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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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NASA NTRS1992

An extensive ab initio study of the structures, vibrational spectra, quadratic force fields, and relative energetics of three isomers of Cl2O2

Quantum mechanical computational methods are employed for an ab initio investigation of: (1) the molecular properties of the lowest isomers of the ClO dimer; and (2) predicted molecular and thermochemical properties. Techniques employed include electron correlation and particularly singles and doubles coupled-cluster (CCSD) theory with or without perturbational estimates of the effects of connected triple excitations. The isomers ClOClO and ClClO2 are found to have higher energies than the ClOOCl isomer, and the theoretical vibrational frequencies of the isomers are well correlated with experimental data. Experimental values of the heat of formation for the isomers are also compared with calculations based on an isodesmic reaction with Cl2O, H2O, and HOOH.

Lee, Timothy J.↗

NASA NTRS2020

Hierarchical Coupling of Molecular Dynamics and Micromechanics to Predict the Elastic Properties of Three-Phase and Four-Phase Silicon Carbide Composites

The results obtained from previously conducted molecular dynamics analysis of silicon carbide (-SiC (6H, 4H, & 2H-SiC), -SiC (3C SiC)), silicon and boron nitride, were utilized as inputs in the MAC/GMC micromechanics software to model and evaluate the elastic properties of three-phase SiC/BN/SiC and four-phase SiC/BN/Si/SiC composites. This method of analysis eliminates the need for back-calculation of the apparent properties of the base constituents from the measured ceramic matrix composites properties. The multiscale models are validated against the available data in literature.

Aluko, Olanrewaju↗

NASA NTRS2023

Thermomechanical Property Prediction of Amorphous and Crystal PEKK via Molecular Dynamics

Traditionally, advanced aerospace composites have been manufactured using thermoset resins. However, recently, thermoplastics have been investigated for use in the manufacturing of composite materials due to their unique manufacturing characteristics. Thermoplastic resins can be reshaped and formed, along with the added benefit of being recyclable, which thermoset resin cannot. Thermoplastic materials undergo a crystallization process during manufacturing which affects the percent crystallinity of the material. The crystallization needs to be understood better to maximize the potential of thermoplastic resins. PEKK is a thermoplastic material with good chemical, thermal, and mechanical loading resistance. PEKK is also a material NASA is interested in for developing new bonded joint technology. The crystalline microstructure of PEKK is at the micrometer length scale, and it is of interest to model the effects of the crystallinity structure on PEKK’s bulk properties. Molecular dynamics (MD) is a simulation tool that allows for property-structure relationships between atomistic structure and nanometer-length portions of a material. This makes MD a useful tool for developing the structure-property relationship of PEKK. However, the micrometer length scale of PEKK’s crystal structure is too large for MD. Thus, a hybrid approach to modeling PEKK’s microstructure is proposed in this work where MD models are built of both the amorphous and crystalline phases of PEKK. The engineering material properties can be obtained using MD at the nanometer length scale. A micromechanics approach can then generate the micrometer length scale of the crystallinity and the effective properties can be homogenized. The objective of this paper is to show the MD model workflow and the MD-predicted properties of PEKK. The properties can then be homogenized with different crystalline percentages to build design graphs that can be used to tailor PEKK for specific composite applications.

poly ether ketone ketone↗

NASA NTRS2021

Machine Learning the COSMO Model for Predicting Thermodynamics of Electrolyte Mixtures

