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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

Enabling Mission Flexibility to Battery Driven Deep Space Endeavors With Generalized Battery-Health-Monitoring Using Physics-Based and Data-Driven Reduced-Order Models

The needs and requirements for an electrochemical energy storage for deep space exploration is well explored. It is often understood that different mission sites and environmental conditions require different battery chemistries or technologies. Additionally, various engineering solutions are deployed to overcome specific chemical challenges. One often overlooked need is the “health” monitoring of an electrochemical storage system. The term generalized health monitoring, as envisioned in this work, refers to the monitoring of various aspects such as electrode health, electrolyte health, reaction pathway health, cooling system health, sensor health, and BMS health [1]. Generalized health monitoring allows mission leads, engineers, and scientists to incorporate flexibility in mission designs, make on-the-fly mission changes, and extend the duration of science missions. Moreover, it enables automation and data-driven decision-making without compromising safety and performance. Recently, our group developed a hierarchy of thermal reduced-order models (TROM) by combining a physics-based modeling approach and data-driven model reduction techniques applied to flight data [2]. The resulting TROMs were found to be not only accurate but also identifiable from the flight data. Consequently, the coefficient of variance of the model parameters is small over the course of hundreds of flights, allowing for monitoring the parameter evolution trajectories as the battery ages and degrades. These parameters constitute the metrics of the generalized health of a battery. Monitoring their evolution allows such models to be used for anomaly detection and prognostics, improving early detection of abnormal behavior and thus enabling timely maintenance, longer battery life, and enhanced battery safety. For this presentation, the practicality of the thermal model will be validated on a pack of 14cells under various topology configurations such as 1S14P, 2P7S, 7S2P, and 1P14S. It is well known that manufacturing and non-uniform aging lead to variability in the performance of a cell, which is exacerbated by cell balancing during active load. Additionally, in extreme scenarios, the paramount objective is to complete the mission, regardless of the stresses on the battery. Topology-induced balancing issues further stress the battery. The goal of this study is to determine if the noise (identifiability) in the reduced-order thermal model parameters is sensitive to topology, cell spacing, cooling strategy, and manufacturing or age variability. The variability in cells is considered by assuming a multimodal distribution for microscopic parameters of a cell (such as porosity, tortuosity, reaction kinetics, volumetric thermal conductivity, and volumetric heat capacity). The compounded effect of manufacturing variability, topological selection, cooling strategies, and cell balancing ages each cell in a battery differently. The study aims to clarify whether the challenge in extracting maximum information depends on the minimum number of sensors or models used for data extraction.

Automation↗
NASA NTRS1994

Compton scattering of the microwave background by quasar-blown bubbles

At least 10% of quasars drive rapid outflows from the central regions of their host galaxies. The mass and energy flow rates in these winds are difficult to measure, but their kinetic luminosities probably exceed 10(exp 45) ergs/s. This kind of outflow easily sunders the interstellar medium of the host and blows a bubble in the intergalactic medium. After the quasar shuts off, the hot bubble continues to shock intergalactic gas until its leading edge merges with the Hubble flow. The interior hot gas Compton scatters microwave background photons, potentially providing a way to detect these bubbles. Assuming that quasar kinetic luminosities scale with their blue luminosities, we integrate over the quasar luminosity function to find the total distortion (y) of the microwave background produced by the entire population of quasar wind bubbles. This calculation of y distortion is remarkably insensitive to the properties of the intergalactic medium (IGM), quasar lifetimes, and cosmological parameters. Current Cosmic Background Explorer (COBE) limits on y constrain the kinetic luminosities of quasars to be less than several times their bolometric radiative luminosities. Within this constraint, quasars can still expel enough kinetic luminosity to shock the entire IGM by z = 0, but cannot heat and ionize the IGM by z = 4 unless omega(sub IGM) much less than 10(exp -2).

