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DOE OSTI2021

RAVEN Theory Manual

RAVEN is a software framework able to perform parametric and stochastic analysis based on the response of complex system codes. The initial development was aimed at providing dynamic risk analysis capabilities to the thermohydraulic code RELAP-7, currently under development at Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose stochastic and uncertainty quantification platform, capable of communicating with any system code. In fact, the provided Application Programming Interfaces (APIs) allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by input files or via python interfaces. RAVEN is capable of investigating system response and explore input space using various sampling schemes such as Monte Carlo, grid, or Latin hypercube. However, RAVEN strength lies in its system feature discovery capabilities such as: constructing limit surfaces, separating regions of the input space leading to system failure, and using dynamic supervised learning techniques. The development of RAVEN started in 2012 when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework arose. RAVEN’s principal assignment is to provide the necessary software and algorithms in order to employ the concepts developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just to identify the frequency of an event potentially leading to a system failure, but the proximity (or lack thereof) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. peak pressure in a pipe) is exceeded under certain conditions. Most of the capabilities, implemented having RELAP-7 as a principal focus, are easily deployable to other system codes. For this reason, several side activates have been employed (e.g. RELAP5-3D, any MOOSE-based App, etc.) or are currently ongoing for coupling RAVEN with several different software. The aim of this document is to provide a set of commented examples that can help the user to become familiar with the RAVEN code usage.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Development of Digital Twin Predictive Model for PWR Components: Updates on Multi Times Series Temperature Prediction Using Recurrent Neural Network, DMW Fatigue Tests, System Level Thermal-Mechanical-Stress Analysis

