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All sources NASA NTRS DOE OSTI

DOE OSTI2022

Thermal conductivity evaluation of ion irradiated Si 3 N 4 and ZrN ceramics using spatial domain thermoreflectance

Nitride ceramics have been investigated for different applications in the nuclear industry, such as space nuclear power, fusion reactor diagnostics and plasma heating, inert matrix fuels, and accident tolerant fuels. Although thermal conductivity remains one of the most important properties to track following irradiation, traditional techniques such as laser flash and xenon flash are limited to bulk sample characterization, which requires lengthy and cost-consuming neutron irradiation. This work used spatial domain thermoreflectance (SDTR) for the micrometer-scale measurement of thermal conductivity in 15 MeV Ni ion-irradiated silicon nitride and zirconium nitride from 1 to 50 dpa and 300 to 700 °C. Here, the SDTR-measured unirradiated thermal conductivity was found to be consistent with the published data on bulk samples. Electrically conductive ZrN exhibits modest reduction after irradiation which is minimal at the highest irradiation temperatures. In electrically insulating Si 3 N 4 , the reduction is more significant and unlike ZrN, the reduction remains significant even at a higher irradiation temperature. The thermal resistance evolution following irradiation was compared with lattice swelling, which was determined using grazing incidence x-ray diffraction, and radiation-induced defects were observed using transmission electron microscopy. A saturation value was observed between 15 and 50 dpa for thermal conductivity degradation in both nitride ceramics and a direct correlation with high-temperature defect recombination was observed, as well as the potential presence of additional carrier scattering mechanisms.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Simulation driven adaptive sampling for neutron-diffraction based strain mapping of additively manufactured parts

Neutron diffraction based strain mapping is a useful technique for measuring residual strains in additively manufactured (AM) metal parts. The measurement is traditionally done by scanning the sample in a point-wise raster pattern to extract the strain at each position. Since the overall scan can span several hours, adaptive sampling approaches using Bayesian optimization based on Gaussian process (BO-GP) regression have been introduced—demonstrating that even with a fraction of the typically made measurements the dominant strain patterns in the sample can be reconstructed. However, the parameters of the BO-GP algorithm have to be carefully chosen for best performance, and the movement time between arbitrary points can offset the time savings from a reduced number of measurement locations. In this paper, we propose algorithms to refine the BO-GP based methods by using simulations of strain patterns in AM parts based on the materials and the process used to print them. We demonstrate that the simulated strain patterns can be used to help choose better parameters for the BO-GP based framework—leading to low reconstruction error for the final strain pattern. Furthermore, we show that the strain mapping experiment can be initialized with a sampling pattern learnt from the simulation data and ordered to reduce movement time, dramatically enabling reduction in the overall time required to run the baseline BO-GP method.

Gaussian process regression↗

DOE OSTI2024

Novel candidate taxa contribute to key metabolic processes in Fennoscandian Shield deep groundwaters

The continental deep biosphere contains a vast reservoir of microorganisms, although a large proportion of its diversity remains both uncultured and undescribed. In this study, the metabolic potential (metagenomes) and activity (metatranscriptomes) of the microbial communities in Fennoscandian Shield deep subsurface groundwaters were characterized with a focus on novel taxa. DNA sequencing generated 1270 de-replicated metagenome-assembled genomes and single-amplified genomes, containing 7 novel classes, 34 orders, and 72 families. The majority of novel taxa were affiliated with Patescibacteria, whereas among novel archaea taxa, Thermoproteota and Nanoarchaeota representatives dominated. Metatranscriptomes revealed that 30 of the 112 novel taxa at the class, order, and family levels were active in at least one investigated groundwater sample, implying that novel taxa represent a partially active but hitherto uncharacterized deep biosphere component. The novel taxa genomes coded for carbon fixation predominantly via the Wood–Ljungdahl pathway, nitrogen fixation, sulfur plus hydrogen oxidation, and fermentative pathways, including acetogenesis. These metabolic processes contributed significantly to the total community’s capacity, with up to 9.9% of fermentation, 6.4% of the Wood–Ljungdahl pathway, 6.8% of sulfur plus 8.6% of hydrogen oxidation, and energy conservation via nitrate (4.4%) and sulfate (6.0%) reduction. Key novel taxa included the UBA9089 phylum, with representatives having a prominent role in carbon fixation, nitrate and sulfate reduction, and organic and inorganic electron donor oxidation. These data provided insights into deep biosphere microbial diversity and their contribution to nutrient and energy cycling in this ecosystem.

