Modernizing Digital Video Systems at the National Ignition Facility (NIF): Success stories, open challenges and future directions
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Engineering, Lasers, Physics, Computer science↗
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Probabilistic learning and updating of a digital twin for composite material systems
This paper presents an approach for characterizing and estimating statistical dependence between a large number of observables in a composite material system. Conditional regression is carried out using the estimated joint density function, permitting a systematic exploration of interdependence between fine scale and coarse observables that can be used for both prognosis and design of complex material systems. An example demonstrates the integration of experimental data with a computational database. The statistical approach is based on the probabilistic learning on manifolds recently developed by the authors. This approach leverages intrinsic structure detected through diffusion on graphs with projected stochastic differential equations to generate samples constrained to that structure.
42 ENGINEERING↗
Position Papers for Inverse Methods for Complex Systems under Uncertainty Workshop
The ability to solve inverse problems – inferring unknown parameters, structures, or states of a system from observed data – is essential for advancing scientific discovery and innovation capabilities for the DOE mission. Basic research needs and challenges are particularly acute in emerging areas such as the interactive, data-driven, modeling and simulation of digital twins; decision support for experiments at DOE scientific user facilities; and for other complex systems and workflows. Inverse problems are at the heart of understanding and controlling complex systems due to factors such as observational data with varying modalities and fidelities, inherent uncertainties in physical measurements and numerical models, and the computational demands of rapid and high-fidelity simulations. The convergence of recent scientific computing trends – scientific machine learning, artificial intelligence, and computing advances such as exascale computing – is creating unprecedented opportunities. These advancements offer the potential to revolutionize how we approach inverse problems to extract actionable insights with the required level of accuracy and computational efficiency. This workshop and the Call for Position Papers are vital steps in bringing together experts to collectively explore and identify the new computational and mathematical directions needed in inverse methods for complex systems under uncertainty.
97 MATHEMATICS AND COMPUTING↗
Prototypes of self-powered radiation detectors employing intrinsic high-energy current
Purpose: The authors experimentally investigate the effect of direct energy conversion of x-rays via selfpowered Auger- and photocurrent, potentially suitable to practical radiation detection and dosimetry in medical applications. Experimental results are compared to computational predictions. The detector the authors consider is a thin-film multilayer device, composed of alternating disparate electrically conductive and insulating layers. This paper focuses on the experiments while a companion paper introduces the fundamental concepts of high-energy current (HEC) detectors. Methods: The energy of ionizing radiation is directly converted to detector signal via electric current induced by high-energy secondary electrons generated in the detector material by the incident primary radiation. The HEC electrons also ionize the dielectric and the resultant charge carriers are selfcollected due to the contact potential of the disparate electrodes. Thus, an electric current is induced in the conductors in two different ways without the need for externally applied bias voltage or amplification. Thus, generated signal in turn is digitized by a data acquisition system. To determine the fundamental properties of the HEC detector and to demonstrate its feasibility for medical applications, the authors used a planar geometry composed of multilayer microstructures. Various detectors with up to seven conducting layers with different combinations of materials (250 μm Al, 35 μm Cu, 100 μm Pb) and air gaps (100 μm) were exposed to nearly plane-parallel 60–120 kVp x-ray beams. For the experimental design and verification, the authors performed coupled electron–photon radiation transport computations. The detector signal was measured using a commercial data acquisition system with 24 bits dynamic range, 0.4 fC sensitivity, and 0.9 ms sampling time. Results: Measured signals for the prototype detector varied depending on the number of layers, material type, and incident photon energy, and it was in the range of 30–150 nA/cm{sup 2} for unit air kerma (1 Gy), which is viable for practical applications. The experiments had an excellent agreement with the computations. Within the examined range of 60–120 kVp, the energy dependence of the HEC (normalized to the x-ray tube output) was relatively small. Conclusions: Based on the experimental results for 100 ms sampling time, it would be possible to measure the time dependence of x-ray beams for x-ray tube current of 0.1 mA or higher. Significant advantages of the HEC device are that generation of its signal does not require external power supply, it can be made in any size and shape, including flexible curvilinear forms, and it is inexpensive. It remains to be determined, which of the potential applications in medical dosimetry (both in vivo and external), or radiation protection would benefit from such selfpowered detectors.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
An Accurate, Error-Tolerant, and Energy-Efficient Neural Network Inference Engine Based on SONOS Analog Memory
In this work, we demonstrate SONOS (silicon-oxide-nitrideoxide- silicon) analog memory arrays that are optimized for neural network inference. The devices are fabricated in a 40nm process and operated in the subthreshold regime for in-memory matrix multiplication. Subthreshold operation enables low conductances to be implemented with low error, which matches the typical weight distribution of neural networks, which is heavily skewed toward near-zero values. This leads to high accuracy in the presence of programming errors and process variations. We simulate the end-to-end neural network inference accuracy, accounting for the measured programming error, read noise, and retention loss in a fabricated SONOS array. Evaluated on the ImageNet dataset using ResNet50, the accuracy using a SONOS system is within 2.16% of floating-point accuracy without any retraining. The unique error properties and high On/Off ratio of the SONOS device allow scaling to large arrays without bit slicing, and enable an inference architecture that achieves 20 TOPS/W on ResNet50, a >10× gain in energy efficiency over state-of-the-art digital and analog inference accelerators.