The long-term operation (LTO) of nuclear power plant (NPP) beyond their original design life of 40 years, can lead to more material damage associated with cyclic fatigue under thermal-mechanical loading cycles and associated long-term exposure of reactor material to the deleterious reactor-coolant environments. However, under this LTO condition the reactor components can still safely operate but may require more frequent Nondestructive Evaluation (NDE) of reactor components. Frequent NDE requirement may lead to frequent shutdown of the NPP. This in turn can lead to power outage and additional NDE-inspection-cost related economic loss. The economic loss can be minimized by reducing uncertainty in life estimation of safety-critical pressure boundary components and by implementing more digital approach such as by using upcoming digital-twin (DT) technology for predicting the structural states (e.g., time and location dependent inside/outside thickness temperature, stress, strain, plastic deformation, etc.) and associated fatigue life of a component in real time. Towards this goal Argonne National Laboratory (ANL) with the sponsorship of DOE Light Water Reactor Sustainability (LWRS) program is working on the development of a DT framework that can be used for real time environmental fatigue prediction of reactor components. The DT framework is based on limited experiment-data, Artificial-intelligence (AI) – Machine-Learning (ML) - Deep-Learning (DL) based techniques and Multiphysics-computational-mechanics such as finite element (FE) based modeling tools. Towards this overall goal, following are some of the major contributions made during the FY21: 1) Multiple 82/182 dissimilar metal weld (DMW) specimens (both solid-weld and joint-weld representing the actual reactor multi-metal nozzles) were fatigue tested. The resulting fatigue lives were compared to the NUREG-6909 based best-fit and design fatigue curves. Additionally, the results of 52/152 DMW fatigue specimens (which were recently tested at Republic of Korea under the sponsorship of International Nuclear Energy Research Initiative - INERI program) were compared to the NUREG-6909 based best-fit and design fatigue curves. From the comparison of 82/182 and 52/152 DMW test data with NUREG-6909 best-fit curve, most of the reported test data fall way away from the NUREG-6909 suggested best-fit or mean curve. The NUREG-6909 suggested best-fit curve is the best-fit curve of austenitic stainless steel and due to lack of enough data on Nickel-based welds, this is currently being used for predicting the life of Nickel-alloy-based welded components. However, the above observation may require higher scaling factor (e.g., ASME suggested factor of 20 on cycles rather than the current NUREG-6909 suggested factor of 12 on cycles) for scaling the austenitic-stainless-steel best-fit-curve for estimating the design or safe-life of a welded component. Accordingly, for example, if a DMW component experience a strain amplitude of 0.6% the PWR-water life of the component would be 52 cycles instead of 85 cycles. However, more DMW tests are required to further ascertain the above-mentioned observations. 2) A system level CAD and finite element model were developed which consists of reactor pressure vessel (RPV), part of steam generator (SG), part of pressurizer (PRZ), hot leg (HL), and surge line (SL). This is with detailed nozzle geometry and thermal-mechanical material properties of different metals to simulate realistic thermal-mechanical stress under connected system global thermal-mechanical boundary conditions. 3) Different system level heat transfer analyses were performed with estimation of relevant heat transfer coefficients. The resulting data were used in subsequent system level thermal-mechanical stress analysis and for generating spatial-temporal training and validation data for a system level digital-twin based temperature predictor. Transient heat transfer analyses were performed considering thermal boundary condition under design-basis (DB) loading and EDF (Électricité de France) data-based grid-load-following (EDF-GLF) loading cycles. 4) System level thermal-mechanical stress analysis was performed for identifying damage-prone hotspots and for future extension of the model for cyclic state prediction. From the system-level model simulation under DB loading cycle it is found that HL and the SL nozzle that connect to the HL can experience significant stress and strain and could be one of the weakest links in the overall reactor coolant system (RCS). 5) An AI/ML based DT model was developed for multi-time-series temperature prediction at any inside/outside thickness locations of PWR pressure boundary components. This is by using Recurrent-neural-network (RNN) and keras machine learning libraries. The RNN model was validated against two laboratory test-based data sets with one obtained through ANL’s in-air fatigue test system and other through PWR-water test loop. The experimentally validated DT model further validated against FE model results to predict thermal scarification related spatialtemporal temperatures at random locations of a component. The well validated DT model was then used for demonstrating spatial-temporal temperature prediction under 100+ years of reactor operation subjected to combined DB, EDF-GLF and randomized grid-load-following (RANDOMGLF) loading Cycles. The expert-elicitation DT model framework was developed assuming field/input/process measurements can be available from a few existing plant sensors and can readily be used by the NPP operators. The above temperature prediction model will feed to the next-step stress analysis model based on which the life of a component can be predicted in realtime, which is one of our future works.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Quaternary Semiconductor Cd1−xZnxTe1−ySey for High-Resolution, Room-Temperature Gamma-Ray Detection

The application of Cd0.9Zn0.1Te (CZT) single crystals, the primary choice for high-resolution, room-temperature compact gamma-ray detectors in the field of medical imaging and homeland security for the past three decades, is limited by the high cost of production and maintenance due to low detector grade crystal growth yield. The recent advent of its quaternary successor, Cd0.9Zn0.1Te1−ySey (CZTS), has exhibited remarkable crystal growth yield above 90% compared to that of ~33% for CZT. The inclusion of Se in appropriate stoichiometry in the CZT matrix is responsible for reducing the concentration of sub-grain boundary (SGB) networks which greatly enhances the compositional homogeneity and growth yield. SGB networks also host defect centers responsible for charge trapping, hence their reduced concentration ensures minimized charge trapping. Indeed, CZTS single crystals have shown remarkable improvement in electron charge transport properties and energy resolution over CZT detectors. However, our studies have found that the overall charge transport in CZTS is still limited by the hole trapping. In this article, we systematically review the advances in the CZTS growth techniques, its performance as room-temperature radiation detector, and the role of defects and their passivation studies needed to improve the performance of CZTS detectors further.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Overview on Transactive Energy—Advantages and Challenges for Weak Power Grids

This document lists some challenges that researchers face when implementing transactive energy in weak power grids. These challenges often include high voltage fluctuations, limited generation, high line loadability, and unbalanced grids. The operation of transactive energy, as well as optimization techniques, are also considered, highlighting the performance and functionalities depending on power grid characteristics and market topology. Some of the most used optimization techniques for market clearing, considering the characteristics and topology, are presented as part of the research work. In addition, this paper compares different market topologies and highlights their advantages and challenges. Furthermore, this paper contains a brief description of the interoperability frameworks applied to a smart grid. As a result, it was determined that interoperability is necessary for the proper functioning of the grid. Moreover, all methods were found to be effective for their purpose from the user’s point of view as each technique has different characteristics relevant to the user and the grid. It was concluded that it is convenient to combine the optimization techniques to consider different constraints in the market clearing.

