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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Non-Parametric Statistical Analysis of Current Waveforms through Power System Sensors

The protection, control, and monitoring of the power grid is not possible without accurate measurement devices. As the percentage of renewable energy sources penetrating the existing grid infrastructure increases, so do uncertainties surrounding their effects on the everyday operation of the power system. Many of these devices are sources of high-frequency transients. These transients may be useful for identifying certain events or behaviors otherwise not seen in traditional analysis techniques. Therefore, the ability of sensors to accurately capture these phenomena is paramount. In this work, two commercial-grade power system distribution sensors are investigated in terms of their ability to replicate high-frequency phenomena by studying their responses to three events: a current inrush, a microgrid “close-in”, and a fault on the terminals of a wind turbine. Kernel density estimation is used to derive the non-parametric probability density functions of these error distributions and their adequateness is quantified utilizing the commonly used root mean square error (RMSE) metric. It is demonstrated that both sensors exhibit characteristics in the high harmonic range that go against the assumption that measurement error is normally distributed.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

A comparison of smartphone and infrasound microphone data from a fuel air explosive and a high explosive

For prompt detection of large (>1 kt) above-ground explosions, infrasound microphone networks and arrays are deployed at surveyed locations across the world. Denser regional and local networks are deployed for smaller explosions, however, they are limited in number and are often deployed temporarily for experiments. With the expanded interest in smaller yield explosions targeted at vulnerable areas such as population centers and key infrastructures, the need for more dense microphone networks has increased. An “attritable” (affordable, reusable, and replaceable) and flexible alternative can be provided by smartphone networks. Explosion signals from a fuel air explosive (thermobaric bomb) and a high explosive with trinitrotoluene equivalent yields of 6.35 and 3.63 kg, respectively, were captured on both an infrasound microphone and a network of smartphones. The resulting waveforms were compared in time, frequency, and time-frequency domains. The acoustic waveforms collected on smartphones produced a filtered explosion pulse due to the smartphone's diminishing frequency response at infrasound frequencies (<20 Hz) and was found difficult to be used with explosion characterization methods utilizing waveform features (peak overpressure, impulse, etc.). However, the similarities in time frequency representations and additional sensor inputs are promising for other explosion signal identification and analysis. As an example, a method utilizing the relative acoustic amplitudes for source localization using the smartphone sensor network is presented.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Affordable Artificial Intelligence-Assisted Machine Supervision System for the Small and Medium-Sized Manufacturers

With the rapid concurrent advance of artificial intelligence (AI) and Internet of Things (IoT) technology, manufacturing environments are being upgraded or equipped with a smart and connected infrastructure that empowers workers and supervisors to optimize manufacturing workflow and processes for improved energy efficiency, equipment reliability, quality, safety, and productivity. This challenges capital cost and complexity for many small and medium-sized manufacturers (SMMs) who heavily rely on people to supervise manufacturing processes and facilities. This research aims to create an affordable, scalable, accessible, and portable (ASAP) solution to automate the supervision of manufacturing processes. The proposed approach seeks to reduce the cost and complexity of smart manufacturing deployment for SMMs through the deployment of consumer-grade electronics and a novel AI development methodology. The proposed system, AI-assisted Machine Supervision (AIMS), provides SMMs with two major subsystems: direct machine monitoring (DMM) and human-machine interaction monitoring (HIM). The AIMS system was evaluated and validated with a case study in 3D printing through the affordable AI accelerator solution of the vision processing unit (VPU).

3D printing↗
DOE OSTI2022

Renewable Energy and Efficiency Technologies in Scenarios of U.S. Decarbonization in Two Types of Models: Comparison of GCAM Modeling and Sector-Specific Modeling

