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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 1,279 records · Page 71
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The challenges of beam polarization and keV-scale centre-of-mass energy calibration at the FCC-ee

The capability to determine the FCC-ee centre-of-mass energies (ECM) at the ppm level using resonant depolarization of the beams is essential for the Z line shape measurements, the W mass and the possible observation of the Higgs boson s-channel production. A first analysis (Blondel A et al Polarization and centre-of-mass energy calibration at FCC-ee. demonstrated the feasibility of this programme, conditional to careful preparation and a number of further developments. The existing simulation codes must be unified; the analysis and design of the instrumentation must be developed; and a detailed planning must be developed for the simultaneous and coordinated operation of the accelerator, of the continuous polarization and depolarization measurements, and of the beam monitoring devices, ensuring a precise extrapolation from beam energies to centre-of-mass energy and energy spread.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Do holographic CFT states have unique semiclassical bulk duals?

Here, we discuss a situation where a holographic CFT state has multiple semiclassical bulk duals. In our example, a given holographic state has two simple, semiclassical descriptions, one with a closed universe, constructed using the gravitational path integral, and one without a closed universe, constructed using the extrapolate dictionary. This highlights an ambiguity in the AdS/CFT dictionary. We propose various options for resolving this tension although none is perfectly satisfactory. We also discuss what this implies for the black hole interior and the gravitational path integral.

AdS/CFT correspondence↗
DOE OSTI2022

Sphaleron transition rates and the chiral magnetic effect

The chiral magnetic effect is a novel quantum phenomenon proposed for high-energy nuclear collisions but which has yet to be observed. We quantify the axial charge relaxation time, due to sphalerons, which enters in simulations of this effect. An extrapolation of weak coupling calculations of the sphaleron rate yields rather different relaxation times than strong coupling AdS/CFT calculations. The AdS/CFT relaxation time is the larger one of the two by an order of magnitude, but the weak coupling relaxation time may not be reliable because it is only marginally bigger than the microscopic thermalization time. The role of quark masses has yet to be accurately assessed.

Physics↗
DOE OSTI2023

Temperature uncertainty modelling with proxy structural data as geostatistical constraints for well siting: an example applied to Granite Springs Valley, NV, USA

Utilizing existing temperature and structural geology information around Granite Springs Valley, Nevada, we build 3D stochastic temperature models with the aims of evaluating the 3D uncertainty of temperature and choosing between candidate exploration well locations. The data used to support the modelling are measured temperatures and structural proxies from 3D geologic modelling (distance to fault, distance to fault intersections and terminations, Coulomb stress change and dilation tendency), the latter considered ‘secondary’ data. Two stochastic geostatistical techniques are explored for incorporating the structural proxies: cosimulation and local varying mean. With both the cosimulation and local varying mean methods, many equally-likely temperature models (i.e. realizations) are produced, from which temperature probability profiles are calculated at candidate well locations. To aid in choosing between the candidate locations, two quantities summarize the temperature probabilities: V prior and entropy. V prior quantifies the likelihood for economic temperatures at each candidate location, whereas entropy identifies where new information has the most potential to reduce uncertainty. In general, the cosimulation realizations have smoother spatial structure, and extrapolate high temperatures at candidate locations that are located along the direction of the longest spatial correlation, which are down dip from existing temperature logs. The smooth realizations result in tight temperature probability profiles that are easier to interpret, but they have unrealistic temperature reversals in some locations because of the dipping ellipsoid shape created and that the cosimulation technique does not enforce a conductive geothermal gradient as a baseline (i.e. linearly increasing temperature with depth). The local varying mean results produce realizations with more realistic geothermal gradients, with temperatures increasing downward since a depth-temperature relationship is included. However, because they have much noisier spatial nature compared to cosimulation, it is harder to interpret the temperature probability profiles. The different local varying mean results allow the geologist to determine which proxy (e.g. dilation v. distance to fault termination) should be used given the specific geothermal system. In general, V prior from local varying mean results identify locations that are close to high values for the structural proxies: areas with higher probabilities for higher temperatures. The entropy results identify where uncertainty is greatest and therefore new drilling information could be most useful. Though these techniques provide useful information, even when applied to areas of sparse data, our comparison of these two techniques demonstrates the need for new geothermal geostatistics techniques that combine the advantages of these two methods and that are tailored to the spatial uncertainty issues inherent in geothermal exploration.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Pareto Optimization of Analog Circuits Using Reinforcement Learning

