Ab initio molecular dynamics simulations of ion–solid interactions in Gd2Zr2O7 and Gd2Ti2O7
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Capture of CO2 from flue gas is considered to be a feasible approach to mitigate the effects of anthropogenic emission of CO2. Herein we report that an isostructural family of metal organic materials (MOMs) of general formula [M(linker)(2)(pillar)], linker = pyrazine, pillar = hexaflourosilicate and M = Zn, Cu, Ni and Co exhibits highly selective removal of CO2 fromdry and wet simulated flue gas. Two members of the family, M = Ni and Co, SIFSIX-3-Ni and SIFSIX-3-Co, respectively, are reported for the first time and compared with the previously reported Zn and Cu analogs.
A large-scale numerical computation of five wind farms was performed as a part of the American Wake Experiment (AWAKEN). This high-fidelity computation used the ExaWind/AMR-Wind LES solver to simulate a 100km × 100km domain containing 541 turbines under unstable atmospheric conditions matching previous measurements. The turbines were represented by Joukowski and OpenFAST coupled actuator disk models. Results of this qualitative comparison illustrate the interactions with wind farms with large scale ABL structures in the flow, as well as the extent of downstream wake penetration in the flow and blockage effects around wind farms.
Power system simulations with long-term data typically have large time steps varying from one second to a few minutes. However, for PV inverter semiconductors, the minimum thermal stress cycle occurs over the fundamental grid frequency (50 or 60 Hz). This requires the time step of the fatigue simulation to be around 100 µs. This small time step requires long computation times to process yearly power production profiles. This paper proposes a fast fatigue simulation for inverter semiconductors using the quasi-static time series (QSTS) simulation concept. The proposed simulation calculates the steady state of the semiconductor junction temperature by using a Fast Fourier Transform (FFT). The small thermal cycling during a switching period and even over the fundamental waveform is disregarded to further accelerate the simulation speed. The resulting time step of the fatigue simulation is 15 minutes, which is consistent with the solar dataset. The error of the proposed simulation is 0.16% compared to the fatigue simulation results using the complete thermal stress profile. A PV inverter that responds to a Transactive Energy System (TES) is simulated to demonstrate the use of the proposed fatigue simulation. The proposed simulation has the potential to co-simulate with system level simulation tools that also adopt the QSTS concept.
Faceted shapes, such as polyhedra, are commonly found in systems of nanoscale, colloidal, and granular particles. Many interesting physical phenomena, like crystal nucleation and growth, vacancy motion, and glassy dynamics are challenging to model in these systems because they require detailed dynamical information at the individual particle level. Within the granular materials community the Discrete Element Method has been used extensively to model systems of anisotropic particles under gravity, with friction. We provide an implementation of this method intended for simulation of hard, faceted nanoparticles, with a conservative Weeks–Chandler–Andersen (WCA) interparticle potential, coupled to a thermodynamic ensemble. This method is a natural extension of classical molecular dynamics and enables rigorous thermodynamic calculations for faceted particles.
We investigate on the procedure of extracting a 'spectral density' from mixed QM/MM calculations and employing it in open quantum systems models. In particular, we study the connection between the energy gap correlation function extracted from ground state QM/MM and the bath spectral density used as input in open quantum system approaches. We introduce a simple model which can give intuition on when the ground state QM/MM propagation will give the correct energy gap. We also discuss the role of higher order correlators of the energy-gap fluctuations which can provide useful information on the bath. Further, various semiclassical corrections to the spectral density, are applied and investigated. Finally, we apply our considerations to the photosynthetic Fenna-Matthews-Olson complex. For this system, our results suggest the use of the Harmonic prefactor for the spectral density rather than the Standard one, which was employed in the simulations of the system carried out to date.
