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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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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Atomistic Modeling of Pd Site Preference in NiTi

An analysis of the site subsitution behavior of Pd in NiTi was performed using the BFS method for alloys. Through a combination of Monte Carlo simulations and detailed atom-by-atom energetic analyses of various computational cells, representing compositions of NiTi with up to 10 at% Pd, a detailed understanding of site occupancy of Pd in NiTi was revealed. Pd subsituted at the expense of Ni in a NiTi alloy will prefer the Ni-sites. Pd subsituted at the expense of Ti shows a very weak preference for Ti-sites that diminishes as the amount of Pd in the alloy increases and as the temperature increases.

Bozzolo, Guillermo↗
NASA NTRS2003

Scalability of a Low-Cost Multi-Teraflop Linux Cluster for High-End Classical Atomistic and Quantum Mechanical Simulations

Scalability of a low-cost, Intel Xeon-based, multi-Teraflop Linux cluster is tested for two high-end scientific applications: Classical atomistic simulation based on the molecular dynamics method and quantum mechanical calculation based on the density functional theory. These scalable parallel applications use space-time multiresolution algorithms and feature computational-space decomposition, wavelet-based adaptive load balancing, and spacefilling-curve-based data compression for scalable I/O. Comparative performance tests are performed on a 1,024-processor Linux cluster and a conventional higher-end parallel supercomputer, 1,184-processor IBM SP4. The results show that the performance of the Linux cluster is comparable to that of the SP4. We also study various effects, such as the sharing of memory and L2 cache among processors, on the performance.

Kikuchi, Hideaki↗
NASA NTRS2012

Development and Application of Interatomic Potentials for Ultra High Temperature Ceramics (UHTC): ZrB2 and HfB2

Ultra high temperature ceramics (UHTC) including ZrB2 and HfB2 are characterized by high melting point, good strength, and reasonable oxidation resistance. These materials are of interest for use as sharp leading edges for hypersonic vehicles among other applications. Progress in computational modeling of UHTCs has been limited in part due to the absence of suitable interatomic potentials. We present a Tersoff style parameterization of such potentials for ZrB2 and HfB2 appropriate for atomistic simulations. Parameters are fit to data generated from ab initio calculations. The accuracy of the potentials is assessed against further ab initio data. As a first non ]trivial application, molecular dynamics simulations are performed to evaluate the thermal conductivity of single crystals and the thermal resistance of high symmetry grain boundaries.

Lawson, John W.↗
NASA NTRS2009

An Overview of the State of the Art in Atomistic and Multiscale Simulation of Fracture

The emerging field of nanomechanics is providing a new focus in the study of the mechanics of materials, particularly in simulating fundamental atomic mechanisms involved in the initiation and evolution of damage. Simulating fundamental material processes using first principles in physics strongly motivates the formulation of computational multiscale methods to link macroscopic failure to the underlying atomic processes from which all material behavior originates. This report gives an overview of the state of the art in applying concurrent and sequential multiscale methods to analyze damage and failure mechanisms across length scales.

Saether, Erik↗
NASA NTRS2019

AladynPi – Adaptive Neural Network Molecular Dynamics Simulation Code with Physically Informed Potential: Computational Materials Mini-Application

This report provides an overview and description of commands used in the Computational Materials mini-application, AladynPi. AladynPi is an extension of a previously released mini-application, Aladyn (https://github.com/nasa/aladyn; Yamakov, V.I., and Glaessgen, E.H., NASA/TM-2018-220104). Aladyn and AladynPi are basic molecular dynamics codes written in FORTRAN 2003, which are designed to demonstrate the use of adaptive neural networks (ANNs) in atomistic simulations. The role of ANNs is to efficiently reproduce the very complex energy landscape resulting from the atomic interactions in materials with the accuracy of the more expensive quantum mechanics-based calculations. The ANN is trained on a large set of atomic structures calculated using the density functional theory method. An input for the ANN is a set of structure coefficients, characterizing the local atomic environment of each atom, for which the atomic energy is obtained in the ANN inference process. In Aladyn, the ANN gives directly the energy of interatomic interactions. In AladynPi, the ANN gives optimized parameters for a predefined empirical function, known as bond-order-potential (BOP). The parameterized BOP function is then used to calculate the energy. AladynPi code is being released to serve as a training testbed for students and professors in academia to explore possible optimization algorithms for parallel computing on multicore central processing unit (CPU) computers or computers utilizing manycore architectures based on graphic processing units (GPUs). The effort is supported by the High Performance Computing incubator (HPCi) project at NASA Langley Research Center.