97 MATHEMATICS AND COMPUTING↗
Recovery and calibration of legacy analog data from the Leo Brady Seismic Network for the Source Physics Experiment
The Leo Brady Seismic Network (LBSN) was established in 1960 by Sandia National Laboratories for monitoring underground nuclear tests (UGTs) at the Nevada Test Site— renamed in 2010 to the Nevada National Security Site (NNSS). The LBSN has been in various configurations throughout its existence, but it has been generally comprised of four to six stations at regional distances from the NNSS with evenly spaced azimuthal coverage. Between 1962 and the early 1980s, the LBSN—and a sister network operated by Lawrence Livermore National Laboratory—were the most comprehensive U.S. source of regional seismic data of UGTs. During the pre-digital era, LBSN data were transmitted as frequency-modulated (FM) audio over telephone lines to the NTS and recorded in analog on hi-fi 8-track AMPEX tapes. These tapes have been stored in temperature-stable buildings or bunkers on the NNSS and Kirtland Air Force Base in Albuquerque, NM for decades and contain the sole record of this irreplaceable data from the analog era; full waveforms of UGTs during this time were never routinely converted to digital form. We have been developing a process over the past few years to recover and calibrate data from these tapes, converting them from FM audio to digital waveforms in ground motion units. The calibration of legacy data from the LBSN is still ongoing. To date, we have digitized tapes from 592 separate UGTs. As a proof-of-concept, we calibrated data from the BOXCAR event.
58 GEOSCIENCES↗
Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials Using MALAMUTE
Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy, aims to develop and qualify additively-manufactured materials for nuclear applications. The key challenges to these efforts are the microstructural variabilities observed on the AM products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-through-put experimental and modeling techniques to accelerate the qualification efforts. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the AM process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture the microstructural variabilities is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning modeling capabilities to develop a digital twin for AM that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation for AM materials. The melting and subsequent solidification that occurs during the AM process is a complex phenomenon that requires multiscale multiphysics analysis. Idaho National Laboratory’s (INL) Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for AM materials in an efficient, reliable, and cost-effective way. This work package focuses on understanding the role of process variabilities on the various microstructural characteristics of the AM materials. Microstructures unique to AM materials, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. In fiscal year (FY) 24, we significantly advanced upon our work in the last fiscal year, both on physics-based and ML models. The alloy solidification model available in MOOSE has been extended to incorporate the thermodynamic properties and free energy relevant to 316SS. The model demonstrates the Cr segregation that occurs during solidifcation. It is demonstrated that rate of solidification and solute segregation is primarily influence by the cooling rate dictating the level of freezing. This work captures the microstructural variabilities at the subgrain level that are often missing in the part-scale models. With an aim to connect the microstructural evolution model to realistic process conditions, a reduced order model is developed for predicting the thermal conditions around meltpool from high-fidelity process simulations. Furthermore, machine learning approach is used to accelerate the temperature prediction during the AM process. In the following years, MALAMUTE will be used to connect different aspects of the models and quantitatively predict the microstructural evolution. The developed ML-based surrogate model will consider the process conditions as the input to predict the microstructural features in a cost-effective way. The generated microstructures can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work help identify the key microstructural features at the subgrain level that are significant in property/performance prediction of the AM products. This work will provide inputs to the large-scale process variability models to reevaluate and validate assumptions/simplifications made in the part-scale models. Furthermore, through active learning this work will help identify the data need from both modeling and experimental sides for development of a robust digital twin for AM.
