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At least 1,585 records · Page 88

Swept-Lookback Deflectometry for High Performance Concentrating Solar Power Optical Metrology

This report describes an initial investigation into a proposed solution to the important problem of performing a detailed evaluation of heliostat optical performance, in situ in a heliostat field. Our approach is to place digital cameras in a position near the receiver where they look back toward the heliostat mirrors. The pixels of each camera sensor identify a set of small cells on the mirror surface, each corresponding to a “mixel.” By either passing reflected sunbeam over the camera or passing the camera through the reflected sunbeam, the cameras intercept sunlight reflected from each mixel. We then analyze the recorded video data to determine times when each mixel transitions from dark to light, and then back to dark. We then use these transitions to construct vectors from the camera to the mixel, and then from the mixel to the edge of the Sun at that moment. We then compute the surface normal at the mixel, which bisects the angle between these vectors. Performing this analysis for all mixels in the mirror yields a high-resolution map of slope across the mirror surface. We have implemented most of this process, successfully collecting data for an example heliostat facet and computing a preliminary estimated slope map. However, more work remains to complete this calculation, since certain factors and transformations are not yet included. Our observations so far support our hypothesis that such a system is possible, but we have not yet completed our quantitative evaluation of the concept.

14 SOLAR ENERGY↗

Digital Twinning and Predictive Modeling of Traffic for Safe, Efficient, and Reliable Intersections

Over the last decade, the advances in connected and autonomous vehicles (CAVs) have far surpassed the technological realm of transportation infrastructure. There is a growing need to have a technologically commensurate transportation infrastructure to enable safe and reliable movement of goods and people. The recent creation of Advanced Research Projects Agency - Infrastructure (ARPA-I) through the Infrastructure Investment and Jobs Act by the U.S. Department of Transportation (USDOT) has further amplified the need to revolutionize the transportation infrastructure system in the US. This need is perhaps felt most at traffic intersections, as more than 50 percent of the combined total of fatal and injury crashes occur at or near intersections. The proposed concept of Infrastructure Perception and Control (IPC) is aimed at bridging this technological gap by building a real-time digital twin of traffic by fusing detections from sensors installed at the intersection. This digital twin can then empower a wide variety of applications such as smart traffic signals or infrastructure-to-everything (I2X) communications. Smart signaling can help avoid crashes through early-prediction, while I2X can augment the CAV sensors under uncertain driving conditions and provide connected vehicles (CVs) with traffic information they can use to optimize their travel.

connected vehicles↗

Machine learning-assisted upscaling analysis of reservoir rock core properties based on micro-computed tomography imagery

Optimum solutions for geologic modeling and reservoir simulation in industries such as oil and gas recovery and carbon capture and storage require accurate characterization of reservoir properties, which are often heterogeneous. In this study, high-quality micro-computed tomography (CT) images (1.475-μm/pixel resolution) of a sandstone core acquired from the Bell Creek oil field, USA, were used to provide nondestructive analysis of pore- and core-scale heterogeneity across measurement scales of 94–566 μm. In addition to characterizing the as-received sample, the core sample was flooded with brine to evaluate the capacity of the core sample to receive injected fluids. The micro-CT images were systematically segmented into pore spaces and grains via machine learning (ML) steps including image preprocessing, label creation using a traditional ML method based on limited manual image annotation, and finally U-Net segmentation. The segmented image stacks were reconstructed into digital cubes of various scales of voxel lengths. The 3D porosity values were calculated for all the digital cubes, and the fractal dimensions of the cubes were estimated using a box-counting method. The results showed that smaller cubes had greater heterogeneity and that the porosity values could be accurately estimated by fractal dimension and voxel lengths using ML models. For the core sample with brine flooding, the ratio of pores filled by brine to the total pore space was related to the porosity and could also be accurately estimated by porosity, fractal dimension, and voxel lengths using ML models. In conclusion, the results of this study demonstrate that the concept of fractal dimension can be a useful vector to perform upscaling analysis of sandstone rock heterogeneity from the pore to core scale and that fractal dimensions can be used to estimate porosity values and pore space-filling capacity across those scales.