14 SOLAR ENERGY↗

DOE OSTI2021

Yttrium Hydride Post-Irradiation Examination Plan

Micro nuclear reactors are expected to use metal hydride moderators to ensure thier compact design for the flexible operational purposes. Department of Energy’s Microreactor program therefore aims to provide infrasture and qualification bed for the development of micro nuclear reactors. Because micro nuclear reactors must be compact to offer a flexible operational purposes, metal hydrides are expected to be used as neutron moderator materials due to their high hydrogen number density. Los Alamos National Laboratory (LANL) directed by the DOE to provide a qualified fabrication techniques of metals hydride, especially the yttrium hydride (YHx) which provides advantages over other metal hydride candiates. Qualification of YHx involves fabrication, property, and performance pillars, which must be met for the deployment of microreactors. In the support of the qualification process metal hydride, LANL has developed fabrication techniques based in direct hydriding and powder compacting. For both manufacturing techniques, fresh material properties of metal hydrides have been reported in the literature. To establish the performance limits and the qualification parameters, an irradiation campaign has been initated at Idaho National Laboratory (INL). The irradation campaign aims to investigate the critical irradiated YHx properties as a function of neutron fluence and temperaute. The first phase of the irradiation campaign includes the effect of the operating temperature under neutron irradiation which provides initial data on the metal hydrides and informs the manufacturing processes. So that, drop-in irradations in Advanced Test Reactor (ATR) has been designed. This document depicts the post-irradation examination (PIE) plan of the ATR irradiated YHx. Objective of the PIE plan is to serve as a qualification basis for YHx as a neutron moderator material by understanding the impacts irradiation has on the physical and thermophysical properties; to establish a link between fabrication and the irradiated YHx properties; to institute a PIE strategy for other solid-state neutron moderator candidates, and; to inform micro nuclear reactor fuel performance and safety codes.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Feasibility and Prospects of the High Resolution Rapid Refresh Model For Dynamic Line Rating

The ampacity of transmission lines is defined as the maximum amount of current the conductor can safely carry. It is necessary for transmission line operators to apply ampacity limits due to the thermal properties of the conductor. Dynamic Line Rating (DLR) is a technology and technique that uses the environmental conditions or a set of the conditions to calculate the ampacity of the conductor. The way the DLR is calculated has depended upon some amount of physical technology to implement the solution. The High Resolution Rapid Refresh (HRRR) Model is an atmospheric model that may provide enough resolution to perform dynamic line rating without any hardware at all. The objective is to use this model to perform Dynamic Line Rating. This will solve two of the most pervasive problems in Dynamic Line Rating, how to instrument a transmission line hundreds of miles long and then how to predict near future ampacity sufficient for transmission operators to effectively use, This technique will use the Idaho National Laboratory site as the test location for analysis. The High Resolution Rapid Refresh model is a reliable, freely available, and well-maintained weather forecasting model for the entire United States of America. The model provides enough special resolution that additional local weather monitoring devices at location are not necessary to conduct dynamic line rating. The HRRR ratings are lower on average than the locally measured ratings which allows for an additional margin of safety. The adoption of using purely digital methods for dynamic line rating could allow for wide scale adoption of the technology with very little overhead expenditure of implementation.

