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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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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Constraints on triple oxygen isotope kinetics

The general assumption that systems are in equilibrium is convenient, but imperfect. Instead, the partial or full expression of kinetic reaction steps may need to be determined. Using an isotope fractionation Monte-Carlo model under the harmonic approximation (Figure 1 envelopes), we find the range of kinetic isotope effects (i.e., resulting from unidirectional chemical reactions) contains and exceeds the range of accessible equilibrium isotope effects. For the 16O-17O-18O system, accessible kinetic isotope effects can yield Δ′17O variations as large 0.8‰ from a single kinetic fractionation step. Reactions that yield the largest deviations from equilibrium are those with the largest molecular mass difference between decomposition fragments, typically the two products, such as a gas and solid generated from the thermal decomposition of an initial solid. For example, previously published experiments on the thermal decomposition of calcite (CaCO3) and dehydroxylation of brucite (Mg(OH)2) are matched by density functional theory models of each kinetic isotope effect, thus revealing their origin in mass-dependent reaction. Beyond oxygen, our approach applies to any system with more than two isotopes.

Oxygen isotopes↗
NASA NTRSDate not supplied

Electron Screened and Enhanced Nuclear Reactions

In 1954, Salpeter calculated nuclear reaction rates would increase via weak and strong electron screening in stars. Astrophysical observations and laboratory astrophysics experiments have confirmed that electron screening reduces the Coulomb Barrier between charged particles. Fermi degenerate strong screening occurs when the electron density exceeds 10e23 electrons/cm3 in the cores of gas giant planets, White Dwarf Stars, the conduction bands of metals, deuterated LENR materials and Inertial Confinement Fusion targets at maximum compression. We have modelled these quantum mechanical effects with Density Functional Theory codes.

electron screening↗
NASA NTRS2006

Theoretical Modeling of Formic Acid (HCOOH), Formate (HCOO(-)), and Ammonia (NH(4)) Vibrational Spectra in Astrophysical Ices

Ions embedded in icy grain mantles are thought to account for various observed infrared spectroscopic features, particularly in certain young stellar objects. The dissociation of formic acid (HCOOH) in astrophysical ices to form the formate ion (HCOO(-)) was modeled with density functional theory cluster calculations. Like isocyanic acid (HOCN), HCOOH was found to spontaneously deprotonate when sufficient water is present to stabilize charge transfer complexes. Both ammonia and water can serve as proton acceptors, yielding ammonium (NH4(+)) and hydronium (H3O(+)) counterions. Computed frequencies of weak infrared features produced by stretching and bending modes in both HCOO(-) and HCOOH were compared with experimental and astronomical data. Our results confirm laboratory assignments that a band at 1381 cm(exp -1) can be attributed to the CH bend in either HCOO(-) or HCOOH, but a band at 1349 cm(exp -1) corresponds to CO stretching in HCOO(-). Another feature at 1710 cm(exp -1) (5.85 m) can possibly be assigned to a CO stretching mode in HCOOH, as suggested by experiment, but the agreement is less satisfactory. In addition, we examine and analyze spectroscopic features associated with NH+4, both as a counterion to HCOO(-) or OCN(-) and in isolation, in order to compare with experimental and astronomical data in the 7 m region.