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Purpose: Reducing the patient dose while maintaining the diagnostic image quality during CT exams is the subject of a growing number of studies, in which simulations of reduced-dose CT with patient data have been used as an effective technique when exploring the potential of various dose reduction techniques. Difficulties in accessing raw sinogram data, however, have restricted the use of this technique to a limited number of institutions. Here, we present a novel reduced-dose CT simulation technique which provides realistic low-dose images without the requirement of raw sinogram data. Methods: Two key characteristics of CT systems, the noise equivalent quanta (NEQ) and the algorithmic modulation transfer function (MTF), were measured for various combinations of object attenuation and tube currents by analyzing the noise power spectrum (NPS) of CT images obtained with a set of phantoms. Those measurements were used to develop a comprehensive CT noise model covering the reduced x-ray photon flux, object attenuation, system noise, and bow-tie filter, which was then employed to generate a simulated noise sinogram for the reduced-dose condition with the use of a synthetic sinogram generated from a reference CT image. The simulated noise sinogram was filtered with the algorithmic MTF and back-projected to create a noise CT image, which was then added to the reference CT image, finally providing a simulated reduced-dose CT image. The simulation performance was evaluated in terms of the degree of NPS similarity, the noise magnitude, the bow-tie filter effect, and the streak noise pattern at photon starvation sites with the set of phantom images. Results: The simulation results showed good agreement with actual low-dose CT images in terms of their visual appearance and in a quantitative evaluation test. The magnitude and shape of the NPS curves of the simulated low-dose images agreed well with those of real low-dose images, showing discrepancies of less than +/−3.2% in terms of the noise power at the peak height and +/−1.2% in terms of the spatial frequency at the peak height. The magnitudes of the noise measured for 12 different combinations the phantom size, tube current, and reconstruction kernel for the simulated and real low-dose images were very similar, with differences of 0.1 to 4.7%. Thep value for a statistical testing of the difference in the noise magnitude ranged from 0.99 to 0.11, showing that there was no difference statistically between the noise magnitudes of the real and simulated low-dose images using our method. The strength and pattern of the streak noise in an anthropomorphic phantom was also consistent with expectations. Conclusions: A novel reduced-dose CT simulation technique was developed which uses only CT images while not requiring raw sinogram data. Our method can provide realistic simulation results under reduced-dose conditions both in terms of the noise magnitude and the textual appearance. This technique has the potential to promote clinical research for patient dose reductions.
In this work, we present results of a large-eddy simulation of the 48 multi-megawatt turbines composing the Lillgrund wind plant. Turbulent inflow wind is created by performing an atmospheric boundary layer precursor simulation, and turbines are modeled using a rotating, variable-speed actuator line representation. The motivation for this work is that few others have done large-eddy simulations of wind plants with a substantial number of turbines, and the methods for carrying out the simulations are varied. We wish to draw upon the strengths of the existing simulations and our growing atmospheric large-eddy simulation capability to create a sound methodology for performing this type of simulation. We used the OpenFOAM CFD toolbox to create our solver. The simulated time-averaged power production of the turbines in the plant agrees well with field observations, except with the sixth turbine and beyond in each wind-aligned. The power produced by each of those turbines is overpredicted by 25-40%. A direct comparison between simulated and field data is difficult because we simulate one wind direction with a speed and turbulence intensity characteristic of Lillgrund, but the field observations were taken over a year of varying conditions. The simulation shows the significant 60-70% decrease in the performance of the turbines behind the front row in this plant that has a spacing of 4.3 rotor diameters in this direction. The overall plant efficiency is well predicted. This work shows the importance of using local grid refinement to simultaneously capture the meter-scale details of the turbine wake and the kilometer-scale turbulent atmospheric structures. Although this work illustrates the power of large-eddy simulation in producing a time-accurate solution, it required about one million processor-hours, showing the significant cost of large-eddy simulation.
