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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

LatticeAnalytics: Strut-Level Visualization and Inspection of Additively Manufactured Lattice Structures

Additive manufacturing (AM) is revolutionizing the production of custom components with complex internal geometries, essential for high-performance applications in diverse fields such as medicine and defense. These AM parts optimize strength while minimizing weight by utilizing internal lattice structures consisting of large quantities of small interconnected struts. However, the complexity of these structures, combined with the challenges of using X-ray Computed Tomography (XCT) data, makes validation of part reliability difficult. This ultimately inhibits the development of novel parts for our collaborating material scientists. Here, we introduce LatticeAnalytics, a novel framework specifically designed for visual inspection of defects in these lattice structures. Our framework offers an end-to-end solution that includes the data management of XCT scans, enables remote access for geographically dispersed teams through a web-based dashboard, and incorporates novel visualizations. Our analysis is facilitated by a coarse alignment between the lattice’s nominal model, a spatial graph, and the XCT data. We employ a simple VR-based approach for fast and rough alignment, followed by an offline registration and identification of the struts. With the nodes and struts aligned and identified in the volume, our framework allows querying of subvolumes containing a single strut at multiple resolutions. This avoids computation over the entire lattice and also allow for easy parallelization of down-stream computations, such as strut-specific metrics. To depict a fast overview of the strut quality, we introduce two innovative visual encodings, crucial for our collaborators’ research in creating novel AM parts: the Contour View and the Roughness Map, which depict critical geometrical and surface features of individual struts in standardized two 2D views. We evaluated the integrated system through expert interviews. The feedback confirms the framework’s practicality and its effectiveness in enhancing current inspection workflows. It solves major bottlenecks for our collaborators, ultimately helping them create novel parts with advanced properties.

Miao, Haichao [Lawrence Livermore National Laborat↗
DOE OSTI2021

High-throughput characterization of Lu-doped zirconia

Lu-doped zirconia (LuDZ) was reported in the 1960s to be a poor oxygen ion conductor. However, we identified this system as worthy of reexamination based on the results of a machine learning-based screen for promising oxygen ion conductors, further supported by a physical argument: because Lu is the heaviest and smallest of the lanthanides, it should offer the possibility of high conductivity due to its high polarizability and size compatibility with the zirconia structure. Here the relationship between dopant concentration and conductivity in the LuDZ system was determined using a high throughput experimental methodology in which spatially resolved conductivity was measured across a compositionally graded film, spanning from Zr 0.93 Lu 0.07 O 1.965 to Zr 0.82 Lu 0.18 O 1.91 . The conductivity broadly peaks with composition at 15 cation % Lu, coinciding with the approximate phase boundary of the fully stabilized cubic phase. The composition-dependent conductivity measurements reveal a surprising increase in activation energy for ion transport with increasing Lu content with no change in this trend at the composition of the conductivity maximum. The pre-exponential factor increases sharply with Lu content, but displays a distinct plateau once the conductivity maximum composition is attained. At 500 °C, the conductivity of Zr 0.85 Lu 0.15 O 1.925 is 2 × 10 –3 S cm –1 , approaching that of optimally doped Sc-stabilized zirconia, which has the highest reported conductivity among zirconia compounds. In comparison to other oxide ion conductors, Lu-stabilized zirconia offers the advantage over ceria of negligible electronic conductivity and is easier to process than (La,Sr)(Ga,Mg)O 3-δ .

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

Demonstration of low-mode shape control in indirect-drive double shell implosions at the NIF

Double shell inertial confinement fusion is a concept for achieving robust thermonuclear burn that uses dense metal shells to compress deuterium-tritium (DT) fuel to fusion conditions. Double shell implosions are typically indirectly driven and involve a target that consists of a low-Z ablator, a foam layer, and a high-Z pusher surrounding the DT fuel. The goal of the campaign is to achieve a volumetric burn as radiation losses from the DT fuel are trapped by the opaque high-Z shell. The overall performance of double shell implosions relies on the efficient collisional transfer of kinetic energy between layers. The efficiency of this transfer (and therefore the overall performance of a given implosion) is degraded by the presence of low-mode asymmetries. P2 asymmetries are often observed in spatially resolved 2D radiographs of nominal double shell implosions. This work discusses three such experiments: one with an oblate P2 asymmetry, one with a prolate P2 asymmetry, and one with an approximate spherical symmetry. After performing a shape analysis of the oblate and prolate implosions to quantify asymmetries, these experimental results were compared with the results of hydrodynamic simulations for the two experiments. Differences between the experiment and simulation were then used to design an approximately spherical implosion by altering the incident laser cone fraction. Radiographs from the experiment that implemented the modified cone fraction show evidence of an implosion that is approximately spherical until bang time. This design is intended to serve as a point design for future studies that will seek to optimize various aspects of the double shell target.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Analytical Models of Frequency and Voltage in Large-Scale All-Inverter Power Systems