Energy system projections from quantitative models inform actions ranging from short-term and local decisions, such as those about technology and infrastructure deployment, and global and long-term negotiations and targets. Computational limits require model designers to balance coverage and resolution (i.e., breadth versus depth). Some models, such as the Global Change Analysis Model (GCAM), represent all energy sources and uses with less resolution than models that focus on a single sector's energy use. GCAM balances global supply and demand of all energy carriers projecting prices using internal calculations for energy sources and costs of greenhouse gas mitigation while capturing interlinkages between the energy system, water, agriculture and land use, the economy, and the climate. This globally comprehensive model was used to frame the Long-Term Strategy of the United States: Pathways to Net-Zero Greenhouse Gas Emissions by 2050, which the White House released in 2021 and has been used to inform national and global economy-wide climate change mitigation discussions and strategy development for decades. Unlike GCAM, sectoral models focus on a portion of the energy sector and with greater detail and resolution. The Regional Energy Deployment System (ReEDS) electricity-sector model, for example, projects electricity system capacity expansion and operation with high-fidelity representation of emerging technologies for deep decarbonization, such as variable renewable energy and energy storage, and integration of these technologies into the electric grid. The Transportation Energy and Mobility Pathway Options (TEMPO) transportation-sector model enables analysis of household choices, with a focus on adoption, charging, and use of electric vehicles. The Scout buildings-sector model supports detailed consideration of the policies and markets that can accelerate the adoption of electrification and energy conservation measures in buildings. Such sector-specific models are instrumental in informing technology research, sectoral planning strategies, and sector-specific aspects of greenhouse gas mitigation strategies in the United States. The integrated multisector and sector-specific modeling approaches represented by GCAM and these sectoral models are complementary. The integrated multisector approach calculates energy pricing and resource allocation within the model, which is important for consistency when future conditions substantially diverge from current conditions in transformative scenarios. The sector-specific approach facilitates representation of granular details across spatial, temporal, technological, and market dimensions that enable exploration of particular interactions and trade-offs. This report presents the results of recent work to explore the differences and trade-offs between these approaches by comparing GCAM with the sector-specific ReEDS, TEMPO, and Scout models. The report compares both model structures and results, and it addresses their potential relevance and applications.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Opportunities for DOE National Laboratory-led QuantISED Experiments

A subset of QuantISED Sensor PIs met virtually on May 26, 2020 to discuss a response to a charge by the DOE Office of High Energy Physics. In this document, we summarize the QuantISED sensor community discussion, including a consideration of HEP science enabled by quantum sensors, describing the distinction between Quantum 1.0 and Quantum 2.0, and discussing synergies/complementarity with the new DOE NQI centers and with research supported by other SC offices. Quantum 2.0 advances in sensor technology offer many opportunities and new approaches for HEP experiments. The DOE HEP QuantISED program could support a portfolio of small experiments based on these advances. QuantISED experiments could use sensor technologies that exemplify Quantum 2.0 breakthroughs. They would strive to achieve new HEP science results, while possibly spinning off other domain science applications or serving as pathfinders for future HEP science targets. QuantISED experiments should be led by a DOE laboratory, to take advantage of laboratory technical resources, infrastructure, and expertise in the safe and efficient construction, operation, and review of experiments. The QuantISED PIs emphasized that the quest for HEP science results under the QuantISED program is distinct from the ongoing DOE HEP programs on the energy, intensity, and cosmic frontiers. There is robust evidence for the existence of particles and phenomena beyond the Standard Model, including dark matter, dark energy, quantum gravity, and new physics responsible for neutrino masses, cosmic inflation, and the cosmic preference for matter over antimatter. Where is this physics and how do we find it? The QuantISED program can exploit new capabilities provided by quantum technology to probe these kinds of science questions in new ways and over a broader range of science parameters than can be achieved with conventional techniques.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Machine learning at the Spallation Neutron Source accelerator and target

We describe the ongoing efforts to apply Machine Learning techniques to improve the performance of our accelerator and target. Specially, we are looking to minimize halo beam losses in the absence of a proper physics model, automatically detect and log anomalies in the target support systems such as cooling, and detect and prevent errant beam pulses in the linac. We also describe the infrastructure we use to acquire and stream data to the GPU cluster for training, our code development cycle, and edge computing for model inference. To minimize halo beam losses, we use a Reinforcement Learning technique tested on a virtual accelerator. The target anomaly detection is trained on archived data using incomplete physics models and is made part of the existing target reporting system. The errant beam prevention analyzes beam current and beam phase waveforms as well as accelerator configuration data to predict errant pulses. We also develop continual learning to adapt to changes in the accelerator.

