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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
DOE OSTI2022

Summary of NDC Capacity Building Workshop and Regional Seismic Travel Time (RSTT) in combination with Data Sharing and Integration Training

The NNSA Seismic Cooperation Program (SCP) sponsored Stephen Myers (LLNL), Michael Begnaud (LANL), Brian Young (SNL) and Istvan Bondar (Research Center for Astronomy and Earth Sciences, Hungary) to serve as a presenters/trainers at the “NDC Capacity Building Workshop and Regional Seismic Travel Time (RSTT) in combination with Data Sharing and Integration Training” September 4-8 2022 in Muscat, Oman (See Appendix A for the agenda). The workshop and training (workshop from here forward) was organized by the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) Provisional Technical Secretariat (PTS). The first half of the week was devoted to NDC workshop activities, and the second half was devoted to RSTT training. Fifty-five participants from 27 countries and the CTBTO-PTS attended the 5-day workshop (See Appendix B for list of participants and countries of origin). Presentations from the PTS described the International Monitoring System (IMS), International Data Centre (IDC) products, and metrics of regional data utilization. Contributed presentations from each country’s scientists included descriptions of regional and national networks, methods of data analysis, and needs for material and technical assistance. Training included an overview of the RSTT method and instruction on how to locate seismic events with the iLoc program, which utilizes RSTT travel times to reduce bias in event location estimates. Methods of seismic tomography and the need for a high-quality tomographic set, including seismological “ground truth”, were emphasized. Seismological “ground truth” or “GT” is a term that has come to mean both events with known location and events with well-characterized locations that are estimated using seismological data, typically with epicenter accuracy of 5 km or better. Notably, the instructional platform has migrated from UNIX shell scripts to Jupyter Notebooks. Jupyter Notebooks have the advantage being more visually intuitive, including display of graphics within the notebook. Each notebook includes every processing step that participants need to reproduce the entire exercise.

58 GEOSCIENCES↗
DOE OSTI2023

Blind photovoltaic modeling intercomparison: A multidimensional data analysis and lessons learned

The Photovoltaic (PV) Performance Modeling Collaborative (PVPMC) organized a blind PV performance modeling intercomparison to allow PV modelers to blindly test their models and modeling ability against real system data. Measured weather and irradiance data were provided along with detailed descriptions of PV systems from two locations (Albuquerque, New Mexico, USA, and Roskilde, Denmark). Participants were asked to simulate the plane-of-array irradiance, module temperature, and DC power output from six systems and submit their results to Sandia for processing. The results showed overall median mean bias (i.e., the average error per participant) of 0.6% in annual irradiation and –3.3% in annual energy yield. While most PV performance modeling results seem to exhibit higher precision and accuracy as compared to an earlier blind PV modeling study in 2010, human errors, modeling skills, and derates were found to still cause significant errors in the estimates.

14 SOLAR ENERGY↗
DOE OSTI2021

How can biosphere models simulate enough vegetation biomass in the mountains of the western United States? Implications of meteorological forcing

Most carbon stocks and fluxes in the western United States are found in mountainous terrain, where observations and modeling are difficult. Terrestrial biosphere models generally underestimate above-ground biomass (AGB) over this region. In this report, we identify methods to reduce this underestimation by focusing upon 1) biases in meteorological datasets, 2) model representation of water stress, and 3) spatial resolution. We adopted the widely-used Community Land Model version 4.5 (CLM 4.5) with six different meteorological datasets and found a 6-fold variation in simulated AGB across Utah/Colorado. Simulations underestimated AGB because of warm and dry biases within the meteorological datasets that reduced water availability and restricted plant growth. To eliminate the AGB underestimation we adopted a meteorological dataset designed for complex terrain (gridMET), combined with a representation of plant hydraulic stress (CLM 5.0). Conversely, changes in spatial resolution (meteorological variables and land surface description) had negligible impact on simulated AGB.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Smoothing Lexis diagrams using kernel functions: A contemporary approach

