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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Parameter Identification for Battery Abuse Reaction Kinetics Models Using DSC Analysis [SWR-23-87]

This code estimates the parameters as inputs for NREL’s abuse reaction kinetics model using Differential scanning calorimetry (DSC) experimental data. The parameters for calculating the rates of abuse reactions among battery cell components at elevated temperatures include frequency factor, activation energy, and reaction orders. The least-squared method is employed to fit the reaction model to DSC data. The code is implemented in MATLAB.

Yang, Chuanbo↗
NASA NTRS1975

Validity of approximate methods in molecular scattering - Thermal HCl-He collisions

Accurate close coupling scattering calculations are presented for thermal energy HCl-He collisions. The interaction potential is obtained from the Gordon-Kim electron gas model, adjusted to have the correct long-range multipole form. A variety of phenomenological cross sections are computed from the close coupling S matrix, and these are compared with results from several commonly employed approximate methods. In particular, it is found that the total integral, total differential, and gas kinetic cross sections are accurately predicted by the central field approximation which retains just the spherical average of the interaction. Integral inelastic cross sections are represented quite accurately by the coupled states approximation of McGuire and Kouri, but only qualitatively by the effective potential method of Rabitz.

Green, S.↗
DOE OSTI2025

A physics-constrained neural ordinary differential equations approach for robust learning of stiff chemical kinetics

The high computational cost associated with solving for detailed chemistry poses a significant challenge for predictive computational fluid dynamics (CFD) simulations of turbulent reacting flows. While deep learning techniques have been explored to develop faster surrogate models, they often fail to integrate reliably with CFD solvers. This instability arises because traditional deep learning approaches optimize for training error without ensuring compatibility with ordinary differential equation (ODE) solvers, resulting in accumulation of errors over time. Recently, neuralODE (NODE) based approaches have been shown to be a promising technique to emulate and accelerate detailed chemistry computations. Here, in the present work, we extend this NODE framework for stiff chemical kinetics by incorporating mass conservation constraints directly into the loss function during training. This ensures that the total mass as well as the individual elemental species masses are conserved in an a-posteriori manner. Proof-of-concept studies are performed with the novel physics-constrained NODE (PC-NODE) approach for homogeneous autoignition of hydrogen-air mixture over a range of composition and thermodynamic conditions. It is demonstrated that the PC-NODE framework not only improves the physical consistency of the resulting data-driven model with respect to mass conservation criteria, but also improves training efficiency. PC-NODE is shown to achieve 2–100× speedup relative to the hydrogen-air detailed chemical mechanism depending on the type of the ODE solver (implicit or explicit) used during autoregressive inference tests. Lastly, a-posteriori studies are performed wherein the trained PC-NODE model is coupled with a CFD solver. It is shown that higher accuracy is achieved with PC-NODE relative to the purely data-driven NODE approach. Moreover, PC-NODE also exhibits robustness and generalizability to unseen initial conditions from within (interpolative capability) as well as outside (extrapolative capability) the training regime.

computational combustion↗
NASA NTRS1991

Combustion kinetics and sensitivity analysis computations

The stiff ordinary differential equation solution methods more commonly used for chemical kinetics problems are examined, and the efficiency and accuracy of several of the methods are compared by applying them to two combustion kinetics problems. The local sensitivity analysis methods developed to date are outlined, and their advantages and difficulties are briefly discussed. Examples are used to make comparisons among the different techniques.

Radhakrishnan, K.↗
NASA NTRS1973

An analysis of a charring ablator with thermal nonequilibrium, chemical kinetics, and mass transfer

The differential equations governing the transient response of a one-dimensional ablative thermal protection system are presented for thermal nonequilibrium between the pyrolysis gases and the char layer and with finite rate chemical reactions occurring. The system consists of three layers (the char layer, the uncharred layer, and an optical insulation layer) with concentrated heat sinks at the back surface and between the second and third layers. The equations are solved numerically by using a modified implicit finite difference scheme to obtain solutions for the thickness of the charred and uncharred layers, surface recession and pyrolysis rates, solid temperatures, porosity profiles, and profiles of pyrolysis-gas temperature, pressure, composition, and flow rate. Good agreement is obtained between numerical results and exact solutions for a number of simplified cases. The complete numerical analysis is used to obtain solutions for an ablative system subjected to a constant heating environment. Effects of thermal, chemical, and mass transfer processes are shown.

Clark, R. K.↗
DOE OSTI2025

Kinetic Deep Learning v0.1

Here, we present a method that uses protein levels to predict times series of metabolite concentrations. Understanding this type of pathway dynamics is important in order to predict the behavior of the pathway and, more pragmatically, to be able to design biological systems (such as strains bioengineered to produce chemical products) reliably. Typically, for this purpose, kinetic models consisting of differential equations based on the Michaelis-Menten dynamics have been used in the past. However, these methods can rarely produce good fits to measured data time series. Possibly, this happens because the kinetic constants are unknown or are different from the ones measured in vivo, or perhaps because Michaelis-Menten dynamics is not a satisfactory description. In order to improve the predictive nature of these kinetic models we have eliminated the Michaelis-Menten description of pathway dynamics and we have substituted it by algorithms that automatically learn these dynamics from previously obtained metabolomics and proteomics data using machine learning approaches. Specifically, kinetic deep learning uses deep learning to map proteomics time series to metabolite concentration time series, instead of learning the first metabolite derivative and integrating in (as in the first version of kinetic learning). This approach is shown to provide good to excellent results with a data set specifically collected for this purpose.