58 GEOSCIENCES↗

A two-stage method for microcalcification cluster segmentation in mammography by deformable models

Purpose: Segmentation of microcalcification (MC) clusters in x-ray mammography is a difficult task for radiologists. Accurate segmentation is prerequisite for quantitative image analysis of MC clusters and subsequent feature extraction and classification in computer-aided diagnosis schemes. Methods: In this study, a two-stage semiautomated segmentation method of MC clusters is investigated. The first stage is targeted to accurate and time efficient segmentation of the majority of the particles of a MC cluster, by means of a level set method. The second stage is targeted to shape refinement of selected individual MCs, by means of an active contour model. Both methods are applied in the framework of a rich scale-space representation, provided by the wavelet transform at integer scales. Segmentation reliability of the proposed method in terms of inter and intraobserver agreements was evaluated in a case sample of 80 MC clusters originating from the digital database for screening mammography, corresponding to 4 morphology types (punctate: 22, fine linear branching: 16, pleomorphic: 18, and amorphous: 24) of MC clusters, assessing radiologists’ segmentations quantitatively by two distance metrics (Hausdorff distance—HDIST{sub cluster}, average of minimum distance—AMINDIST{sub cluster}) and the area overlap measure (AOM{sub cluster}). The effect of the proposed segmentation method on MC cluster characterization accuracy was evaluated in a case sample of 162 pleomorphic MC clusters (72 malignant and 90 benign). Ten MC cluster features, targeted to capture morphologic properties of individual MCs in a cluster (area, major length, perimeter, compactness, and spread), were extracted and a correlation-based feature selection method yielded a feature subset to feed in a support vector machine classifier. Classification performance of the MC cluster features was estimated by means of the area under receiver operating characteristic curve (Az ± Standard Error) utilizing tenfold cross-validation methodology. A previously developed B-spline active rays segmentation method was also considered for comparison purposes. Results: Interobserver and intraobserver segmentation agreements (median and [25%, 75%] quartile range) were substantial with respect to the distance metrics HDIST{sub cluster} (2.3 [1.8, 2.9] and 2.5 [2.1, 3.2] pixels) and AMINDIST{sub cluster} (0.8 [0.6, 1.0] and 1.0 [0.8, 1.2] pixels), while moderate with respect to AOM{sub cluster} (0.64 [0.55, 0.71] and 0.59 [0.52, 0.66]). The proposed segmentation method outperformed (0.80 ± 0.04) statistically significantly (Mann-Whitney U-test, p < 0.05) the B-spline active rays segmentation method (0.69 ± 0.04), suggesting the significance of the proposed semiautomated method. Conclusions: Results indicate a reliable semiautomated segmentation method for MC clusters offered by deformable models, which could be utilized in MC cluster quantitative image analysis.

60 APPLIED LIFE SCIENCES↗

Communication-Constrained Robust Control and Learning of Grid-Connected

The electric grid of things (EGoT) promises great potential for innovative grid services by tapping into vast load flexibility. However, the unique characteristics of EGoT, being a part of the cyber-physical electric power system, present both opportunities and challenges, especially concerning supply-demand balancing, stability, and communication constraints. Traditionally, centralized control was employed to ensure balance and stability in power systems. However, with the massive influx of EGoT devices, new strategies are needed to efficiently coordinate and control these distributed devices for optimal grid operations. While some studies have explored efficiency and economic models, there remains a gap in ensuring reliability under everyday operations and resilience during extreme conditions. Addressing this gap, this project develops the technology for an Energy Service Interface (ESI) that includes novel pricing, control, learning, and distributed optimization algorithms, which will enable utilities to recruit EGoT assets for crucial grid services such as load flexibility, voltage regulation, and situation-awareness. The key novelty of the proposed technology is the careful distribution of learning and control functions across utility and EGoT asset owners such that provably efficient and resilient grid operations are attained while respecting communication and information-exchange constraints. Specifically, the project team develops machine-learning enhanced load modeling methods to allow EGoT asset owners to learn their load capability and flexibility, and develops pricing-based and decentralized learning-based control so that asset owners can coordinate to meet system-wide demand-supply balance and reliability goals. For extreme situations involving high-impact, low-probability catastrophic events (termed the “black-sky” events), the team also develops (1) a “Feeder-Operating Center-on-a-Laptop” (FOCAL) software that can assist utility personnel in leveraging EGoT assets to accelerate the service recovery of damaged feeders, and (2) distributed optimization algorithms that can coordinate the operation points of EGoT devices under severe communication constraints. The proposed technology has been extensively tested and evaluated through simulations and on a testbed. In summary, as we transition into a more interconnected and digital power grid era, our project’s findings and developments offer a pivotal step toward guaranteeing both efficiency and resilience in the face of both everyday operations and rare “black-sky” events.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Deep learning-assisted modeling for χ (2) nonlinear optics