Accelerator Physics↗
DOE OSTI2023

Complete Evaluation on Advanced Reactor Machine Learning Subversion Attacks (Final)

Navigating through the world of Artificial Intelligence (AI) in nuclear reactors and their Instrumentation and Control (I&C) systems demands a careful, deliberate journey. AI’s capability to manage massive datasets and streamline control systems has indeed carved out a significant role in various sectors, including nuclear energy. However, while AI, and particularly Large Language Models (LLMs), bring a lot to the table in terms of operational efficiency and anomaly detection, they also expose the sector to a new breed of cybersecurity threats, like Inference Attacks, Adversarial Attacks, and Trojan Attacks. This guide is designed to be a straightforward manual, diving deep into the intertwining worlds of AI and cybersecurity within nuclear reactors, and tailoring insights for three crucial audiences: I&C Vendors/Developers, Nuclear Regulators, and Nuclear Reactor Operators and Cyber Defense Teams. (1) Section 2, directed at I&C Vendors/Developers, will provide a clear and focused look at several cybersecurity attacks, offering practical recommendations and detailed scenarios related to AI cybersecurity. This section isn’t just about identifying problems but also about giving solid, usable solutions. (2) Section 3, meant for Nuclear Regulators, gets straight to the point about regulations, policy suggestions, and guidelines that are needed to lay down a robust, secure, and ethical foundation for the application of AI in nuclear operations. The focus is on making sure that everything adheres to international standards and laws while being practicable and clear-cut. (3) Section 4, aimed at Nuclear Reactor Operators and Cyber Defense Teams, offers an exhaustive exploration and technical reports, with clear recommendations and scenario analyses vital to protect operational environments and guarantee the secure application of AI in nuclear reactor operations. The goal is simple: as we step into an era where AI becomes a fundamental element of our technological and energy infrastructures, this guide is here to act as a clear, direct handbook, ensuring that AI is implemented within the nuclear sector in a manner that is secure, responsible, and practical. It’s about striking a balance – optimizing the undeniable benefits offered by AI while securing and shielding against potential cyber threats as we move through this new and complex landscape.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Advancing ocean monitoring and knowledge for societal benefit: the urgency to expand Argo to OneArgo by 2030

The ocean plays an essential role in regulating Earth’s climate, influencing weather conditions, providing sustenance for large populations, moderating anthropogenic climate change, encompassing massive biodiversity, and sustaining the global economy. Human activities are changing the oceans, stressing ocean health, threatening the critical services the ocean provides to society, with significant consequences for human well-being and safety, and economic prosperity. Effective and sustainable monitoring of the physical, biogeochemical state and ecosystem structure of the ocean, to enable climate adaptation, carbon management and sustainable marine resource management is urgently needed. The Argo program, a cornerstone of the Global Ocean Observing System (GOOS), has revolutionized ocean observation by providing real-time, freely accessible global temperature and salinity data of the upper 2,000m of the ocean (Core Argo) using cost-effective simple robotics. For the past 25 years, Argo data have underpinned many ocean, climate and weather forecasting services, playing a fundamental role in safeguarding goods and lives. Argo data have enabled clearer assessments of ocean warming, sea level change and underlying driving processes, as well as scientific breakthroughs while supporting public awareness and education. Building on Argo’s success, OneArgo aims to greatly expand Argo’s capabilities by 2030, expanding to full-ocean depth, collecting biogeochemical parameters, and observing the rapidly changing polar regions. Providing a synergistic subsurface and global extension to several key space-based Earth Observation missions and GOOS components, OneArgo will enable biogeochemical and ecosystem forecasting and new long-term climate predictions for which the deep ocean is a key component. Driving forward a revolution in our understanding of marine ecosystems and the poorly-measured polar and deep oceans, OneArgo will be instrumental to assess sea level change, ocean carbon fluxes, acidification and deoxygenation. Emerging OneArgo applications include new views of ocean mixing, ocean bathymetry and sediment transport, and ecosystem resilience assessment. Implementing OneArgo requires about $100 million annually, a significant increase compared to present Argo funding. OneArgo is a strategic and cost-effective investment which will provide decision-makers, in both government and industry, with the critical knowledge needed to navigate the present and future environmental challenges, and safeguard both the ocean and human wellbeing for generations to come.