17 WIND ENERGY↗

DOE OSTI2021

Shear Assisted Processing and Extrusion of Aluminum Alloy 7075 Tubing at High Speed

Conventional extrusion of aluminum alloy 7075 is limited to 1-2 meters per minute in order to avoid surface tearing and cracking. An emerging technique called Shear Assisted Processing and Extrusion (ShAPE) was used to extrude aluminum alloy 7075 tubing at a speed of 7.4 meters per minute without inducing surface defects. The faster extrusion speed is attributed to the unique flow characteristics inherent to the ShAPE process compared to conventional extrusion. Tubes with an inner diameter of 10 mm, outer diameter of 12 mm, and length of 2 meters were extruded at temperatures ranging from 340°C to 466°C. Tensile testing was performed per ASTM B557-15 with strain measured using digital image correlation. An ultimate tensile strength of 565 ± 4.6 MPa, yield strength of 496 ± 8.7 MPa, and elongation of 16.4 ± 1.0 % were measured for extrusion made at 362°C and heat treated to the T6 condition.

Shear Assisted Processing and Extrusion, Friction ↗

DOE OSTI2022

Kinetic theory of particle-in-cell simulation plasma and the ensemble averaging technique

Abstract We derive the kinetic theory of fluctuations in physically and numerically stable particle-in-cell (PIC) simulations of electrostatic plasmas. The starting point is the single-time correlation at the start of the simulation between the statistical fluctuations of the weighted densities of macroparticle centers in the plasma particle phase-space. The fluctuations are associated with different initial conditions, typically due to the random initial conditions (in velocity space) of the macroparticles/simulation plasma, assigned according to their initial distribution of probability. The single-time correlations at all time steps and in each spatial grid cell are then determined from the Laplace–Fourier transforms of the discretized Klimontovich-like equation for the macroparticles and Maxwell’s equations for the fields, as computed by modern PIC codes. We recover the expressions for the electrostatic field and the plasma particle density fluctuation autocorrelation spectra as well as the kinetic equations describing the average evolution of PIC-simulated plasma particles, first derived by Langdon (1970b Proc. 4th Conf. Numerical Simulation of Plasmas ) using a test macroparticle approach perturbing a discretized Vlasovian plasma and then averaging the obtained physical quantity over the initial macroparticle velocity distribution. We generalize and extend these results to the modern algorithms in PIC codes using arbitrary macroparticle weights. Analytical estimates of statistical fluctuation amplitudes are derived as a function of the plasma simulation parameters, using the central limit theorem in the limit of a large number of macroparticles per cell. The theory is then used to analyze the ensemble averaging technique of PIC simulations where statistical averages are performed over ensembles of PIC simulations, modeling the same plasma physics problem but using different statistical realizations of the initial distribution functions of the macroparticles. This method is illustrated by linear Landau damping uncovering (from noise, which is usually considered numerical) the physical fluctuations driven by a single small amplitude electrostatic wave perturbing a PIC simulation plasma in equilibrium.

fluctuations correlations↗

DOE OSTI2022

Real-time image denoising of mixed Poisson–Gaussian noise in fluorescence microscopy images using ImageJ

Fluorescence microscopy imaging speed is fundamentally limited by the measurement signal-to-noise ratio (SNR). To improve image SNR for a given image acquisition rate, computational denoising techniques can be used to suppress noise. However, common techniques to estimate a denoised image from a single frame either are computationally expensive or rely on simple noise statistical models. These models assume Poisson or Gaussian noise statistics, which are not appropriate for many fluorescence microscopy applications that contain quantum shot noise and electronic Johnson–Nyquist noise, therefore a mixture of Poisson and Gaussian noise. In this paper, we show convolutional neural networks (CNNs) trained on mixed Poisson and Gaussian noise images to overcome the limitations of existing image denoising methods. The trained CNN is presented as an open-source ImageJ plugin that performs real-time image denoising (within tens of milliseconds) with superior performance (SNR improvement) compared to conventional fluorescence microscopy denoising methods. The method is validated on external datasets with out-of-distribution noise, contrast, structure, and imaging modalities from the training data and consistently achieves high-performance ( > <#comment/> 8 d B ) denoising in less time than other fluorescence microscopy denoising methods.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Suppressing simulation bias in multi-modal data using transfer learning

