Molecular Beams To Simulate Impinging Rocket Exhaust
Report discusses development of small-scale apparatus to generate molecular beams for use in simulating impingement of rocket-engine exhaust on nearby spacecraft surfaces.
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Report discusses development of small-scale apparatus to generate molecular beams for use in simulating impingement of rocket-engine exhaust on nearby spacecraft surfaces.
Here we report flow statistics and visualizations from molecular-gas-dynamics simulations using the direct simulation Monte Carlo (DSMC) method for turbulent Couette flow in a minimal domain where the lower wall is replaced by an idealized permeable fibrous substrate representative of thermal-protection-system materials for which the Knudsen number is O(10 -1 ). Comparisons are made with smooth-wall DSMC simulations and smooth-wall direct numerical simulations (DNS) of the Navier-Stokes equations for the same conditions. Roughness, permeability, and noncontinuum effects are assessed. In the range of Reynolds numbers considered herein, the scalings of the skin friction on the permeable substrate and of the mean flow within the substrate suggest that they are dominated by viscous effects. While the regenerative cycle characteristic of smooth-wall turbulence remains intact for all cases considered, we observe that the near-wall velocity fluctuations are modulated by the permeable substrate with a wavelength equal to the pore spacing. Additionally, the flow within the substrate shows significant rarefaction effects, resulting in an apparent permeability that is 13% larger than the intrinsic permeability. In contrast, the smooth-wall DSMC and DNS simulations exhibit remarkably good agreement for the statistics examined, despite the Knudsen number based on the viscous length scale being as large as O(10 -1 ). This latter result is at variance with classical estimates for the breakdown of the continuum assumption and calls for further investigations into the interaction of noncontinuum effects and turbulence.
While drug resistance mutations can often be attributed to the loss of direct or solvent-mediated protein−ligand interactions in the drug-mutant complex, in this study we show that a resistance mutation for the picomolar HIV-1 capsid (CA)-targeting antiviral (GS-6207) is mainly due to the free energy cost of the drug-induced protein side chain reorganization in the mutant protein. Among several mutations, M66I causes the most suppression of the GS-6207 antiviral activity (up to ~84,000-fold), and only 83- and 68-fold reductions for PF74 and ZW-1261, respectively. To understand the molecular basis of this drug resistance, we conducted molecular dynamics free energy simulations to study the structures, energetics, and conformational free energy landscapes involved in the inhibitors binding at the interface of two CA monomers. To minimize the protein−ligand steric clash, the I66 side chain in the M66I−GS-6207 complex switches to a higher free energy conformation from the one adopted in the apo M66I. In contrast, the binding of GS-6207 to the wild-type CA does not lead to any significant M66 conformational change. Based on an analysis that decomposes the absolute binding free energy into contributions from two receptor conformational states, it appears that it is the free energy cost of side chain reorganization rather than the reduced protein−ligand interaction that is largely responsible for the drug resistance against GS-6207.
A code for the simulation of nonequilibrium plasmas is being developed, with the capability to couple the plasma fluid-dynamics for a single fluid with a collisional-radiative model, where electronic states are treated as separate species. The model allows for non-Boltzmann distribution of the electronic states. Deviations from the Boltzmann distributions are expected to occur in the rapidly ionizing regime behind a strong shock or in the recombining regime during a fast expansion. This additional step in modeling complexity is expected to yield more accurate predictions of the nonequilibrium state and the radiation spectrum and intensity. An attempt at extending the code to molecular plasma flows is presented. The numerical techniques used, the thermochemical model, and the results of some numerical tests are described.