Modeling second-order (χ(2)) nonlinear optical processes remains computationally expensive due to the need to resolve fast field oscillations and simulate wave propagation using methods such as the split-step Fourier method (SSFM). This can become a bottleneck in real-time applications, such as high-repetition-rate laser systems requiring rapid feedback and control. We present a long short-term memory-based surrogate model trained on SSFM simulations generated from a start-to-end model of the photocathode drive laser at SLAC National Accelerator Laboratory’s Linac Coherent Light Source II. The model achieves over 250× speedup while maintaining high fidelity, enabling future real-time optimization and laying the foundation for data-integrated modeling frameworks and digital twins of laser systems.

Accelerator Physics (physics.acc-ph)↗

MO-FG-202-01: A Fast Yet Sensitive EPID-Based Real-Time Treatment Verification System

Purpose: To create a real-time EPID-based treatment verification system which robustly detects treatment delivery and patient attenuation variations. Methods: Treatment plan DICOM files sent to the record-and-verify system are captured and utilized to predict EPID images for each planned control point using a modified GPU-based digitally reconstructed radiograph algorithm which accounts for the patient attenuation, source energy fluence, source size effects, and MLC attenuation. The DICOM and predicted images are utilized by our C++ treatment verification software which compares EPID acquired 1024×768 resolution frames acquired at ∼8.5hz from Varian Truebeam™ system. To maximize detection sensitivity, image comparisons determine (1) if radiation exists outside of the desired treatment field; (2) if radiation is lacking inside the treatment field; (3) if translations, rotations, and magnifications of the image are within tolerance. Acquisition was tested with known test fields and prior patient fields. Error detection was tested in real-time and utilizing images acquired during treatment with another system. Results: The computational time of the prediction algorithms, for a patient plan with 350 control points and 60×60×42cm^3 CT volume, is 2–3minutes on CPU and <27 seconds on GPU for 1024×768 images. The verification software requires a maximum of ∼9ms and ∼19ms for 512×384 and 1024×768 resolution images, respectively, to perform image analysis and dosimetric validations. Typical variations in geometric parameters between reference and the measured images are 0.32°for gantry rotation, 1.006 for scaling factor, and 0.67mm for translation. For excess out-of-field/missing in-field fluence, with masks extending 1mm (at isocenter) from the detected aperture edge, the average total in-field area missing EPID fluence was 1.5mm2 the out-of-field excess EPID fluence was 8mm^2, both below error tolerances. Conclusion: A real-time verification software, with EPID images prediction algorithm, was developed. The system is capable of performing verifications between frames acquisitions and identifying source(s) of any out-of-tolerance variations. This work was supported in part by Varian Medical Systems.