Voit, G. Mark↗
NASA NTRS1975

Theoretical study of production of unique glasses in space

The potential of producing the glassy form of selected materials in the weightless, containerless nature of space processing is examined through the development of kinetic relationships describing nucleation and crystallization phenomena. Transformation kinetics are applied to a well-characterized system (SiO2), an excellent glass former (B2O3), and a poor glass former (Al2O3) by conventional earth processing methods. Viscosity and entropy of fusion are shown to be the primary materials parameters controlling the glass forming tendency. For multicomponent systems diffusion-controlled kinetics and heterogeneous nucleation effects are considered. An analytical empirical approach is used to analyze the mullite system. Results are consistent with experimentally observed data and indicate the promise of mullite as a future space processing candidate.

Larsen, D. C.↗
DOE OSTI2024

KETCHUP: Parameterizing of large-scale kinetic models using multiple datasets with different reference states

Large-scale kinetic models provide the computational means to dynamically link metabolic reaction fluxes to metabolite concentrations and enzyme levels while also conforming to substrate level regulation. However, the development of broadly applicable frameworks for efficiently and robustly parameterizing models remains a challenge. Challenges arise due to both the heterogeneity, paucity, and difficulty in obtaining flux and/or concentration data but also due to the computational difficulties of the underlying parameter identification problem. Both the computational demands for parameterization, degeneracy of obtained parameter solutions and interpretability of results has so far limited widespread adoption of large-scale kinetic models despite their potential. Herein, we introduce the Kinetic Estimation Tool Capturing Heterogeneous Datasets Using Pyomo (KETCHUP), a flexible parameter estimation tool that leverages a primal-dual interior-point algorithm to solve a nonlinear programming (NLP) problem that identifies a set of parameters capable of recapitulating the (non)steady-state fluxes and concentrations in wild-type and perturbed metabolic networks. KETCHUP is benchmarked against previously parameterized large-scale kinetic models demonstrating an at least an order of magnitude faster convergence than the tool K-FIT while at the same time attaining better data fits. Finally, this versatile toolbox accepts different kinetic descriptions, metabolic fluxes, enzyme levels and metabolite concentrations, under either steady-state or instationary conditions to enable robust kinetic model construction and parameterization. KETCHUP supports the SBML format and can be accessed at https://github.com/maranasgroup/KETCHUP.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1997

Kinetics and Thermochemistry of ClCO Formation from the Cl + CO Association Reaction

Laser flash photolysis of Cl2/CO/M mixtures (M = N2, CO, Ar, CO2) has been employed in conjunction with Cl((sup 2)P(sub J)) detection by time-resolved resonance fluorescence spectroscopy to investigate equilibration kinetics in the reactions Cl((sup 2)P(sub J)) + CO ClCO as a function of temperature (185-260 K) and pressure (14-200 Torr). The association and dissociation reactions are found to be in the low-pressure limit over the range of experimental conditions investigated. In N2 and/or CO buffer gases, the temperature dependences of the ClCO formation and dissociation reaction rate constants are described by the Arrhenius expressions k(sub 1) = (1.05 +/- 0.36) x 10(exp -34) exp[(810 +/- 70)/T] cm(exp 6)/molecules(exp 2).s and k(sub -1) = (4.1 +/- 3.1) x 10(exp -10) exp[(-2960 +/- 60)/T]cu cm/(molecule.s) (errors are 2 sigma). Second- and third-law analyses of the temperature dependence of the equilbrium constant (k/k-1) lead to the following thermodynamic parameters for the association reaction: Delta-H(sub 298) = -7.7 +/- 0.6 kcal/mol, Delta-H(sub 0) = -6.9 +/- 0.7 kcal/mol, Delta-S(sub 298) = -23.8 +/- 2.0 cal/mole.K, Delta-H(sub f,298)(ClCO) = 5.2 +/- 0.6 kcal/mol (errors are 2 sigma). The results repported in this study significantly reduce the uncertainties in all reported kinetic and thermodynamic parameters.