Biomolecular simulations at millisecond and longer time-scales can provide vital insights into functional mechanisms. Because post-simulation analyses of such large trajectory datasets can be a limiting factor in obtaining biological insights, there is an emerging need to identify key dynamical events and relating these events to the biological function online, that is, as simulations are progressing. Recently, we have introduced a novel computational technique, quasi-anharmonic analysis (QAA) (Ramanathan et al., PLoS One 2011;6:e15827), for partitioning the conformational landscape into a hierarchy of functionally relevant sub-states. The unique capabilities of QAA are enabled by exploiting anharmonicity in the form of fourth-order statistics for characterizing atomic fluctuations. In this article, we extend QAA for analyzing long time-scale simulations online. In particular, we present HOST4MD—a higher-order statistical toolbox for molecular dynamics simulations, which (1) identifies key dynamical events as simulations are in progress, (2) explores potential sub-states, and (3) identifies conformational transitions that enable the protein to access those sub-states. We demonstrate HOST4MD on microsecond timescale simulations of the enzyme adenylate kinase in its apo state. HOST4MD identifies several conformational events in these simulations, revealing how the intrinsic coupling between the three subdomains (LID, CORE, and NMP) changes during the simulations. Further, it also identifies an inherent asymmetry in the opening/closing of the two binding sites. We anticipate that HOST4MD will provide a powerful and extensible framework for detecting biophysically relevant conformational coordinates from long time-scale simulations.
Sooting tendencies of a series of nitrogen-containing hydrocarbons (NHCs) have been recently characterized experimentally using the yield sooting index (YSI) methodology. This work aims to identify soot-relevant reaction pathways for three selected C6H15N amines, namely, dipropylamine (DPA), diisopropylamine (DIPA), and 3,3-dimethylbutylamine (DMBA) using ReaxFF molecular dynamics (MD) simulations and quantum mechanical (QM) calculations and to interpret the experimentally observed trends. ReaxFF MD simulations are performed to determine the important intermediate species and radicals involved in the fuel decomposition and soot formation processes. QM calculations are employed to extensively search for chemical reactions involving these species and radicals based on the ReaxFF MD results and also to quantitatively characterize the potential energy surfaces. Specifically, ReaxFF simulations are carried out in the NVT ensemble at 1400, 1600, and 1800 K, where soot has been identified to form in the YSI experiment. These simulations account for the interactions among test fuel molecules and pre-existing radicals and intermediate species generated from rich methane combustion, using a recently proposed simulation framework. ReaxFF simulations predict that the reactivity of the amines decrease in the order DIPA > DPA > DMBA, independent of temperature. Both QM calculations and ReaxFF simulations predict that C2H4, C3H6, and C4H8 are the main nonaromatic soot precursors formed during the decomposition of DPA, DIPA, and DMBA, respectively, and the associated reaction pathways are identified for each amine. Both theoretical methods predict that sooting tendency increases in the order DPA, DIPA, and DMBA, consistent with the experimentally measured trend in YSI. This work demonstrates that sooting tendencies and soot-relevant reaction pathways of fuels with unknown chemical kinetics can be identified efficiently through combined ReaxFF and QM simulations. Overall, predictions from ReaxFF simulations and QM calculations are consistent, in terms of fuel reactivity, major intermediates, and major nonaromatic soot precursors.