Park, Jin-Young↗
NASA NTRS1999

Effective Forces Between Colloidal Particles

Colloidal suspensions have proven to be excellent model systems for the study of condensed matter and its phase behavior. Many of the properties of colloidal suspensions can be investigated with a systematic variation of the characteristics of the systems and, in addition, the energy, length and time scales associated with them allow for experimental probing of otherwise inaccessible regimes. The latter property also makes colloidal systems vulnerable to external influences such as gravity. Experiments performed in micro-ravity by Chaikin and Russell have been invaluable in extracting the true behavior of the systems without an external field. Weitz and Pusey intend to use mixtures of colloidal particles with additives such as polymers to induce aggregation and form weak, tenuous, highly disordered fractal structures that would be stable in the absence of gravitational forces. When dispersed in a polarizable medium, colloidal particles can ionize, emitting counterions into the solution. The standard interaction potential in these charged colloidal suspensions was first obtained by Derjaguin, Landau, Verwey and Overbeek. The DLVO potential is obtained in the mean-field linearized Poisson-Boltzmann approximation and thus has limited applicability. For more precise calculations, we have used ab initio density functional theory. In our model, colloidal particles are charged hard spheres, the counterions are described by a continuum density field and the solvent is treated as a homogeneous medium with a specified dielectric constant. We calculate the effective forces between charged colloidal particles by integrating over the solvent and counterion degrees of freedom, taking into account the direct interactions between the particles as well as particle-counterion, counterion-counterion Coulomb, counterion entropic and correlation contributions. We obtain the effective interaction potential between charged colloidal particles in different configurations. We evaluate two- and three-body forces in the bulk as well as study the influence of soft walls. We qualitatively explain the effects of the walls on the forces and demonstrate that many-body effects are negligible in our system. With adjustments in the parameters, the DLVO pair-potential can describe the results quantitatively. Besides electrostatic interactions, entropic depletion effects that arise from (hard-core) exclusion play an important role in determining the behavior of multi-component colloidal suspensions. A standard theory for depletion forces is due to Asakura and Oosawa and is based on the ideal gas approximation. To go beyond this approximation, we have studied entropic forces in molecular dynamics simulations of systems of hard spheres (the effects of the solvent have been ignored). The effective depletion forces for these systems can be found either from equilibrium distribution functions or from direct momentum transfer calculations. Our results obtained by either method show qualitative differences from the Asakura-Oosawa forces, indicating a longer range, higher value at contact and most importantly a more complicated structure, comprising of several maxima and minima. Our calculations include the determination of effective forces between two spheres, a hard sphere and a wall, and the behavior of a hard sphere near a step-edge and a corner. We also demonstrate that such entropic forces do not necessarily satisfy pairwise additivity.

Tehver, Riina↗
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 NTRS2003

Influence of Disorder on DNA Conductance

Disorder along a DNA strand due to non uniformity associated with the counter ion type and location, and in rise and twist are investigated using density functional theory. We then model the conductance through a poly(G) DNA strand by including the influence of disorder. We show that the conductance drops by a few orders of magnitude between typical lengths of 10 and 100 nm. Such a decrease occurs with on-site potential disorder that is larger than 100 meV.

Adessi, Christophe↗
NASA NTRS2021

Thermodynamics of the Lu2O3 – SiO2 system and comparison to other Rare Earth Silicates

Environmental barrier coatings are necessary to protect SiC based ceramics and composites from water vapor degradation in harsh engine environments. Currently, rare earth (RE) silicates are the most promising systems to protect SiC based ceramics and composites. This protection is largely based on reduced silica activity in these rare earth silicates which results in a lowered reactivity with water vapor. To that end, previous Knudsen effusion mass spectrometry (KEMS) studies have explored RE = Yb, Y silicates to measure the reduced silica activity and subsequent water vapor reactivity. Similarly, this work employs the KEMS technique to measure the SiO(g) vapor pressure in Lu containing RE silicates to calculate the activity of silica within the monosilicate [log(a(SiO2)) = -2351.1*1/T-1.6731] and the disilicate (log(a(SiO2)) = -4884.0*1/T + 2.2208) as a function of temperature. The enthalpies of formation for Lu monosilicate from the oxides and the elements were calculated to be -45±3 kJ/mol at 1550 K and -2831.1±12 kJ/mol at 298 K, respectively. The measured enthalpy of formation and those found in literature are compared to modeled values from density functional theory and those estimated using electronegativity.