Nicovich, J. M.↗
DOE OSTI2022

Joint estimation of biogeochemical model parameters from multiple experiments: A bayesian approach applied to mercury methylation

Here, to characterize complex biogeochemical systems, results from multiple experiments, where each targets a specific subprocess, are commonly combined. The resulting datasets are interpreted through the calibration of biogeochemical models for process inference and predictions. Commonly used calibration approaches of fitting datasets from individual experiments to subprocess models one at a time is prone to missing information shared between datasets and incomplete uncertainty propagation. We propose a Bayesian joint-fitting scheme addressing the above-mentioned concerns by jointly fitting all the available datasets, thus calibrating the entire biogeochemical model in one go using Markov Chain Monte Carlo (MCMC). The identification of null spaces in the parameter distributions from MCMC guided the simplification of certain subprocess models. For example, fast kinetic sorption was replaced by equilibrium sorption, and Monod demethylation was replaced by first-order demethylation. Joint fitting of datasets resulted in complete uncertainty propagation with parameter estimates informed by all available data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Convergence in simulating global soil organic carbon by structurally different models after data assimilation

Abstract Current biogeochemical models produce carbon–climate feedback projections with large uncertainties, often attributed to their structural differences when simulating soil organic carbon (SOC) dynamics worldwide. However, choices of model parameter values that quantify the strength and represent properties of different soil carbon cycle processes could also contribute to model simulation uncertainties. Here, we demonstrate the critical role of using common observational data in reducing model uncertainty in estimates of global SOC storage. Two structurally different models featuring distinctive carbon pools, decomposition kinetics, and carbon transfer pathways simulate opposite global SOC distributions with their customary parameter values yet converge to similar results after being informed by the same global SOC database using a data assimilation approach. The converged spatial SOC simulations result from similar simulations in key model components such as carbon transfer efficiency, baseline decomposition rate, and environmental effects on carbon fluxes by these two models after data assimilation. Moreover, data assimilation results suggest equally effective simulations of SOC using models following either first‐order or Michaelis–Menten kinetics at the global scale. Nevertheless, a wider range of data with high‐quality control and assurance are needed to further constrain SOC dynamics simulations and reduce unconstrained parameters. New sets of data, such as microbial genomics‐function relationships, may also suggest novel structures to account for in future model development. Overall, our results highlight the importance of observational data in informing model development and constraining model predictions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Assessing the numerical stability of physics models to equilibrium variation through database comparisons on DIII-D

High fidelity kinetic equilibria are crucial for tokamak modeling and analysis. Manual workflows for constructing kinetic equilibria are time consuming and subject to user error, motivating development of automated equilibrium reconstruction tools to provide accurate and consistent reconstructions for downstream physics analysis. These automated tools also provide access to kinetic equilibria at large database scales, which enables the quantification of general uncertainties arising from equilibrium reconstruction techniques. In this paper, we compare a large database of DIII-D kinetic equilibria generated manually by physics experts to equilibria from automated kinetic reconstruction tools, assessing the impact of reconstruction method on equilibrium parameters and resulting magnetohydrodynamic stability calculations. We find agreement among scalar parameters, whereas profile quantities, such as the bootstrap current, show larger disagreements. We analyze ideal kink and classical tearing stability with DCON and STRIDE respectively, finding that the kink stability calculation is generally more robust than the tearing index Δ' calculation. We find that in 90% of cases, both kink stability classifications are unchanged between the manual expert and automated kinetic equilibria.

CAKE↗
DOE OSTI2025

Measurement of post-disruption runaway electron kinetic energy and pitch angle during final loss instability in DIII-D

Post-disruption runaway electron (RE) kinetic energy K and pitch angle sin$\vartheta$ are critical parameters for determining resulting first wall material damage during wall strikes, but are very challenging to measure experimentally. During the final loss instability, confined RE K and sin$\vartheta$ are reconstructed during center-post wall strikes for both high impurity (high-Z) and low impurity (low-Z) plasmas by combining soft x-ray, hard x-ray, synchrotron emission, and total radiated power measurements. Deconfined (wall impacting) RE sin$\vartheta$ is then reconstructed for these shots by using time-decay analysis of infra-red imaging. Additionally, deconfined RE K and sin$\vartheta$ are reconstructed for a low-Z downward loss shot by analyzing resulting damage to a sacrificial graphite dome limiter. The damage analysis uses multi-step modeling simulating plasma instability, RE loss orbits, energy deposition, and finally material expansion (MARS-F, KORC, GEANT-4, and finally COMSOL). Overall, mean kinetic energies are found to be in the range $\langle$$K$$\rangle$ $≈ 3-4$ MeV for confined REs. KORC simulations indicate that the final loss instability process does not change individual RE kinetic energy K. In conclusion, confined RE pitch angles are found to be fairly low initially pre-instability, $\langle$sin$\vartheta$$\rangle$ $≈ 0.1 – 0.2$, but appear to increase roughly 2x, to $\langle$sin$\vartheta$$\rangle$ $≈ 0.3 – 0.4$ for both confined and deconfined REs during instability onset in the low-Z case; this increase is not observed in the high-Z case.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Stochastic Model for High Temperature Oxidation of Cr–Ni Austenitic Steels Assisted by Spallation