Lexis diagrams are rectangular arrays of event rates indexed by age and period. Analysis of Lexis diagrams is a cornerstone of cancer surveillance research. Typically, population-based descriptive studies analyze multiple Lexis diagrams defined by sex, tumor characteristics, race/ethnicity, geographic region, etc. Inevitably the amount of information per Lexis diminishes with increasing stratification. Several methods have been proposed to smooth observed Lexis diagrams up front to clarify salient patterns and improve summary estimates of averages, gradients, and trends. In this article, we develop a novel bivariate kernel-based smoother that incorporates two key innovations. First, for any given kernel, we calculate its singular values decomposition, and select an optimal truncation point—the number of leading singular vectors to retain—based on the bias-corrected Akaike information criterion. Second, we model-average over a panel of candidate kernels with diverse shapes and bandwidths. The truncated model averaging approach is fast, automatic, has excellent performance, and provides a variance-covariance matrix that takes model selection into account. We present an in-depth case study (invasive estrogen receptor-negative breast cancer incidence among non-Hispanic white women in the United States) and simulate operating characteristics for 20 representative cancers. The truncated model averaging approach consistently outperforms any fixed kernel. Our results support the routine use of the truncated model averaging approach in descriptive studies of cancer.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Microstructure Scale Lithium-Ion Battery Modeling, Part IV: The Representativity of Microstructure Parameters and Electrochemical Response

Lithium-ion battery electrochemical models require an accurate description of the electrodes microstructures to be predictive, that can be achieved through nanoscale imaging. Such observations are however limited by their field of view (FOV), as they provide only a subset of the whole electrode volume that does not necessarily represent the whole electrode microstructure heterogeneity, and therefore can bias the analysis. A representativity analysis has been performed on the microstructure parameters and, in a novel way, on the full cell electrochemical response to evaluate the predictions representativeness, and thus relevance, of a microstructure scale electrochemical model. The microstructure parameter deviation propagations to the electrochemical response have been quantified for different charge rates. This defines a threshold for the microstructure parameters FOV for a desired maximum deviation of the electrochemical response. Electrochemical model shows cell representative section areas are increasing with C-rate, due to higher in-plane heterogeneities, indicating larger FOVs are required specifically for fast charge modeling. Representativity analysis determines a cell FOV of 144.4 × 154.4 μm 2 is large enough to establish a convergence on the representative section areas for low-intermediate C-rate (≤2.5 C), therefore positively concludes on the model representativeness for these rates, but is not large enough to conclude for higher rates.

25 ENERGY STORAGE↗
DOE OSTI2024

On the Representativity of Electrode Microstructure Parameters and Their Electrochemical Response for Lithium Ion Batteries