Candidatus↗

DOE OSTI2023

Forecasting constraints on the mean free path of ionizing photons at z ≥ 5.4 from the Lyman-α forest flux autocorrelation function

ABSTRACT Fluctuations in Lyman-α (Ly α) forest transmission towards high-z quasars are partially sourced from spatial fluctuations in the ultraviolet background, the level of which are set by the mean free path of ionizing photons (λmfp). The autocorrelation function of Ly α forest flux characterizes the strength and scale of transmission fluctuations and, as we show, is thus sensitive to λmfp. Recent measurements at z ∼ 6 suggest a rapid evolution of λmfp at z > 5.0 which would leave a signature in the evolution of the autocorrelation function. For this forecast, we model mock Ly α forest data with properties similar to the XQR-30 extended data set at 5.4 ≤ z ≤ 6.0. At each z, we investigate 100 mock data sets and an ideal case where mock data matches model values of the autocorrelation function. For ideal data with λmfp = 9.0 cMpc at z = 6.0, we recover $\lambda _{\text{mfp}}=12^{+6}_{-3}$ cMpc. This precision is comparable to direct measurements of λmfp from the stacking of quasar spectra beyond the Lyman limit. Hypothetical high-resolution data leads to a $\sim 40~{{\ \rm per\ cent}}$ reduction in the error bars over all z. The distribution of mock values of the autocorrelation function in this work is highly non-Gaussian for high-z, which should caution work with other statistics of the high-z Ly α forest against making this assumption. We use a rigorous statistical method to pass an inference test, however future work on non-Gaussian methods will enable higher precision measurements.

Astronomy & Astrophysics↗

DOE OSTI2022

A new route to obtain fluorescence X-ray absorption spectra of compounds and to remove the self-absorption induced nonlinearity in the spectra

A new route to obtain fluorescence X-ray absorption spectra of compounds and to remove the self-absorption induced nonlinearity in the spectra is described. The fluorescent intensity If is linearly proportional to the absorption coefficient μ. For studies of surface structures around an element (κ) the fluorescence detection is often the mode of choice. However, the measurement may suffer from a self-absorption (SA) effect which nonlinearly distorts the spectra. The effect is severe when κ is concentrated or the measurements are carried out in certain geometries. Here, the correlations among emission events in compounds are examined following resonance X-ray core-electron excitation within κ. Under conditions leading to SA, If emitted from κ apparently has a conjugated relationship with the fluorescent intensities simultaneously emitted from other elements (ξ). Normalizing the former (κ) by the latter (ξ) will largely remove SA effects and reduce this nonlinear problem to a tractable linear problem. This does result in a moderate reduction of the spectral amplitude due to the so-called secondary emission from ξ excited by the emission from κ. Nonetheless, the resulting spectra will allow one to accurately determine bond distances and disorder and, in some respects, can be superior to spectra obtained via the absorption channel. For μξ < μκ and grazing incidence geometry, the amplitude reduction can be small and simple normalization is sufficient to restore the spectral integrity with remarkable accuracy. This has been instrumental in unravelling the surface and subsurface structures around cations in amorphous Ga–In–O and Zn–Sn–O films which are otherwise inaccessible due to severe SA effects. This method has also been applied to several samples with μξ ≃ μκ to examine its applicability. For these samples, the amplitude reduction is 12 ± 4% versus their standards for the data measured with the classical 45°/45° geometry. This experimental method is easy to implement. Since If from κ and ξ are measured by the same detector system, it is also superior to other methods in removing systematic errors such as detector system nonlinearity, electronic noise, and some beam instabilities, and in removing spectral imperfections due to, for example, SA effects, diffraction effects and sample inhomogeneity. The distortions resulting from the latter can be severe in the spectra measured in transmission mode.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Real-Time Regional PV Spinning Reserve Estimator with AGC Look-Ahead Windows

Curtailed PV generation is a zero-marginal cost spinning reserve that can be used for a number of active power control services. However, unlike the traditional spinning reserve providers, i.e., fossil-fueled generators, who have well-defined operating characteristics, e.g., available headroom or potential high limit (PHL), PV plants have by nature variable and uncertain operating characteristics. To ensure the effective coordination between PV plants and the system operator during an active power control event, accurate forecasts of the PV PHL are essential. A novel reference-control grouping based scaling method has been proposed by NREL to estimate the PV PHL in real-time. This work further enhances the methodology by: 1) improving the model accuracy through machine learning; 2) considering look-ahead windows introduced by the computation and communication latencies; 3) applying the method to regional spinning reserve estimation. A significant performance improvement, over 99% of estimation error reduction, has been observed based on real-world data collected by CAISO and PV plant operators.