Low-order frequency response models for power systems have a decades-long history in optimization and control problems such as unit commitment, economic dispatch, and wide-area control. With a few exceptions, these models are built upon the Newtonian mechanics of synchronous generators, assuming that the frequency dynamics across a system are approximately homogeneous, and assume the dynamics of nodal voltages for most operating conditions are negligible, and thus are not directly computed at all buses. As a result, the use of system frequency models results in the systematic underestimation of frequency minimum nadir and maximum RoCoF, and provides no insight into the reactive power-voltage dynamics. This paper proposes a low-order model of both frequency and voltage response in grid-forming inverter-dominated power systems. The proposed model accounts for spatial-temporal variations in frequency and voltage behavior across a system and as a result, demonstrates the heterogeneity of frequency response in future renewable power systems. Electromagnetic transient (EMT) simulations are used to validate the utility, accuracy, and computational efficiency of these models, setting the basis for them to serve as fast, scalable alternatives to EMT simulation, especially when dealing with very large-scale systems, for both planning and operational studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Design Principles to Govern Electrode Fabrication for the Lithium Trivanadate Cathode

A full depth of discharge mathematical model for the lithium trivanadate cathode, considering lithiation of the layered α-phase, phase change, and lithiation of the rock-salt like β-phase at lower potentials, is developed. The coupled electrode-scale and crystal-scale model is fit to electrochemical data, and additionally validated with operando EDXRD. There is good agreement between the simulated and measured spatial variation of the volume fraction of the β-phase. This mathematical model is used to guide electrode fabrication, accounting for both ionic and electronic transport effects. Values of three design parameters—electrode thickness, porosity, and volume fraction of conductor—are identified, and the sensitivity of the energy density to these design parameters is quantified. The model is also used to investigate electrode design to create electrodes that deliver the maximum achievable energy density under the constraint that the α to β-phase transition is avoided, since phase change has been demonstrated to reduce cycle life. The energy density sacrificed to avoid phase change decreases at higher discharge rates, but the target values for electrode fabrication remain the same as those when optimizing the electrode for the full depth of discharge.

25 ENERGY STORAGE↗
DOE OSTI2023

When Spectral Modeling Meets Convolutional Networks: A Method for Discovering Reionization-era Lensed Quasars in Multiband Imaging Data

Over the last two decades, around 300 quasars have been discovered at z ≳ 6, yet only one has been identified as being strongly gravitationally lensed. We explore a new approach—enlarging the permitted spectral parameter space, while introducing a new spatial geometry veto criterion—which is implemented via image-based deep learning. We first apply this approach to a systematic search for reionization-era lensed quasars, using data from the Dark Energy Survey, the Visible and Infrared Survey Telescope for Astronomy Hemisphere Survey, and the Wide-field Infrared Survey Explorer. Our search method consists of two main parts: (i) the preselection of the candidates, based on their spectral energy distributions (SEDs), using catalog-level photometry; and (ii) relative probability calculations of the candidates being a lens or some contaminant, utilizing a convolutional neural network (CNN) classification. The training data sets are constructed by painting deflected point-source lights over actual galaxy images, to generate realistic galaxy–quasar lens models, optimized to find systems with small image separations, i.e., Einstein radii of θ E ≤ 1''. Visual inspection is then performed for sources with CNN scores of P lens > 0.1, which leads us to obtain 36 newly selected lens candidates, which are awaiting spectroscopic confirmation. These findings show that automated SED modeling and deep learning pipelines, supported by modest human input, are a promising route for detecting strong lenses from large catalogs, which can overcome the veto limitations of primarily dropout-based SED selection approaches.

