Simulation of Deployment Dynamics of Inflatable Structures
Analytical simulation of the inflation process of inflatable structures is key to assessing their robust deployment in space environment.
deployment↗
SEARCH · Engineering Papers
Results for “Dynamic Simulation”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
REACTER 2.0: Quantum-Informed Reaction Constraints and Automated Interaction Typing
REACTER is a heuristic method for modeling chemical reactions in classical molecular dynamics simulations, implemented in LAMMPS as fix bond/react. The authors recently extended LAMMPS to support alphanumeric labels for atom types, bond types etc., which enables the pre- and post-reaction templates required by the REACTER protocol to be portable between different simulations and greatly simplifies the task of creating simulation-ready reaction templates. To further increase the generality of reaction templates, support for wildcard characters within atom types has been added, along with the automatic assignment of interaction types for new bonds, angles, etc. based on the involved atom types. In some cases, this feature can express a class of reactions with one pair of reaction templates, where previously dozens may have been required. Advanced reaction constraints have also been added, including an Arrhenius constraint to enforce an effective activation energy, a root-mean-square-deviation option for complex geometrical constraints, as well as a custom constraint that leverages LAMMPS’ powerful built-in variable framework. Other new features include variable support for various inputs (e.g., to allow reaction rates or cutoffs to be dependent on overall conversion), on-the-fly update of molecule IDs, and the ability to create new atoms positioned with respect to the reaction site. The new features are applied to modeling polymeric, thermosetting and composite materials, and advanced applications of the new reaction constraints are demonstrated. For example, REACTER is shown to accurately reproduce mechanically-induced bond breaking, as characterized by third-order DFT-based tight-binding (DFTB3) simulations, via a constraint on the total potential energy of the involved atoms.
polymer simulations↗
Dynamic Modeling, Simulation, and Analysis of Orbiting Sample Capture for Potential Mars Sample Return
The current notional architecture for the Mars Sample Return (MSR) campaign would require autonomous on-orbit rendezvous and capture of a sample container after it has been delivered to Mars orbit by a preceding MSR mission. As part of the complete orbital payload known as the Capture, Contain, and Return System (CCRS), a concept has been developed for autonomously sensing and capturing this Orbiting Sample (OS) container. The concept, comprised of a capture mechanism and multiple optical break-beam arrays, would detect and enclose the OS into CCRS, and engage a dust-tight seal to prevent the escape of the OS or other unsterilized particles. A challenging goal for the system is to close the lid prior to the OS coming into physical contact after entering CCRS in order to reduce the risk of unsterilized Mars particles on the surface of the OS from contaminating the outside of the spacecraft. The mechanical and sensor performance requirements were driven by the broader architecture of CCRS, OS rendezvous parameters, and Planetary Protection requirements regarding the interaction and handling of the OS. The mechanism and break-beam array concepts were designed accordingly, and kinematic simulations of the mechanism’s behavior, combined with a Monte-Carlo simulation of OS rendezvous/collision behaviors and corresponding breakbeam sensor responses verify compliance.
Ishigo, Alyssa↗
Quantum Mechanical Simulations of Dynamics of Electronic Excitations in Low-Dimensional Photovoltaic Materials
Explore the source record for details and available documents.
Zhigang Wu↗
Machine Learning Based Upscaling of Finite-Box Size Molecular Dynamics Diffusion Simulations.
Abstract not provided.
Leverant, Calen John↗
Quantifying Uncertainty in Transmissional Simulator-Based Dynamic Substructuring.
Abstract not provided.