Knudesen Effusion Mass Spectrometry↗
NASA NTRS1991

Center for Modeling of Turbulence and Transition (CMOTT). Research briefs: 1990

Brief progress reports of the Center for Modeling of Turbulence and Transition (CMOTT) research staff from May 1990 to May 1991 are given. The objectives of the CMOTT are to develop, validate, and implement the models for turbulence and boundary layer transition in the practical engineering flows. The flows of interest are three dimensional, incompressible, and compressible flows with chemistry. The schemes being studied include the two-equation and algebraic Reynolds stress models, the full Reynolds stress (or second moment closure) models, the probability density function models, the Renormalization Group Theory (RNG) and Interaction Approximation (DIA), the Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS).

Povinelli, Louis A.↗
NASA NTRS1992

Center for Modeling of Turbulence and Transition (CMOTT): Research Briefs, 1992

The progress is reported of the Center for Modeling of Turbulence and Transition (CMOTT). The main objective of the CMOTT is to develop, validate and implement the turbulence and transition models for practical engineering flows. The flows of interest are three-dimensional, incompressible and compressible flows with chemical reaction. The research covers two-equation (e.g., k-e) and algebraic Reynolds-stress models, second moment closure models, probability density function (pdf) models, Renormalization Group Theory (RNG), Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS).

Liou, William W.↗
NASA NTRS1994

Center for modeling of turbulence and transition: Research briefs, 1993

This research brief contains the progress reports of the research staff of the Center for Modeling of Turbulence and Transition (CMOTT) from June 1992 to July 1993. It is also an annual report to the Institute for Computational Mechanics in Propulsion located at Ohio Aerospace Institute and NASA Lewis Research Center. The main objectives of the research activities at CMOTT are to develop, validate, and implement turbulence and transition models for flows of interest in propulsion systems. Currently, our research covers eddy viscosity one- and two-equation models, Reynolds-stress algebraic equation models, Reynolds-stress transport equation models, nonequilibrium multiple-scale models, bypass transition models, joint scalar probability density function models, and Renormalization Group Theory and Direct Interaction Approximation methods. Some numerical simulations (LES and DNS) have also been carried out to support the development of turbulence modeling. Last year was CMOTT's third year in operation. During this period, in addition to the above mentioned research, CMOTT has also hosted the following programs: an eighteen-hour short course on 'Turbulence--Fundamentals and Computational Modeling (Part I)' given by CMOTT at the NASA Lewis Research Center; a productive summer visitor research program that has generated many encouraging results; collaborative programs with industry customers to help improve their turbulent flow calculations for propulsion system designs; a biweekly CMOTT seminar series with speakers from within and without the NASA Lewis Research Center including foreign speakers. In addition, CMOTT members have been actively involved in the national and international turbulence research activities. The current CMOTT roster and organization are listed in Appendix A. Listed in Appendix B are the abstracts of the biweekly CMOTT seminar. Appendix C lists the papers contributed by CMOTT members.

Liou, William W.↗
NASA NTRS2004

Photoionization of Benzene and Small Polycyclic Aromatic Hydrocarbons in Ultraviolet-Processed Astrophysical Ices: A Computational Study

We employed density functional theory (DFT) calculations to model the photoionization behavior of benzene and small polycyclic aromatic hydrocarbons when they are embedded in a matrix of water ice in order to investigate issues raised by recent experimental work by Gudipati and Allamandola. The ionization energies of benzene, naphthalene, anthracene, and pyrene were found to be lowered by 1.5-2.1 eV in water ice. Low-lying vertical electronic excitation energies were computed with time-dependent DFT for both neutral and ionized species and are found in both cases to be remarkably unaffected by the ice matrix. Chemical behavior in ultraviolet-photoprocessed ices is also discussed, with a focus on electron recombination and pathways leading to phenol and analogous products.