36 MATERIALS SCIENCE↗
Miniature bulge test and energy release rate in HIPed aluminum/aluminum interfacial fracture
We summarize the development of a technique of using miniature bulge test combined with three-dimensional digital image correlation (3D-DIC) for measuring energy release rate or fracture toughness of bimaterial interface of thin metal foils. Furthermore, the energy release rate associated with the HIPed aluminum/aluminum interfacial delamination is determined experimentally using the proposed technique. Detailed discussions of the schemes of preparing and conducting the bulge test, and computing various quantities required for the determination of the energy release rate are presented.
36 MATERIALS SCIENCE↗
Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) Winter Intensive Operational Period: Aerodynamic Particle Sizer Field Campaign Report
An aerodynamic particle sizer spectrometer (APS, model 3321 by TSI Inc., Shoreview, Minnesota) was deployed at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility Eastern North Atlantic (ENA) observatory during the second (winter) intensive operational period (IOP) of the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign. Data were collected from January 9 through February 26, 2018, covering the second deployment of the ARM Aerial Facility (AAF) aircraft within ACE-ENA. The standard ENA instrument suite does not provide measurements of aerosol particle size distribution for diameters greater than one micron. Properties of coarse-mode (super-micron) aerosols are rarely measured, although super-micron particles can play an important role in the aerosol life cycle, affecting aerosol optical properties and cloud nuclei activity (e.g., Clarke et al. 2003). The APS was deployed to fill this gap in particle size measurements: it is capable of measuring size-resolved concentration of the aerosols in the 0.5-20 μm range (aerodynamic diameter). The APS was installed in the aerosol trailer to sample through the main stack. Special attention was given to selection of the instrument location (just below the main stack) to minimize particle losses in the connecting lines. Most atmospheric aerosols are hygroscopic, so the measurement of the aerosol size distributions should either be performed in a controlled humidity environment or accompanied by concurrent measurement of the temperature and humidity of the sample air. The latter strategy was used during this deployment: a high-precision humidity/temperature transmitter (model EE08 by E+E Elektonik GMBH, Engerwitzdorf, Germany) was mounted in the APS sampling line immediately upstream of the APS inlet. A dedicated laptop computer was used for data collection and communications. The APS was controlled with vendor-supplied software (Aerosol Instrument Manager, ver. 10.1, TSI); aerosol size distribution data were collected over a period of 55 seconds and converted to one-minute averages. Temperature and humidity analog signals were acquired via analog-to-digital converter (ADC) module (model WTAIN-M by Weeder Technologies, Fort Walton Beach, Florida) using vendor-supplied software (ModCom) with 1- sec resolution. In post-processing, the APS data were exported from TSI proprietary format into “comma-separated value” American Standard Code for Information Interchange (ASCII) files and matched with appropriately averaged temperature and humidity data. Two significant periods of missing data were caused by local power outages. Vendor-supplied software was not designed for unattended operation: it needs to be manually restarted including sensor configuration setup.
ACE-ENA, Azores, ultra-high-sensitivity aerosol sp↗
Embedded Fluidic Sensing and Control with Soft Open‐Cell Foams
Abstract The synthesis of soft matter intelligence with circuit‐driven logic has enabled a new class of robots that perform complex tasks or conform to specialized form factors in unique ways that cannot be realized through conventional designs. Translating this hybrid approach to fluidic systems, the present work addresses the need for sheet‐based circuit materials by leveraging the innate porosity of foam—a soft material—to develop pneumatic components that support digital logic, mixed‐signal control, and analog force sensing in wearables and soft robots. Analytical tools and experimental techniques developed in this work serve to elucidate compressible gas flow through porous sheets, and to inform the design of centimeter‐sized foam resistors with fluidic resistances on the order of 10 9 Pa s m −3 . When embedded inside soft robots and wearables, these resistors facilitate diverse functionalities spanning both sensing and control domains, including digital logic using textile logic gates, digital‐to‐analog signal conversion using ladder networks, and analog sensing of forces up to 40 N via compression‐induced changes in resistance. By combining features of both circuit‐based and materials‐based approaches, foam‐enabled fluidic circuits serve as a useful paradigm for future hybrid robotic architectures that fully embody the sensing and computing capabilities of soft fluidic materials.