Bottom-up design of electrolyte mixtures for battery systems requires predicting macro thermodynamic properties from molecular constituents. For instance, molten salt electrolyte batteries require conditions far above room temperature to operate. Therefore, discovering mixtures with increasingly lower eutectic melting points is desirable. A model that can approximate chemical activity is a valuable tool to search through the vast compositional design space. Machine learning can predict properties of materials such as vibrational free energies, electronic energy gaps, and thermal conductivities. Moreover, they can learn physical models such as interatomic potentials. The COSMO-SAC model uses theory and empirical parameterization to predict liquid-vapor and liquid-solid properties using first-principles calculations. However, obtaining activity coefficients required for parameterizing the COSMO-SAC model is costly and limited to a select chemical space. In this work, we explored if machine learning methods could improve the COSMO-SAC model and bridge density functional theory calculations to liquid phase thermodynamic properties. Our data-driven approach uses existing databases for sigma-profiles of organic solvents and reconciles their methodological differences via ensemble averaging. First, an optimal machine learning model is constructed for each dataset. Our machine learning algorithms use the sigma-profile as an input feature to predict binary mixtures' activity coefficients using multi-output regression. Each dataset uses different choices of functionals, methods, and basis sets. Therefore, our ensemble model attempts to predict corrected activity coefficients given the combination of all the model outputs. The activity coefficients used for training are generated using the COSMO-SAC model. This approach enables the extraction of meaningful information from the existing datasets to improve the COSMO-SAC model for obtaining thermodynamic properties of electrolyte mixtures. With the liquid phase activities, we can identify electrolyte mixtures that meet desired phase equilibria conditions.

Thermodynamics↗

NASA NTRS1999

Linear Scaling Density Functional Calculations with Gaussian Orbitals

Recent advances in linear scaling algorithms that circumvent the computational bottlenecks of large-scale electronic structure simulations make it possible to carry out density functional calculations with Gaussian orbitals on molecules containing more than 1000 atoms and 15000 basis functions using current workstations and personal computers. This paper discusses the recent theoretical developments that have led to these advances and demonstrates in a series of benchmark calculations the present capabilities of state-of-the-art computational quantum chemistry programs for the prediction of molecular structure and properties.

Scuseria, Gustavo E.↗

NASA NTRS2005

Prediction of Mechanical Properties of Polymers With Various Force Fields

The effect of force field type on the predicted elastic properties of a polyimide is examined using a multiscale modeling technique. Molecular Dynamics simulations are used to predict the atomic structure and elastic properties of the polymer by subjecting a representative volume element of the material to bulk and shear finite deformations. The elastic properties of the polyimide are determined using three force fields: AMBER, OPLS-AA, and MM3. The predicted values of Young s modulus and shear modulus of the polyimide are compared with experimental values. The results indicate that the mechanical properties of the polyimide predicted with the OPLS-AA force field most closely matched those from experiment. The results also indicate that while the complexity of the force field does not have a significant effect on the accuracy of predicted properties, small differences in the force constants and the functional form of individual terms in the force fields determine the accuracy of the force field in predicting the elastic properties of the polyimide.

Odegard, Gregory M.↗

NASA NTRS2016

Molecular Dynamics Modeling of Carbon Nanotube Composite Fracture Using ReaxFF

Carbon nanotube (CNT) fiber reinforced composites with specific tensile strengths and moduli approaching those of aerospace grade carbon fiber composites have recently been reported. This achievement was enabled by the emerging availability of high N/tex yarns in kilometer-scale quantities. While the production of this yarn is an impressive advance, its strength is still much lower than that of the individual CNTs comprising the yarn. Closing this gap requires understanding load transfer between CNTs at the nanometer dimensional scale. This work uses reactive molecular dynamics simulations to gain an understanding at the nanometer scale of the key factors that determine CNT nanocomposite mechanical performance, and to place more realistic upper bounds on the target properties. While molecular dynamics simulations using conventional force fields can predict elastic properties, the ReaxFF reactive forcefield can also model fracture behavior because of its ability to accurately describe bond breaking and formation during a simulation. The upper and lower bounds of CNT composite properties are investigated by comparing systems composed of CNTs continuously connected across the periodic boundary with systems composed of finite length CNTs. These lengths, effectively infinite for the continuous tubes and an aspect ratio of 13 for the finite length case, result from practical limitations on the number of atoms that can be included in a simulation. Experimentally measured aspect ratios are typically on the order of 100,000, so the calculated results should represent upper and lower limits on experimental mechanical properties. Finally, the effect of various degrees of covalent crosslinking between the CNTs and amorphous carbon matrix is considered to identify the amount of CNT-matrix covalent bonding that maximizes overall composite properties.