Woon, D. E.↗
NASA NTRS2002

Computational Thermochemistry of Jet Fuels and Rocket Propellants

The design of new high-energy density molecules as candidates for jet and rocket fuels is an important goal of modern chemical thermodynamics. The NASA Glenn Research Center is home to a database of thermodynamic data for over 2000 compounds related to this goal, in the form of least-squares fits of heat capacities, enthalpies, and entropies as functions of temperature over the range of 300 - 6000 K. The chemical equilibrium with applications (CEA) program written and maintained by researchers at NASA Glenn over the last fifty years, makes use of this database for modeling the performance of potential rocket propellants. During its long history, the NASA Glenn database has been developed based on experimental results and data published in the scientific literature such as the standard JANAF tables. The recent development of efficient computational techniques based on quantum chemical methods provides an alternative source of information for expansion of such databases. For example, it is now possible to model dissociation or combustion reactions of small molecules to high accuracy using techniques such as coupled cluster theory or density functional theory. Unfortunately, the current applicability of reliable computational models is limited to relatively small molecules containing only around a dozen (non-hydrogen) atoms. We propose to extend the applicability of coupled cluster theory- often referred to as the 'gold standard' of quantum chemical methods- to molecules containing 30-50 non-hydrogen atoms. The centerpiece of this work is the concept of local correlation, in which the description of the electron interactions- known as electron correlation effects- are reduced to only their most important localized components. Such an advance has the potential to greatly expand the current reach of computational thermochemistry and thus to have a significant impact on the theoretical study of jet and rocket propellants.

Crawford, T. Daniel↗
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 NTRS2023

An Ensemble Neural Network Model for Predicting Rare-Earth Oxide and Silicate Heat Capacities at High Temperature

In this work, a neural network model was developed to predict the constant pressure heat capacity for materials in the rare-earth oxide—silica material space. Several model architectures were trained and tested on heat capacity data generated from first-principles density functional theory calculations. Hyperparameter optimization was performed, and the optimal model was selected for heat capacity predictions. The optimal model architecture was found to have a root-mean-squared error of 5.12 ± 3.37 J/mol-K. The optimal model architecture was then used in a bagging ensemble model trained using the leave-one-group-out method to provide error estimates for model predictions. The out-of-bag score for the ensemble model was 0.997. The predicted heat capacities agree well with the DFT and experimental results and were computed orders of magnitude faster than DFT simulations. Machine learning shows the potential to provide a suitable surrogate model for thermochemical property predictions for candidate environmental barrier coating materials but refining of input material features and model architectures could further improve accuracy for these models.

environmental barrier coatings↗
NASA NTRSDate not supplied

Electrochemical Recycling of CO2

Carbon dioxide (CO2) is a molecule that plays multiple roles in our universe. Currently, its impact on global warming has rightfully given CO2 a label as a harmful waste product that must be amply handled. Nonetheless, under other circumstances, and from other perspectives, CO2 could be regarded as a valuable resource. As example, in nature CO2 is an essential building block in photosynthesis ultimately yielding molecules and materials that we harvest for numerous applications ranging from energy storage and construction to medicines and food. In a similar manner, there is a growing interest to utilize atmospheric CO2 to generate renewable fuels and platform chemicals through artificial processes. These processes can replace fossil based industrial processes on earth alleviating climate impact, but they can also find uses far away from our planet. In space, CO2 is an important source of precious oxygen, however, it can also serve as feedstock for carbon-based molecules and materials that are needed to sustain societies at distant planets such as Mars. In this presentation, I will discuss the contemporary understanding of electrochemical reduction of CO2 for selective production of desired molecules. We will zoom in to the very atomic scale to gain mechanistic understanding of how the structure and composition of the cathode electrode affects product distributions. This information is used as guide in the design of new materials that convert CO2 more efficiently and more selectively. On this basis, opportunities for recycling CO2 under different conditions will be discussed, as will remaining challenges for the ultimate application towards the sustainable human living on earth and in space.