Abstract Many problems in science and engineering require making predictions based on few observations. To build a robust predictive model, these sparse data may need to be augmented with simulated data, especially when the design space is multi-dimensional. Simulations, however, often suffer from an inherent bias. Estimation of this bias may be poorly constrained not only because of data sparsity, but also because traditional predictive models fit only one type of observed outputs, such as scalars or images, instead of all available output data modalities, which might have been acquired and simulated at great cost. To break this limitation and open up the path for multi-modal calibration, we propose to combine a novel, transfer learning technique for suppressing the bias with recent developments in deep learning, which allow building predictive models with multi-modal outputs. First, we train an initial neural network model on simulated data to learn important correlations between different output modalities and between simulation inputs and outputs. Then, the model is partially retrained, or transfer learned, to fit the experiments; a method that has never been implemented in this type of architecture. Using fewer than 10 inertial confinement fusion experiments for training, transfer learning systematically improves the simulation predictions while a simple output calibration, which we design as a baseline, makes the predictions worse. We also offer extensive cross-validation with real and carefully designed synthetic data. The method described in this paper can be applied to a wide range of problems that require transferring knowledge from simulations to the domain of experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Design and performance of a variable gap system for thermal conductivity measurements of high temperature, corrosive, and reactive fluids

We report high-temperature fluids such as molten salts, liquid metals, and gasses are being proposed for many advanced energy systems including thermal energy storage devices, concentrating solar plants, and advanced nuclear reactor designs. However, the chemical behavior and thermophysical properties of many of these fluids have not been well characterized, which hinders the design, modeling, safety analysis, and deployment of these systems. Thermal conductivity is a property that is especially limited by existing measurement capabilities, which are subject to errors caused by convection, material interaction, radiative heat transfer, and instrument degradation. Therefore, there is a lack of standard, systematic measurement techniques for high-temperature, reactive, and corrosive fluids. In this work, the development of a variable gap thermal conductivity measurement system is detailed. The system is designed to measure the thermal conductivity of highly corrosive and reactive fluids, and survive operation between 100 °C and 800 °C. The effects of convection are minimized by limiting the thickness of the specimen to thin sizes (<0.3 mm). Corrections for radiative heat transfer were included in the working equations to consider specimens with varying optical properties. The design, construction, instrumentation, operating principles, and data analysis techniques are discussed in detail. The system was tested up to 500 °C using helium gas and molten KNO 3 -NaNO 3 to verify the measurement technique and determine the sources of error. At 300 and 400 °C KNO 3 -NaNO 3 , results showed maximum relative error of 6% when compared to results in the literature. The helium results were within 13% of those in the literature at 300 and 400 °C. Higher errors were observed at 500 °C for both fluids, and the sources of these errors are discussed.