Experimental studies of collective dynamics in lipid bilayers have been challenging due to the energy resolution required to observe these low-energy phonon-like modes. However, inelastic X-ray scattering (IXS) measurements — a technique for probing vibrations in soft and biological materials — are now possible with sub-meV resolution, permitting direct observation of low energy, phonon-like modes in lipid membranes. Here, in this study, IXS measurements with sub-meV energy resolution reveal a low-energy optic-like phonon mode at roughly 3 meV in the liquid-ordered (L o ) and liquid-disordered (L d ) phases of a ternary lipid mixture. This mode is only observed experimentally at momentum transfers greater than 5 nm -1 in the L o system. A similar gapped mode is also observed in all-atom molecular dynamics (MD) simulations of the same mixture, indicating that the simulations accurately represnt the fast, collective dynamics in the L o phase. It’s optical nature and the Q range of the gap together suggest that the observed mode is due to the coupled motion of cholesterol-lipid pairs, separated by several hydrocarbon chains within the membrane plane. Analysis of the simulations provide molecular insight into the origin of the mode in transient, nanoscale substructures of hexagonally packed hydrocarbon chains. This nanoscale hexagonal packing was previously reported based on molecular dynamics simulations and later by NMR measurements. Here, however, the integration of IXS and MD simulations identifies a new signature of the L o substructure in the collective lipid dynamics, thanks to the recent confluence of IXS sensitivity and MD simulation capabilities.
Ranges of validity for the continuum-beam model, the length-scale effects and continuum assumptions are analyzed in the framework of scaling analysis of NT structure. Two coupled criteria for the applicability of the continuum model are presented. Scaling analysis of NT buckling and geometric parameters (e.g., diameter and length) is carried out to determine the key non-dimensional parameters that control the buckling strains and modes of NT buckling. A model applicability map, which represents two classes of NTs, is constructed in the space of non-dimensional parameters. In an analogy with continuum mechanics, a mechanical law of geometric similitude is presented for two classes of beam-like NTs having different geometries. Expressions for the critical buckling loads and strains are tailored for the distinct groups of NTs and compared with the data provided by the molecular dynamics simulations. Implications for molecular dynamics simulations and the NT-based scanning probes are discussed.
The application of asphalt materials in pavement engineering has been increasingly widespread and sophisticated over the past several decades. Variations in the properties of asphalt binder during mixing, transportation, and paving can affect the performance of asphalt pavement. However, the asphalt material is a non-homogeneous and complex organic substance, consisting of various molecules with widely various molecular weights, elemental compositions, and structures. This complexity leads to difficulties for researchers to clearly and immediately understand the properties of asphalt materials and their variations. The multi-scale research approach combines macroscopic experimental data and microscopic simulation results from a practical engineering perspective. It helps to improve the understanding of asphalt materials. The molecular dynamics (MD) simulation proposes a corresponding molecular model of asphalt material based on experimental data, and the simulation algorithm is able to derive properties similar to those of real asphalt. Here, this paper provides a comprehensive review of the current studies on MD simulation of asphalt materials, including modeling, properties, and multi-scale analysis. As a key part of the computational simulation, this paper discusses the typical asphalt binder and asphalt-aggregate interface models constructed by different groups, and also presents their differences from real samples and their feasibility based on fundamental properties. After the introduction of molecular models, the extensive work made by researchers based on molecular models is categorically reviewed and discussed. The strengths and weaknesses of MD simulation methods in the study of asphalt materials are also summarized in order to provide the reader with a more comprehensive understanding of the relevant contents and to guide subsequent research.