electrochemistry↗
NASA NTRS2024

Theoretical Prediction of Thermal Expansion Anisotropy for Y 2 Si 2 O 7 Environmental Barrier Coatings Using a Deep Neural Network Potential and Comparison to Experiment

Environmental barrier coatings (EBCs) are an enabling technology for silicon carbide (SiC)-based ceramic matrix composites (CMCs) in extreme environments such as gas turbine engines. However, development of new coating systems is hindered by the large design space and difficulty in predicting properties for these materials. Density Functional Theory (DFT) has successfully been used to model and predict some thermodynamic and thermo-mechanical properties of high-temperature ceramics for EBCs, although these calculations are challenging due to their high computational costs. In this work, we use machine learning to train a deep neural network potential (DNP) for Y 2 Si 2 O 7 , which is then applied to calculate thermodynamic and thermo-mechanical properties at near-DFT accuracy much faster and using less computational resources than DFT. We use this DNP to predict phonon-based thermodynamic properties of Y 2 Si 2 O 7 with good agreement to DFT and experiments. We also utilize the DNP to calculate the anisotropic, lattice direction-dependent coefficients of thermal expansion (CTEs) for Y 2 Si 2 O 7 . Molecular dynamics trajectories using the DNP correctly demonstrate accurate prediction of the anisotropy of the CTE in good agreement with diffraction experiments. In the future, this DNP could be applied to accelerate additional property calculations for Y 2 Si 2 O 7 compared to DFT or experiments.

rare earth silicates↗
NASA NTRSDate not supplied

Quantum Hardware-Enabled Molecular Dynamics via Transfer Learning

The ability to perform ab initio molecular dynamics simulations using potential energy surfaces provided by quantum computers would open the door to virtually exact dynamics for a variety of chemical and biochemical systems, with impacts on catalysis and biophysics. Nonetheless, performing molecular dynamics on surfaces produced by quantum hardware has been hampered by the noisy energies typically produced by quantum computers and challenges associated with computing gradients and scaling to large systems interest. A recent set of advances in machine learning, known as transfer learning, provides a new path forward for molecular dynamics simulations on quantum hardware. Transfer learning offers a workaround, where one first trains models on larger, less accurate classical datasets and then refines them on smaller, more accurate quantum datasets. We explore this approach by training machine learning models to predict a molecule's potential energy based on its geometric structure using Behler-Parrinello neural networks. When successfully trained, the model enables energy gradient predictions necessary for dynamic simulations. To reduce the quantum resources needed, the model is initially trained with data derived from classical density functional theory and subsequently refined with a smaller dataset obtained from a variational quantum eigensolver optimization of the unitary coupled cluster ansatz. We show that this approach significantly reduces the size of the needed quantum training dataset while capturing the high accuracies needed within quantum chemistry simulations. The success of this two-step training method opens more opportunities to apply machine learning models on quantum data, a significant stride towards efficient quantum-classical hybrid computational models.

quantum computing↗
NASA NTRSDate not supplied

A materials-informatics based study of solid electrolytes and protective coatings for Li batteries

All-solid-state batteries with Li metal anode can address the safety issues surrounding traditional Li-ion batteries as well as the demand for higher energy densities. However, the development of solid electrolytes and protective coatings simultaneously possessing high ionic conductivity and wide electrochemical stability has proven to be a challenge. Here, we present a data-driven approach to explore the Li compound space for promising solid electrolytes and coatings. This is accomplished through the generation of a large database of battery-related materials properties of Li compounds by computing Li+ migration barriers using bond-valence-based pair potentials, and stability windows using density functional theory energies. Using this database, we implement machine learning models that can accurately predict migration barriers and electrochemical stability windows for any new Li compound. Through feature engineering, we ensure that our models are both accurate and interpretable. We perform feature importance analysis on our models to highlight materials properties that can be tuned for future design of coatings/electrolytes. Our database and informatics approach provide a valuable tool for the rapid discovery of new solid-state battery chemistries.

Solid state batteries↗