ARGO↗
DOE OSTI2021

Building Neutron Scattering Infrastructure in Louisiana for Advanced Materials (Final Report)

The main objective of the Louisiana Consortium for Neutron Scattering (LaCNS) is to build a major neutron scattering infrastructure capable of treating both soft and hard materials. The goal is to create a sustainable effort having the quality, breadth, and depth necessary to produce competitive proposals including collaborative and center type proposals. Our scientific aim is to understand the role of coupling in emergent complex materials and its impact on the structure/property relationship and to explore how to tune the key couplings to guide the design of materials with the desired properties. This naturally includes building a base of users of the Spallation Neutron Source (SNS) and the High Flux Isotope Reactor (HFIR) in Louisiana; to train highly talented graduate and post-doctoral students in synthesis, neutron scattering, and simulation and modeling techniques, thereby helping to produce the next generation of scientist who use neutron scattering techniques as a crucial part of their research. The goal of our hard matter program is to understand the interplay between spin, charge, orbital, and lattice degrees of freedom in carefully selected complex materials. We have made considerable progress on number of complex systems including the oxide Sr3(Ru1-xMnx)2O7 where short-range magnetic ordering with anisotropic spin texture is initiated at the metal-insulator transition that clearly indicates a strong spin-charge coupling. Another critical area is in quantum materials. For example, in the topological semimetal BaMnSb2, we found a 3D canted antiferromagnetic Weyl semimetal with a 2D electronic structure and a nontrivial Berry phase. In addition, in the hexagonal chiral Mn1/3NbS2 system, we found soliton/soliton and soliton/antisoliton domain walls where the application of small fields or small currents can be used to control nanoscopic magnetic domains where the control of domain walls is crucially important for information storage. In addition, chemical transformation investigations were performed on the VISION instrument at Spallation Neutron Source (SNS). The soft matter effort was focused on understanding the role of non-covalent interactions on the structure and dynamics of fluid-based soft matter. One key focus was on sequence-defined (SD) amphiphilic peptoid polymers that allow encoding of molecular interactions and thereby systematic investigations of how charge directs the solution self-assembly of amphiphilic polymers in water. Another important area was on the dynamics of lipids self-assembled into membranes for exploring the permeability and mechanical using both neutron spin echo (NSE) spectroscopy and quasielastic neutron scattering (QENS) to distinguished between viscoelasticity and permeability at the molecular scale. Overall, the LaCNS project was quite successful, generating 145 publications and 245 presentations. Our graduate and post-doctoral students were well trained resulting in positions in national laboratories (Oak Ridge National Laboratory (ORNL), Argonne National Laboratory and Los Alamos National Laboratory), major research universities, and industry. We also developed a uniaxial pressure cell along with ORNL for SNS. A critical goal of this project was to establish a foundation for competing nationally in federally funded research programs. To this end, we were quite successful in generating over twenty-two federally and non-federally funded grants including awards from NSF, DOE, and DOD, and two early career awards. Equally important, we were able to secure a key major piece of instrumentation via a large ARO grant for a state-of-the-art electron microscope.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Coolant Pump Predictive Data Analytics from Signatures Generated by the Recursive Short Time Fast Fourier Transform

Although a nuclear reactor is a hostile environment for sensors and signal transmissions, the reactor core is amenable to acoustic communication. An acoustic measurement infrastructure installed at the Advanced Test Reactor (ATR) nozzle trench area records acoustic signals that can capture reactor operating states. The distinct states produce unique signatures that can be identified and tracked using data processing and data analytics. The infrastructure relies on acoustic transmission through ATR in-pile structural components, piping, and coolant that transmit acoustically modified signals generated by the coolant pumps. This paper will discuss results from using the Recursive Short Time Fast Fourier Transform (RSTFFT) technique used to process acoustic signals and provide signatures that are identified and monitored by analytics. The RSTFFT is applied to ATR data to understand the vibration levels and signatures for different operating regimes as displayed by the spectrogram. The combination of coolant pumps for normal and high-power operation generate unique signatures. These acoustic signatures are used to develop machine learning approaches to automatically classify operating regimes. Two machine-learning models, Support Vector Machines and Linear Discriminant Analysis, were developed to classify two event classes. Class 1 is a normal steady-state operation, and Class 2 is any event that is due to start up, shut down, or other actions. Both types of machine learning models had over a 96% prediction accuracy for the two classes. These results lay the foundation for predictive analytic frameworks that can be leveraged by ATR to optimize operations and maintenance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Enabling a Physical Twin for Control Methods Evaluation