Rajappan, Anoop↗
Planck 2018 results: III. High frequency instrument data processing and frequency maps
This paper presents the High Frequency Instrument (HFI) data processing procedures for the Planck 2018 release. Major improvements in mapmaking have been achieved since the previous Planck 2015 release, many of which were used and described already in an intermediate paper dedicated to the Planck polarized data at low multipoles. These improvements enabled the first significant measurement of the reionization optical depth parameter using Planck-HFI data. This paper presents an extensive analysis of systematic effects, including the use of end-to-end simulations to facilitate their removal and characterize the residuals. The polarized data, which presented a number of known problems in the 2015 Planck release, are very significantly improved, especially the leakage from intensity to polarization. Calibration, based on the cosmic microwave background (CMB) dipole, is now extremely accurate and in the frequency range 100-353 GHz reduces intensity-to-polarization leakage caused by calibration mismatch. The Solar dipole direction has been determined in the three lowest HFI frequency channels to within one arc minute, and its amplitude has an absolute uncertainty smaller than 0.35 μK, an accuracy of order 10 -4 . This is a major legacy from the Planck HFI for future CMB experiments. The removal of bandpass leakage has been improved for the main high-frequency foregrounds by extracting the bandpass-mismatch coefficients for each detector as part of the mapmaking process; these values in turn improve the intensity maps. This is a major change in the philosophy of "frequency maps", which are now computed from single detector data, all adjusted to the same average bandpass response for the main foregrounds. End-to-end simulations have been shown to reproduce very well the relative gain calibration of detectors, as well as drifts within a frequency induced by the residuals of the main systematic effect (analogue-to-digital convertor non-linearity residuals). Using these simulations, we have been able to measure and correct the small frequency calibration bias induced by this systematic effect at the 10 -4 level. There is no detectable sign of a residual calibration bias between the first and second acoustic peaks in the CMB channels, at the 10 -3 level.
79 ASTRONOMY AND ASTROPHYSICS↗
Historical NTS digitizer file format
The historical NTS *.dig data formal is not widely known. I document the proper *.dig file format. I include the MATLAB code used to read *.dig files with the historical data analysis software, "eXreme Las Vegas".
97 MATHEMATICS AND COMPUTING↗
Computed Tomography Scanning and Petrophysical Measurements of Illinois Basin Coal Wells
The computed tomography (CT) facilities and the Multi-Sensor Core Logger (MSCL) at the U.S. Department of Energy’s (DOE) National Energy Technology Laboratory (NETL) in Morgantown, West Virginia, were used to characterize core from four wells that represent coal resources across Illinois. The primary impetus of this work was to capture a detailed digital representation of the core from the Brush Creek Quarry, E. Miller/Hanna City, Morris, and Weatherford Wells. The collaboration between the NETL and the Illinois State Geological Survey (ISGS) enables other research entities to access information about this potential carbon ore, rare earth, and critical mineral resource play in the Illinois Basin.
01 COAL, LIGNITE, AND PEAT↗
White Paper: Scalable Digital Twin Capabilities for Aging and Surveillance of Engineered Systems
This white paper presents a multi-year initiative to develop practical, secure, and scalable digital twin capabilities for engineered systems in aging and surveillance contexts—an approach pioneered at the National Nuclear Security Administration (NNSA) Lawrence Livermore National Laboratory (LLNL) that maps directly onto the needs and ambitions of the Navy for ship- and fleet-level digital twins. LLNL’s work in building part- and process-level digital twins for advanced manufacturing, with a vision to scale up to entire factory floors and, ultimately, enterprise-wide digital twins, offers an adaptable pathway for the Navy as it seeks to modernize lifecycle management, readiness, and predictive maintenance across ships and fleets. For our application, we integrate physics-based modeling with automated data ingestion, processing, and AI-driven calibration, creating hybrid models that are both interpretable and data responsive. We modernized legacy workflows, established centralized data infrastructure, automated experimental pipelines, and demonstrated end-to-end coupling of accelerated aging data with finite element simulations via optimization and surrogate modeling. The result is a generalizable framework that supports part-level digital twins today and lays the groundwork for future system-level twins suitable for Navy applications.