Lithium-ion battery electrochemical models require an accurate description of the electrodes microstructures to be predictive that can be achieved through nanoscale imaging. Such observations are however limited by their field of view (FOV), as they provide only a subset of the whole electrode volume that does not necessarily represent the whole electrode microstructure heterogeneity, and therefore can bias the microstructure analysis. A microstructure scale electrochemical model was used to investigate lithium plating onset, material non-uniform utilization, and in-plane heterogeneities for an NMC-graphite full cell. To evaluate the representativeness, and thus relevance, of these model predictions, a coupled representativity analysis has been performed on the microstructure parameters and, in a novel way, on the full cell electrochemical response. Electrode microstructure parameters representativeness has been first quantified using the representative volume element (RVE) methodology. The RVE major flaw is that ultimately it can only conclude if a FOV contains representative subvolumes of the FOV, but not if the FOV itself is representative of the electrode volume. Analysis can conclude negatively ('FOV is not representative'), but not positively ('FOV is representative'). One major contribution of this work was to quantify the convergence of the RVE size with the FOV, to actually investigate the FOV representativeness and thus partly remedy this intrinsic limitation. The analysis determined that performing a standard RVE calculation, without exploring its FOV convergence, is likely to strongly underestimate the actual RVE size. The new RVE methodology has been automated in the NREL open-source Microstructure Analysis Toolbox (MATBOX) and is available to the battery community. Representativeness of microstructure parameters is however only an intermediate step, as the end-results of an electrochemical model are performances predictions. Indeed, what is the practical consequence of a given deviation for a microstructure parameter? The microstructure parameter deviation propagations to the 3D microstructure scale electrochemical response have been then quantified for different charge rates. This defines a threshold for the microstructure parameters FOV for a desired maximum deviation of the electrochemical response. Such deviation propagation analysis is analogous to error propagation analysis and is necessary to determine the relevance of microstructure scale model predictions for macroscale predictions. Electrochemical model shows cell representative section areas are increasing with C-rate, due to higher in-plane heterogeneities, indicating larger FOVs are required specifically for fast charge modeling. Therefore, we introduced the novel concept of electrochemical RVE (eRVE) that is a function of the operating conditions (thus defined as a dynamic RVE), with an increasing dependence with the C-rate. Representativity analysis of the investigated cell determined a FOV of 144.4 x 54.4 m2 is large enough to establish a convergence on the representative section areas for low to intermediate C-rate (=2.5C), but not large enough to conclude for higher rates. This work aims to emphasize the importance of representativity analysis for LIB electrode microstructures, as it is required to estimate the error, and thus the relevance, of microstructure parameters intended to be used in macroscale models. The methodology and results can help researchers to select the relevant imaging and associated FOV required to provide accurate enough microstructure parameters.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Clustering analysis of medium-band selected high-redshift galaxies

Next-generation large-scale structure spectroscopic surveys will probe cosmology at high redshifts (2.3 < z < 3.5), relying on abundant galaxy tracers such as Lyα emitters (LAEs) and Lyman break galaxies (LBGs). Medium-band photometry has emerged as a potential technique for efficiently selecting these high-redshift galaxies. In this work, we present clustering analysis of medium-band selected galaxies at high redshift, utilizing photometric data from the Intermediate Band Imaging Survey (IBIS) and spectroscopic data from the Dark Energy Spectroscopic Instrument (DESI). We interpret the clustering of such samples using both Halo Occupation Distribution (HOD) modeling and a perturbation theory description of large-scale structure. Our modeling indicates that the current target sample is composed from an overlapping mixture of LAEs and LBGs with emission lines. Despite differences in target selection, we find that the clustering properties are consistent with previous studies, with correlation lengths r 0 ≃ 3-4 h -1 Mpc and a linear bias of b ∼ 1.8-2.5. Finally, we discuss the simulation requirements implied by these measurements and demonstrate that the properties of the samples would make them excellent targets to enhance our understanding of the high-z universe.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Imaging nanoscale carrier, thermal, and structural dynamics with time-resolved and ultrafast electron energy-loss spectroscopy

Time-resolved and ultrafast electron energy-loss spectroscopy (EELS) is an emerging technique for measuring photoexcited carriers, lattice dynamics, and near-fields across femtosecond to microsecond timescales. When performed in either a specialized scanning transmission electron microscope or ultrafast electron microscope (UEM), time-resolved and ultrafast EELS can directly image charge carriers, lattice vibrations, and heat dissipation following photoexcitation or applied bias. Yet, recent advances in theoretical calculations and electron optics are often required to realize the full potential of ultrafast EEL spectrum imaging. Here, in this review, we present a comprehensive overview of the recent progress in the theory and instrumentation of time-resolved and ultrafast EELS. We begin with an introduction to the technique, followed by a physical description of the loss function. We outline approaches for calculating and interpreting ground-state and transient EEL spectra spanning low-loss plasmons to core-level excitations analogous to x-ray absorption. We then survey the current state of time-resolved and ultrafast EELS techniques beyond photon-induced near-field electron microscopy, highlighting abilities to image carrier and thermal dynamics. Finally, we examine future directions enabled by emerging technologies, including electron beam monochromation, in situ and operando cells, laser-free UEM, and high-speed direct electron detectors. These advances position time-resolved and ultrafast EELS as a critical tool for uncovering nanoscale dynamic processes in quantum materials and solar energy conversion devices.

