetic sounds. A linear method applied to the same data incorrectly produces networks with information flow to non-neural tissue and over paths known not to exist. To our knowledge, this study represents the first biologically validated demonstration of an algorithm to successfully infer neural information flow networks.

Computational Inference of Genome-Wide Protein-DNA Interactions Using High-Throughput Genomic Data

Transcriptional regulation has been studied intensively in recent decades. One important aspect of this regulation is the interaction between regulatory proteins, such as transcription factors (TF) and nucleosomes, and the genome. Different high-throughput techniques have been invented to map these interactions genome-wide, including ChIP-based methods (ChIP-chip, ChIP-seq, etc.), nuclease digestion methods (DNase-seq, MNase-seq, etc.), and others. However, a single experimental technique often only provides partial and noisy information about the whole picture of protein-DNA interactions. Therefore, the overarching goal of this dissertation is to provide computational developments for jointly modeling different experimental datasets to achieve a holistic inference on the protein-DNA interaction landscape.

We first present a computational framework that can incorporate the protein binding information in MNase-seq data into a thermodynamic model of protein-DNA interaction. We use a correlation-based objective function to model the MNase-seq data and a Markov chain Monte Carlo method to maximize the function. Our results show that the inferred protein-DNA interaction landscape is concordant with the MNase-seq data and provides a mechanistic explanation for the experimentally collected MNase-seq fragments. Our framework is flexible and can easily incorporate other data sources. To demonstrate this flexibility, we use prior distributions to integrate experimentally measured protein concentrations.

We also study the ability of DNase-seq data to position nucleosomes. Traditionally, DNase-seq has only been widely used to identify DNase hypersensitive sites, which tend to be open chromatin regulatory regions devoid of nucleosomes. We reveal for the first time that DNase-seq datasets also contain substantial information about nucleosome translational positioning, and that existing DNase-seq data can be used to infer nucleosome positions with high accuracy. We develop a Bayes-factor-based nucleosome scoring method to position nucleosomes using DNase-seq data. Our approach utilizes several effective strategies to extract nucleosome positioning signals from the noisy DNase-seq data, including jointly modeling data points across the nucleosome body and explicitly modeling the quadratic and oscillatory DNase I digestion pattern on nucleosomes. We show that our DNase-seq-based nucleosome map is highly consistent with previous high-resolution maps. We also show that the oscillatory DNase I digestion pattern is useful in revealing the nucleosome rotational context around TF binding sites.

Finally, we present a state-space model (SSM) for jointly modeling different kinds of genomic data to provide an accurate view of the protein-DNA interaction landscape. We also provide an efficient expectation-maximization algorithm to learn model parameters from data. We first show in simulation studies that the SSM can effectively recover underlying true protein binding configurations. We then apply the SSM to model real genomic data (both DNase-seq and MNase-seq data). Through incrementally increasing the types of genomic data in the SSM, we show that different data types can contribute complementary information for the inference of protein binding landscape and that the most accurate inference comes from modeling all available datasets.

This dissertation provides a foundation for future research by taking a step toward the genome-wide inference of protein-DNA interaction landscape through data integration.

Monotone scheme and boundary conditions for finite volume simulation of magnetohydrodynamic internal flows at high Hartmann number

A monotone scheme for finite volume simulation of magnetohydrodynamic internal flows at high Hartmann number is presented. The numerical stability is analysed with respect to the electromagnetic force. Standard central finite differences applied to finite volumes can only be numerically stable if the vector products involved in this force are computed with a scheme using a fully staggered grid. The electromagnetic quantities (electric currents and electric potential) must be shifted by half the grid size from the mechanical ones (velocity and pressure). An integral treatment of the boundary layers is used in conjunction with boundary conditions for electrically conducting walls. The simulations are performed with inhomogeneous electrical conductivities of the walls and reach high Hartmann numbers in three-dimensional simulations, even though a non-adaptive grid is used.