Temperature Qualification Device Conceptual Design
This powerpoint is documenting the conceptual design of the Temperature Qualification Device (TQD) as developed under the the Advanced Sensors and Instrumentation (ASI) program.
42 ENGINEERING↗
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High Speed Data Acquisition For Fiber Optic Pressure Sensor
Presentation documenting the advanced sensors and instrumentation fiber optic pressure sensor's improved data acquisition system.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Proof-of-Concept for Sensor Modeling in MOOSE for the Design of Autonomous Nuclear Reactor Control
Autonomous operation is essential for the deployment of microreactors and fission batteries, both in terrestrial and space applications. However, prototypes of microreactors and fission batteries do not exist yet, and even the design space has not been narrowed down conclusively, making the instrumentation and control system design difficult. For this reason, there is a need for flexible computational capabilities to create a numerical stand-in of potential microreactor and fission battery designs. The latter can be used to design and test control strategies to support autonomous operations. In this paper, we describe the initial implementation of a pluggable sensor system for the easy implementation of realistic sensor models in the multiphysics object-oriented simulation environment (MOOSE) framework. This new capability will enable MOOSE users to create a numerical stand-in of microreactors and fission batteries, ultimately allowing them to easily test new control algorithms, and instrumentation strategies for advanced systems in the design phase
22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
Passive Temperature Sensors for Nuclear Applications
In April 2007, the Department of Energy (DOE) designated the Advanced Test Reactor (ATR) a National Scientific User Facility (NSUF) to advance US leadership in nuclear science and technology. By attracting new users from universities, laboratories, and industry, this program supports basic and applied nuclear research to help address the nation's energy security needs. In support of this program, the Idaho National Laboratory (INL) established in-house capabilities to develop, fabricate, test, and qualify new and enhanced temperature sensors for irradiation testing. This effort is continuing today through the DOE?s Advanced Sensors and Instrumentation (ASI) program. Although most efforts emphasize sensors capable of providing real-time data, selected tasks have been completed to enhance passive sensors for irradiations where instrumentation leads cannot be included. These sensors include silicon carbide (SiC) monitors, melt wires and the sublime temperature monitor. SiC monitors are available to detect peak irradiation temperatures between 200°C and 800°C in reactor locations where instrumentation leads cannot be used. SiC monitors may be evaluated using specialized equipment installed at INL?s Measurement Sciences Laboratory (MSL). A melt wire inventory is also maintained at MSL. This inventory contains wires for specific use in irradiation experiments ranging in temperatures from 30°C to 1500°C. Melt wires and SiC monitors have had decades of research and application. Recent research has produced a passive monitor known as the sublime temperature monitor. This passive sensor has the capability of recording temperature gradients. This paper will discuss passive temperature sensors currently being researched and implemented under the ASI program.
11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Proof-of-Concept for Sensor Modeling in MOOSE for the Design of Autonomous Nuclear Reactor Control
Autonomous operation is essential for the deployment of microreactors and fission batteries, both in terrestrial and space applications. For this reason, recent studies have investigated autonomous control by using adaptive model predictive control and multi-objective optimization for heat pipe–cooled microreactors under normal and heat pipe failure conditions. However, prototypes of microreactors and fission batteries do not exist yet, and even the design space has not been narrowed down conclusively, making the instrumentation and control system design difficult. For this reason, there is a need for flexible computational capabilities to create a numerical stand-in of potential microreactor and fission battery designs. The latter can be used to design and test control strategies to support autonomous operations. In this poster, we describe the initial implementation of a pluggable sensor system for the easy implementation of realistic sensor models in the multiphysics object-oriented simulation environment (MOOSE) framework. This new capability will enable MOOSE users to create a numerical stand-in of microreactors and fission batteries, ultimately allowing them to easily test new control algorithms, and instrumentation strategies for advanced systems in the design phase.
22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
Field-Assisted Embedding of Fiber Bragg Gratings with Compressive Strain for Sensing at High Temperatures
This document summarizes embedding of fiber Bragg grating sensors and temperature sensing. This document is for the Advanced Sensors and Instrumentation (ASI) program Spring 2025 Newsletter.