42 ENGINEERING↗

DOE OSTI2025

White Paper: Research & Development for the Time at Temperature Approach

Recent advancements in nuclear power research are greatly improving reactor safety and performance through the development of Accident Tolerant Fuel (ATF) and Low-Enriched Uranium Plus (LEU+). These innovations can address Departure from Nucleate Boiling (DNB) margins, which are vital for reactor safety. DNB happens when the coolant switches to film boiling, significantly decreasing heat transfer and posing a risk of fuel cladding failure. The U.S. Nuclear Regulatory Commission (NRC) employs conservative DNB criteria, which can potentially restrict the operational flexibility and efficiency of reactors. The Time at Temperature (TaT) approach could provide a more detailed and adaptable operational guideline by establishing acceptable time-temperature limits, accounting for the duration a material can withstand elevated temperatures without losing its integrity. This method allows reactors to operate more efficiently and safely, offering additional operational margins, faster power adjustments, and improved fuel cycle economics. TaT criteria allow for higher power levels and more flexible responses to operational transients, particularly applicable for anticipated operational occurrences (AOOs) that result in short durations of post-DNB conditions. It enhances plant operational flexibility, allows faster startup times, and enables quicker power level adjustments, optimizing fuel loading patterns and improving fuel cycle economics. Implementing TaT limits reduces core design constraints, lowers fuel usage, and reduces costs, essential for the long-term sustainability of Light Water Reactors (LWRs). TaT maximizes the use of advanced fuel technologies like ATF and LEU+, further enhancing their economic and environmental benefits. To apply the TaT approach in existing LWRs, collaborative research activities among various DOE-sponsored programs are essential. These efforts should incorporate fuel experiments, physics-based high-fidelity modeling, ML-based surrogate modeling, and optimization techniques. This whitepaper proposes four research and development areas: 1) Investigation of the feasibility of new operations of LWR with updated safety limits; 2) Assessment of reactor operation limits through uncertainty reduction; 3) Evaluation of power uprate in virtual environment; and 4) Lattice and reactor core design for power uprate. Each area includes why this research is in need and a suggested scope of work. These comprehensive research areas ensure practical and beneficial advancements for existing reactors, translating innovations in nuclear fuel and cladding technology into improved reactor performance and safety.

42 - ENGINEERING↗

DOE OSTI2023

Knowledge Distillation for Anomaly Detection

Unsupervised deep learning techniques are widely used to identify anomalous behaviour. The performance of such methods is a product of the amount of training data and the model size. However, the size is often a limiting factor for the deployment on resource-constrained devices. Here, we present a novel procedure based on knowledge distillation for compressing an unsupervised anomaly detection model into a supervised deployable one and we suggest a set of techniques to improve the detection sensitivity. Compressed models perform comparably to their larger counterparts while significantly reducing the size and memory footprint.

Pol, Adrian Alan↗

DOE OSTI2023

Multidisciplinary concurrent optimization framework for multi-phase building design process

Modern day building design projects require multidisciplinary expertise from architects and engineers across various phases of the design (conceptual, preliminary, and detailed) and construction processes. The Architecture Engineering and Construction (AEC) community has recently shifted gears toward leveraging design optimization techniques to make well-informed decisions in the design of buildings. However, most of the building design optimization efforts are either multidisciplinary optimization confined to just a specific design phase (conceptual/preliminary/detailed) or single disciplinary optimization (structural/thermal/daylighting/energy) spanning across multiple phases. Complexity in changing the optimization setup as the design progresses through subsequent phases, interoperability issues between modeling and physics-based analysis tools used at later stages, and the lack of an appropriate level of design detail to get meaningful results from these sophisticated analysis tools are few challenges that limit multi-phase multidisciplinary design optimization (MDO) in the AEC field. Here this paper proposes a computational building design platform leveraging concurrent engineering techniques such as interactive problem structuring, simulation-based optimization using meta models for energy and daylighting (machine learning based) and tradespace visualization. The proposed multi-phase concurrent MDO framework is demonstrated by using it to design and optimize a sample office building for energy and daylighting objectives across multiple phases. Furthermore, limitations of the proposed framework and future avenues of research are listed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Stochastic Vector Techniques in Ground-State Electronic Structure

Herein we review a suite of stochastic vector computational approaches for studying the electronic structure of extended condensed matter systems. These techniques help reduce algorithmic complexity, facilitate efficient parallelization, simplify computational tasks, accelerate calculations, and diminish memory requirements. While their scope is vast, we limit our study to ground-state and finite temperature density functional theory (DFT) and second-order many-body perturbation theory. More advanced topics, such as quasiparticle (charge) and optical (neutral) excitations and higher-order processes, are covered elsewhere. We start by explaining how to use stochastic vectors in computations, characterizing the associated statistical errors. Next, we show how to estimate the electron density in DFT and discuss effective techniques to reduce statistical errors. Finally, we review the use of stochastic vectors for calculating correlation energies within the second-order Møller-Plesset perturbation theory and its finite temperature variational form. Example calculation results are presented and used to demonstrate the efficacy of the methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Micro- and nano-scale techniques to explore bacteria and fungi interactions with silicate minerals