We report a series of simulations of the formation of a star cluster similar to the Orion Nebula Cluster (ONC), including both radiative transfer and protostellar outflows, and starting from both smooth and self-consistently turbulent initial conditions. Each simulation forms >150 stars and brown dwarfs, yielding a stellar mass distribution that ranges from <0.1 M{sub Sun} to >10 M{sub Sun }. We show that a simulation that begins with self-consistently turbulent density and velocity fields embedded in a larger turbulent volume, and that includes protostellar outflows, produces an initial mass function (IMF) that is consistent both with that of the ONC and the Galactic field, at least within the statistical power provided by the number of stars formed in our simulations. This is the first simulation published to date that reproduces the observed IMF in a cluster large enough to contain massive stars, and where the peak of the mass function is determined by a fully self-consistent calculation of gas thermodynamics rather than a hand-imposed equation of state. This simulation also produces a star formation rate that, while still somewhat too high, is much closer to observed values than if we omit either the larger turbulent volume or the outflows. Moreover, we show that the combination of outflows, self-consistently turbulent initial conditions, and turbulence continually fed by motions on scales larger than that of the protocluster yields an IMF that is in agreement with observations and invariant with time, resolving the 'overheating' problem in which simulations without these features have an IMF peak that shifts to progressively higher masses over time as more and more of the gas is heated, inconsistent with the observed invariance of the IMF. The simulation that matches the observed IMF also qualitatively reproduces the observed trend of stellar multiplicity strongly increasing with mass. We show that this simulation produces massive stars from distinct massive cores whose properties are consistent with those of observed massive cores. However, the stars formed in these cores also undergo dynamical interactions as they accrete that naturally produce Trapezium-like hierarchical multiple systems of massive stars.
This paper describes a numerical study of flow-induced vibration in a helical coil steam generator experiment conducted at Argonne National Laboratory in the 1980s. In the experiment, a half-scale sector model of a steam generator helical coil tube bank was subjected to still and flowing air and water, and the vibrational characteristics were recorded. The research detailed in this document utilizes the multi-physics simulation toolkit SHARP developed at Argonne National Laboratory, in cooperation with Lawrence Livermore National Laboratory, to simulate the experiment. SHARP uses the spectral element code Nek5000 for fluid dynamics analysis and the finite element code DIABLO for structural analysis. The flow around the coil tubes is modeled in Nek5000 by using a large eddy simulation turbulence model. Transient pressure data on the tube surfaces is sampled and transferred to DIABLO for the structural simulation. The structural response is simulated in DIABLO via an implicit time-marching algorithm and a combination of continuum elements and structural shells. Tube vibration data (acceleration and frequency) are sampled and compared with the experimental data. Currently, only one-way coupling is used, which means that pressure loads from the fluid simulation are transferred to the structural simulation but the resulting structural displacements are not fed back to the fluid simulation
Ten 3D cloud-resolving model (CRM) simulations and four 3D limited area model (LAM) simulations of an intense mesoscale convective system observed on January 23-24, 2006 during the Tropical Warm Pool – International Cloud Experiment (TWP-ICE) are compared with each other and with observed radar reflectivity fields and dual-Doppler retrievals of vertical wind speeds in an attempt to explain published results showing a high bias in simulated convective radar reflectivity aloft. This high bias results from ice water content being large, which is a product of large, strong convective updrafts, although hydrometeor size distribution assumptions modulate the size of this bias. Snow reflectivity can exceed 40 dBZ in a two-moment scheme when a constant bulk density of 100 kg m-3 is used. Making snow mass more realistically proportional to area rather than volume should somewhat alleviate this problem. Graupel, unlike snow, produces high biased reflectivity in all simulations. This is associated with large amounts of liquid water above the freezing level in updraft cores. Peak vertical velocities in deep convective updrafts are greater than dual-Doppler retrieved values, especially in the upper troposphere. Freezing of large rainwater contents lofted above the freezing level in simulated updraft cores greatly contributes to these excessive upper tropospheric vertical velocities. Strong simulated updraft cores are nearly undiluted, with some showing supercell characteristics. Decreasing horizontal grid spacing from 900 meters to 100 meters weakens strong updrafts, but not enough to match observational retrievals. Therefore, overly intense simulated updrafts may partly be a product of interactions between convective dynamics, parameterized microphysics, and large-scale environmental biases that promote different convective modes and strengths than observed.