36 - MATERIALS SCIENCE↗
Field Testing of Safeguards Technologies in the Hot Fuel Examination Facility
Recent developments in nuclear fuel reprocessing techniques have yielded more efficient processes and fuel cycle options that strengthen the nuclear industry and production of clean energy. One such area of interest is pyroprocessing of used oxide fuel. However, with these advances in the back end of the nuclear fuel cycle, advances in safeguards instrumentation, measurements, and approaches are needed to ensure special nuclear material (SNM) is accounted for according to regulatory requirements. As a high-level overview of a nominal pyroprocessing approach, used oxide fuel from commercial light water reactors (LWR) is mechanically removed from the metallic cladding. Then the fuel is crushed and randomized representative samples are taken and sent to an analytical lab for analysis. The analytical results of the feed material are used for input accountancy into the rest of the process. The crushed oxide fuel is then moved to the oxide reduction (OR) furnace where it is reduced from an oxide to metallic form. The OR product is distilled to remove salt and then moved to an electrorefiner (ER), where it is immersed in a eutectic mixture of lithium chloride potassium chloride (LiCl-KCl) that typically ranges between 450-550 ?. Within the ER, the usable uranium is electrochemically transported through the molten salt from the anode to the cathode, and then subsequently removed as a relatively pure U product.. A simplified model of pyroprocessing techniques with added emphasis on the safeguards can be seen below in Fig. 1
11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
An Integrated Framework for Risk Assessment of Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants: Methodology Advancement and Application
This report documents activities performed by Idaho National Laboratory (INL) during fiscal year (FY) 2024 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk Informed Systems Analysis (RISA) Pathway, Digital Instrumentation and Control (DI&C) Risk Assessment project. The goal of the RISA Pathway is to optimize safety margins and minimize uncertainties to achieve economic efficiencies while maintaining high levels of safety. This is accomplished by providing scientific basis to better represent safety margins and factors that contribute to cost and safety, and by developing new technologies that reduce operating costs. The research efforts for FY 2024 encompass methodology refinement and exploration. The efforts include: (1) The implementation of a natural language processing tool to expedite key aspects of the reliability analysis methods developed by INL; (2) advances to support intersystem CCF analysis by providing guidance for and identification of coupling mechanisms that may contribute to CCF; (3) the investigation of how generative artificial intelligence tools can aid in hazard analysis and diversity and defense in depth (i.e., D3) assessments; (4) Industry collaboration, allowing the demonstration of and INL's risk assessment tools to support risk assessment of DI&C systems at early and late stages of development; (4) a roadmap for the development of a software for each of INL's risk assessment tools; (5) The development of a theory and methodology manual for a risk quantification methodology; (6) the development of a reliability analysis for machine learning (ML)-integrated control systems.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Nuclear data uncertainty propagation applied to the versatile test reactor conceptual design
We report the Versatile Test Reactor (VTR) currently under development is a 300 MWth sodium-cooled fast reactor (SFR) fueled with ternary metal alloy fuel, which aims to accelerate the testing of advanced nuclear fuels, materials, instrumentation, and sensors in high flux environments that are necessary to license the next generation of advanced reactor concepts. To support the VTR design process, uncertainties associated with the nuclear data has been propagated through the reactor core neutronics calculation to global parameters of interest, such as the core multiplication factor, kinetic parameters, and various reactivity feedback coefficients, following the sensitivity based uncertainty propagation approach. By folding the sensitivity coefficients, separately computed by the generalized perturbation theory code PERSENT and Monte Carlo code Serpent 2, with the variance-covariance matrices from COMMARA-2.0, we obtain the reaction-wise, isotope-wise, and overall uncertainties for each response of interest due to nuclear data uncertainty. With Serpent 2, the statistical error of the uncertainty is obtained by propagating the statistical error of the sensitivity coefficients through the same process using a newly developed uncertainty propagation method. From both codes, the overall top uncertainty contributors are found to be the cross section of Fe-56 elastic scattering, Na-23 elastic scattering, and U 238 inelastic scattering. The large contributions of the Fe-56 elastic scattering cross sections to global parameters are due to its relatively large relative uncertainty of 5–10% in nuclear data and the large volume of Fe-containing reflector assemblies in the fairly compact VTR core design. Both codes agreed well for the overall uncertainty estimates of all responses of interest, except the delayed neutron fraction, prompt neutron generation time, and the coolant density feedback coefficient, where Serpent 2 yielded a much larger value than PERSENT due to the large statistical error of sensitivity coefficients. The calculated uncertainties are also compared to those associated with other SFR cores. Another outcome of this study is a variance-covariance matrix of reactivity coefficients, which can be used in the subsequent uncertainty propagation to the system level to investigate the impact of identified uncertainties on system responses in the safety analysis.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
tomoCAM : fast model-based iterative reconstruction via GPU acceleration and non-uniform fast Fourier transforms