Analog circuit optimization and design presents a unique set of challenges in the IC design process. Many applications require the designer to optimize for multiple competing objectives, which poses a crucial challenge. Motivated by these practical aspects, we propose a novel method to tackle multi-objective optimization for analog circuit design in continuous action spaces. In particular, we propose to (i) extrapolate current techniques in Multi-Objective Reinforcement Learning to continuous state and action spaces and (ii) provide for a dynamically tunable trained model to query user defined preferences in multi-objective optimization in the analog circuit design context.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Remark on Algorithm 1012: Computing Projections with Large Datasets

In ACM TOMS Algorithm 1012, the DELAUNAYSPARSE software is given for performing Delaunay interpolation in medium to high dimensions. When extrapolating outside the convex hull of the training set, DELAUNAYSPARSE calls the nonnegative least squares solver DWNNLS to compute projections onto the convex hull. However, DWNNLS and many other available sum-of-squares optimization solvers were not intended for usage with many variable problems, which result from the large training sets that are typical in machine learning applications. Thus, a new PROJECT subroutine is given, based on the highly customizable quadratic program solver BQPD. This solution is shown to be as robust as DELAUNAYSPARSE for projection onto both synthetic and real-world datasets, where other available solvers frequently fail. Although it is intended as an update for DELAUNAYSPARSE, due to the difficulty and prevalence of the problem, this solution is likely to be of external interest as well.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

The Predictive Power of Exact Constraints and Appropriate Norms in Density Functional Theory

Ground-state Kohn-Sham density functional theory provides, in principle, the exact ground-state energy and electronic spin densities of real interacting electrons in a static external potential. In practice, the exact density functional for the exchange-correlation (xc) energy must be approximated in a computationally efficient way. About 20 mathematical properties of the exact xc functional are known. In this work, we review and discuss these known constraints on the xc energy and hole. By analyzing a sequence of increasingly sophisticated density functional approximations (DFAs), we argue that (a) the satisfaction of more exact constraints and appropriate norms makes a functional more predictive over the immense space of many-electron systems and (b) fitting to bonded systems yields an interpolative DFA that may not extrapolate well to systems unlike those in the fitting set. We discuss both how the class of well-described systems has grown along with constraint satisfaction and the possibilities for future functional development.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Fast Charging of Li-Ion Cells: Part V. Design and Demonstration of Protocols to Avoid Li-Plating

Fast charging of Li-ion batteries would make “fueling” of electric vehicles comparable in time to fueling of gasoline-powered cars, increasing consumer appeal of the new technology. Taking the US Department of Energy goal of safe 6 C charging to 80% capacity as a guide, we describe approaches that can mitigate Li plating on the graphite anode. To make this possible, a variable-rate anode potential charging protocol has been implemented by using a microprobe reference electrode to continuously monitor and adjust the current, in this way avoiding low anode potentials that favor Li deposition. Various implementations of the anode potential control are considered using electrochemical modeling and compared with the experimental data. For charge to 80% capacity at 30 °C, an average C-rate of 4.97 C was obtained for an NCM523/graphite cell with 70 μ m thick graphite electrode and 7.40 C for a cell with 47 μ m thick graphite electrode. Our electrochemical model accounts for these observations and provides a means to extrapolate the approach to other cell designs and operation regimes, drawing the maximum average fast charging rates that can still avoid Li plating.

25 ENERGY STORAGE↗
DOE OSTI2021

Lithium-Ion Battery Life Model with Electrode Cracking and Early-Life Break-in Processes

This paper develops a physically justified reduced-order capacity fade model from accelerated calendar- and cycle-aging data for 32 lithium-ion (Li-ion) graphite/nickel-manganese-cobalt (NMC) cells. The large data set reveals temperature-, charge C-rate-, depth-of-discharge-, and state of charge (SOC)-dependent degradation patterns that would be unobserved in a smaller test matrix. Model structure is informed by incremental capacity analysis that shows loss of lithium inventory and cathode-material loss as the dominant capacity fade mechanisms. The model includes terms attributable to solid-electrolyte interface (SEI) growth, electrode cracking, cycling-driven acceleration of SEI growth, and "break-in" mechanisms that slightly decrease or increase available Li inventory early in life. The study explores what mathematical couplings of these mechanisms best describe calendar aging, cycle aging, and mixed calendar/cycle aging. Various approaches are discussed for extracting relevant stress factors from complex cycling profiles to predict lifetime during real-world battery loads using models trained on constant-current laboratory test results. The complexity of the present human-driven model identification process motivates future work in machine learning to more widely search and statistically discern the optimal model that correctly extrapolates capacity fade based on physical knowledge.