Lopp, Garret↗
Extraction of Dynamic Inflow Models for Coaxial and Tandem Rotors from CFD Simulations
The dynamic inflow coupling with rotor/body dynamics is crucial in the analysis of stability and control law design for helicopters. Over the past several decades, finite-state inflow models for single rotor configurations in hover, forward flight, and maneuver have developed (Ref.1-3). By capturing the interference effects between rotors, the extension of pressure potential finite state inflow model has promising result for coaxial rotor configuration (Ref.4-6). Recently, the focus of the dynamic inflow modeling has shifted to tandem rotor configurations (Ref.7, 8). The development of the dynamic inflow models for tandem rotor configuration still have some limitations due to the lack of knowledge of rotor-to-rotor interference, and rotor-wake interference. Experimental methods, and computational fluid dynamics methods are commonly used to understand the rotor performance and rotor airload variations, and measure or predict inflow velocity distributions at the rotor desk. The inflow distributions are subsequently used to improve the dynamic inflow models. Tandem rotor configurations have been studied experimentally and computationally for several decades (Ref.9-12). Sweet (Ref.10) observed that a tandem rotor with 76-percent-radius overlap required 14% more induced power at hovering condition, relative to an isolated rotor of equivalent disk area. Sweet also found that, above a shaft-to-shaft distance of 1.03 diameter, the performance of the tandem rotor was nearly the same as two isolated rotors. The objective of the present study is to apply computational fluid dynamics simulations of tandem rotors for the extraction of dynamic inflow models. The extended methodology is first validated by comparing the computed induced power against test data. Subsequently inflow distributions and wake structures are analyzed.
Extraction of Dynamic Inflow Models↗
Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene
Abstract We present a way to dramatically accelerate Gaussian process models for interatomic force fields based on many-body kernels by mapping both forces and uncertainties onto functions of low-dimensional features. This allows for automated active learning of models combining near-quantum accuracy, built-in uncertainty, and constant cost of evaluation that is comparable to classical analytical models, capable of simulating millions of atoms. Using this approach, we perform large-scale molecular dynamics simulations of the stability of the stanene monolayer. We discover an unusual phase transformation mechanism of 2D stanene, where ripples lead to nucleation of bilayer defects, densification into a disordered multilayer structure, followed by formation of bulk liquid at high temperature or nucleation and growth of the 3D bcc crystal at low temperature. The presented method opens possibilities for rapid development of fast accurate uncertainty-aware models for simulating long-time large-scale dynamics of complex materials.
Chemistry↗
Molecular Dynamics Free Energy Simulations Reveal the Mechanism for the Antiviral Resistance of the M66I HIV-1 Capsid Mutation
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.
Sun, Qinfang (ORCID:0000000173621543)↗
Solution Structure and Scaling Laws of Cylindrical and Tapered Bottlebrush Polymers
Bottlebrush polymers are a unique class of macromolecular architectures with a plethora of potential industrial and pharmaceutical applications that critically depend on the bottlebrush shape and dimensions. Here, a systematic series of 12 cylindrical and 12 cone-shaped (tapered) bottlebrush polymers with poly(tert-butyl acrylate) (PtBA) or polystyrene (PS) side chains were synthesized using the sequential addition of macromonomers ring-opening metathesis polymerization (SAM-ROMP) grafting-through method. Small-angle neutron scattering (SANS) studies on dilute solutions of the two types of bottlebrush polymers provided noninvasive characterization of their structural dimensions and chain conformations. Simulated SANS traces, generated using coarse-grained molecular dynamics simulations, reproduced the distinctive features observed in the experimental SANS signals and provided necessary validation for data modeling. The combined analysis of experimental and simulated SANS signals yielded key structural and conformational parameters, including the bottlebrush radius, length, and Kuhn length as well as the excluded volume parameter and the correlation length of the polymer side chains. Importantly, the obtained structural parameters followed well-defined scaling laws as a function of the backbone and side chain degrees of polymerization, as predicted by mean field theories. In conclusion, these findings provide clear experimental and computational evidence of the interdependence of structural and conformational properties in an important class of polymer architectures.
