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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Classical molecular dynamics simulations coupled with umbrella sampling reveal the atomistic processes and free-energy profiles of the displacement of carbonate groups in the Ca 2 UO 2 (CO 3 ) 3 complex by amidoxime-based ligands in a 0.5 M NaCl solution.
Biomacromolecules are inherently dynamic, and their dynamics are interwoven into function. The fast collective vibrational dynamics in proteins occurs in the low picosecond timescale corresponding to frequencies of ~5-50 cm -1 . This sub-to-low THz frequency regime covers the low-amplitude collective breathing motions of a whole protein and vibrations of the constituent secondary structure elements, such as α-helices, β-sheets and loops. We have used inelastic neutron scattering experiments in combination with molecular dynamics simulations to demonstrate the vibrational dynamics softening of HIV-1 protease, a target of HIV/AIDS antivirals, upon binding of a tight clinical inhibitor darunavir. Changes in the vibrational density of states of matching structural elements in the two monomers of the homodimeric protein are not identical, indicating asymmetric effect of darunavir on the vibrational dynamics. Three of the 11 major secondary structure elements contribute over 40% to the overall changes in the vibrational density of states upon darunavir binding. Molecular dynamics simulations informed by experiments allowed us to estimate that the altered vibrational dynamics of the protease would contribute -3.6 kcal/mol -1 at 300 K, or 25%, to the free energy of darunavir binding. As HIV-1 protease drug resistance remains a concern, our results open a new avenue to help establish a direct quantitative link between protein vibrational dynamics and drug resistance.
We propose an efficient quantum algorithm for simulating the dynamics of general Hamiltonian systems. Our technique is based on a power series expansion of the time-evolution operator in its off-diagonal terms. The expansion decouples the dynamics due to the diagonal component of the Hamiltonian from the dynamics generated by its off-diagonal part, which we encode using the linear combination of unitaries technique. Our method has an optimal dependence on the desired precision and, as we illustrate, generally requires considerably fewer resources than the current state-of-the-art. We provide an analysis of resource costs for several sample models.
A spectral method for kinetic plasma simulations based on the expansion of the velocity distribution function in a variable number of Hermite polynomials is presented. The method is based on a set of non-linear equations that is solved to determine the coefficients of the Hermite expansion satisfying the Vlasov and Poisson equations. In this paper, we first show that this technique combines the fluid and kinetic approaches into one framework. Second, we present an adaptive strategy to increase and decrease the number of Hermite functions dynamically during the simulation. The technique is applied to the Landau damping and two-stream instability test problems. Performance results show 21% and 47% saving of total simulation time in the Landau and two-stream instability test cases, respectively.
Direct molecular dynamics simulation of atomic deposition under realistic conditions is notoriously challenging because of the wide range of time scales that must be captured. Numerous simulation approaches have been proposed to address the problem, often requiring a compromise between model fidelity, algorithmic complexity, and computational efficiency. Coarse projective integration, an example application of the “equation-free” framework, offers an attractive balance between these constraints. Here, periodically applied, short atomistic simulations are employed to compute time derivatives of slowly evolving coarse variables that are then used to numerically integrate differential equations over relatively large time intervals. A key obstacle to the application of this technique in realistic settings is the “lifting” operation in which a valid atomistic configuration is recreated from knowledge of the coarse variables. Using Ge deposition on amorphous SiO{sub 2} substrates as an example application, we present a scheme for lifting realistic atomistic configurations comprised of collections of Ge islands on amorphous SiO{sub 2} using only a few measures of the island size distribution. The approach is shown to provide accurate initial configurations to restart molecular dynamics simulations at arbitrary points in time, enabling the application of coarse projective integration for this morphologically complex system.
Electron transfer (ET) dictates a wide variety of energy-conversion processes in biological systems. Visualizing ET dynamics could provide key insight into understanding and possibly controlling these processes. We present a computational framework named VizBET to visualize biological ET dynamics, using an outer-membrane Mtr–Omc cytochrome complex in Shewanella oneidensis MR-1 as an example. Starting from X-ray crystal structures of the constituent cytochromes, molecular dynamics simulations are combined with homology modeling, protein docking, and binding free energy computations to sample the configuration of the complex as well as the change of the free energy associated with ET. This information, along with quantum-mechanical calculations of the electronic coupling, provides inputs to kinetic Monte Carlo (KMC) simulations of ET dynamics in a network of heme groups within the complex. Visualization of the KMC simulation results has been implemented as a plugin to the Visual Molecular Dynamics (VMD) software. Finally, VizBET has been used to reveal the nature of ET dynamics associated with novel nonequilibrium phase transitions in a candidate configuration of the Mtr–Omc complex due to electron–electron interactions.