Computational methods↗
DOE OSTI2022

A critical comparison of CH∙∙∙π versus π∙∙∙π interactions in the benzene dimer: obtaining benchmarks at the CCSD(T) level and assessing the accuracy of lower scaling methods

In this study, we have established CCSD(T)/CBS (Complete Basis Set) limits for 3 stationary points on the benzene dimer potential energy surface, corresponding to the π∙∙∙π (parallel displaced or PD(C 2h ), minimum) and CH∙∙∙π (T-shaped or T(C 2v ), transition state) and tilted T-shaped (or TT(Cs), minimum) bonding scenarios considering both the structure and binding energy. The CCSD(T)/CBS binding energies are -2.65 ± 0.02 (PD), -2.74 ± 0.03 (T), and -2.83 ± 0.01 kcal mol -1 (TT). To this end, the CH∙∙∙π is ~0.2 kcal mol -1 stronger than the π∙∙∙π interaction, whereas the tilting of the CH donating benzene molecule with respect to the other benzene is worth 0.1 kcal mol -1 . As previously discussed in the literature, the MP2 level of theory does not provide a close match for either the energy or structure, yet the SCS-MP2 yields structures in excellent agreement with respect to the CCSD(T) result. It is found that the SCS-MI-MP2 also gives optimized structures very close to SCS-MP2 (within ~0.01 Å of the benchmark). Despite the closer match in structure, the spin-biased MP2 methods (SCS-, SCS-MI-, and SOS-MP2) incorrectly predict the relative stabilities of the isomers. That said, none of the spin biased MP2 methods offers a good compromise between energy and structure for the systems examined. Finally, the CCSD(T)/CBS benchmarks were used to assess the performance of 13 DFT functionals selected from different rungs of Jacob's ladder. Several functionals such as TPSS-D3, B3LYP-D3, B97-D, B97-D3, and B2PLYP-D3 provided a good description of the binding energies for both CH∙∙∙π and π∙∙∙π interactions, yielding values within 6% of the CCSD(T)/CBS benchmark values. Unlike the MP2 methods, these functionals correctly predict the relative stability of the PD(C 2h ) and T(C 2v ) dimers. Further, we find that there is no systematic improvement as Jacob's ladder is ascended (increased complexity of functional). The best functionals that result in a good compromise between structure and energy accuracy are B97-D3 and B2PLYP-D3 for both the CH∙∙∙π and π∙∙∙π interaction. Despite the impressive performance of these functionals, a challenge that remains is ensuring the transferability of these density functionals in accurately describing the interaction between dimers of larger aromatic molecules, the latter requiring high-level benchmarks for these systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

The PDF perspective on the tracer-matter connection: Lagrangian bias and non-Poissonian shot noise

ABSTRACT We study the connection of matter density and its tracers from the probability density function (PDF) perspective. One aspect of this connection is the conditional expectation value 〈δtracer|δm〉 when averaging both tracer and matter density over some scale. We present a new way to incorporate a Lagrangian bias expansion of this expectation value into standard frameworks for modelling the PDF of density fluctuations and counts-in-cells statistics. Using N-body simulations and mock galaxy catalogues we confirm the accuracy of this expansion and compare it to the more commonly used Eulerian parametrization. For haloes hosting typical luminous red galaxies, the Lagrangian model provides a significantly better description of 〈δtracer|δm〉 at second order in perturbations. A second aspect of the matter-tracer connection is shot-noise, i.e. the scatter of tracer density around 〈δtracer|δm〉. It is well known that this noise can be significantly non-Poissonian and we validate the performance of a more general, two-parameter shot-noise model for different tracers and simulations. Both parts of our analysis are meant to pave the way for forthcoming applications to survey data.