Abstract Cr–Ni austenitic steels offer significant high temperature corrosion protection by forming a surface oxide layer. However, above critical service conditions (temperature, atmosphere, thermal cycling), oxidized surface can experience intensive degradation because of scale spallation, which could be detrimental to the in-service life. To predict the effect of scale spallation on oxidation kinetics, a simulation was implemented using a stochastic model. The model considers topological parameters and intensity of spallation which can occur, while delivering a true oxidation constant. The experimental procedure identified the amount of formed spalled scale and topology of spallation based on the use of element mapping of the surface. This information was used to determine a true kinetic constant for a corresponding spallation intensity in oxidized Cr–Ni austenitic steel. To illustrate the capability of the stochastic model, a parametric analysis was performed. The model verified how the spallation parameters could change the oxidation processes from parabolic growth of an adhered oxide layer without spallation to a mixed linear-parabolic, or with a constant thickness of residual scale at high spallation intensity. The spallation model will be used in a separate article to characterize high temperature surface degradation of several Cr–Ni austenitic steels during harsh oxidation environments.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Single-Well Push–Pull Tracer Test Analyses to Determine Aquifer Reactive Transport Parameters at a Former Uranium Mill Site (Grand Junction, Colorado)

At a former uranium mill site where tailings have been removed, prior work has determined several potential ongoing secondary uranium sources. These include locations with uranium sorbed to organic carbon, uranium in the unsaturated zone, and uranium associated with the presence of gypsum. To better understand uranium mobility controls at the site, four single-well push–pull tests (with a drift phase) were completed with the goal of deriving aquifer flow and contaminant transport parameters for inclusion in a future sitewide reactive transport model. This goes beyond the traditional use of a constant sorption distribution coefficient (K d ) and allows for the evaluation of alternative remedial injection fluids, which can produce variable K d values. Dispersion was first removed from the resulting data to determine possible reactions before conducting reactive transport simulations. These initial analyses indicated the potential need to include cation exchange, uranium sorption, and gypsum dissolution. A reactive transport model using multiple layers to account for partially penetrating wells was completed using the PHT-USG reactive transport modeling code and calibrated using PEST. The model results quantify the hydraulic conductivity and dispersion parameters using the injected tracer concentrations. Uranium sorption, cation exchange, and gypsum dissolution parameters were quantified by comparing the simulated versus observed geochemistry. All simulations required some cation exchange and calcite equilibrium, and one simulation required gypsum dissolution to improve the model fit for calcium and sulfate. Uranium sorption parameters were not strongly influenced by the other parameter values but were highly influenced by uranium concentrations during the drift phase, with possible kinetic rate limitations. Thus, a future recommendation for such push–pull tests is to collect more geochemical data during the drift phase. The final uranium sorption parameters were within the range of values determined from prior column testing. The flow and transport parameters derived from these single-well push–pull tests will provide initial parameters for any future sitewide reactive transport model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A deep learning approach to fast analysis of collective Thomson scattering spectra