potential high limit↗

DOE OSTI2021

The Effect of Pressure on NO x Entitlement and Reaction Timescales in a Premixed Axial Jet-In-Crossflow

This paper investigates the pressure dependency of a lean premixed jet injected into a lean vitiated crossflow with an experimentally verified detailed chemistry computational fluid dynamics (CFD) model and 53 species considered. Experimental data were taken in an axially staged combustor with an optically accessible test section, allowing the use of particle image velocimetry (PIV) and CH* chemiluminescence techniques as well as point measurement of species concentration, temperature, and pressure. The experimental data cases at one, three, and five atmospheres were selected to describe the flame stabilization dependency on pressure and gain the required knowledge for an extrapolation to engine condition. Simulated exit nitrogen oxide levels were validated with experimental emission data, and a global emission trend for the NO reduction at elevated pressure and constant turbine inlet temperature level was defined. The nitrogen oxide benefit at elevated operating pressure was justified with the significantly smaller flame surface area: the analysis of the simulated spanwise and top-view profiles showed a relatively short receded core flame with nitrogen oxide production in the center at high pressure relative to a longer and larger shear layer flame at atmospheric condition that produced NO toward the inner and outer side of the flame. Furthermore, decomposition of the Damköhler number revealed the strong influence of the reaction timescales with higher reaction rates at elevated pressure, along with a moderate influence of the turbulent timescales, showing higher turbulence intensity in the lee-side recirculation zone at lower pressure.

03 NATURAL GAS↗

DOE OSTI2024

Examining Performance Loss Mechanisms in Lithium-Ion Batteries with the High-Voltage Mn-Rich Spinel Positive Electrodes

The high-voltage spinel, with the nominal composition of LiNi 0.5-x Mn 1.5+x O 4 (LNMO), could be a sustainable alternative to the layered-oxide positive electrodes used in lithium-ion batteries. However, commercial acceptance has been limited as LNMO cells display rapid performance loss during cycling. To examine reasons for this loss we prepared cells with LNMO-based positive and either graphite or lithium titanate (LTO) based negative electrodes. Our initial cells displayed high impedance and rapid impedance rise during cycling. Adding single walled carbon nanotubes in the positive lowered initial cell impedance but impedance increased during cycling because of coating delamination from the Al foil. Using a primed Al current collector solved the delamination problem: cells with this current collector showed only a small impedance rise. Regarding capacity, cells with the LTO negative showed higher initial capacities and smaller fade. Our data indicate that cell capacity retention is determined by reduction reactions at the negative electrode that lower capacity and oxidation reactions at the positive electrode that increase capacity: both types of reactions deplete the cell electrolyte during extended cycling. All things considered, species generated at high voltages, either in the electrolyte or at the LNMO electrode, drive cell capacity fade.

25 ENERGY STORAGE↗

DOE OSTI2020

New Yield Estimates for Nuclear Detonations Over Water

One of the primary methods used by the United States to estimate the yields of atmospheric nuclear tests performed in the 1940’s, 50’s, and 60’s was to film each detonation with a high-speed camera, measure the blast radius as a function of time, and then use Taylor’s spherical blast wave equation to calculate the source energy (i.e., the yield). For surface detonations, the yield in Taylor’s equation was assumed to be twice as large as the actual yield to account for the energy reflected from the surface, assuming the ground was a perfect reflector. Recent simulations of surface detonations over water (referred to as barge shots) have shown that Taylor’s equation must be modified further to account for material entrainment and heat losses. We have computed this lost energy using a high-order radiation-hydrodynamics code to determine the reduction in blast radius vs. time. By analyzing simulation data in the same manner as film data, we have determined that a yield correction factor of 1.27± 0.04 should be applied to the barge shots.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2020

Advanced Anode for Internal Reforming and Thermal Management in Solid Oxide Fuel Cells