Friedrich, Oliver↗
DOE OSTI2023

Amazon drought resilience – emerging results point to new empirical needs

Will climate change turn tropical forests from a carbon sink to a source (Pan et al., 2011)? Tropical forests cycle more carbon, water, and energy than any other biome (Bonan, 2008). The Amazon is the largest tropical forest, spanning one-third of South America, and any perturbation in its function has repercussions on the global climate. Mild and severe droughts in the Amazon are predicted to double and triple in area, respectively, by 2100, while the area under wet extremes will increase (Duffy et al., 2015). Our understanding of the role of plants' hydrological environments in determining their drought response, however, is limited (Chitra-Tarak et al., 2018, 2021). An important review by Costa et al. (2023; pp. 714–733) published in this issue of New Phytologist highlights that shallow-water table (WT) forests constitute c. 50% of the Amazon and may act as hydrological refugia during droughts, yet they have been neglected in Amazon forest research to date. Emerging results suggest that the shallow-WT forests that dominate the Amazon basin may increase in productivity under moderate droughts due to relief from hypoxia, suggesting a potential buffer from drought relative to deeper WT forests (Fig. 1). During severe droughts, however, shallow-WT forests may be vulnerable to collapse due to drought-intolerant traits. Addressing the underrepresentation of relatively drought-resilient shallow-WT forests in the Amazon's forest inventories may help resolve the much-debated incongruence in recent studies of the Amazon's drought resilience. Data from forest inventories (biased toward deep-WT forests) indicate that the Amazon's carbon sink is declining, partially because of drought impacts (Phillips et al., 2009; Brienen et al., 2015), whereas basin-wide satellite-based measures of gross primary productivity, which include shallow-WT forests, indicate varied regional responses to droughts (Saleska et al., 2007; Brando et al., 2010). Costa et al.'s (2023) descriptive insights of the intricate balance of geology, topography, hydrology, vegetation, and drought on ecosystem function, as well as their conceptual predictive framework, are useful for developing new empirical research in these understudied ecosystems and improving Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Up‐And‐Coming Advances in Optical and Microwave Nonreciprocity: From Classical to Quantum Realm

Reciprocity is a fundamental physical principle that roots in the time‐reversal symmetry of physical laws. It allows making predictions on any arbitrary complex system's response and operation and hence simplifies the analysis. However, there are many practical situations in which it is advantageous to break reciprocity, e.g., isolators preventing wave scattering back to lasers and generators, full‐duplex systems for multiplexing transmission and receiving in the same channel, nonreciprocal cavity excitation, and protection of fragile states of superconductor quantum computers from thermal noise. The most widespread approach to time‐reversal symmetry breaking and nonreciprocity based on magnetic field biasing suffers from bulkiness, cost ineffectiveness, and loss, motivating researchers and engineers to search for more practical approaches. Herein, the up‐and‐coming advances in optical nonreciprocity, including new materials (Weyl semimetals, topological insulators, metasurfaces), active structures, time‐modulation, parity‐time (PT)‐symmetry breaking, nonlinearity combined with a structural asymmetry, quantum nonlinearity, unidirectional gain and loss, chiral quantum states and valley polarization are overviewed. A general description of nonreciprocal systems is provided and the pros and cons of the mentioned approaches to nonreciprocity are discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Kinetic simulations of ignited mode cesium vapor thermionic converters

Cesium vapor thermionic converters are an attractive method of converting high-temperature heat directly to electricity, but theoretical descriptions of the systems have been difficult due to the multi-step ionization of Cs through inelastic electron–neutral collisions. This work presents particle-in-cell simulations of these converters, using a direct simulation Monte Carlo collision model to track 52 excited states of Cs. Here, these simulations show the dominant role of multi-step ionization, which also varies significantly based on both the applied voltage bias and pressure. The electron energy distribution functions are shown to be highly non-Maxwellian in the cases analyzed here. A comparison with previous approaches is presented, and large differences are found in ionization rates due especially to the fact that previous approaches have assumed Maxwellian electron distributions. Finally, an open question regarding the nature of the plasma sheaths in the obstructed regime is discussed. The one-dimensional simulations did not produce stable obstructed regime operation and thereby do not support the double-sheath hypothesis.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2026