Advanced nuclear reactors play an important role in the energy future of the United States and the rest of the world. They are designed and operated based on a different model than that of the current operating fleet, thus enabling deployment in remote locations and allowing for safe semi-autonomous or autonomous operations. Such characteristics require the development of a new reactor control paradigm. A significant factor in the development of control technologies and methods is integration of the various technologies and methods with each other and with hardware (both reactor system hardware and control hardware). A recent workshop on control of advanced reactors identified the lack of a flexible, expandable software/hardware infrastructure to enable such integration as a key gap. A previous phase of the current effort involved developing and demonstrating the Control and Optimization Modular Modeling Application for Nuclear Deployment (COMMAND) platform, which is capable of integrating autonomous-control-enabling technologies and methods, without the constraints imposed by existing software solutions. This platform will enable advanced reactor developers to deploy and test any developed technologies and methods by employing a common framework, and to couple them with their own models and hardware. The present phase of this effort entails using the Microreactor Automated Control System (MACS) platform, which was developed by the U.S. Department of Energy (DOE) Microreactor Program, to serve as a control method testbed. MACS can be used by advanced reactor developers to integrate their control related research activities with any reactor system. For the present effort, MACS was customized to mirror Idaho National Laboratory (INL)’s Microreactor Applications Research Validation and Evaluation (MARVEL) microreactor, and COMMAND was leveraged to enable MACS to emulate the physics of MARVEL, thus positioning MACS as a physical twin of MARVEL.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

The Scientific Justification for a U.S. Domestic High-Performance Reactor-Based Research Facility

The Basic Energy Science Advisory Committee (BESAC) was charged with forming a subcommittee to assess the scientific justification for a U.S. domestic high-performance reactor-based research facility in order to continue providing the U.S scientific community with leading neutron capabilities in support of DOE's missions in science, energy, environment, and national security. The assessment included consideration of current international plans and existing domestic facility infrastructure. The subcommittee held a series of meetings from August 19, 2019 to April 24, 2020 that included DOE senior officials, leaders of national and international neutron facilities (SNS, HFIR, NIST, ILL, FRM-II), chairs of the NAS and POPA HEU-LEU committees, and outside experts on important areas of science, technology, and industry where high flux nuclear reactor facilities make important contributions. Also included were tours of neutron facilities (SNS, HFIR, NIST, BR2 reactor, and the planned Jules Horowitz Reactor). This July 2020 (revised 10-28-2020) report describes scientific use cases, brief summaries of existing and planned neutron facilities in the US and Europe, a comprehensive review of HFIR, a comprehensive discussion of the current state of progress on HEU-LEU conversion, user information from NIST and ORNL, and three recommendations to DOE for moving forward.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Expanded Capabilities and Opportunities for Virtual Beamline Code