36 MATERIALS SCIENCE↗
Automatic Lane-Level Road Network Extraction from Aerial Imagery for Transportation Digital Twins
Accurate road networks are essential for credible traffic microsimulation and transportation digital twins, yet high-definition maps are often difficult to obtain due to limited availability, high cost, or proprietary restrictions. Some build networks from crowdsourced data, such as OpenStreetMap, but these sources often contain geometric and semantic inconsistencies. Others create networks manually, a process that is labor-intensive and difficult to scale. To address these limitations, this work presents an end-to-end pipeline that automatically extracts georeferenced, lane-level road networks from publicly available high-resolution satellite imagery and converts them into simulation-ready assets. The developed end-to-end pipeline has three primary modules: (1) A computer-vision-based module first detects directed lane geometries and intersection layouts. (2) A heuristic-based topology construction module then identifies approach and exit legs and establishes conflict-free lane-to-lane connections. (3) Finally, an automatic simulation-building module converts the extracted network into standard formats, e.g., OpenDRIVE, and generates routable SUMO networks. The framework supports both complete network construction from scratch and local-scale refinement of existing networks through lane-count correction, transition recovery, and geometric regularization. The proposed pipeline provides a practical pathway to generate traffic simulation networks from satellite imagery, significantly reducing manual reconstruction effort and enabling scalable, continuously updated transportation digital twins.
Guo, Hetian [University of Georgia, Athens] (ORCID↗
Sub-nanosecond signal propagation in anisotropy-engineered nanomagnetic logic chains
Energy efficient nanomagnetic logic (NML) computing architectures propagate binary information by relying on dipolar field coupling to reorient closely spaced nanoscale magnets. In the past, signal propagation in nanomagnet chains were characterized by static magnetic imaging experiments; however, the mechanisms that determine the final state and their reproducibility over millions of cycles in high-speed operation have yet to be experimentally investigated. Here we present a study of NML operation in a high-speed regime. We perform direct imaging of digital signal propagation in permalloy nanomagnet chains with varying degrees of shape-engineered biaxial anisotropy using full-field magnetic X-ray transmission microscopy and time-resolved photoemission electron microscopy after applying nanosecond magnetic field pulses. Moreover, an intrinsic switching time of 100 ps per magnet is observed. In conclusion these experiments, and accompanying macrospin and micromagnetic simulations, reveal the underlying physics of NML architectures repetitively operated on nanosecond timescales and identify relevant engineering parameters to optimize performance and reliability.
97 MATHEMATICS AND COMPUTING↗
Alternative approach to quantum imaginary time evolution
There is increasing interest in quantum algorithms (QAs) that are based on the imaginary time evolution (ITE), a successful classical numerical approach to obtain ground states. However, most of the proposals so far require heavy postprocessing computational steps on a classical computer, such as solving linear equations. Here we provide an alternative approach to implement ITE. A key feature in our approach is the use of an orthogonal basis set: the propagated state is efficiently expressed in terms of orthogonal basis states at every step of the evolution. We argue that the number of basis states needed at those steps to achieve an accurate solution can be kept on the order of n , the number of qubits, by controlling the precision (number of significant digits) and the imaginary time increment. The number of quantum gates per imaginary time step is estimated to be polynomial in n . Additionally, while in many QAs the locality of the Hamiltonian is a key assumption, in our algorithm this restriction is not required. This characteristic of our algorithm renders it useful for studying highly nonlocal systems, such as the occupation-representation nuclear shell model. Here, we illustrate our algorithm through numerical implementation on an IBM quantum simulator.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Active interlocking metasurfaces enabled by shape memory alloys
Interlocking metasurfaces (ILMs) are a newly developed joining technology that relies on arrays of interlocking features that transmit force and constrain motion between adjoining bodies in one or more directions. This study explores harnessing the shape memory effect (SME) in Nickel-Titanium shape memory alloys (NiTi SMAs) in structures fabricated using additive manufacturing (AM) to advance the development of active ILMs by creating unit cells that open or close at specific temperatures. The study encompasses designing and fabricating two distinct interlocking array configurations using near-equiatomic NiTi powder and the laser powder bed fusion (L-PBF) AM technique, following a previously developed AM process optimization framework to manufacture defect-free parts. To guide the design process, finite element analysis (FEA) was employed to predict strain values during engage-disengage cycles. The martensitic transformation characteristics of the ILMs were characterized. Thermomechanical testing revealed that the ILMs demonstrate high locking force once engaged, coupled with complete shape recovery and good cyclic stability. Digital image correlation (DIC) was also employed to validate the FEA predictions during the engage-disengage cycles. The results indicate that NiTi SMA-based ILMs can be designed and fabricated into complex shapes using L-PBF. By leveraging the SME, the functionality of an ILM can be improved upon. The combination of computational modeling, additive manufacturing, and thermomechanical and physical property characterization provides a framework for designing future ILMs out of active materials.
Additive manufacturing↗