Fast analysis of collective Thomson scattering ion acoustic wave features using a deep convolutional neural network model is presented. The network was trained from spectra to predict the plasma parameters, including ion velocities, population fractions, and ion and electron temperatures. A fully kinetic particle-in-cell simulation was used to model a laboratory astrophysics experiment and simulate a diagnostic image of the ion acoustic wave feature. Network predictions were compared with Bayesian inference of the plasma model parameters for both the simulated and experimentally measured images. Both approaches were fairly accurate predicting the simulated image and the network predictions matched a good portion of the Bayesian results for the experimentally measured image. The Bayesian approach is more robust to noise and motivates future work to train deep learning models with realistic noise. The advantage of the deep learning model is making thousands of predictions in a few hundred milliseconds, compared to a few seconds to minutes per prediction for the optimization and Bayesian approaches presented here. The results demonstrate promising capabilities of deep learning models to analyze Thomson data orders of magnitude faster than conventional methods when using the neural network for standalone analysis. If more rigorous analysis is needed, neural network predictions can be used to quickly initialize other optimization methods and increase chances of success. This is especially useful when the dataset becomes very large or highly dimensional and manually refining initial conditions for the entire dataset are no longer tractable.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1997

Unsteady Crystal Growth Due to Step-Bunch Cascading

Based on our experimental findings of growth rate fluctuations during the crystallization of the protein lysozym, we have developed a numerical model that combines diffusion in the bulk of a solution with diffusive transport to microscopic growth steps that propagate on a finite crystal facet. Nonlinearities in layer growth kinetics arising from step interaction by bulk and surface diffusion, and from step generation by surface nucleation, are taken into account. On evaluation of the model with properties characteristic for the solute transport, and the generation and propagation of steps in the lysozyme system, growth rate fluctuations of the same magnitude and characteristic time, as in the experiments, are obtained. The fluctuation time scale is large compared to that of step generation. Variations of the governing parameters of the model reveal that both the nonlinearity in step kinetics and mixed transport-kinetics control of the crystallization process are necessary conditions for the fluctuations. On a microscopic scale, the fluctuations are associated with a morphological instability of the vicinal face, in which a step bunch triggers a cascade of new step bunches through the microscopic interfacial supersaturation distribution.

Vekilov, Peter G.↗
DOE OSTI2023

Observationally Constrained Modeling of the Reactive Uptake of Isoprene-Derived Epoxydiols under Elevated Relative Humidity and Varying Acidity of Seed Aerosol Conditions

Isoprene is the non-methane volatile organic compound (VOC) emitted in largest amounts to the atmosphere, and it is a significant source of secondary organic aerosol (SOA) mass. The uptake of isoprene oxidation products followed by multiphase chemistry in fine particles is the key pathway to form isoprene epoxydiol-derived SOA (IEPOX-SOA). However, many parameters that relate to diffusion and reaction of IEPOX in the particle phase remain uncertain, since reaction kinetics previously measured in bulk aqueous phase solutions might be different from atmospheric aerosols. Here, we use simultaneous environmental chamber measurements of multiple parameters governing IEPOX-SOA formation at timescales of ~hours: particle size distribution, composition, and volatility of IEPOX-SOA to constrain the key parameters governing IE-POX-SOA formation under humid (i.e., 50% relative humidity, RH) and varying seed aerosol acidity conditions. Reducing the 2-methyltetrol (tetrol) reaction rate constants by a factor of 4 brings the model predictions in agreement with the IEPOX-SOA measurements with acidified ammonium bisulfate seed aerosols. For less acidic ammonium sulfate aerosols both the organo-sulfate (OS) and tetrol reaction rate constants need to be reduced to bring model predictions closer to chamber observations. Using the measured non-volatile content of IEPOX-SOA we constrain the oligomerization timescale of 2-methyltetrols. We find that the oligomerization timescale is 4 hours with acidified seed aerosols, but a much longer time scale of 24 hours is needed for non-acidified seed aerosols, indicating that the aerosol acidity greatly affects the oligomerization rate of tetrols. Here we show that the actual kinetics of IEPOX-SOA formation rate on aerosol seeds consisting of both ammonium bisulfate and ammonium sulfate are a factor of 4~5 slower under 50-60% RH conditions compared to their application in previous models, which were based on bulk aqueous solution measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Skyrmion lattice formation and destruction mechanisms probed with TR-SANS