We present a visualization-driven simulation system that tightly couples systems dynamics simulations with an immersive virtual environment to allow analysts to rapidly develop and test hypotheses in a high-dimensional parameter space. To accomplish this, we generalize the two-dimensional parallel-coordinates statistical graphic as an immersive 'parallel-planes' visualization for multivariate time series emitted by simulations running in parallel with the visualization. In contrast to traditional parallel coordinate's mapping the multivariate dimensions onto coordinate axes represented by a series of parallel lines, we map pairs of the multivariate dimensions onto a series of parallel rectangles. As in the case of parallel coordinates, each individual observation in the dataset is mapped to a polyline whose vertices coincide with its coordinate values. Regions of the rectangles can be 'brushed' to highlight and select observations of interest: a 'slider' control allows the user to filter the observations by their time coordinate. In an immersive virtual environment, users interact with the parallel planes using a joystick that can select regions on the planes, manipulate selection, and filter time. The brushing and selection actions are used to both explore existing data as well as to launch additional simulations corresponding to the visually selected portions of the input parameter space. As soon as the new simulations complete, their resulting observations are displayed in the virtual environment. This tight feedback loop between simulation and immersive analytics accelerates users' realization of insights about the simulation and its output.
In a recent paper, the experimental 2D-Raman-THz response of liquid water at ambient conditions has been presented [J. Savolainen, S. Ahmed, and P. Hamm, Proc. Natl. Acad. Sci. U. S. A. 110, 20402 (2013)]. Here, all-atom molecular dynamics simulations are performed with the goal to reproduce the experimental results. To that end, the molecular response functions are calculated in a first step, and are then convoluted with the laser pulses in order to enable a direct comparison with the experimental results. The molecular dynamics simulation are performed with several different water models: TIP4P/2005, SWM4-NDP, and TL4P. As polarizability is essential to describe the 2D-Raman-THz response, the TIP4P/2005 water molecules are amended with either an isotropic or a anisotropic polarizability a posteriori after the molecular dynamics simulation. In contrast, SWM4-NDP and TL4P are intrinsically polarizable, and hence the 2D-Raman-THz response can be calculated in a self-consistent way, using the same force field as during the molecular dynamics simulation. It is found that the 2D-Raman-THz response depends extremely sensitively on details of the water model, and in particular on details of the description of polarizability. Despite the limited time resolution of the experiment, it could easily distinguish between various water models. Albeit not perfect, the overall best agreement with the experimental data is obtained for the TL4P water model.
Atomistic mechanisms underlying the reaction time and intermediate reaction products of detonating high explosives far from equilibrium have been elusive. This is because detonation is one of the hardest multiscale physics problems, in which diverse length and time scales play important roles. Here, large spatiotemporal-scale reactive molecular dynamics simulations validated by quantum molecular dynamics simulations reveal a two-stage reaction mechanism during the detonation of cyclotrimethylenetrinitramine crystal. Rapid production of N{sub 2} and H{sub 2}O within ∼10 ps is followed by delayed production of CO molecules beyond ns. We found that further decomposition towards the final products is inhibited by the formation of large metastable carbon- and oxygen-rich clusters with fractal geometry. In addition, we found distinct unimolecular and intermolecular reaction pathways, respectively, for the rapid N{sub 2} and H{sub 2}O productions.
Both steady-state and dynamic simulations have been widely used by HVAC&R industry to support product/equipment development for decades. Steady-state simulation focuses on the system mass, energy and momentum balance of an equilibrium state. It is based on high-fidelity components models, and thus is suitable for system and component design optimization. Dynamic simulation studies the system transient response and is generally used for controls development and verification. It usually does not require rigorous component models of high accuracy because 1) the commonly used PID control is feedback control whose control performance evaluation doesn’t require high fidelity system/plant model; 2) high-fidelity dynamic model significantly increases the number of equations and variables and creates tremendous challenge for math solver. For supervisory control, transactive control or optimization of an integrated building system, the HVAC&R equipment is often one of the sub-components to be controlled. High-fidelity equipment models are required for accurately evaluating control strategies. In addition, building equipment manufacturers have developed a lot of high-fidelity steady-state equipment/component models per their expertise. Thus, a platform that can integrate OEM high-fidelity steady-state model with dynamic building simulation and/or electric power system & grid simulation to support the development and verification of supervisory control for integrated building systems is necessary. In this study, ORNL’s heat pump design tool (HPDM) is utilized to develop a co-simulation platform for supervisory control and optimization in integrated building systems. It is based on a model that integrates high-fidelity steady-state simulation equipment models with dynamic building simulation. A practical case of using the proposed co-simulation platform to develop and evaluate the supervisory control and optimization is presented and discussed.