High-redshift galaxies↗
DOE OSTI2024

Optimized Substrate Positioning Enables Switches in the C–H Cleavage Site and Reaction Outcome in the Hydroxylation–Epoxidation Sequence Catalyzed by Hyoscyamine 6β-Hydroxylase

Hyoscyamine 6β-hydroxylase (H6H) is an Fe(II)- and 2-oxoglutarate-dependent (Fe/2OG) oxygenase that catalyzes the last two steps in the biosynthesis of scopolamine, a prolifically administered anti-nausea drug. After its namesake first reaction, H6H couples the newly installed C6-bonded oxygen to C7 to form the epoxide of scopolamine. Oxoiron(IV) (ferryl) intermediates initiate both reactions by cleaving C–H bonds, but it remains unclear how the enzyme switches target site and promotes (C6)O–C7 coupling in preference to C7 hydroxylation in the second step. In one possible epoxidation mechanism, the C6 oxygen would – analogously to mechanisms proposed for the Fe/2OG halogenases and, in the preceding paper, N-acetylnorloline synthase (LolO) – coordinate as alkoxide to the C7–H-cleaving ferryl intermediate to enable alkoxyl coupling to the ensuing C7 radical. Here we provide structural and kinetic evidence that H6H instead exploits the distinct spatial dependencies of competitive C–H-cleavage (C6 vs C7) and C–O-coupling (oxygen rebound vs cyclization) steps to promote the two-step sequence without substrate coordination or repositioning for the epoxidation step. Structural comparisons of ferryl-mimicking vanadyl complexes of wild-type H6H and a variant that preferentially hydroxylates C7 of 6-hydroxyhyoscyamine suggest that only a modest (~ 10°) shift in the Fe–O–H(C7) approach angle is sufficient to change the outcome. Finally, the observation that, in wild-type H6H, 2 H 2 O solvent also increases the C7-hydroxylation:epoxidation ratio by ~ 8-fold implies that the latter outcome requires cleavage of the alcohol O-H bond, which, unlike in the LolO oxacyclization, is not accomplished in advance of C–H cleavage.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Deciphering the Evolution of Current Distribution in Hybrid Silver Vanadium Oxide / Carbon Monofluoride Cathodes within Lithium Primary Batteries

For batteries to function effectively all active material must be accessible requiring both electron and ion transport to each particle. A common approach to generating the needed conductive network is the addition of carbon to create a composite electrode. An alternative approach is the electrochemically induced formation of conductive reaction products where the electrochemically generated materials are in intimate contact with the active material contributing to effective connection of each active particle. Furthermore, this study probes silver vanadium oxide (Ag 2 V 4 O 11 , SVO), carbon monofluoride (CF x ), and hybrid SVO/CF x electrodes in lithium batteries. Ex situ XRD identifies Ag 0 as a reduction product from SVO and LiF from CF x that can be followed as a function of depth-of-discharge (DOD). Spatially-resolved operando energy dispersive x-ray diffraction reveals that the presence of SVO alleviates reaction heterogeneity in the electrodes which are electron transfer limited in the absence of sufficient Ag 0 . Synchrotron X-ray tomography on discharged cathodes reveals the distribution of silver particles where the particles are more closely spaced near the current collector indicating multiple nucleation sites for their formation. Finally, operando isothermal microcalorimetry is used to determine the heat dissipation of the parent and hybrid battery types. Using material enthalpy potentials, we determine the current distribution between the two active materials for the discharging hybrid cathode adding further insight to the diffraction analysis. Taken together, these results provide a comprehensive understanding of hybrid SVO/CF x cathodes and give guidance on optimal compositions that balance power and energy density considerations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Location-Dependent Cobalt Deposition in Smartphone Cells upon Long-Term Fast-Charging Visualized by Synchrotron X-ray Fluorescence

In this work, we investigate the transition-metal dissolution of the layered cathode material LiCoO 2 upon repeated fast-charging of three smartphone batteries from different manufacturers using synchrotron micro X-ray fluorescence (μ-XRF). Using this spatially resolved technique, dissolution of Co and subsequent location-dependent deposition on the anode are observed. μ-XRF mapping of selected parts of the anode electrode sheets, such as electrode folds and edges of the jelly roll, reveals the difference in the way Co is deposited on specific regions of the anode electrode. While some folds show no depositions, edges of the anode show gradually accumulating Co depositions. Furthermore, careful quantification of the dissolved Co reveals that the capacity loss scales with the amount of deposited Co on the anode, that is, total Co loss from within the cathode. Soft X-ray absorption spectroscopy of the Co depositions on the anode shows that Co is mainly deposited in a reduced 2 + state. While optimization of the fast-charging protocol mitigates Li plating on the anode, no significant difference in the amount of deposited Co can be observed between an optimized and a nonoptimized fast-charging algorithm.