Garcia Martin, Hector [Joint BioEnergy Institute (↗
DOE OSTI2021

Computational Investigation of the Role of Active Site Heterogeneity for aSupported Organovanadium(III) Hydrogenation Catalyst

A crucial consideration for supported heterogeneous catalysts is the nonuniformity of the active sites, particularly for supported organometallic catalysts. Standard spectroscopic techniques, such as X-ray absorption spectroscopy, reflect the nature of the most populated sites, which are often intrinsically structurally distinct from the most active catalytic sites. Additionally, with computational models, often, only a few representative structures are used to depict catalytic active sites on a surface, even though there are numerous observable factors of surface heterogeneity that contribute to the kinetically favorable active species. A previously reported study on the mechanism of a surface organovanadium-(III) catalyst [(SiO 2 )V III (Mes)(THF)] for styrene hydrogenation yielded two possible mechanisms: heterolytic cleavage and redox cycling. These two mechanistic scenarios are challenging to differentiate experimentally since the kinetic readouts of the catalyst are identical. To showcase the importance of modeling surface heterogeneity and its effect on catalytic activity, density functional theory (DFT) computational models of a series of potential active sites of [(SiO 2 )V III (Mes)(THF)] for the reaction pathways are applied in combination with kinetic Monte Carlo (kMC) simulations. Computed results were then compared to the previously reported experimental kinetic study: (1) DFT free-energy reaction pathways indicated the likely active site and pathway for styrene hydrogenation, a heterolytic cleavage pathway requiring a bare tripodal vanadium site. (2) From the kMC simulations, a mixture of different bond lengths from the support oxygen to the metal center was required to qualitatively describe the experimentally observed kinetic aspects of a supported organovanadium(III) catalyst for olefin hydrogenation. This work underscores the importance of modeling surface heterogeneity in computational catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Surface Equilibration Mechanism Controls the Stability of a Model Codeposited Glass Mixture of Organic Semiconductors

While previous work has identified the conditions for preparing ultrastable single-component organic glasses by physical vapor deposition (PVD), little is known about the stability of codeposited mixtures. Here, we prepared binary PVD glasses of organic semiconductors, TPD (N,N'-Bis(3-methylphenyl)-N,N'-diphenylbenzidine) and m-MTDATA (4,4',4"-Tris[phenyl(m-tolyl)amino]triphenylamine), with a 50:50 mass concentration over a wide range of substrate temperatures (T sub ). The enthalpy and kinetic stability are evaluated with differential scanning calorimetry and spectroscopic ellipsometry. Binary organic semiconductor glasses with exceptional thermodynamic and kinetic stability comparable to the most stable single-component organic glasses are obtained when deposited at T sub = 0.78–0.90T g (where T g is the conventional glass transition temperature). When deposited at 0.94T g , the enthalpy of the m-MTDATA/TPD glass equals that expected for the equilibrium liquid at that temperature. Thus, the surface equilibration mechanism previously advanced for single-component PVD glasses is also applicable for these codeposited glasses. Furthermore, these results provide an avenue for designing high-performance organic electronic devices.

36 MATERIALS SCIENCE↗
NASA NTRS1988

Theoretical studies of solar lasers and converters

The geometry and setup for the n-C3F7I iodine laser are illustrated. The mathematical modeling of this system is described. The chemical kinetics are summarized. A sensitivity analysis was performed on the parameters occurring in the differential equations describing the chemical kinetics.

Heinbockel, John↗
DOE OSTI2023

Machine learning of hidden variables in multiscale fluid simulation

Solving fluid dynamics equations often requires the use of closure relations that account for missing microphysics. For example, when solving equations related to fluid dynamics for systems with a large Reynolds number, sub-grid effects become important and a turbulence closure is required, and in systems with a large Knudsen number, kinetic effects become important and a kinetic closure is required. By adding an equation governing the growth and transport of the quantity requiring the closure relation, it becomes possible to capture microphysics through the introduction of ‘hidden variables’ that are non-local in space and time. The behavior of the ‘hidden variables’ in response to the fluid conditions can be learned from a higher fidelity or ab-initio model that contains all the microphysics. In our study, a partial differential equation simulator that is end-to-end differentiable is used to train judiciously placed neural networks against ground-truth simulations. We show that this method enables an Euler equation based approach to reproduce non-linear, large Knudsen number plasma physics that can otherwise only be modeled using Boltzmann-like equation simulators such as Vlasov or particle-in-cell modeling.