Jensen, Benjamin D.↗

NASA NTRS1969

Empirical Observations on the Sensitivity of Hot Cathode Ionization Type Vacuum Gages

A study of empirical methods of predicting tile relative sensitivities of hot cathode ionization gages is presented. Using previously published gage sensitivities, several rules for predicting relative sensitivity are tested. The relative sensitivity to different gases is shown to be invariant with gage type, in the linear range of gage operation. The total ionization cross section, molecular and molar polarizability, and refractive index are demonstrated to be useful parameters for predicting relative gage sensitivity. Using data from the literature, the probable error of predictions of relative gage sensitivity based on these molecular properties is found to be about 10 percent. A comprehensive table of predicted relative sensitivities, based on empirical methods, is presented.

Summers, R. L.↗

NASA NTRS1993

Numerical simulation of rarefied flow through a slit. I - Direct simulation Monte Carlo results

The pressure-driven flow of a rarefied monatomic gas through a two-dimensional slit is simulated using the direct simulation Monte Carlo technique. Of particular interest is the change in flow field structure as pressure ratio and Knudsen number are varied. Comparisons are made to quantify the limits of validity of free-molecular theory and approximate, nearly free-molecular iterative methods. Also addressed is the sensitivity of the numerical solutions to grid structure and boundary conditions. The free-molecular theory is found to predict quantitative flow field properties (e.g., centerline velocities or downstream flux profiles) reasonably well for large finite Knudsen number with the error dependent on the pressure ratio. The nearly free-molecular corrections are shown to have limited range of applicability. A previously derived parameter is found to correlate total mass flux well as a function of pressure ratio and Knudsen number over a large portion of the transitional regime.

Wadsworth, D. C.↗

NASA NTRS1972

A molecular theory of elastomer deformation and rupture.

The mechanical properties of elastomers, including rupture and its time dependence, can be semiquantitatively predicted from nine molecular parameters which are characteristic for a given species, from the initial molecular weight of a sample before cross-linking and from the effective chain concentration (which must still be determined for each vulcanizate). Only two empirical quantities are involved - the Plazek retardation function for entanglement slippage and a related constant which serves to locate this function on the time scale. The theory leads to a new method of estimating fatigue lifetimes from short-time data.

Landel, R. F.↗

NASA NTRS1990

Sulfur at nickel-alumina interfaces - Molecular orbital theory

Previous studies on Al-Ni alloys containing sulfur as an impurity suggest that, when S is in the interface between a metal and an oxide scale, it weakens the chemical bonding between them. This paper investigates factors responsible for this effect, using a molecular orbital theory to predict sulfur structures and electronic properties on the Ni-Al2O3 interface. It is shown that, in absence of S, the basal plane of Al2O3 will bind strongly through the Al(3+) cation surface to Ni (111). When segregated S impurity is present on the Ni surface, there are too few interfacial AlS bonds to effect good adhesion, leading to an inhibition of the oxide scale adhesion in NiCrAl alloys.

Hong, S. Y.↗

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 NTRS2001

Crosslink Density and Molecular Weight Effects on the Viscoelastic Response of a Glassy High-Performance Polyimide

Durability and long-term performance are among the primary concerns for the use of advanced polymer matrix composites (PMCs) in modern aerospace structural applications. For a PMC subJected to long-term exposure at elevated temperatures. the viscoelastic nature of the polymer matrix will contribute to macroscopic changes in composite stiffness, strength and fatigue life. Over time. changes in the polymer due to physical aging will have profound effects on tile viscoelastic compliance of the material, hence affecting its long-term durability. Thus, the ability to predict material performance using intrinsic properties, such as crosslink density and molecular weight, would greatly enhance the efficiency of design and development of PMCs. The objective of this paper is to discuss and present the results of an experimental study that considers the effects of crosslink density, molecular weight and temperature on the viscoelastic behavior including physical aging of an advanced polymer. Five distinct variations in crosslink density were used to evaluate the differences in mechanical performance of an advanced polyimide. The physical aging behavior was isolated by conducting sequenced, short-term isothermal creep compliance tests in tension. These tests were performed over a range of sub-glass transition temperatures. The material constants, material master curves and physical aging-related parameters were evaluated as a function of temperature crosslink density and molecular weight using time-temperature and time-aging time superposition techniques.