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

Nowcasting Earthquakes: Imaging the Earthquake Cycle in California With Machine Learning

We propose a new machine learning-based method for nowcasting earthquakes to image the time-dependent earthquake cycle. The result is a timeseries that may correspond to the process of stress accumulation and release. The timeseries are constructed by using principal component analysis of regional seismicity. The patterns are found as eigenvectors of the cross-correlation matrix of a collection of seismicity timeseries in a coarse grained regional spatial grid (pattern recognition via unsupervised machine learning). The eigenvalues of this matrix represent the relative importance of the various eigenpatterns. Using the eigenvectors and eigenvalues, we compute the weighted correlation timeseries of the regional seismicity. This timeseries has the property that the weighted correlation generally decreases prior to major earthquakes in the region, and increases suddenly just after a major earthquake occurs. As in a previous paper, we find that this method produces a nowcasting timeseries that resembles the hypothesized regional stress accumulation and release process characterizing the earthquake cycle. We then address the problem of whether the timeseries contain information regarding future large earthquakes. For this, we compute a receiver operating characteristic and determine the decision thresholds for several future time periods of interest (optimization via supervised machine learning). We find that signals can be detected that can be used to characterize the information content of the timeseries. These signals may be useful in assessing present and near-future seismic hazards.

58 GEOSCIENCES↗
DOE OSTI2021

Accurate prediction of mega-electron-volt electron beam properties from UED using machine learning

To harness the full potential of the ultrafast electron diffraction (UED) and microscopy (UEM), we must know accurately the electron beam properties, such as emittance, energy spread, spatial-pointing jitter, and shot-to-shot energy fluctuation. Owing to the inherent fluctuations in UED/UEM instruments, obtaining such detailed knowledge requires real-time characterization of the beam properties for each electron bunch. While diagnostics of these properties exist, they are often invasive, and many of them cannot operate at a high repetition rate. Here, we present a technique to overcome such limitations. Employing a machine learning (ML) strategy, we can accurately predict electron beam properties for every shot using only parameters that are easily recorded at high repetition rate by the detector while the experiments are ongoing, by training a model on a small set of fully diagnosed bunches. Applying ML as real-time noninvasive diagnostics could enable some new capabilities, e.g., online optimization of the long-term stability and fine single-shot quality of the electron beam, filtering the events and making online corrections of the data for time-resolved UED, otherwise impossible. This opens the possibility of fully realizing the potential of high repetition rate UED and UEM for life science and condensed matter physics applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Improving deep learning performance for predicting large-scale geological ${{CO}_{2}}$ sequestration modeling through feature coarsening

Physics-based reservoir simulation for fluid flow in porous media is a numerical simulation method to predict the temporal-spatial patterns of state variables (e.g. pressure p) in porous media, and usually requires prohibitively high computational expense due to its non-linearity and the large number of degrees of freedom (DoF). This work describes a deep learning (DL) workflow to predict the pressure evolution as fluid flows in large-scale 3-dimensional(3D) heterogeneous porous media. In particular, we develop an efficient feature coarsening technique to extract the most representative information and perform the training and prediction of DL at the coarse scale, and further recover the resolution at the fine scale by spatial interpolation. We validate the DL approach to predict pressure field against physics-based simulation data for a field-scale 3D geologic CO 2 sequestration reservoir model. We evaluate the impact of feature coarsening on DL performance, and observe that the feature coarsening not only decreases the training time by >74% and reduces the memory consumption by >75%, but also maintains temporal error 0.63% on average. Besides, the DL workflow provides predictive efficiency with 1406 times speedup compared to physics-based numerical simulation. The key findings from this research significantly improve the training and prediction efficiency of deep learning model to deal with large-scale heterogeneous reservoir models, and thus it can also be further applied to accelerate workflows of history matching and reservoir optimization for close-loop reservoir management.