Magnetic skyrmions are topologically protected, nanoscale whirls of the spin configuration that tend to form hexagonally ordered arrays. As a topologically non-trivial structure, the nucleation and annihilation of the skyrmion, as well as the interaction between skyrmions, varies from conventional magnetic systems. Recent works have suggested that the ordering kinetics in these materials occur over millisecond or longer timescales, which is unusually slow for magnetic dynamics. The current work investigates the skyrmion ordering kinetics, particularly during lattice formation and destruction, using time-resolved small angle neutron scattering (TR-SANS). Evaluating the time-resolved structure and intensity of the neutron diffraction pattern reveals the evolving real-space structure of the skyrmion lattice and the timeframe of the formation. Measurements were performed on three prototypical skyrmion materials: MnSi, (Fe,Co)Si, and Cu 2 OSeO 3 . To probe lattice formation and destruction kinetics, the systems were prepared in the stable skyrmion state, and then a square-wave magnetic field modulation was applied. The measurements show that the skyrmions quickly form ordered domains, with a significant distribution in lattice parameters, which then converge to the final structure; the results confirm the slow kinetics, with formation times between 10 ms and 99 ms. Comparisons are made between the measured formation times and the fundamental material properties, suggesting the ordering temperature, saturation magnetization and magnetocrystalline anisotropy may be driving the timeframes. Micromagnetic simulations were also performed and support a scaling of the kinetics with sample volume, a behavior which is caused by the reconciling of misaligned domains.

Liyanage, W. L. N. C.↗
DOE OSTI2022

The role of energy transfer and competing bimolecular reactions in characterizing the unimolecular dissociations of allylic radicals

Resonantly stabilized radicals (RSR's) such as allylic radicals typically have bond energies exceeding 45 kcal/mol and are therefore reasonably long-lived up to quite high temperatures. Consequently, kinetics of the unimolecular dissociations and bimolecular reactions of these long-lived RSR's assume significance in high temperature chemically reacting systems such as in flames. Our recent analyses (J. Cho et al., Proc. Comb. Inst., 2022) of the uncertainty propagated by the energy transfer parameters for 1-methylallyl (1MA) dissociation to the laminar flame speed, indicates an intricate coupling between the kinetics of 1MA dissociation (chain propagation) and its reaction with H-atoms (chain-termination). Here, this work extends such analyses to other C 3 – C 5 allylic radicals: allyl, 2-methylallyl, 1,1-dimethylallyl, 1,2-dimethylallyl, and 1,3-dimethylallyl. Theoretical kinetics for the C 3 – C 4 allylic radicals were taken from prior literature studies (J.A. Miller et al., J. Phys. Chem., 2008; J. Cho et al., Proc. Comb. Inst., 2022; R.S. Tranter et al., Proc. Comb. Inst., 2017). Potential energy surfaces were newly computed for the dissociations of the larger C5 allylic radicals and the reactions of these radicals with H-atoms. These PES's were then used to calculate rate constants for the relevant unimolecular and bimolecular reactions. Energy transfer has a crucial role to play in these pressure-dependent reactions and therefore the present kinetics calculations relied on a-priori predictions of collisional energy transfer parameters to best characterize the branching between chain propagating unimolecular dissociations of these allylic radicals and chain terminating bimolecular reactions of these RSR's with H-atoms. Prompt dissociations are now established to be a universal feature of free radical dissociations. Here we have also characterized the propensity for prompt dissociations in these more-stable allylic RSR's and their role in combustion simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1980

Physical and dynamical conditions in interstellar clouds

Techniques for measuring the local and global parameters that describe molecular clouds are presented and illustrated by selected results. The local parameters which characterize a given location in a cloud are gas kinetic temperature, dust temperature, total density, magnetic field strength, abundance of species i, volume cooling and heating rates for gas and dust, and the thermal and turbulent velocities. Global properties which characterize the cloud as a whole include the size expressed as a length or area, the orientation and shape, the mass, and the integrated heating and cooling rates. If the cloud as a whole is collapsing or rotating, then the collapse velocity and the rotational velocity or angular momentum are global properties. An intermediate class of parameters which shares features of local and global properties includes the column density of species i, the total column density, and the average density.

Evans, N. J., II↗