Yamakov, Vesselin I.↗
NASA NTRSDate not supplied

Predicting Melt Properties Using Atomistic Simulations With A Highly Accurate Physically Informed Neural Network Interatomic Potential

The use of a recently developed machine learning (ML) interatomic potential for molecular dynamics simulations of aluminum melt properties will be presented. Such properties are critical for process modeling in additive manufacturing, including the melt pool size, solidification, and formation of solidification microstructures. Direct first-principles modeling of these processes is computationally prohibitive whereas simulations employing ML potentials combine the high accuracy of quantum-mechanical methods with high computational speeds. The physically-informed neural network (PINN) method used herein, integrates a high-dimensional regression implemented by an artificial neural network with a physics-based bond-order interatomic potential. PINN potentials can accurately reproduce many properties of aluminum in both crystalline-solid and liquid phases. We examine the accuracy of a PINN Al potential in predicting the density, self-diffusivity, viscosity, and the tension of the liquid surface and liquid-solid interfaces. Comparison with experimental data and ab initio molecular dynamics calculations shows very good agreement for all properties tested.

molecular dynamics↗
NASA NTRS2004

Nanoscale Bio-engineering Solutions for Space Exploration: The Nanopore Sequencer

Characterization of biological systems at the molecular level and extraction of essential information for nano-engineering design to guide the nano-fabrication of solid-state sensors and molecular identification devices is a computational challenge. The alpha hemolysin protein ion channel is used as a model system for structural analysis of nucleic acids like DNA. Applied voltage draws a DNA strand and surrounding ionic solution through the biological nanopore. The subunits in the DNA strand block ion flow by differing amounts. Atomistic scale simulations are employed using NASA supercomputers to study DNA translocation, with the aim to enhance single DNA subunit identification. Compared to protein channels, solid-state nanopores offer a better temporal control of the translocation of DNA and the possibility to easily tune its chemistry to increase the signal resolution. Potential applications for NASA missions, besides real-time genome sequencing include astronaut health, life detection and decoding of various genomes.

Stolc, Viktor↗
NASA NTRS2005

Nanoscale Bioengineering Solutions for Space Exploration the Nanopore Sequencer

Characterization of biological systems at the molecular level and extraction of essential information for nano-engineering design to guide the nano-fabrication of solid-state sensors and molecular identification devices is a computational challenge. The alpha hemolysin protein ion channel is used as a model system for structural analysis of nucleic acids like DNA. Applied voltage draws a DNA strand and surrounding ionic solution through the biological nanopore. The subunits in the DNA strand block ion flow by differing amounts. Atomistic scale simulations are employed using NASA supercomputers to study DNA translocation. with the aim to enhance single DNA subunit identification. Compared to protein channels, solid-state nanopores offer a better temporal control of the translocation of DNA and the possibility to easily tune its chemistry to increase the signal resolution. Potential applications for NASA missions, besides real-time genome sequencing include astronaut health, life detection and decoding of various genomes. http://phenomrph.arc.nasa.gov/index.php

Ioana, Cozmuta↗
NASA NTRS1996

New Theoretical Technique Developed for Predicting the Stability of Alloys

When alloys are being designed for aeronautical and other applications, a substantial experimental effort is necessary to make incremental changes in the desired alloy properties. A scheme to narrow the field to the most promising candidates would substantially reduce the high cost of this experimental screening. Such a method for determining alloy properties, called the BFS (Bozzolo, Ferrante, and Smith) method, has been developed at the NASA Lewis Research Center. This method was used to calculate the thermal stability and mechanical strength of 200 alloys of Ni3Al, with Cu and Au impurities forming ternary and quaternary compounds. With recent advances in the method, almost any metallic impurity and crystal structure can be addressed. In addition, thermal effects can be addressed with Monte Carlo techniques. At present, an experimental program is in progress to verify these results. The method identified a small number of the most promising candidates from the 200 alloys with the largest negative heat of formation and the highest bulk modulus. This calculation required only 5 min of CPU time on a VAX computer. It is clear that semi-empirical methods have achieved the level of development and reliability to warrant examining this new approach to the problem of alloy design. The present work was meant to demonstrate, perhaps in a rather simple way, this power. This type of application of atomistic simulation methods can narrow the gap and improve the feedback between theoretical predictions and laboratory experimentation.