58 GEOSCIENCES↗
DOE OSTI2024

Imaging the Meissner effect in hydride superconductors using quantum sensors

By directly altering microscopic interactions, pressure provides a powerful tuning knob for the exploration of condensed phases and geophysical phenomena. Here, the megabar regime represents an interesting frontier, in which recent discoveries include high-temperature superconductors, as well as structural and valence phase transitions. However, at such high pressures, many conventional measurement techniques fail. Here we demonstrate the ability to perform local magnetometry inside a diamond anvil cell with sub-micron spatial resolution at megabar pressures. Our approach uses a shallow layer of nitrogen-vacancy colour centres implanted directly within the anvil; crucially, we choose a crystal cut compatible with the intrinsic symmetries of the nitrogen-vacancy centre to enable functionality at megabar pressures. We apply our technique to characterize a recently discovered hydride superconductor, CeH 9 . By performing simultaneous magnetometry and electrical transport measurements, we observe the dual signatures of superconductivity: diamagnetism characteristic of the Meissner effect and a sharp drop of the resistance to near zero. By locally mapping both the diamagnetic response and flux trapping, we directly image the geometry of superconducting regions, showing marked inhomogeneities at the micron scale. Our work brings quantum sensing to the megabar frontier and enables the closed-loop optimization of superhydride materials synthesis.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Coupled Experimental and High-Temperature Discrete-Element Method Modeling Studies of Aluminosilicate Particle Handling in Concentrated Solar Power Environments

Chemically inert, aluminosilicate based particles have been investigated as both a thermal transport and sensible energy storage medium for concentrated solar power facilities. These particles will experience a wide range of operating temperatures (300-1000 K) and handling conditions (dense to dilute falling particle curtains, dense granular flows, or dense structures), requiring specially-designed and optimized infrastructures. The relative influence of collisional and frictional interactions between particles varies based on temperature-dependent particulate properties and greatly impacts the bulk, granular flow behavior. These underlying physics are captured using discrete element method modeling tools. However, this modeling method is computationally expensive as each particle position and interaction is tracked during the simulation. These modeling methods are further complicated by introducing temperature-dependent particle properties, high-temperature radiative exchange, and directional irradiation sources experienced by granular flows in concentrated solar power environments. Coupled experimental and numerical studies of aluminosilicate particles in rotary kilns and dense particle curtains were performed for bulk temperatures up to 1073 K. The three particle types investigated included Carbobead HSP 30 /60, Carbobead CP 30/60, and Granusil 4030. Temperature, spatial, and velocity profile data were extracted from experimental runs using embedded K-type thermocouple probes and particle image velocimetry techniques. Experimental and numerical studies were compared using spatial temperature profiles, velocity fields, and shape profiles of the bulk, granular flows. Numerical models were developed using commercially available discrete element method modeling software, Aspherix®. Existing Aspherix® functionality was expanded by introducing coupled radiative exchange modeling tools. The laboratory-scale rotary kiln was developed to investigate the steady-state heat and mass transfer performance of aluminosilicate particles based on particle type, bulk handling temperature, and wall roughness. The rotational speed of the rotary kiln was varied to control the relative impact of collisional and frictional effects upon the granular flow behavior. Heat and mass transfer performance was categorized based on the Froude number and the observed flow regimes of slipping, rolling, cascading, and centrifuging. Coupled discrete element method modeling studies were used to evaluate the effects of temperature-dependent, particulate mechanical properties upon bulk flow behavior and upon the relative effects of radiative, advective, and/or conductive heat transfer. A high-temperature (< 1073 K) falling particle curtain was similarly fabricated to investigate the heat and mass transfer performance of aluminosilicate particles in particle handling situations dominated by inter-particle collisions. The impact of particle type, flow preheat temperatures (< 1073K), and bulk mass flow rates were investigated upon the particle curtain shape, temperature, and velocity profiles. Coupled discrete element method modeling studies were performed to evaluate the varying impact of temperature-dependent, particulate mechanical properties on the bulk flow behavior and the temperature profile of the particle curtain.