60 APPLIED LIFE SCIENCES↗

Galaxy clusters in the SDSS Stripe 82 based on photometric redshifts

Based on a recent photometric redshift galaxy catalogue, we have searched for galaxy clusters in the Stripe ~82 region of the Sloan Digital Sky Survey by applying the Adami & MAzure Cluster FInder (AMACFI). Extensive tests were made to fine-tune the AMACFI parameters and make the cluster detection as reliable as possible. The same method was applied to the Millennium simulation to estimate our detection efficiency and the approximate masses of the detected clusters. Considering all the cluster galaxies (i.e. within a 1 Mpc radius of the cluster to which they belong and with a photoz differing by less than 0.05 from that of the cluster), we stacked clusters in various redshift bins to derive colour-magnitude diagrams and galaxy luminosity functions (GLFs). For each galaxy with absolute magnitude brighter than -19.0 in the r band, we computed the disk and spheroid components by applying SExtractor, and by stacking clusters we determined how the disk-to-spheroid flux ratio varies with cluster redshift and mass. We also detected 3663 clusters in the redshift range 0.15 13 and a few 10 14 solar masses. Furthermore, by stacking the cluster galaxies in various redshift bins, we find a clear red sequence in the (g'-r') versus r' colour-magnitude diagrams, and the GLFs are typical of clusters, though with a possible contamination from field galaxies. The morphological analysis of the cluster galaxies shows that the fraction of late-type to early-type galaxies shows an increase with redshift (particularly in high mass clusters) and a decrease with detection level, i.e. cluster mass. From the properties of the cluster galaxies, the majority of the candidate clusters detected here seem to be real clusters with typical cluster properties.

79 ASTRONOMY AND ASTROPHYSICS↗

Star formation bimodality in early-type galaxies

We compute the properties of a sample of 221 local, early-type galaxies with a spectral energy distribution (SED) modeling software, CIGALEMC. Concentrating on the star-forming (SF) activity and dust contents, we derive parameters such as the specific star formation rate (sSFR), the dust luminosity, dust mass, and temperature. In our sample, 52% is composed of elliptical (E) galaxies and 48% of lenticular (S0) galaxies. We find a larger proportion of S0 galaxies among galaxies with a large sSFR and large specific dust emission. The stronger activity of S0 galaxies is confirmed by larger dust masses. We investigate the relative proportion of active galactic nuclei (AGNs) and SF galaxies in our sample using spectroscopic Sloan Digital Sky Survey data and near-infrared selection techniques, and find a larger proportion of AGN-dominated galaxies in the S0 sample than the E one. This could corroborate a scenario where blue galaxies evolve into red ellipticals by passing through an S0 AGN active period while quenching its star formation. Finally, we find a good agreement comparing our estimates with color indicators.

79 ASTRONOMY AND ASTROPHYSICS↗

A platform for quantum information and large-scale entanglement with Rydberg atoms in programmable optical potentials

Large-scale quantum systems with controllable interactions are important for understanding complex phenomena in nature, and are the basis for advanced quantum technologies. Realizing a controllable platform for controlling, understanding, and ultimately harnessing the entanglement is an outstanding challenge in quantum science. This project demonstrated reconfigurable arrays of individually-trapped ultracold atoms, thus realizing a platform that could demonstrate large-scale quantum entanglement with the addition of strong inter-atomic interactions. Arrays of more than 50 trap sites were formed via digital holography and a high-numerical aperture imaging system that featured in-situ trap diagnostics and single-atom imaging resolution. We further discovered a new implementation of a controlled-phase gate that utilized coherent excitation to Rydberg states. This method will enable robust entanglement protocols in many-atom systems such as the one developed here.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Colloidal Atomic Layer Deposition with Stationary Reactant Phases Enables Precise Synthesis of “Digital” II–VI Nano-heterostructures with Exquisite Control of Confinement and Strain

In contrast to molecular systems, which are defined with atomic precision, nanomaterials generally show some heterogeneity in size, shape, and composition. The sample inhomogeneity translates into a distribution of energy levels, band gaps, work functions, and other characteristics, which detrimentally affect practically every property of functional nanomaterials. Here, we introduce a novel synthetic strategy, colloidal atomic layer deposition (c-ALD) with stationary reactant phases, which largely circumvents the limitations of traditional colloidal syntheses of nano-heterostructures with atomic precision. This method allows for significant reduction of inhomogeneity in nanomaterials in complex nanostructures without compromising their structural perfection and enables the synthesis of epitaxial nano-heterostructures of unprecedented complexity. The improved synthetic control ultimately enables bandgap and strain engineering in colloidal nanomaterials with close to atomic accuracy. To demonstrate the power of the new c-ALD method, we synthesize a library of complex II–VI semiconductor nanoplatelet heterostructures. By combining spectroscopic and computational studies, we elucidate the subtle interplay between quantum confinement and strain effects on the optical properties of semiconductor nanostructures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Towards string order melting of spin-1 particle chains in superconducting transmons using optimal control