Solid oxide fuel cell (SOFC) is an efficient and clean electrical power generation system compared to conventional combustion based technologies with energy efficiency reaching as high as 85-90% in co-generation mode (electricity and heat). Other advantages of SOFCs are hybridization, modularity of construction, small CO 2 foot print per kWh of generated electricity and fuel flexibility. Hydrocarbons present in the gaseous fuel is utilized in SOFCs by internal or external reforming. There are two different internal reforming concepts: Direct Internal Reforming (DIR) and Indirect Internal Reforming (IIR). For DIR operation, the endothermic reforming reaction and the exothermic reaction from the oxidation reaction are operated together in the single unit eliminating the requirement for a separate fuel reformer. This configuration also simplifies the overall system design, making SOFC more attractive and efficient means of producing electrical power. The main advantage of the DIR type of operation is that the H 2 or CO consumption by the electrochemical reaction could directly promote the conversion of methane at the anode side of the fuel cell resulting in high conversion and high efficiency. The DIR operation, however, requires an anode material that has desired dual catalytic (hetero and electro) properties for reforming reaction and electrochemical reactions. The anode materials also need to remain resistant to carbon formation at the operating temperature and atmosphere with stable cell performance. Another requirement is to match the reforming reactions and electrochemical reactions to avoid local cooling or overheating, which can result in mechanical failure due to thermally induced stresses. Direct internal reforming (DIR) of hydrocarbon fuels simplifies the overall SOFC system design making it more attractive and efficient for producing electrical power. Low cost alloy anodes for distributed internal reforming of methane and other hydrocarbon fuels offer increased fuel-flexibility, reliability, and long term performance stability of solid oxide fuel cells (SOFC). The research program examined modification of the chemical compositions and microstructure of high entropy alloy (HEA) anode materials using thermochemical calculations and process simulation and modeling to achieve distributed reforming over the entire anode to eliminate hot zones. Cell fabrication and testing of the HEA anodes using button cell configuration has been performed to demonstrate the advantages of new anode over traditional Ni-YSZ anodes for distributed reforming and carbon free operation. Technical accomplishments include identification and synthesis of HEA carbon-resistant anode, demonstration of reduction in reforming rate confirmed by GC and modelling data validating effectiveness for thermal management in cell/stacks, electrochemical testing of HEA anode in single cell (HEA-GDC||YSZ||LSM-YSZ) and Characterization of pretest and posttest anode materials by TEM and SEM-EDS confirming carbon-free operation.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2024

AEOLUS: Advances in Experimental Design, Optimal Control, and Learning for Uncertain Complex Systems

Sustained advances in the mathematics of modeling and simulation have resulted in the capability today for routine simulation of a number of large scale complex DOE-relevant systems. As remarkable as this capability for solving the so-called forward problem is, it is typically only the first step-an inner loop within an outer loop that explores the simulation model's parameter space and decision space to characterize uncertainty in the model's predictions, learn unknown model parameters from data, design the most informative experiments, determine optimal control strategies, and create optimal designs. Broadly, what unifies all of these outer loop problems is that they are, in one form or another, optimization problems over parameter/control/design space that are constrained by complex uncertain models. To fully realize the power of scientific simulation as a basis for scientific discovery, technological innovation, and rational decision-making, it is imperative to move beyond simulation to tackle the outer loop of optimization for learning from data, experimental design, and control with complex uncertain models. When the models under consideration are large-scale and complex, and when the optimization variable and uncertain parameter spaces are high (or infinite) dimensional, this constitutes a grand challenge of the highest order, and is intractable with conventional methods. To overcome these challenges, the AEOLUS Center was established to develop a unified mathematical, computational, and statistical framework for (1) Learning predictive models from complex data via Bayesian inference and optimization, and (2) Optimizing experiments, processes, and designs using the resulting uncertain models. These problems are intractable with conventional methods, for several reasons: (1) The simulation problems that govern the inner loops of the optimization problems are expensive to execute (due to severe nonlinearity, heterogeneity, multiphysics/multiscale coupling); (2) The optimization variable and uncertain parameter spaces are high dimensional, often stemming from discretizations of infinite dimensional fields such as initial conditions, sources, or material properties. We argue that the key to overcoming these challenges is to develop new mathematical, computational, and statistical methods that exploit the structure of the Bayesian inference and optimization problems mediated by their underlying complex uncertain models. This structure includes the regularity, sparsity, geometry, low intrinsic dimensionality, and multifidelity nature of the maps from uncertain parameter/optimization variable spaces to the specific objectives targeted: Bayesian inference, optimal experimental design, and optimal control design. Black box methods developed as generic tools are incapable of exploiting this structure. To be successful, we must create, integrate, and cross-fertilize ideas across multiple areas of applied math--including approximation theory, Bayesian inference, data science, experimental design, information theory, machine learning, model reduction, optimal control theory, parallel algorithms, PDE-constrained optimization, randomized algorithms, stochastic optimization, and uncertainty quantification--all while exploiting the structure of the problems at hand. With this goal in mind, we have marshaled a team of leading authorities in these areas. While the methods we develop will be broadly applicable across a wide spectrum of DOE problems in which experiments inform models and the systems those models describe must be optimized under uncertainty, we have chosen a specific area, advanced manufacturing and materials, to drive our work. AMM is characterized by complex models across multiple scales, and is a rich source of challenging problems in inference, experimental design, and optimal control, requiring multifaceted and integrated advances in applied mathematics. As such, AMM serves as an excellent vehicle to motivate and demonstrate the advances in applied mathematics developed by our center.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Application of the Approximate Bayesian Computation Algorithm to Gamma-Ray Spectroscopy