In the 25 years since LLNL broke ground on the National Ignition Facility (NIF) - a cornerstone of the National Nuclear Security Administration’s Stockpile Stewardship Program - the NIF and Photon Science (NIF&PS) Directorate has steadily pushed the boundaries of laser physics, nonlinear optics, and photonics in service of inertial confinement fusion experiments, and advanced photon sources development. Beyond the 192 beamlines of the primary laser facility with its world-record energy and fusion yields, NIF&PS capabilities have expanded to include chirped-pulse amplification, kilojoule petawatt-class short-pulse systems to generate hard x-ray radiographic probes, high-average power lasers as secondary sources for generation or wake field particle acceleration along directed energy applications, and other scientific pursuits. The Computing Directorate partners with NIF&PS on numerous projects and technologies from diagnostic measurements and high performance control systems to data analysis, information technology infrastructure, and scientific simulation code development. “This strong collaboration enables cutting-edge science, particularly with regard to physics-based modeling and simulation,” says physicist Jean-Michel Di Nicola. One crucial capability for NIF&PS is the Virtual Beamline (VBL) laser simulation code. It can model all the major laser physics and technology involved in the design optimization, commissioning, and operations of advanced laser architectures from tabletop to NIF-scale. VBL provides researchers with high-fidelity models and high-resolution calculations of laser performance predictions—including for the entire NIF laser system, the Advanced Radiographic Capability, parts of the High Repetition-Rate Advanced Petawatt Laser System laser delivered to the Czech Republic, and the Optical Science Laser. After more than two decades of experimentally verified physics and computing enhancements, this unique workhorse code is wrapping up another major milestone: migration from Java to C++ with a flurry of user interface and optimization features, as well as laser physics and high-resolution enhancements thanks to parallel execution on Livermore Computing platforms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Neutron Transmission Imaging with a Portable D-T Neutron Generator

Fast-neutron transmission imaging provides complementary information to x-ray transmission imaging. While fast neutron imaging resolution is generally below x-ray imaging, 14-MeV neutrons have an advantage over portable x-ray systems. Neutrons have higher transmission through high-Z materials due to a more uniform attenuation as a function of material atomic number Z compared to X-rays, and can therefore image low-Z materials inside high-Z materials. As a result, portable neutron transmission imaging has many applications, including inspection of concrete and welds for corrosion in vehicles, bridges, and other infrastructure, measurement of material levels in containers, and inspection of suspicious packages. Fast-neutron imaging is also more practical for field use than thermal-neutron imaging due to the size and shielding requirements typical of thermal-imaging systems compared to the availability of small 14.1 MeV D-T neutron generators. However, there are limitations in portable fast-neutron imaging systems, including limited neutron output, limited light produced by neutron scintillators, and lower resolution due to neutron source spot size and 2-3 mm scintillator thickness. In addition, digital-panel dark-noise is roughly 100x higher than neutron scintillator light, and variations in noise across the panel and in time is comparable to the imaging signal. Here we discuss recent efforts in developing a portable fast-neutron radiography system, including an improved neutron scintillator, mitigation of panel noise, and new commercial portable D-T neutron generators. We also present MCNP efforts to model neutron imaging, including scintillator resolution and the effects of neutron scattering from the object and surrounding materials.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Torsional force microscopy of van der Waals moirés and atomic lattices

In a stack of atomically thin van der Waals layers, introducing interlayer twist creates a moiré superlattice whose period is a function of twist angle. Changes in that twist angle of even hundredths of a degree can dramatically transform the system’s electronic properties. Setting a precise and uniform twist angle for a stack remains difficult; hence, determining that twist angle and mapping its spatial variation is very important. Techniques have emerged to do this by imaging the moiré, but most of these require sophisticated infrastructure, time-consuming sample preparation beyond stack synthesis, or both. In this work, we show that torsional force microscopy (TFM), a scanning probe technique sensitive to dynamic friction, can reveal surface and shallow subsurface structure of van der Waals stacks on multiple length scales: the moirés formed between bi-layers of graphene and between graphene and hexagonal boron nitride (hBN) and also the atomic crystal lattices of graphene and hBN. In TFM, torsional motion of an Atomic Force Microscope (AFM) cantilever is monitored as it is actively driven at a torsional resonance while a feedback loop maintains contact at a set force with the sample surface. TFM works at room temperature in air, with no need for an electrical bias between the tip and the sample, making it applicable to a wide array of samples. It should enable determination of precise structural information including twist angles and strain in moiré superlattices and crystallographic orientation of van der Waals flakes to support predictable moiré heterostructure fabrication.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Integrated sample-handling and mounting system for fixed-target serial synchrotron crystallography