36 MATERIALS SCIENCE↗
Characterization of partial wetting by CMAS droplets using multiphase many-body dissipative particle dynamics and data-driven discovery based on PINNs
The molten sand that is a mixture of calcia, magnesia, alumina and silicate, known as CMAS, is characterized by its high viscosity, density and surface tension. The unique properties of CMAS make it a challenging material to deal with in high-temperature applications, requiring innovative solutions and materials to prevent its buildup and damage to critical equipment. Here, we use multiphase many-body dissipative particle dynamics simulations to study the wetting dynamics of highly viscous molten CMAS droplets. The simulations are performed in three dimensions, with varying initial droplet sizes and equilibrium contact angles. We propose a parametric ordinary differential equation (ODE) that captures the spreading radius behaviour of the CMAS droplets. The ODE parameters are then identified based on the physics-informed neural network (PINN) framework. Subsequently, the closed-form dependency of parameter values found by the PINN on the initial radii and contact angles are given using symbolic regression. Finally, we employ Bayesian PINNs (B-PINNs) to assess and quantify the uncertainty associated with the discovered parameters. In brief, this study provides insight into spreading dynamics of CMAS droplets by fusing simple parametric ODE modelling and state-of-the-art machine-learning techniques.
Mechanics↗
Molecular Simulations Probing the Adsorption and Diffusion of Ammonia, Nitrogen, Hydrogen, and Their Mixtures in Bulk MFI Zeolite and MFI Nanosheets at High Temperature and Pressure
Recent advances in the synthesis of MFI zeolite nanosheets have led to highly selective membranes that are promising candidates for small-scale ammonia separation from ammonia/nitrogen/hydrogen mixtures in distributed green ammonia production plants. Using force-field-based molecular simulations, we evaluate the performance of bulk all-silica MFI zeolite and a 3 nm thick all-silica MFI nanosheet for ammonia/nitrogen/hydrogen separations at moderate temperature and pressure conditions (T = 373 K, p = 5 bar) as well as conditions relevant for a membrane-based reactor–separator process (T = 523 or 623 K, p = 80 bar). Isobaric–isothermal Gibbs ensemble Monte Carlo (GEMC) simulations were carried out to understand the selective adsorption behavior for these mixtures. Molecular dynamics simulations in the canonical ensemble were performed to examine the transport behavior of ammonia in a mixture with nitrogen or hydrogen at loadings obtained from the GEMC simulations. Our results show that both bulk MFI and the nanosheet with explicit surface silanols are highly selective toward ammonia adsorption, but the adsorption selectivity decreases by factors of 4 and 10 for ammonia/nitrogen and ammonia/hydrogen mixtures as the temperature is increased from 373 to 623 K. Conversely, the diffusion selectivities toward ammonia are more favorable at process-relevant temperatures and for the MFI nanosheet. Here, at T = 523 K and p = 80 bar, our simulations predict overall separation factors of 3.8 ± 0.2 and 4.5 ± 0.3 for ammonia/nitrogen and ammonia/hydrogen mixtures, respectively, in the bulk MFI zeolite, and separation factors of 8.6 ± 0.4 and 12.5 ± 0.8, respectively, for the MFI nanosheet.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Quantum fluctuations in dense plasma simulations
Molecular dynamics (MD) simulations are a powerful tool for modeling warm and hot dense matter. Density functional theory (DFT) MD simulations are often preferred in dense plasmas in order to accurately model quantum electronic structure. However, DFT-MD simulations neglect interaction effects due to fluctuations in excited states. In this work, we present an MD approach that uses excited state method pseudoatoms to run dense plasma simulations with many different core-electron configurations at classical MD speeds. We also allow for transitions between different configurations in our simulations and find that these fluctuations are especially important for highly excited states. Our results suggest that finite configuration lifetimes that are comparable to the inverse ion plasma frequency need to be accounted for in order to accurately model ion distributions in dense plasma simulations. We also demonstrate that excited state fluctuations have a direct impact on ion plasma microfields, generate different plasma microfields for different excitation levels, and thereby induce absorption–emission line shape asymmetries even in steady-state plasmas.