Radioisotope identification (RIID) algorithms for gamma-ray spectroscopy aim to infer what isotopes are present and in what amounts in test items. RIID algorithms either use all energy channels in the analysis region or only energy channels in and near identified peaks. Because many RIID algorithms rely on locating peaks and estimating each peak’s net area, peak location and peak area estimation algorithms continue to be developed for gamma-ray spectroscopy. This paper shows that approximate Bayesian computation (ABC) can be effective for peak location and area estimation. Algorithms to locate peaks can be applied to raw or smoothed data, and among several smoothing options, the iterative bias reduction algorithm (IBR) is recommended; the use of IBR with ABC is shown to potentially reduce uncertainty in peak location estimation. Extracted peak locations and areas can then be used as summary statistics in a new ABC-based RIID. ABC allows for easy experimentation with candidate summary statistics such as goodness-of-fit scores and peak areas that are extracted from relatively high dimensional gamma spectra with photopeaks (1024 or more energy channels) consisting of count rates versus energy for a large number of gamma energies.

Burr, Tom↗

DOE OSTI2021

GHG policy impacts for Seattle’s buildings: targets, timing, and scope

Many US cities are addressing climate change by setting goals to reduce their greenhouse gas (GHG) emissions by a specified amount within a specified time period. In order to achieve these goals, reducing emissions from existing buildings is crucial. Many cities are passing legislation to target existing buildings through benchmarking, auditing, retuning, or energy or emissions performance standards. As cities design legislation, they must consider the timing of the policies, how to prioritize building types and sizes, and how these design decisions will impact the city’s emissions. This paper addresses these questions for one particular US city: Seattle, Washington. A model of Seattle’s building stock was created with benchmarking and tax assessor data. It was then used to predict GHG emissions reductions due to different policy implementations for existing commercial and multifamily buildings. Key findings are: (1) the proposed emissions policy is expected to reduce cumulative emissions from buildings by 19% between 2020 and 2050; (2) delaying the implementation of the policy by five years could limit savings to 12%; and (3) including smaller buildings in the policy could increase savings to 34%. The lessons learned and how this can be used by other cities are discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Real-Time Regional PV Spinning Reserve Estimator with AGC Look-Ahead Windows: Preprint

Curtailed PV generation is a zero-marginal cost spinning reserve that can be used for a number of active power control services. However, unlike the traditional spinning reserve providers, i.e., fossil-fueled generators, who have well-defined operating characteristics, e.g., available headroom or potential high limit (PHL), PV plants have by nature variable and uncertain operating characteristics. To ensure the effective coordination between PV plants and the system operator during an active power control event, accurate forecasts of the PV PHL are essential. A novel reference-control grouping based scaling method has been proposed by NREL to estimate the PV PHL in real-time. This work further enhances the methodology by: 1) improving the model accuracy through machine learning; 2) considering look-ahead windows introduced by the computation and communication latencies; 3) applying the method to regional spinning reserve estimation. A significant performance improvement, over 1.6% of estimation error reduction, has been observed based on real-world data collected by CAISO and PV plant operators.