Protein-DNA Interaction Workflow (PDI Workflow), a pipeline that focuses on generating high-quality docking and molecular dynamics simulations for Protein-DNA complexes. This allows us to take DNA sequences with unknown tertiary structures, accurately predict their structure, dock them with the desired target protein, and then simulate their interactions using molecular dynamics simulations
The output of a molecular dynamics simulation is a data file containing properties of the motion of the simulated particles as a function of time (e.g., positions, velocities). From this output, scientists can extract various properties which give insight into the physics of the system; for example, the radial distribution function can be computed from the particle positions, which gives insight into how close the system is to melting or freezing. Many publicly available codes to perform molecular dynamics simulations exist and have been well adopted by the scientific community (e.g., LAMMPS, VASP). However, it remains standard practice for scientists to write their own post-processing scripts to extract various properties of interest from the molecular dynamics simulation data. Since in many cases scientists are interested in the same set of “textbook” properties, there is a large duplication of effort in writing these post-processing scripts. Our proposed code, FP-Tools, is a C++ toolkit designed to help scientists extract and analyze quantities of interest from the output of a molecular dynamics simulation, eliminating the need for them to write these codes themselves and thus reducing this duplication of effort. The quantities which are computed by our code are well documented in the literature (either in publications or textbooks), and the algorithms we implement to compute these properties are also well known in the field. We are not introducing new science or methods here; rather, our primary goal is to provide a useful tool to the community in the form of a well-documented, easy to use package.
Understanding the microscopic driving force of water wetting is challenging and important for design of materials. The relations between structure, dynamics and hydrogen bonds of interfacial water can be investigated using molecular dynamics simulations. Here, contact angles at the alumina (0001) and ($11\bar{2}0$) surfaces are studied using both classical molecular dynamics simulations and experiments. To test the superhydrophilicity, the free energy cost of removing waters near the interfaces are calculated using the density fluctuations method. The strength of hydrogen bonds is determined by their lifetime and geometry. Both surfaces are superhydrophilic and the (0001) surface is more hydrophilic. Interactions between surfaces and interfacial waters promote a templating effect whereby the latter are aligned in a pattern that follows the underlying lattice of the surfaces. Translational and rotational dynamics of interfacial water molecules are slower than in bulk water. Hydrogen bonds between water and both surfaces are asymmetric, water-to-aluminol ones are stronger than aluminol-to-water ones. Molecular dynamics simulations eliminate the impacts of surface contamination when measuring contact angles and the results reveal the microscopic origin of the macroscopic superhydrophilicity of alumina surfaces: strong water-to-aluminol hydrogen bonds.
Layered aluminosilicates play a dominant role in the mechanical and gas storage properties of the subsurface, are used in diverse industrial applications, and serve as model materials for understanding solvent-ion-support systems. Although expansion in the presence of H2O is well known to be systematically correlated with the hydration free energy of the interlayer cation, in environments dominated by non-polar solvents (i.e. CO2), uptake into the interlayer is not well-understood. Using novel high pressure capabilities, we investigated the interaction of super-critical CO2 with Na+-, NH4+-, and Cs+-saturated montmorillonite, comparing results with predictions from molecular dynamics simulations. Despite the known trend in H2O, and that cation solvation energies in CO2 suggest a stronger interaction with Na+, both the NH4+- and Cs+-clays readily absorbed CO2 and expanded while the Na+-clay did not. The apparent inertness of the Na+-clay was not due to kinetics, as experiments seeking a stable expanded state showed that none exists. Molecular dynamics simulations revealed a large endothermicity to CO2 intercalation in the Na+-clay, but little or no energy barrier for the NH4+- and Cs+-clays. Consequently, we have shown for the first time that in the presence of a low dielectric constant gas swelling depends more on the strength of the interaction between interlayer cation and aluminosilicate sheets and less on that with solvent. The finding suggests a distinct regime in layered aluminosilicates swelling behavior triggered by low solvent polarizability, with important implications in geomechanics, storage and retention of volatile gases, and across industrial uses in gelling, decoloring, heterogeneous catalysis, and semi-permeable reactive barriers.