36 MATERIALS SCIENCE↗
The importance of long-timescale simulations for driven systems: An example of He bubble growth at a W GB
Accelerated Molecular Dynamics (AMD) is used to study complex systems under realistic conditions by extending the timescales accessible by Molecular Dynamics. However, some studies rely instead on driving atomic systems harder with higher temperature, faster growth, etc. Here, we study He bubble growth at a W grain boundary as an illustration of harnessing AMD methods to avoid consequences of over-driving the system. The growth mechanisms observed for a He bubble grown under realistic conditions are compared to bubbles-grown orders of magnitude faster, at rates typical of conventional molecular dynamics simulations. We find that progressive growth mechanisms and bubble structures depend on the rate at which the bubble is grown providing further evidence that care must be taken when simulating the dynamics of driven systems such as this one.
36 MATERIALS SCIENCE↗
A local dynamic model for large eddy simulation
The dynamic model is a method for computing the coefficient C in Smagorinsky's model for the subgrid-scale stress tensor as a function of position from the information already contained in the resolved velocity field rather than treating it as an adjustable parameter. A variational formulation of the dynamic model is described that removes the inconsistency associated with taking C out of the filtering operation. This model, however, is still unstable due to the negative eddy-viscosity. Next, three models are presented that are mathematically consistent as well as numerically stable. The first two are applicable to homogeneous flows and flows with at least one homogeneous direction, respectively, and are, in fact, a rigorous derivation of the ad hoc expressions used by previous authors. The third model in this set can be applied to arbitrary flows, and it is stable because the C it predicts is always positive. Finally, a model involving the subgrid-scale kinetic energy is presented which attempts to model backscatter. This last model has some desirable theoretical features. However, even though it gives results in LES that are qualitatively correct, it is outperformed by the simpler constrained variational models. It is suggested that one of the constrained variational models should be used for actual LES while theoretical investigation of the kinetic energy approach should be continued in an effort to improve its predictive power and to understand more about backscatter.
Ghosal, Sandip↗
Verification and Validation of Simulation Models for High-Speed Flow Fields
This paper is largely based on the paper titled "Guide to Credible Computational Fluid Dynamics Simulations (Invited)" (AIAA Paper 95-2225). The significance of computational fluid dynamics (CFD) simulations depends solely on their credibility. A customer of CFD products -- simulations and software -- expects that these products are credible for the intended use. Simulation model verification and validation are critical in establishing the credibility of simulations and in certifying simulation software. The only thing that matters in establishing the credibility is uncertainty, not veracity or validity. The sensitivity-uncertainty analysis is the key to the establishment of this credibility. Assessing the credibility of complex simulation results poses a significant challenge. Terminology, concepts, framework, and guidelines are presented for addressing this challenge. Verification assesses whether the problem is solved correctly and estimates the level of computational accuracy of simulations; validation determines whether the right problem is solved and assesses the level of validity of the simulation model by estimating the degree to which simulations accurately represent reality. These concepts and the related guidelines are discussed with examples from high-speed flow fields.
Mehta, Unmeel B.↗
Quantum Simulation of Molecular Dynamics Processes─A Benchmark Study Using a Classical Simulator and Present-Day Quantum Hardware
Here, we explore how the fundamental problems in quantum molecular dynamics can be modeled using classical simulators (emulators) of quantum computers and the actual quantum hardware available to us today. The list of problems we tackle includes propagation of a free wave packet, vibration of a harmonic oscillator, and tunneling through a barrier. Each of these problems starts with the initial wave packet setup. Although Qiskit provides a general method for initializing wave functions, in most cases it generates deep quantum circuits. While these circuits perform well on noiseless simulators, they suffer from excessive noise on quantum hardware. To overcome this issue, we designed a shallower quantum circuit for preparing a Gaussian-like initial wave packet, which improves the performance of real hardware. Next, quantum circuits are implemented to apply the kinetic and potential energy operators for the evolution of a wave function over time. The results of our modeling on classical emulators of quantum hardware agree perfectly with the results obtained using the traditional (classical) methods. This serves as a benchmark and demonstrates that the quantum algorithms and Qiskit codes we developed are accurate. However, the results obtained on the actual quantum hardware available today, such as IBM’s superconducting qubits and IonQ’s trapped ions, indicate large discrepancies due to hardware limitations. This work highlights both the potential and challenges of using quantum computers to solve fundamental quantum molecular dynamics problems.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