potential high limit↗

DOE OSTI2025

Dual-Fuel Ammonia Equivalence Ratio Sweep Data

Ammonia (NH3) has garnered significant interest as an alternative fuel for meeting international emissions reduction mandates in sectors with high weight and distance requirements, such as shipping. Technical barriers and unanswered questions remain on the combustion strategies that can maximize ammonia utilization and minimize emissions. Prior research studies at the US Department of Energy’s Oak Ridge National Laboratory have shown strong performance with NH3 under dual-fuel mode using conventional diesel combustion (CDC) manifold air pressure settings. Diesel airflow was initially used to simplify retrofitting (no turbocharger modification), which resulted in air-fuel equivalence ratios (λ) greater than 1.5. To characterize potential improvements in dual-fuel NH3 combustion performance at richer in-cylinder conditions, a global λ sweep compared the use of early (E-pilot) and late (L-pilot) single diesel injections. The experiments were conducted at 1200 RPM and 12.8 ± 0.2 bar (75 % load), and λ was varied by decreasing the commanded air flow to the engine at greater than 90 % ammonia energy substitution level. A diesel injection timing sweep was conducted for both the injection strategies at fixed λ, and the timing with the lowest engine-out N2O emissions was identified. The results indicated an optimal balance between CO2,eq and thermal efficiency benefits both E-pilot and L-pilot injection strategy cases compared with CDC at a λ of 1.4. The indicated nitrogen-based emissions exhibited a strong correlation to the ratio of CA5–50 and ignition delay for L-pilot, but no apparent trend emerged for the E-pilot injection strategy at the tested boundary conditions. This dataset includes the raw experimental data and documentation of the experiment conditions and methods.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2021

Active learning strategy for high fidelity short-term data-driven building energy forecasting

The quality of a data-driven model is heavily dependent on the quality of data. Data from building operation often have data bias problems, which means that the data sample is collected in a way that some members of the intended data population are less likely to be included than others. Data-driven energy forecasting models built on such data hence are biased and could lead to large forecasting errors. Active learning—an effective method to defying data bias—is rarely studied or applied in the area of data-driven building energy forecasting modeling. This paper attempts to fill this gap and explores the application of active learning in data-driven building energy forecasting. The developed strategy in this paper efficiently generate informative training data within a time budget and uses block design to passively consider weather disturbances. The developed active learning strategy is applied and evaluated in both virtual and real-building testbeds against traditional data-driven methods. Via these virtual and real-building evaluation cases, we have demonstrated that the data bias problem typically exists in building operation data is resolved by applying the developed active learning strategy. Furthermore, building energy forecasting models trained from data generated from the active learning strategy have shown improved performances in both model accuracy and model extendibility perspectives. The effectiveness of the block design module is also validated to effectively consider the impact of weather conditions on active learning design.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

A measurement of stellar surface gravity hidden in radial velocity differences of comoving stars

The gravitational redshift induced by stellar surface gravity is notoriously difficult to measure for non-degenerate stars, since its amplitude is small in comparison with the typical Doppler shift induced by stellar radial velocity. In this study, we make use of the large observational data set of the Gaia mission to achieve a significant reduction of noise caused by these random stellar motions. By measuring the differences in velocities between the components of the pairs of comoving stars and wide binaries, in this work we are able to statistically measure the combined effects of gravitational redshift and convective blueshifting of spectral lines, and nullify the effect of the peculiar motions of the stars. For the subset of stars considered in this study, we find a positive correlation between the observed differences in Gaia radial velocities and the differences in surface gravity and convective blueshift inferred from effective temperature and luminosity measurements. The results rule out a null signal at the 5σ level for our full data set. Additionally, we study the subdominant effects of binary motion, and possible systematic errors in radial velocity measurements within Gaia. Results from the technique presented in this study are expected to improve significantly with data from the next Gaia data release. Such improvements could be used to constrain the mass–luminosity relation and stellar models that predict the magnitude of convective blueshift.

(stars:) binaries: general↗

DOE OSTI2022

Geometric learning for computational mechanics Part II: Graph embedding for interpretable multiscale plasticity

The history-dependent behaviors of classical plasticity models are often driven by internal variables evolved according to phenomenological laws. The difficulty to interpret how these internal variables represent a history of deformation, the lack of direct measurement of these internal variables for calibration and validation, and the weak physical underpinning of those phenomenological laws have long been criticized as barriers to creating realistic models. In this work, geometric machine learning on graph data (e.g. finite element solutions) is used as a means to establish a connection between nonlinear dimensional reduction techniques and plasticity models. Geometric learning-based encoding on graphs allows the embedding of rich time-history data onto a low-dimensional Euclidean space such that the evolution of plastic deformation can be predicted in the embedded feature space. Finally, a corresponding decoder can then convert these low-dimensional internal variables back into a weighted graph such that the dominating topological features of plastic deformation can be observed and analyzed.

42 ENGINEERING↗