Serial synchrotron crystallography (SSX) is enabling the efficient use of small crystals for structure–function studies of biomolecules and for drug discovery. An integrated SSX system has been developed comprising ultralow background-scatter sample holders suitable for room and cryogenic temperature crystallographic data collection, a sample-loading station and a humid `gloveless' glovebox. The sample holders incorporate thin-film supports with a variety of designs optimized for different crystal-loading challenges. These holders facilitate the dispersion of crystals and the removal of excess liquid, can be cooled at extremely high rates, generate little background scatter, allow data collection over >90° of oscillation without obstruction or the risk of generating saturating Bragg peaks, are compatible with existing infrastructure for high-throughput cryocrystallography and are reusable. The sample-loading station allows sample preparation and loading onto the support film, the application of time-varying suction for optimal removal of excess liquid, crystal repositioning and cryoprotection, and the application of sealing films for room-temperature data collection, all in a controlled-humidity environment. The humid glovebox allows microscope observation of the sample-loading station and crystallization trays while maintaining near-saturating humidities that further minimize the risks of sample dehydration and damage, and maximize working times. This integrated system addresses common problems in obtaining properly dispersed, properly hydrated and isomorphous microcrystals for fixed-orientation and oscillation data collection. Its ease of use, flexibility and optimized performance make it attractive not just for SSX but also for single-crystal and few-crystal data collection. Fundamental concepts that are important in achieving desired crystal distributions on a sample holder via time-varying suction-induced liquid flows are also discussed.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Development of a Cloud-based Application to Enable a Scalable Risk-informed Predictive Maintenance Strategy at Nuclear Power Plants

Light-water reactor operations and maintenance (O&M) costs are prohibitively high, thus contributing to the premature decommissioning of nuclear power plants (NPPs). This is partly due to how the equipment is monitored. In recent years, cloud computing has emerged as a dominant technology by virtue of its low costs, computing and storage adaptability, and ability to host applications over numerous types of virtual infrastructures. Cloud computing can be a cost-effective alternative to onsite storage and diagnostics. This paper conducts a techno-economic assessment of a provisional cloud deployment architecture for a NPP predictive monitoring (PdM) system. The cloud-based monitoring system would enable maintenance and diagnostics (M&D) analysts and other authorized plant users to remotely monitor equipment functionality so as to enable PdM practices and early detection of faults. The Microsoft Azure cloud platform is included in the proposed cloud architecture to provide data processing and storage, sensor device networking, and database management; however, this analysis could be extended to other cloud computing service providers as well. For the techno-economic assessment, technical feasibility is measured in terms of network performance metrics such as response time, latency, and throughput, whereas economic feasibility is measured in terms of operational costs and capital expenditures. Finally, this report covers certain regulatory and security aspects that may concern licensees looking to implement cloud computing. The report focuses on the integration of sensor database storage, the application of cloud resources to PdM, and the identification of technological and economic hurdles associated with moving to a cloud-computing-based architecture.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Evidence of Nonlinear Seismic Effects in the Earth from Downhole Distributed Acoustic Sensors

Seismic velocities and elastic moduli of rocks are known to vary significantly with applied stress, which indicates that these materials exhibit nonlinear elasticity. Monochromatic waves in nonlinear elastic media are known to generate higher harmonics and combinational frequencies. Such effects have the potential to be used for broadening the frequency band of seismic sources, characterization of the subsurface, and safety monitoring of civil engineering infrastructure. However, knowledge on nonlinear seismic effects is still scarce, which impedes the development of their practical applications. To explore the potential of nonlinear seismology, we performed three experiments: two in the field and one in the laboratory. The first field experiment used two vibroseis sources generating signals with two different monochromatic frequencies. The second field experiment used a surface orbital vibrator with two eccentric motors working at different frequencies. In both experiments, the generated wavefield was recorded in a borehole using a fiber-optic distributed acoustic sensing cable. Both experiments showed combinational frequencies, harmonics, and other intermodulation products of the fundamental frequencies both on the surface and at depth. Laboratory experiments replicated the setup of the field test with vibroseis sources and showed similar nonlinear combinations of fundamental frequencies. Amplitudes of the nonlinear signals observed in the laboratory showed variation with the saturating fluid. These results confirm that nonlinear components of the wavefield propagate as body waves, are likely to generate within rock formations, and can be potentially used for reservoir fluid characterization.

58 GEOSCIENCES↗