Unraveling Fundamental Activity–Stability Relationships in Rutile Oxides

The oxygen evolution reaction (OER) is a key anodic half-cell reaction that accompanies several critical electrochemical reduction reactions of interest to a variety of applications. Despite steady advances in understanding and qualitatively predicting OER activity and selectivity trends, a comprehensive description or prediction of material aqueous (in)stability and degradation mechanisms remains elusive, even though these processes critically influence device lifetime and economic feasibility. In this work, we investigate the interplay, or lack thereof, between OER activity and material aqueous stability across rutile oxides, with a particular focus on iridium oxide (IrO 2 ). By applying a Born–Haber cycle, we calculate the thermodynamic driving force for metal dissolution as a function of the applied bias and electrolyte conditions. We apply interpretable machine learning techniques, including principal component analysis and symbolic regression, to analyze trends across rutile oxides and find that key thermodynamic descriptors for OER activity and surface stability are only very weakly correlated. Instead, the local atomic environment─especially electronic structure signatures for interactions between the active site and its neighbors─plays a more important role in predicting material stability. Leveraging these insights, we investigate the impact of doping IrO 2 with a range of transition metals and show that the stability of Ir active sites can be tuned largely independently of its predicted OER activity. These insights lay the foundation for material design to improve stability with respect to corrosion, with the ultimate aim to enhance long-term stability without sacrificing catalytic performance in the OER.

evolution reactions↗
DOE OSTI2021

Fermi-Hubbard Transport Solver v. 1.0

SAND2021-1511 O. The code takes a description of a system comprised of two leads that are sources and sinks of electrons, a set of abstract sites that those electrons can occupy, and their connectivity. It then predicts the current passing between the two leads through the set of abstract sites. There are numerical parameters that define the rate at which electrons tunnel between sites and another parameter that determines the energy of having two electrons on the same site. When the ratio of these energies and biases are set just right, they can be used to predict the existence of Coulomb blockade in which no current flows because the energetic penalty would be too high. That is, the code predicts a current and the absence of a current which is predictive of Coulomb blockade. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Brickson, MitchellI↗
DOE OSTI2023

Cost-optimized Automated Variance Reduction for Highly Angle-dependent Radiation Transport Analyses [Dissertation]

Monte Carlo variance-reduction techniques that directly bias particle direction have historically received limited attention despite being useful for many radiation transport applications such as those in which radiation travels through large regions with a low probability of colliding. One such technique is known as DXTRAN (short for deterministic transport) in the MCNP Monte Carlo radiation transport code. Until now, effectively applying DXTRAN in calculations is largely based on empirical observations of computational performance. Optimal DXTRAN parameters are identified through manual iteration. This work develops new mathematical descriptions of the DXTRAN variance-reduction process and demonstrates a new automated variance-reduction method that applies these mathematical formulations to determine the optimal application of DXTRAN in a given problem.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Exact reaction coordinates for flap opening in HIV-1 protease

The primary goal of protein science is to understand how proteins function, which requires understanding the functional dynamics responsible for transitions between different functional structures of a protein. A central concept is the exact reaction coordinates that can determine the value of committor for any protein configuration, which provide the optimal description of functional dynamics. Despite intensive efforts, identifying the exact reaction coordinates (RCs) in complex molecules remains a formidable challenge. Using the recently developed generalized work functional, we report the discovery of the exact RCs for an important functional process—the flap opening of HIV-1 protease. Our results show that this process has six RCs, each one is a linear combination of ~240 backbone dihedrals, providing the precise definition of collectivity and cooperativity in the functional dynamics of a protein. Applying bias potentials along each RC can accelerate flap opening by 10 3 to 10 4 folds. The success in identifying the RCs of a protein with 198 residues represents a significant progress beyond that of the alanine dipeptide, currently the only other complex molecule for which the exact RCs for its conformational changes are known. Our results suggest that the generalized work functional (GWF) might be the fundamental operator of mechanics that controls protein dynamics.

Science & Technology - Other Topics↗