A novel technique, which directly and nondestructively maps polar domains using electron backscatter diffraction (EBSD) is described and demonstrated. Through dynamical diffraction simulations and quantitative comparison to experimental EBSD patterns, the absolute orientation of a non-centrosymmetric crystal can be determined. With this information, the polar domains of a material can be mapped. The technique is demonstrated by mapping the non-ferroelastic, or 180°, ferroelectric domains in periodically poled LiNbO 3 single crystals. Furthermore, the authors demonstrate the possibility of mapping polarity using this technique in other polar materials system.
Simulation models can greatly improve decisions meant to control the consequences of disruptions to critical infrastructures. We describe a dynamic flow model on networks purposed to inform analyses by those concerned about consequences of disruptions to infrastructures and to help policy makers design robust mitigations. We conceptualize the adaptive responses of infrastructure networks to perturbations as market transactions and business decisions of operators. We approximate commodity flows in these networks by a diffusion equation, with nonlinearities introduced to model capacity limits. To illustrate the behavior and scalability of the model, we show its application first on two simple networks, then on petroleum infrastructure in the United States, where we analyze the effects of a hypothesized earthquake.
This Modeling Archive is in support of an NGEE Arctic publication "Simulated hydrological dynamics and coupled iron redox cycling impact methane production in an Arctic soil" in the Journal of Geophysical Research-Biogeosciences. We simulated biogeochemical cycling in arctic soils using the PFLOTRAN geochemical model combined with measurements from previous NGEE Arctic incubations of polygonal permafrost soils in northern Alaska (Zheng et al., 2018). Simulated iron cycling, carbon dioxide production, and methane production were compared with incubation measurements and the parameterized model was then used to simulate coupled iron and carbon cycling over repeated oxic-anoxic cycles at different levels of carbon substrate availability and pH. The most recent data version (2.0) in the archive incorporates changes to the model and simulations as suggested by reviewers during the manuscript review process. These changes include an updated parameterization of the model; a new set of simulations omitting the iron cycle for direct evaluation of how iron cycle processes affect modeled outcomes; and a set of simulations testing different scenarios of carbon substrate availability in addition to scenarios of initial soil pH. This archive contains simulation code, model output, and analysis code for PFLOTRAN simulations. All scripts are python except the batch script for submitting multiprocessor jobs. Note that the model also requires compiled versions of the Alquimia interface and the NGEE Arctic fork of the PFLOTRAN geochemical simulator (see the README_INSTALL document for basic instructions). The Output directory contains eight data files in netCDF format generated by the model. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 10-year research effort (2012-2022) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
Imagine using a real-time virtual simulator to learn to fly a space shuttle or rebuild your car's transmission without touching a piece of equipment or getting your hands dirty. Now, apply this concept to learning how to operate and control a state-of-the-art, electricity-producing power plant capable of carbon dioxide (CO{sub 2}) capture. That's what the National Energy Technology Laboratory's (NETL) Advanced Virtual Energy Simulation Training and Research (AVESTAR) Center (www.netl.doe.gov/avestar) is designed to do. Established as part of the Department of Energy's (DOE) initiative to advance new clean energy technology for power generation, the AVESTAR Center focuses primarily on providing simulation-based training for process engineers and energy plant operators, starting with the deployment of a first-of-a-kind operator training simulator for an integrated gasification combined cycle (IGCC) power plant with CO{sub 2} capture. The IGCC dynamic simulator builds on, and reaches beyond, conventional power plant simulators to merge, for the first time, a 'gasification with CO{sub 2} capture' process simulator with a 'combined-cycle' power simulator. Based on Invensys Operations Management's SimSci-Esscor DYNSIM software, the high-fidelity dynamic simulator provides realistic training on IGCC plant operations, including normal and faulted operations, as well as plant start-up, shutdown and power demand load changes. The highly flexible simulator also allows for testing of different types of fuel sources, such as petcoke and biomass, as well as co-firing fuel mixtures. The IGCC dynamic simulator is available at AVESTAR's two locations, NETL (Figure 1) and West Virginia University's National Research Center for Coal and Energy (www.nrcce.wvu.edu), both in Morgantown, W.Va. By offering a comprehensive IGCC training program, AVESTAR aims to develop a workforce well prepared to operate, control and manage commercial-scale gasification-based power plants with CO{sub 2} capture. The facility and simulator at West Virginia University promotes NETL's outreach mission by offering hands-on simulator training and education to researchers and university students.