Source record↗
NASA NTRSDate not supplied

Machine-Learned Committor Functions for Reactive Molecular Dynamics

Reactive molecular dynamics (MD) is a powerful tool for atomistic-scale modeling of a diverse range of chemical processes. However, scaling these simulations to large systems and long times scales remains a challenge because of the complexity of the potential energy function required. The authors previously developed a heuristic approach, called REACTER, that incorporates reactivity in MD simulations in a less general but much more computationally efficient manner. REACTER uses standard, fixed valence force fields as the underlying potentialenergy surface for describing all interatomic interactions but adds a procedure for enforcing user-defined reactions that occur when certain geometric constraints on relative atomic positions are satisfied. Further, these bonding changes can be accepted or rejected with a probability related tothe local thermal energy. This work seeks to generalize this approach by replacing the set of user defined geometric constraints and energetic criteria with a committor function that specifies the probability of a reaction occurring on the basis of the local atomic configuration. The committor function is a useful mathematical tool for modeling rare events but, unfortunately, is very difficult to compute for realistic systems in a general way. This work describes a method for approximating the committor function using a machine learning approach, specifically a deep neural network trained with data from reactive MD and DFT-based dynamics simulations. This network is coupled to the existing REACTER protocol, as implemented in the LAMMPS MD package, and used to make on-the-fly predictions of reaction probabilities without the more extensive user input previously required. The new method is demonstrated using the polymerization of polystyrene as a case study. Although very dependent on the quality and quantity of training data, machine-learned committor functions show promise as a method for incorporating reaction probability from higher level calculations into highly scalable MD simulations.

polymer simulations↗
NASA NTRS2002

Atomistic Modeling of RuAl and (RuNi) Al Alloys

Atomistic modeling of RuAl and RuAlNi alloys, using the BFS (Bozzolo-Ferrante-Smith) method for alloys is performed. The lattice parameter and energy of formation of B2 RuAl as a function of stoichiometry and the lattice parameter of (Ru(sub 50-x)Ni(sub x)Al(sub 50)) alloys as a function of Ni concentration are computed. BFS based Monte Carlo simulations indicate that compositions close to Ru25Ni25Al50 are single phase with no obvious evidence of a miscibility gap and separation of the individual B2 phases.

Gargano, Pablo↗
NASA NTRS2012

Multiscale Modeling of Grain Boundaries in ZrB2: Structure, Energetics, and Thermal Resistance

A combination of ab initio, atomistic and finite element methods (FEM) were used to investigate the structures, energetics and lattice thermal conductance of grain boundaries for the ultra high temperature ceramic ZrB2. Atomic models of idealized boundaries were relaxed using density functional theory. Information about bonding across the interfaces was determined from the electron localization function. The Kapitza conductance of larger scale versions of the boundary models were computed using non-equilibrium molecular dynamics. The interfacial thermal parameters together with single crystal thermal conductivities were used as parameters in microstructural computations. FEM meshes were constructed on top of microstructural images. From these computations, the effective thermal conductivity of the polycrystalline structure was determined.

Lawson, John W.↗
NASA NTRS2002

Surface Composition of NiPd Alloys

Surface segregation in Ni-Pd alloys has been studied using the BFS method for alloys. Not only does the method predict an oscillatory segregation profile but it also indicates that the number of Pd-enriched surface planes can vary as a function of orientation. The segregation profiles were computed as a function of temperature, crystal face, and composition. Pd enrichment of the first layer is observed in (111) and (100) surfaces, and enrichment of the top two layers occurs for (110) surfaces. In all cases, the segregation profile shows oscillations that are actually related to weak ordering tendencies in the bulk. An atom-by-atom analysis was performed to identify the competing mechanisms leading to the observed surface behaviors. Large-scale atomistic simulations were also performed to investigate the temperature dependence of the segregation profiles as well as for analysis of the bulk structures. Finally, the observed surface behaviors are discussed in relation to the bulk phase structure of Ni-Pd alloys, which exhibit a tendency to weakly order.

Noebe, Ronald D.↗
NASA NTRSDate not supplied

Fracture Prediction of Epoxy Resin using Morse Bond Potential Embedded in GAFF using Molecular Dynamics Simulations

A generic method is developed to investigate the fracture behavior of an epoxy resin using atomistic molecular (MD) dynamics simulations. The epoxy system consisted of the stoichiometric mixture of tetra-/tri-functional epoxies and di-functional hardener molecules and was cured with a step-growth crosslinking algorithm. A hybrid force field (hFF) in which Morse bond function were added in second generation-general Amber Force Field (GAFF2) was used to capture bond breaking of the crosslinked epoxy system under uniaxial deformation. The Morse bond parameters for covalent bonds in the crosslinked backbone were fit to dissociation curves computed by CASPT2/6-311+G**. After the systems in various crosslink densities were obtained and equilibrated, the full length of stress-strain (𝜎-𝜀) curve was produced including initial elastic regime, yielding, plastic flow, strain hardening, and progressive failure. We found that the hFF was effective in studying the dissociation of crosslinked polymers and understanding the failure mechanism. In the results, the hFF delivered the same elastic property obtained from the unmodified force field GAFF. The stress-strain curve, however, began to be distinct in the plastic regime due to broken covalent bonds capable of altering the tensile behavior. Once the stress reached maximum, it progressively decreased until the ultimate failure (𝜎=0). Since hFF employed the existing GAFF functions with including a minimal change of its bond potential, this approach is easier to perform and computationally more efficient for unveiling the fracture behavior of polymer materials at the molecular level than other approaches with reactive force fields. We expect this approach will be utilized to accelerate the material-by-design process for thermosets by incorporate data from molecular models.