Utilizing optimal control to simulate a model Hamiltonian is an emerging strategy that leverages the intrinsic physics of a device with digital quantum simulation methods. Here we evaluate optimal control for probing the nonequilibrium properties of symmetry-protected topological (SPT) states simulated with superconducting hardware. Assuming a tunable transmon architecture, we cast the evolution of these SPT states as a series of one- and two-site pulse optimization problems that are solved in the presence of leakage constraints. From the generated pulses, we classically simulate the time-dependent melting of the perturbed SPT string order across a six-site model with an average state infidelity of 10 -3 . The feasibility of these pulses as well as their efficient application indicate that high-fidelity simulations of string order melting are within reach of current quantum computing systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Super-resolution image display using diffractive decoders

High-resolution image projection over a large field of view (FOV) is hindered by the restricted space-bandwidth product (SBP) of wavefront modulators. We report a deep learning–enabled diffractive display based on a jointly trained pair of an electronic encoder and a diffractive decoder to synthesize/project super-resolved images using low-resolution wavefront modulators. The digital encoder rapidly preprocesses the high-resolution images so that their spatial information is encoded into low-resolution patterns, projected via a low SBP wavefront modulator. The diffractive decoder processes these low-resolution patterns using transmissive layers structured using deep learning to all-optically synthesize/project super-resolved images at its output FOV. This diffractive image display can achieve a super-resolution factor of ~4, increasing the SBP by ~16-fold. We experimentally validate its success using 3D-printed diffractive decoders that operate at the terahertz spectrum. This diffractive image decoder can be scaled to operate at visible wavelengths and used to design large SBP displays that are compact, low power, and computationally efficient.

36 MATERIALS SCIENCE↗

Strategy to Develop a Control Scheme for Core Thermal Performance Optimization

This report outlines a three-year research plan for optimizing reactor core thermal performance through use of a Digital Twin (DT) model and a set of neutron detectors to correspondingly adapt the reactor control strategy. It identifies reactor features and achievable neutron measurements that factor into this optimization task. It considers spatial effects that are important and how they can be managed by a real-time algorithm that controls reactivity actuators. The report presents a set of tasks, along with corresponding methods, for accomplishing this objective. The final task in this set is to validate the combined optimization procedure in the Purdue University’s PUR-1 reactor, for which we have an agreement of understanding. Additionally, we describe an alternative approach for overcoming some of the limitations inherent in the above approach, which includes the computational costs of high-fidelity simulations and achievable integration of neutron measurements into the DT. The proposed algorithms lower the technology readiness level (TRL) of the overall approach as some additional analysis and the development of a new neutron detector concept are necessary. In particular, a sensor measuring the gradient of the neutron flux is required, and the prototype is expected to be validated in the PUR-1 reactor. The potential benefits and the opportunity to significantly push forward the state-of-the-art make this approach worthy of further exploration in the upcoming years.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

RCSED—A Value-added Reference Catalog of Spectral Energy Distributions of 800,299 Galaxies in 11 Ultraviolet, Optical, and Near-infrared Bands: Morphologies, Colors, Ionized Gas, and Stellar Population Properties

We present RCSED, the value-added Reference Catalog of Spectral Energy Distributions of galaxies, which contains homogenized spectrophotometric data for 800,299 low- and intermediate-redshift galaxies (0.007<z<0.6) selected from the Sloan Digital Sky Survey spectroscopic sample. Accessible from the Virtual Observatory (VO) and complemented with detailed information on galaxy properties obtained with state-of-the-art data analysis, RCSED enables direct studies of galaxy formation and evolution over the last 5 Gyr. We provide tabulated color transformations for galaxies of different morphologies and luminosities, and analytic expressions for the red sequence shape in different colors. RCSED comprises integrated k-corrected photometry in up to 11 ultraviolet, optical, and near-infrared bands published by the GALEX, SDSS, and UKIDSS wide-field imaging surveys; results of the stellar population fitting of SDSS spectra including best-fitting templates, velocity dispersions, parameterized star formation histories, and stellar metallicities computed for instantaneous starburst and exponentially declining star formation models; parametric and non-parametric emission line fluxes and profiles; and gas phase metallicities. We link RCSED to the Galaxy Zoo morphological classification and galaxy bulge+disk decomposition results of Simard et al. We construct the color–magnitude, Faber–Jackson, and mass–metallicity relations; compare them with the literature; and discuss systematic errors of the galaxy properties presented in our catalog. RCSED is accessible from the project web site and via VO simple spectrum access and table access services using VO-compliant applications. We describe several examples of SQL queries to the database. Finally, we briefly discuss existing and future scientific applications of RCSED and prospective catalog extensions to higher redshifts and different wavelengths.