Using all-atom molecular dynamics simulations and a variety of experimental methods, we previously reported on a linear polyethylene with pendant phenyl sulfonated groups precisely on every fifth carbon along the backbone. With increasing relative humidity this fluorine-free polymer self-assembled to form nanoscale water channels and exhibited exceptional proton conductivity. Expanding upon those findings, here we explore partially sulfonated random copolymers, referred to as p 5PhSH-Y. Using either acetyl sulfate or sulfuric acid, a wide range of sulfonation levels were prepared ( Y = 34−98%) corresponding to ion-exchange capacities (IEC) of 2.0−4.4 mmol/g. Combining experimental techniques and all-atom molecular dynamics simulations, we study the effect of Y on water uptake, nanoscale morphology, and the proton/water transport properties of p5PhSH- Y . The proton conductivity of p 5PhSH- Y increases with relative humidity and with Y and achieves values in excess of 0.1 S/cm. These high conductivities are attributed to high IEC and welldeveloped nanoscale percolated hydrophilic domains made possible by the flexible backbone. We quantitatively describe the nature of the water channels using the characteristic distance, channel width distribution, the area per sulfonate group at the hydrophilic/ hydrophobic interface, and the fractal dimension. Notably, the channel widths and the areas per sulfonate group are nominally independent of the level of sulfonation, while depending significantly on the level of hydration. The fractal dimension of the water channels correlates strongly with the water diffusion coefficients calculated from the molecular dynamics (MD) simulations. These findings demonstrate that the p 5PhSH- Y hydrocarbon copolymers can be modified to tune properties, particularly proton conductivity.
Using ab initio molecular dynamics (AIMD) simulations, classical molecular dynamics (CMD) simulations, small-angle X-ray scattering (SAXS), and pulsed-field gradient nuclear magnetic resonance (PFG-NMR), the solvation structure and ion dynamics of magnesium bis(trifluoromethanesulfonyl)imide (Mg(TFSI) 2 ) aqueous electrolyte at 1, 2, and 3 m concentrations are investigated. From AIMD and CMD simulations, the first solvation shell of an Mg 2+ ion is found to be composed of six water molecules in an octahedral configuration and the solvation shell is rather rigid. The TFSI - ions prefer to stay in the second solvation shell and beyond. Meanwhile, the comparable diffusion coefficients of positive and negative ions in Mg(TFSI) 2 aqueous electrolytes have been observed, which is mainly due to the formation of the stable [Mg(H 2 O) 6 ] 2+ complex, and, as a result, the increased effective Mg ion size. Finally, the calculated correlated transference numbers are lower than the uncorrelated ones even at the low concentration of 2 and 3 m, suggesting the enhanced correlations between ions in the multivalent electrolytes. This work provides a molecular-level understanding of how the solvation structure and multivalency of the ion affect the dynamics and transport properties of the multivalent electrolyte, providing insight for rational designs of electrolytes for improved ion transport properties.
A brief synopsis of the low Earth orbit (LEO) satellite environment is presented including neutral and ionic species. Two ground based atomic and molecular beam instruments are described which are capable of simulating the interaction of spacecraft surfaces with the LEO environment and detecting the results of these interactions. The first detects mass spectrometrically low level fluxes of reactively and nonreactively surface scattered species as a function of scattering angle and velocity while the second ultrahigh velocity (UHV) molecular beam, laser induced fluorescence apparatus is capable of measuring chemiluminescence produced by either gas phase or gas-surface interactions. A number of proposed experiments are described.
Many scientific applications are inherently multiscale in nature. Such complex physical phenomena often require simultaneous execution and coordination of simulations spanning multiple time and length scales. This is possible by combining expensive small-scale simulations (such as molecular dynamics simulations) with larger scale simulations (such continuum limit/hydro solvers) to allow for considerably larger systems using task and data parallelism. However, the granularity of the tasks can be very large and often leads to load imbalance. Traditionally, we use approximations to streamline the computation of the more costly interactions and this introduces trade-offs between simulation cost and accuracy. In recent years, the available computational power and the advances in machine learning have made computing these scale-bridging interactions and multiscale simulations more feasible. One driving application has been plasma modeling in inertial confinement fusion (ICF), which is fundamentally multiscale in nature. This requires deep understanding of how to extrapolate microscopic information into macroscopically relevant scales. For example, in ICF one needs an accurate understanding of the connection between experimental observables and the underlying microphysics. The properties of the larger scales are often affected by the microscale behavior incorporated usually into the equations of state and ionic and electronic transport coefficients (Liboff, 1959; Rinderknecht et al., 2014; Rosenberg et al., 2015; Ross et al., 2017). Instead of incorporating this information using reliable molecular dynamics (MD) simulations, one often needs to use theoretical models, due to the inability of MD to reach engineering scales (Glosli et al., 2007; Marinak et al., 1998). One approach to resolve this issue is by coupling two MD simulations of different scales via force interpolation, e.g., the AdResS method (Krekeler et al., 2018; Nagarajan et al., 2013). Another approach, which we will pursue in the scope of this work, is by enabling scale bridging between MD simulations and meso/macro-scale models through the development and support of application programming interfaces that these different applications can interact through.