Changwoon Jang↗
NASA NTRS2019

Physically-Informed Artificial Neural Networks for Atomistic Modeling of Materials

A new approach is presented for the development of classical interatomic potentials using physically-informed neural networks (PINN) combined with an analytical bond-order atomic interaction model. Due to the strong physical underpinnings, the PINN potentials demonstrate much better transferability than the existing machine-learning potentials while drastically improving the accuracy in comparison with traditional potentials. PINN potentials can be constructed for both metallic and covalent materials in a unified manner. A number of applications of PINN potentials to large-scale molecular dynamics and Monte Carlo simulations and calculation of thermal and mechanical properties of diverse materials are demonstrated. Some of the specific materials systems include silicon and aluminum, as well as alloys and compounds. Computational aspects of PINN potentials are discussed and future developments in this field are outlined.

J Hickman↗
NASA NTRS2006

Computer Simulation of Fracture in Aerogels

Aerogels are of interest to the aerospace community primarily for their thermal properties, notably their low thermal conductivities. While the gels are typically fragile, recent advances in the application of conformal polymer layers to these gels has made them potentially useful as lightweight structural materials as well. In this work, we investigate the strength and fracture behavior of silica aerogels using a molecular statics-based computer simulation technique. The gels' structure is simulated via a Diffusion Limited Cluster Aggregation (DLCA) algorithm, which produces fractal structures representing experimentally observed aggregates of so-called secondary particles, themselves composed of amorphous silica primary particles an order of magnitude smaller. We have performed multi-length-scale simulations of fracture in silica aerogels, in which the interaction b e e n two secondary particles is assumed to be described by a Morse pair potential parameterized such that the potential range is much smaller than the secondary particle size. These Morse parameters are obtained by atomistic simulation of models of the experimentally-observed amorphous silica "bridges," with the fracture behavior of these bridges modeled via molecular statics using a Morse/Coulomb potential for silica. We consider the energetics of the fracture, and compare qualitative features of low-and high-density gel fracture.

Good, Brian S.↗
NASA NTRS2015

Computational Modeling and Experimental Characterization of Martensitic Transformations in Nicoal for Self-Sensing Materials

Fundamental changes to aero-vehicle management require the utilization of automated health monitoring of vehicle structural components. A novel method is the use of self-sensing materials, which contain embedded sensory particles (SP). SPs are micron-sized pieces of shape-memory alloy that undergo transformation when the local strain reaches a prescribed threshold. The transformation is a result of a spontaneous rearrangement of the atoms in the crystal lattice under intensified stress near damaged locations, generating acoustic waves of a specific spectrum that can be detected by a suitably placed sensor. The sensitivity of the method depends on the strength of the emitted signal and its propagation through the material. To study the transition behavior of the sensory particle inside a metal matrix under load, a simulation approach based on a coupled atomistic-continuum model is used. The simulation results indicate a strong dependence of the particle's pseudoelastic response on its crystallographic orientation with respect to the loading direction and suggest possible ways of optimizing particle sensitivity. The technology of embedded sensory particles will serve as the key element in an autonomous structural health monitoring system that will constantly monitor for damage initiation in service, which will enable quick detection of unforeseen damage initiation in real-time and during onground inspections.

Wallace, T. A.↗
NASA NTRS2010

GUI to Facilitate Research on Biological Damage from Radiation

A graphical-user-interface (GUI) computer program has been developed to facilitate research on the damage caused by highly energetic particles and photons impinging on living organisms. The program brings together, into one computational workspace, computer codes that have been developed over the years, plus codes that will be developed during the foreseeable future, to address diverse aspects of radiation damage. These include codes that implement radiation-track models, codes for biophysical models of breakage of deoxyribonucleic acid (DNA) by radiation, pattern-recognition programs for extracting quantitative information from biological assays, and image-processing programs that aid visualization of DNA breaks. The radiation-track models are based on transport models of interactions of radiation with matter and solution of the Boltzmann transport equation by use of both theoretical and numerical models. The biophysical models of breakage of DNA by radiation include biopolymer coarse-grained and atomistic models of DNA, stochastic- process models of deposition of energy, and Markov-based probabilistic models of placement of double-strand breaks in DNA. The program is designed for use in the NT, 95, 98, 2000, ME, and XP variants of the Windows operating system.

Cucinotta, Frances A.↗