42 ENGINEERING↗
NASA NTRS1977

Dissociative excitation of H2, HD, and D2 by electron impact

Time-of-flight techniques have been used to investigate the electron-impact dissociation of H2, HD, and D2 in order to determine the effect of isotopic mass variation in the target molecule on the dissociative excitation process. At incident electron energies near 100 eV, the time-of-flight spectrum produced from each molecule consists of atoms in the metastable 2s state and in high-lying long-lived Rydberg levels. The individual time-of-flight distributions, kinetic-energy spectra, and relative differential cross sections for these two species resulting from each molecule have been measured. The kinetic-energy spectrum of the Rydberg atoms produced from dissociative excitation of H2 was notably dissimilar in shape from the corresponding distributions produced from HD and D2. Also the 2s and Rydberg production cross sections differed between the three molecules. In the dissociation of the heteronuclear HD molecule, the ratio of fast H(2s) atoms to D(2s) atoms was about 1 to 1, while the same ratio comparing the Rydberg atoms was nearly 2 to 1. These differences indicate the influence of the mass variation on the position of the Franck-Condon region in the production of 2s atoms and on the competition between autoionization and dissociation in the formation of Rydberg fragments.

Carnahan, B. L.↗
DOE OSTI2021

CSPlib - A Software Toolkit for the Analysis of Dynamical Systems and Chemical Kinetic Models

CSPlib is an open source software library for analyzing general ordinary differential equation (ODE) systems and detailed chemical kinetic ODE systems. It relies on the computational singular perturbation (CSP) method for the analysis of these systems. The software provides support for: General ODE models (gODE model class) for computing source terms and Jacobians for a generic ODE system; TChem model (ChemElemODETChem model class) for computing source term, Jacobian, other necessary chemical reaction data, as well as the rates of progress for a homogenous batch reactor using an elementary step detailed chemical kinetic reaction mechanism. This class relies on the TChem [2] library; A set of functions to compute essential elements of CSP analysis (Kernel class). This includes computations of the eigensolution of the Jacobian matrix, CSP basis vectors and co-vectors, time scales (reciprocals of the magnitudes of the Jacobian eigenvalues), mode amplitudes, CSP pointers, and the number of exhausted modes. This class relies on the Tines library; A set of functions to compute the eigensolution of the Jacobian matrix using Tines library GPU eigensolver; A set of functions to compute CSP indices (Index Class). This includes participation indices and both slow and fast importance indices.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1972

Galactic cosmic ray modulation from 1965-1970.

Numerical solutions of the cosmic-ray equation of transport within the solar cavity and including the effects of diffusion, convection, and energy losses due to adiabatic deceleration, have been used to reproduce the modulation of galactic electrons, protons, and helium nuclei observed during the period from 1965 to 1970. Kinetic energies between 10 and 10,000 MeV/nucleon are considered. Computed and observed spectra are given for the years 1965, 1968, 1969, and 1970 together with the diffusion coefficients. These diffusion coefficients are assumed to be of separable form in rigidity and radial dependence, and are consistent with the available magnetic-field power spectra. The force-field solutions are given for these diffusion coefficients and galactic spectra and are compared with the numerical solutions. It is shown that the energy losses and convection lead to near-earth nuclei spectra at kinetic energies less than or equal to 100 MeV/nucleon in which the differential intensity is proportional to the kinetic energy with little dependence on the form of the galactic spectrum. This dependence is in agreement with the observed spectra of all species of atomic nuclei and it is argued that this provides strong observational evidence for the presence of energy losses in the propagation process, and for the exclusion of low-energy galactic nuclei from near earth.

Urch, I. H.↗
NASA NTRS1990

Preosteoblast production 55 hours after a 12.5-day spaceflight on Cosmos 1887

The influence of 12.5 days of spaceflight and a 55 h stressful recovery period (at 1 g) on fibroblastlike osteoblast precursor cells was assessed in the periodontal ligament (PDL) of rats that were 91 days old at launch. Nuclear morphometry was used as a marker for precursor cell differentiation in 3 microns sections cut in the midsagittal plane from the maxillary first molar. According to nuclear volume, cells were classified as preosteoblasts (C + D cells, greater than or equal to 120 microns 3) and less differentiated progenitor cells (A + A' cells, 40-79 microns 3). Compared with synchronous controls (simulated flight conditions), the 55 h postflight recovery period at 1 g resulted in a 40% decrease in the A + A' cell population, a 42% increase in the C + D cells, and a 39% increase in the number of PDL fibroblastlike cells near the bone surface. These results are consistent with a postflight osteogenic response in PDL. This recovery response occurred despite physiological stress in the flight animals that resulted in a highly significant (P less than or equal to 0.001) increase in adrenal weight. The data suggest that after spaceflight there is a strong and rapid recovery mechanism for osteoblast differentiation that is not suppressed by physiological stress.

Cosmos 1887 Project↗