We report the ability to make sense of the massive amounts of high-dimensional data generated from molecular dynamics simulations is heavily dependent on the knowledge of a low-dimensional manifold (parameterized by a reaction coordinate or RC) that typically distinguishes between relevant metastable states, and which captures the relevant slow dynamics of interest. Methods based on machine learning and artificial intelligence have been proposed over the years to deal with learning such low-dimensional manifolds, but they are often criticized for a disconnect from more traditional and physically interpretable approaches. To deal with such concerns, in this work we propose a deep learning based state predictive information bottleneck approach to learn the RC from high-dimensional molecular simulation trajectories. We demonstrate analytically and numerically how the RC learnt in this approach is connected to the committor in chemical physics and can be used to accurately identify transition states. A crucial hyperparameter in this approach is the time delay or how far into the future the algorithm should make predictions about. Through careful comparisons for benchmark systems, we demonstrate that this hyperparameter choice gives useful control over how coarse-grained we want the metastable state classification of the system to be. We thus believe that this work represents a step forward in systematic application of deep learning based ideas to molecular simulations.
A molecular level understanding of the structure and energetics of the monovalent and divalent metal ion complexes is of great importance for development of next-generation batteries. Here, Density Functional Theory (DFT) simulations at the ωb97xD/6-31 + G(d,p) level of theory are performed to investigate the interaction of metal ions (Li + , Na + , K + , Mg 2+ , Ca 2+ , Zn 2+ ) with 26 organic solvent molecules. The reduction energetics (electron affinity and reduction potential) and structural responses of the solvent molecules and the molecular complexes are discussed. The DFT calculations are carried out to investigate the structure, energetics and electron affinities of chelated complexes of water (H 2 O), tetrahydrofuran (THF) and di-methoxy ethane (DME) solvent molecules. Additionally, ab initio dynamic simulations (AIMD) at 298 K using atom centered density matrix propagation (ADMP) formalism are performed to understand the spontaneous structure formation upon electron attachment of the metal ion-solvent complexes. The ADMP simulations indicate the decomposition of Mg + -(DME) 3 complex via cleavage of C–O bond of one of the three DME molecules indicating irreversible decomposition of DME in the presence of the Mg + radical. We believe that the data collected as part of this investigation serves as a library of fundamental knowledge towards a deeper understanding of the electrode-electrolyte interfacial reactions.
New polymeric blend membranes for CO 2 separation were synthesized based on insights from molecular dynamics simulation. A molecular-level structure-property relationship in polymers of intrinsic microporosity (PIM) based blend membranes, was investigated in detail computationally. Calculated local density profiles and energy of interaction of the blend membranes, composed of PIM-1 and various polyphosphazenes, showed that using the polyphosphazene with a higher concentration of ether side chains can improve the compatibility with PIM-1. Furthermore, based on the findings of computational studies, blend membranes were experimentally fabricated from PIM-1 and polyphosphazenes with various polyether side chain concentrations. Polyether concentration in polyphosphazenes was correlated with the film properties and gas transport performance of the blend membranes. Blend membranes showed very high CO 2 permeability (3100-5300 barrer) and improved CO 2 /N 2 selectivity (24-28), outperforming all other PIM-based blend membranes reported to date. Moreover, the CO 2 permeability performance of the blend membranes was tested 566 hours under real post-combustion flue gas from a coal-fired power plant, including CO 2 , N 2 , H 2 O, O 2 , SO x and NO x .