This is a survey paper about the state-of-the-art in large-scale network simulation. Networks for the smart grids are characterized by millions of sensor nodes exchanging information about the status of the grid. This information exchange must be realized reliably and efficiently due to the mission critical nature of the power grid. Hence, the applications and the network protocols developed for the smart grid need go through rigorous testing and analysis before deployment. Developers usually do not have access to such a large-scale network that can be used as a controlled test-bed; therefore, network simulation becomes an essential tool for testing. Network simulation is a well studied problem in the literature and there are various widely used network simulators. These simulators can be adopted for testing applications and protocols of the smart grid. Due to the scale of these networks, parallel/distributed simulations need to be conducted. Even though most network simulators support distributed simulations, generating a large-scale network model to simulate can still be a cumbersome task. In this survey, we describe a selection of commonly used network simulators and evaluate them with respect to the following features that can aid users in distributed simulations of large-scale networks: transparency of setting up distributed simulation, automated topology generation, information hiding, lightweight routing protocols, network error simulation, evaluation of the network model during simulation and trace analysis tools. As a complementary result, we identify two issues with network simulators that can be addressed with runtime steering methods.
Subsurface simulation of multiphase extraction from wells is notoriously difficult. Explicit representation of well geometry requires small grid resolution, potentially leading to large computational demands. To reduce the problem dimensionality, multiphase extraction is mostly modeled using vertically-averaged approaches. In this paper, a multiphase well model approach is presented as an alternative to simplify the application. The well model, a multiphase extension of the classic Peaceman model, has been implemented in the STOMP simulator. The numerical solution approach accounts for local conditions and gradients in the exchange of fluids between the well and the aquifer. Advantages of this well model implementation include the option to simulate the effects of well characteristics and operation. Simulations were conducted investigating the effects of extraction location, applied vacuum pressure, and a number of hydraulic properties. The obtained results were all consistent and logical. A major outcome of the test simulations is that, in contrast with common recommendations to extract from either the gas-NAPL or the NAPL-aqueous phase interface, the optimum extraction location should be in between these two levels. Further, the new model implementation was also used to simulate extraction at a field site in Brazil. The simulation shows a good match with the field data, suggesting that the new STOMP well module may correctly represent oil removal. The field simulations depend on the quality of the site conceptual model, including the porous media and contaminant properties and the boundary and extraction conditions adopted. The new module may potentially be used to design field applications and analyze extraction data.
With the rise of interest in thermal neutron scattering data for advanced reactor, criticality safety, and shielding applications, new experimental data are required for evaluation of new materials or for re-evaluation (or validations) of previously evaluated materials. New experimental data are evaluated in a three-step process: (1) computing the phonon characteristics, (2) computing the dynamic structure factor (DSF) from the data, and (3) using the experimental setup to simulate the experimental data. All three steps have challenges, ranging from the need for a sufficiently general material simulation code—a processing code that can compute the corresponding DSF—to having a detailed layout of the instrument/beamline/facility where the data were measured. Whereas phonon characteristics of materials can be computed using various methods (molecular dynamics, density functional theory, etc.), a high-fidelity computation of the DSF and the simulation of the experiment based on the DSF is vital to the accuracy of the evaluation. The latter two steps can be achieved by using the two corresponding code systems developed by instrument scientists at the Spallation Neutron Source (SNS) at Oak Ridge National Laboratory: (1) OCLIMAX, a program that calculates the dynamic structure factor from DFT and MD simulation results, and (2) MCViNE, a Monte Carlo neutron ray-tracing program designed to simulate neutron scattering experiments. Recently, polyethylene and yttrium hydride were measured at the Wide Angular-Range Chopper (ARCS) and SEQUOIA instrument stations of the SNS. These experiments are simulated using the density functional theory code, the Cambridge Serial Total Energy Package (CASTEP), to compute its phonon characteristics (eigenvalues/vectors and PDOS), which is then processed using OCLIMAX to yield the DSF, and finally the data at each instrument station are simulated by the MCViNE for comparison to the measured data for evaluation. For comparison to conventional evaluation methods, the scattering data processed from OCLIMAX are compared against those processed from the LEAPR module of NJOY, and the results from MCViNE simulations are compared against previously used simplified beamline models implemented in the Monte Carlo N-Particle (MCNP) code.