25 ENERGY STORAGE↗
DOE OSTI2022

Elementally Resolved Dissolution Kinetics of a Ni-Fe-Cr-Mn-Co Multi-Principal Element Alloy in Sulfuric Acid Using AESEC-EIS

Atomic emission spectroelectrochemistry (AESEC) combined with linear sweep voltammetry (LSV) and electrochemical impedance spectroscopy (EIS) provided insights on both active and passive dissolution of Ni-Fe-Cr-Mn-Co multi-principal element alloy. Elemental dissolution rates measured by AESEC during open circuit experiment were in agreement with those extrapolated from AESEC-LSV and indicated element-specific dissolution tendencies. AESEC-EIS at open circuit potential showed nearly in-phase elemental dissolution during potential modulation which suggests direct dissolution from the alloy surface to the electrolyte. In the passive potential domain, no oscillation of the elemental dissolution rate was detected by AESEC-EIS, suggesting non-oxidative chemical dissolution of the outer layer of the passive film. In this case, dissolution at the passive film/electrolyte interface was equal to the metal oxidation rate (passive current density) at the metal/passive film interface and the passive current density was independent of potential.

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

Assessing Electrolyte Fluorination Impact on Calendar Aging of Blended Silicon-Graphite Lithium-Ion Cells Using Potentiostatic Holds

Silicon-based lithium-ion batteries have started to meet cycle life metrics, but they exhibit poor calendar life. Here, electrolyte fluorination impact on calendar fade of blended silicon-graphite anodes is explored using a LiPF 6 in EC:EMC:FEC electrolyte vs LiBOB in EC:EMC electrolyte. We utilize a combined experimental-modeling approach applying potentiostatic voltage holds (V-hold) to evaluate electrolyte suitability for calendar life in a shortened testing timeframe (~2 months). Our theoretical framework deconvolutes the irreversible parasitic capacity losses (lithium lost to the solid electrolyte interphase) from the V-hold electrochemical data. Unfluorinated electrolyte (dominant LiBOB reduction) exhibits higher cell resistance as compared to fluorinated electrolyte (dominant FEC reduction). Both systems have similar irreversible capacities during the voltage hold duration with slower rate of parasitic capacity loss for the LiBOB system. Extrapolation of the parasitic losses to end of life capacity fade of 20% shows LiBOB electrolyte outperforming LiPF 6 electrolyte in calendar life. The results demonstrate the applicability of the V-hold protocol as a rapid material screening tool providing semi-quantitative calendar lifetime estimates.

25 ENERGY STORAGE↗
DOE OSTI2023

Revealing the Electrochemical Kinetics of Electrolytes in Nanosized LiFePO 4 Electrodes

Lithium-ion battery rate performance is ultimately limited by the electrolyte, yet the behaviors of electrolytes during high-rate (dis)charge remain elusive to electrochemical measurement. Herein, we develop and study a nanosized LiFePO 4 model system in which the electrolyte completely controls the electrochemical kinetics of the porous electrode. Impedance spectroscopy, cyclic voltammetry, and rate performance testing prove that ion transport in the electrolyte is the sole rate-limiting process, even in thin electrodes. A novel pseudo-steady-state extrapolation (S3E) method for Tafel analysis shows that LiFePO 4 obeys Butler-Volmer kinetics with a transfer coefficient of 3. The combination of these unexpectedly rapid interfacial kinetics and an activation barrier for phase transformation causes extreme reaction heterogeneity, which manifests as a moving reaction zone. Resistance versus capacity analysis enables direct measurement of electrolyte resistance growth during high-rate (dis)charge, revealing how the interaction between concentration polarization and a moving reaction zone controls electrolyte rate performance in LiFePO 4 electrodes. This work elucidates the profound impacts of the electrolyte on electrochemical measurements in porous battery electrodes: when the active material is not rate limiting, it is impossible to directly measure the intrinsic kinetics of the active material, but conversely, it becomes possible to directly measure the kinetics of the electrolyte.

Electrochemistry↗
DOE OSTI2024

Leveraging Temperature-Dependent (Electro)Chemical Kinetics for High-Throughput Flow Battery Characterization

The library of redox-active organics that are potential candidates for electrochemical energy storage in flow batteries is exceedingly vast, necessitating high-throughput characterization of molecular lifetimes. Demonstrated extremely stable chemistries require accurate yet rapid cell cycling tests, a demand often frustrated by time-denominated capacity fade mechanisms. We have developed a high-throughput setup for elevated temperature cycling of redox flow batteries, providing a new dimension in characterization parameter space to explore. We utilize it to evaluate capacity fade rates of aqueous redox-active organic molecules, as functions of temperature. We demonstrate Arrhenius-like behavior in the temporal capacity fade rates of multiple flow battery electrolytes, permitting extrapolation to lower operating temperatures. Collectively, these results highlight the importance of accelerated decomposition protocols to expedite the screening process of candidate molecules for long lifetime flow batteries.