14 SOLAR ENERGY↗
DOE OSTI2023

Supplementary spatial data for Available Land for Cellulosic Biofuel Production: A Supply Chain Centered Comparison

Collection of the underlying spatially explicit data for Available Land for Cellulosic Biofuel Production: A Supply Chain Centered Comparison. Includes raw biomass yield data and soil carbon sequestration potential data for three types of marginal land for the USA midwest at the field level including field areas. Collection also includes raw land rasters for the three types of marginal land, model parameters for the MILP model used in the study, and results used to generate the figures in the paper.

bioenergy landscape design↗
DOE OSTI2026

A cell-centered AMR-ALE framework for 3D multi-material hydrodynamics. Part II: linesweep ALE rezoning for nonconformal block-structured AMR meshes

The simulation of flows presenting contact discontinuities, vorticity, and large variations in spatial scales can be performed in a framework coupling Arbitrary Lagrangian Eulerian (ALE) algorithms and Adaptive Mesh Refinement (AMR). This coupling requires adaptation of ALE rezoning techniques to meshes containing nonconformal nodes arising from both the AMR topology and the junction of mesh blocks. Here, in this paper, we present an ALE rezoning strategy that is compatible with such meshes, and that can also act as a disentangling algorithm. Emphasis is put on an algorithm that respects intrinsic Lagrangian mesh properties in order to preserve accuracy around discontinuities. To that end, we adapt the weighted linesweep algorithm to nonconformal block-structured AMR meshes. Then, we present control parameters introduced in the method for it to be applicable in practical situations. Notably, the method is coupled to a specific metric optimization in order to palliate some shortcomings of the linesweep method. Finally, numerical test cases are presented that feature the capabilities of the ALE-AMR algorithm for flows that present discontinuities, vorticity, and a variety of scales. Notably, we show that our ALE-AMR algorithm gives results at least similar to Euler-AMR, but provides better accuracy in cases where discontinuities are involved, thanks to a method that respects the Lagrangian features of the mesh. Additionally, it enables Euler-AMR-like computations on domains with temporally varying domain boundaries.

Adaptive mesh refinement↗
DOE OSTI2025

Building partnerships for development of sustainable energy systems with atmospheric measurements

Atmospheric dynamics often play a critical role in the sustainability and reliability of diverse forms of energy production. This is especially true for the growing number of renewable energy deployments that harness aspects of the environment for power production. While the University of Memphis has a strong research background in energy systems, we have little experience working with the Earth and Environmental Systems Science Division (EESSD) and their associated User Facilities. Of particular interest to us is the Atmospheric Science Research and the Atmospheric Radiation Measurement (ARM) user facility to address surface-boundary layer interactions and physical phenomena. One of the major challenges for understanding and developing energy systems and management platforms is accurate modeling/forecasting of atmospheric conditions across disparate spatial and temporal scales. These conditions are often required to understand the lowest levels of the atmospheric boundary layer, but are also important to understand higher atmospheric conditions where aerosols affect cloud development. The objective of this work was to develop partnerships with national laboratories for collaboration on environmental science and its intersection with sustainable energy systems, as well as to leverage the ARM user facility data repositories to enhance our research capabilities in energy systems and their inter-dependence on environmental systems for future engagement with EESSD. Specifically, we accomplished these objectives by (1) developing collaborations with Oakridge National Laboratory ARM Data Science and Integration Group which resulted in student internships, (2) employed ARM data to develope modeling of the atmospheric boundary layer optical turbulence, and (3) optimally-sized large-scale renewable energy systems and their associated energy storage systems with ARM repository data.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Wave optics simulations for new soft x-ray beamlines at NSLS-II

In this work, we performed fully- and partially-coherent synchrotron emission and propagation simulations with the "Synchrotron Radiation Workshop" computer code to analyze the performance of two soft X-ray beamlines under development at the National Synchrotron Light Source II: Soft X-ray Nanoprobe (SXN), and Angle-Resolved Photoemission Spectroscopy (ARPES) and Resonant Inelastic X-ray Scattering (RIXS) Imaging (ARI). The SXN beamline intends to provide high flux and high spatial resolution coherent soft X-ray imaging capabilities using both zone plate and lensless coherent imaging techniques. The ARI beamline aims to perform high flux ARPES and RIXS experiments with a focal spot size at the sample approaching 100 nm using highly-demagnifying mirrors in Kirkpatrick-Baez geometry. To accurately calculate the resolution and the degree of X-ray coherence provided by the two state-of-the-art beamlines, partial coherence effects are required to be taken into account in wave optics simulations for these two beamlines. In this talk, beamline performance parameters such as spot size, degree of coherence, flux, and energy resolution at the sample are presented. The effects of mirror surface slope errors on beamline performance were studied and some suggestions for further optimization are discussed.

36 MATERIALS SCIENCE↗