In an earlier paper, Khare and Sagan reported the production of a brownish polymeric material from the near-ultraviolet irradiation of simulated jovian atmospheres with a low hydrogen abundance. Examination of this product indicates that hydrogen sulfide is the initial photon acceptor; the powder resulting after extraction with benzene is 84 percent sulfur, largely S8. In results reported here, the remaining 16 percent was pyrolyzed and then examined by gas chromatography-mass spectrometry. Pyrolysis at 450 C yielded a series of alkanes, alkenes, C3-alkylbenzenes, aromatics, thiophenes, alkylthiophenes, alkylmercaptans, alkyldisulfides, together with the nitrogenous compounds hydrogen cyanide, methyl cyanide, alkylisothiocyanates, acrylonitrile, and allylisothiocyanates. Some of these compounds might be sought on Jupiter and Saturn and their satellites by remote infrared and ultraviolet spectroscopy and directly by entry probes.
The presence of water in propellant-grade MON-3 is a concern in the Aerospace Industry. NASA Johnson Space Center (JSC), White Sands Test Facility (WSTF) Propulsion Department has evaluated many types of molecular sieves for control of iron, the corrosion product of water in Mixed Oxides of Nitrogen (MON-3). In 1995, WSTF initiated laboratory and pilot-scale testing of molecular sieve type 3A for removal of water and iron. These tests showed sufficient promise that a series of continuous recycle tests were conducted at WSTF. Periodic samples of the circulating MON-3 solution were analyzed for water (wt %) and iron (ppm, wt). This test column was modeled as a series of transfer units; i. e., each unit represented the height equivalent of a theoretical plate. Such a model assumes there is equilibrium between the adsorbent material and the effluent stream from the unit. Operational and design parameters were derived based on the simulation results. These parameters were used to predict the design characteristics of a proposed molecular sieve column for removal of water and iron from MON-3 at the NASA Kennedy Space Center (KSC). In addition, these parameters were used to simulate a small, single-pass operation column at KSC currently used for treating MON-3. The results of this work indicated that molecular sieve type 3A in 1/16 in. diameter pellets, in a column 2.5 ft. in diameter, 18 ft. in height, and operated at 25 gpm is adequate for the required removal of water and iron from MON-3.
Hydrogen is a clean alternative to fossil fuels, emitting only water vapor during combustion. In a future hydrogen economy, large-scale storage will be an important component of the supply chain. Due to its low volumetric density, conventional surface storage methods are inadequate. Underground hydrogen storage (UHS) offers a viable solution, enabling the storage of millions of cubic meters. Among potential geological sites, including salt caverns, aquifers, and depleted gas reservoirs, depleted oil reservoirs show promise. Studying hydrogen interactions with residual oil and reservoir minerals is vital for understanding the properties of hydrogen and the reservoir post-injection. In this work, we employed molecular dynamics simulations to gain molecular-level insights into these interactions. We investigated hydrogen dissolution in oil, adsorption at kaolinite/oil interfaces, the role of CO 2 as a cushion gas, and the influence of kaolinite’s hydrophobicity on H 2 behavior. The main findings include: (1) hydrogen dissolves more in oil than in water, (2) the introduction of CO 2 suppresses hydrogen dissolution in oil and reduces the interfacial tension (IFT) between oil and gas, (3) CO 2 decreases H 2 partitioning near kaolinite surfaces due to its strong affinity for the hydrophilic gibbsite surface of kaolinite, and (4) CO 2 is more effective than H 2 in reducing IFT between kaolinite and the oil–gas mixture. These findings emphasize the effectiveness of CO 2 as a cushion gas and the important role of clay hydrophobicity in UHS, providing insights that are challenging to obtain experimentally.
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