Nicholson, Lee M.↗

NASA NTRS2018

Multiscale Modeling of PEEK Using Reactive Molecular Dynamics Modeling and Micromechanics

Polyether ether ketone (PEEK) is a high-performance, semi-crystalline thermoplastic that is used in a wide range of engineering applications, including some structural components of aircraft. The design of new PEEK-based materials requires a precise understanding of the multiscale structure and behavior of semi-crystalline PEEK. Molecular Dynamics (MD) modeling can efficiently predict bulk-level properties of single phase polymers, and micromechanics can be used to homogenize those phases based on the overall polymer microstructure. In this study, MD modeling was used to predict the mechanical properties of the amorphous and crystalline phases of PEEK. The hierarchical microstructure of PEEK, which combines the aforementioned phases, was modeled using a multiscale modeling approach facilitated by NASA's MSGMC. The bulk mechanical properties of semi-crystalline PEEK predicted using MD modeling and MSGMC agree well with vendor data, thus validating the multiscale modeling approach.

Pisani, William A.↗

NASA NTRS1985

Understanding the high pressure properties of molecular solids and molecular surfaces deposited on hetrogeneous substrates

Work directed toward understanding the high pressure properties of molecular solids and molecular surfaces deposited on hetrogeneous substrates is reported. The motivation, apart from expanding our basic knowledge about these systems, was to understand and predict the properties of new materials synthesized at high pressure, including pressure induced metallic and superconducting states. As a consequence, information about the states of matter of the Jovian planets and their satellites, which are natural high pressure laboratories was also provided. The work on molecular surfaces and finite two and three dimensional clusters of atoms and molecules was connected with the composition and behavior of planetary atmospheres and on the processes involved in forming surface layers, which is vital to the development of composite materials and microcircuitry.

Etters, R. D.↗

NASA NTRS1986

Numerical studies of collapsing interstellar clouds

Numerical simulation of the structure and evolution of interstellar clouds was initiated. Steps were taken toward an integrated treatment of the dynamical, thermal, and chemical processes entering model calculations. A detailed study was made of radiative transfer in molecular lines to allow model predictions to be tested against empirical data. The calculations have successfully reproduced and explained several observed cloud properties, including abundances of complex molecular species and the apparent depletion of CO in dense cores.

Bodenheimer, P. H.↗

NASA NTRS1992

Prediction of nonlinear optical properties of large organic molecules

The preparation of materials with large nonlinear responses usually requires involved synthetic processes. Thus, it is very advantageous for materials scientists to have a means of predicting nonlinear optical properties. The prediction of nonlinear optical properties has to be addressed first at the molecular level and then as bulk material. For relatively large molecules, two types of calculations may be used, which are the sum-over-states and the finite-field approach. The finite-field method was selected for this research, because this approach is better suited for larger molecules.

Cardelino, Beatriz H.↗

NASA NTRS2011

Using FT-IR Spectroscopy to Elucidate the Structures of Ablative Polymers

The composition and structure of an ablative polymer has a multifaceted influence on its thermal, mechanical and ablative properties. Understanding the molecular level information is critical to the optimization of material performance because it helps to establish correlations with the macroscopic properties of the material, the so-called structure-property relationship. Moreover, accurate information of molecular structures is also essential to predict the thermal decomposition pathways as well as to identify decomposition species that are fundamentally important to modeling work. In this presentation, I will describe the use of infrared transmission spectroscopy (FT-IR) as a convenient tool to aid the discovery and development of thermal protection system materials.

Fan, Wendy↗