Biogeochemical interfaces such as bacteria-mineral, fungi-mineral, and water-mineral are hot spots for important processes and reactions in all environments. Studying these interfaces are useful over a range of spatial and temporal scales in order to understand elemental cycles, soil formation, restoration, and bioremediation. Advancement of micro- and nano-scale techniques lead to new discoveries about reactions and interactions at the heterogeneous interface of bacteria, fungi, and minerals. The current state-of-art of analytical techniques allows to study solid, liquid, and gas phases and their interactions with biological components and often permits simultaneous study of chemical and physical properties in a site-specific manner. Improved techniques not only increased spatial and temporal resolution with site specificity but also decreased sample preparation, artifact generation, sample size requirements and analytical time. There are limitations and challenges that still need to be overcome while studying the very heterogeneous microbe-mineral interface, but improved accessibility to beam time at the synchrotron facilities and instrument time at the user laboratories offer some solutions. This review chapter focuses on a subset of the available micro- and nano-scale techniques: electron and atomic force microscopy, and X-ray based spectroscopy of traditional and synchrotron-based applications.

Slicate minerals, microscopy, microbes↗

DOE OSTI2024

Cyclotron radiation emission spectroscopy of electrons from tritium $β$ decay and 83m Kr internal conversion

Project 8 has developed a novel technique, cyclotron radiation emission spectroscopy (CRES), for direct neutrino mass measurements. A CRES-based experiment on the beta spectrum of tritium has been carried out in a small-volume apparatus. Here, we provide a detailed account of the experiment, focusing on systematic effects and analysis techniques. In a Bayesian (frequentist) analysis, we measure the tritium endpoint as ${18}$ ${553}_{—19}^{+18}$ (${18}$ ${548}_{—19}^{+19}$) eV and set upper limits of 155 (152) eV (90% C.L.) on the neutrino mass. No background events are observed beyond the endpoint in 82 days of running. We also demonstrate an energy resolution of 1.66 ± 0.19 eV in a resolution-optimized magnetic trap configuration by measuring 83m Kr 17.8-keV internal-conversion electrons. These measurements establish CRES as a low-background, high-resolution technique with the potential to advance neutrino mass sensitivity

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Extraction of Vibration Data with Imaging

To date, the primary sensing technology used to measure the vibration response has been accelerometers and strain gages mounted directly to the structure and using either wired or, more recently, wireless telemetry. Cost issues with these sensors and the associated data acquisition systems typically limit the numbers that are deployed on in situ structures. Although there are a few structures with larger sensing counts that in some cases exceed over 1000 sensors, more typical numbers range from ten to one hundred sensors resulting in low spatial resolution when they are applied to physically large systems. When one considers that nuclear power plant structures usually have complex geometries, material properties, connectivity and boundary conditions, it is clear these current approaches to vibration measurements can only provide limited information about a system’s dynamics response characteristics. As an alternative, many non-contact measurement technologies have emerged, including point wise measurement methods such as Global Positioning System (GPS), microwave interferometry, and laser Doppler vibrometry (LDV), as well as simultaneous full-field measurement methods such as electronic speckle pattern interferometry, holography interferometry, and muon tomography, some of which can provide high spatial resolution measurements. Among these methods, digital video imaging techniques have emerged as a feasible solution for full-field vibration measurements that provide significantly more detailed dynamic response information because every pixel becomes a measurement point. Furthermore, recent advances in image processing and computer vision algorithms have been successfully used to process video data for experimental and operational modal analysis. Such full-field measurements have the potential to significantly improve many current structural assessment procedures including system identification (modal parameter estimation), structural health monitoring, load reconstruction, model validation, and model updating. Furthermore, more recent full-field imaging techniques can be accomplished with relatively low-cost, commercially-available off-the-shelf cameras. However, these measurement procedures have other limitations that must be considered such as the ability to only measure visibly accessible points on a structure and a more limited dynamic range and bandwidth than can be achieved with accelerometers or strain gages.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