25 ENERGY STORAGE↗
DOE OSTI2024

Significant Improvements to Si Calendar Lifetime Using Rapid Electrolyte Screening via Potentiostatic Holds

Silicon-based lithium-ion batteries exhibit severe time-based degradation resulting in poor calendar lives. This has been identified as the major impediment towards commercialization with cycle life considered a solved issue through nanosizing and protective coatings allowing over 1000 cycles of life to be achieved. In this work, rapid screening of sixteen electrolytes for calendar life extension of Si-rich systems (70 wt% Si) is performed using the voltage hold (V-hold) protocol. V-hold significantly shortens the testing duration over the traditional open circuit voltage reference performance test allowing us to screen electrolytes within a span of two months. We find a novel ethylene carbonate (EC) free electrolyte formulation containing lithium hexafluorophosphate (LiPF 6 ) salt, and binary solvent mix of fluoroethylene carbonate (FEC), ethyl methyl carbonate (EMC) that extends calendar life of Si cells as compared to conventional EC based electrolyte. Our coupled experimental-theoretical analysis framework provides a decoupling of the parasitic currents during V-hold, allowing us to extrapolate the capacity loss to predict semiquantitative calendar lifetimes. Subsequently, cycle aging and oxidative stability tests of the EC free system also show enhanced performance over baseline electrolyte.

25 ENERGY STORAGE↗
DOE OSTI2026

Conditional Latent Diffusion for High-Resolution Prediction of Electrochemical Surface Morphology

A conditionally guided generative latent diffusion process that is trained on a set of experimental processing parameters and their associated resulting electron microscope images of the electrodeposition process is able to interpolate between processing parameters in a physically consistent way. Electrodeposition of rhenium with pulse and pulse-reverse waveforms is used as a model system, and the process is adaptable to other electrodeposition, electropolishing, or corrosion processes. The method is able to extrapolate, predicting estimates of material morphologies for experimental setups unseen in the training data. The results are demonstrated with experimental data.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The updated ITPA global H-mode confinement database: description and analysis

The multi-machine ITPA Global H-mode Confinement Database has been upgraded with new data from JET with the ITER-like wall and ASDEX Upgrade with the full tungsten wall. This paper describes the new database and presents results of regression analysis to estimate the global energy confinement scaling in H-mode plasmas using a standard power law. Various subsets of the database are considered, focusing on type of wall and divertor materials, confinement regime (all H-modes, ELMy H or ELM-free) and ITER-like constraints. Apart from ordinary least squares, two other, robust regression techniques are applied, which take into account uncertainty on all variables. Regression on data from individual devices shows that, generally, the confinement dependence on density and the power degradation are weakest in the fully metallic devices. Using the multi-machine scalings, predictions are made of the confinement time in a standard ELMy H-mode scenario in ITER. The uncertainty on the scaling parameters is discussed with a view to practically useful error bars on the parameters and predictions. One of the derived scalings for ELMy H-modes on an ITER-like subset is studied in particular and compared to the IPB98(y,2) confinement scaling in engineering and dimensionless form. Transformation of this new scaling from engineering variables to dimensionless quantities is shown to result in large error bars on the dimensionless scaling. Regression analysis in the space of dimensionless variables is therefore proposed as an alternative, yielding acceptable estimates for the dimensionless scaling. The new scaling, which is dimensionally correct within the uncertainties, suggests that some dependencies of confinement in the multi- machine database can be reconciled with parameter scans in individual devices. This includes vanishingly small dependence of confinement on line-averaged density and normalized plasma pressure (β), as well as a noticeable, positive dependence on effective atomic mass and plasma triangularity. Extrapolation of this scaling to ITER yields a somewhat lower confinement time compared to the IPB98(y, 2) prediction, possibly related to the considerably weaker dependence on major radius in the new scaling (slightly above linear). Further studies are needed to compare more flexible regression models with the power law used here. In addition, data from more devices concerning possible ‘hidden variables’ could help to determine their influence on confinement, while adding data in sparsely populated areas of the parameter space may contribute to further disentangling some of the global confinement dependencies in tokamak plasmas.

Database↗
DOE OSTI2020

Hylia: Time-Series Library for Network Operations (Hylia) v1

The ability to analyze time-series data in network operations is critical to ensure optimal operations and management in wide area networks, sensor networks, and cloud networks. Hylia is a collection of multiple time-series prediction algorithms designed specifically to work with network monitoring data sets such as SNMP, NETFLOW and other monitoring data. It is designed to recognize these features and perform the extrapolation of the data.

Kiran, Mariam↗