79 ASTRONOMY AND ASTROPHYSICS↗

Harnessing ferro-valleytricity in pentalayer rhombohedral graphene for memory and compute

Two-dimensional materials with multiple degrees of freedom, including spin, valleys, and orbitals, open up an exciting avenue for engineering multifunctional devices. Beyond spintronics, these degrees of freedom can lead to novel quantum effects such as valley-dependent Hall effects and orbital magnetism, which could revolutionize next-generation electronics. However, achieving independent control over valley polarization and orbital magnetism has been a challenge due to the need for large electric fields. A recent breakthrough involving pentalayer rhombohedral graphene has demonstrated the ability to individually manipulate anomalous Hall signals and orbital magnetic hysteresis, forming what is known as a valley-magnetic quartet. Here, we leverage the electrically tunable ferro-valleytricity of pentalayer rhombohedral graphene to develop nonvolatile memory and in-memory computation applications. We propose an architecture for a dense, scalable, and selector-less nonvolatile memory array that harnesses the electrically tunable ferro-valleytricity. In our designed array architecture, nondestructive read and write operations are conducted by sensing the valley state through two different pairs of terminals, allowing for independent optimization of read/write peripheral circuits. The power consumption of our PRG-based array is remarkably low, with only ∼6 nW required per write operation and ∼2.3 nW per read operation per cell. This consumption is orders of magnitude lower than that of the majority of state-of-the-art cryogenic memories. Additionally, we engineer in-memory computation by implementing majority logic operations within our proposed nonvolatile memory array without modifying the peripheral circuitry. In conclusion, our framework presents a promising pathway toward achieving ultra-dense cryogenic memory and in-memory computation capabilities.

2D materials↗

ADAM Program Execution Plan LANL Inputs (FY2022)

The National Security Research Center (NSRC) is Los Alamos National Laboratory’s classified library. There are two groups associated with the NSRC, both of who work for LANL’s Weapons Research Services (WRS) division (WRS-SIS and WRS-WMT). These groups are funded in part by the NNSA Archives Program. The NSRC’s collections include tens of millions of documents from the Manhattan Project era through today. It is staffed with an expert, highly trained staff of librarians, archivists, digitizers, historians, and communications specialists. The NSRC traces its lineage to the wartime Technical Library created by J. Robert Oppenheimer during in 1943. Today, it supports a broad range of researchers within the LANL Weapons Program and beyond. The NSRC also has customers across other National Nuclear Security Administration labs and sites, and partners in the Department of Defense. This report highlights LANL accomplishments through NSRC.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

Artificial Intelligence for Enhancing Multiscale Analysis: Buildings Focus

This project aims to develop multi-scale building energy data, potentially improving the representation of the U.S. buildings sector in GCAM-USA, an U.S.-focused human-energy-Earth systems model. Existing building energy datasets are typically limited to national or regional levels, which constrains the ability of models to capture fine-scale human-energy-Earth systems interactions and reduces their relevance for decision-making on issues such as energy security, resilience, and energy planning. By leveraging AI and advanced data integration methods, this work fuses multiple existing datasets to enhance the physical and geographic representation of both residential and commercial building energy use. So far, progress includes processing residential building data, designing the data structure for commercial buildings, and testing AI approaches for integrating datasets and addressing spatial-temporal gaps. This effort can not only advances GCAM-USA’s capability in modeling the buildings sector but also supports broader DOE missions, such as developing digital testbeds, enhancing grid resilience analysis, and improving building–energy system modeling at decision-relevant scales.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