X-ray-based computed tomography is a well established technique for determining the three-dimensional structure of an object from its two-dimensional projections. In the past few decades, there have been significant advancements in the brightness and detector technology of tomography instruments at synchrotron sources. These advancements have led to the emergence of new observations and discoveries, with improved capabilities such as faster frame rates, larger fields of view, higher resolution and higher dimensionality. These advancements have enabled the material science community to expand the scope of tomographic measurements towards increasingly in situ and in operando measurements. In these new experiments, samples can be rapidly evolving, have complex geometries and restrictions on the field of view, limiting the number of projections that can be collected. In such cases, standard filtered back-projection often results in poor quality reconstructions. Iterative reconstruction algorithms, such as model-based iterative reconstructions (MBIR), have demonstrated considerable success in producing high-quality reconstructions under such restrictions, but typically require high-performance computing resources with hundreds of compute nodes to solve the problem in a reasonable time. Here, tomoCAM , is introduced, a new GPU-accelerated implementation of model-based iterative reconstruction that leverages non-uniform fast Fourier transforms to efficiently compute Radon and back-projection operators and asynchronous memory transfers to maximize the throughput to the GPU memory. The resulting code is significantly faster than traditional MBIR codes and delivers the reconstructive improvement offered by MBIR with affordable computing time and resources. tomoCAM has a Python front-end, allowing access from Jupyter -based frameworks, providing straightforward integration into existing workflows at synchrotron facilities.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
ORNL Second Target Station Project: Biological & Environmental Science Workshop
Recent advances in neutron sources and instrumentation have opened up many new opportunities for the application of neutron scattering techniques in the biological and environmental sciences. Neutrons enable studies of the structure and dynamics of biological and environmental samples with a particular sensitivity to light elements, such as hydrogen, which is a key component of biological and environmental samples. Studies using neutrons are complementary to X-rays and have the unique advantage of being non-destructive and highly-penetrating. Oak Ridge National Laboratory’s upcoming Spallation Neutron Source (SNS) Second Target Station (STS) will provide high brightness cold neutron sources that significantly advance the scientific capabilities of neutron scattering instruments. The STS will advance our understanding of biological and environmental processes across spatial and temporal scales. The capabilities will enhance our ability to discover, design, and develop new materials essential for advanced sustainable technologies to address society’s most pressing needs. This report summarizes the discussions and recommendations from a joint workshop held by the STS Project and the Biological and Environmental Systems Science Directorate (BESSD) in June 2022. The purpose of the workshop was to explore science opportunities and capabilities related to biological and environmental systems that could be incorporated into both current and future STS instrument designs, as well as additional instruments at the SNS First Target Station (FTS) and High Flux Isotope Reactor (HFIR). With six breakout sessions, each with two invited plenary speakers from other institutions, the participants discussed a wide range of topics relevant to biological and environmental research. Based on the input from participants, a number of recommendations on instrumentation, sample environments, complementary multi-modal methods, data processing and analysis and sample deuteration are provided in the report. The participants also identified science opportunities that are emerging from the planned instrument capabilities at STS. Selected recommendations and science opportunities are listed in the Executive Summary.
54 ENVIRONMENTAL SCIENCES↗
Opportunities for retrieval and tool augmented large language models in scientific facilities
Upgrades to advanced scientific user facilities such as next-generation x-ray light sources, nanoscience centers, and neutron facilities are revolutionizing our understanding of materials across the spectrum of the physical sciences, from life sciences to microelectronics. However, these facility and instrument upgrades come with a significant increase in complexity. Driven by more exacting scientific needs, instruments and experiments become more intricate each year. This increased operational complexity makes it ever more challenging for domain scientists to design experiments that effectively leverage the capabilities of and operate on these advanced instruments. Large language models (LLMs) can perform complex information retrieval, assist in knowledge-intensive tasks across applications, and provide guidance on tool usage. Using x-ray light sources, leadership computing, and nanoscience centers as representative examples, we describe preliminary experiments with a Context-Aware Language Model for Science (CALMS) to assist scientists with instrument operations and complex experimentation. With the ability to retrieve relevant information from facility documentation, CALMS can answer simple questions on scientific capabilities and other operational procedures. With the ability to interface with software tools and experimental hardware, CALMS can conversationally operate scientific instruments. By making information more accessible and acting on user needs, LLMs could expand and diversify scientific facilities’ users and accelerate scientific output.
97 MATHEMATICS AND COMPUTING↗
Machine-Learning-aided Approach for Predicting the Thermal Expansion Behaviors in Advanced Test Reactor Capsules (NURETH-20 full paper)
Instrumented experiments at test reactors are essential to deploying new advanced reactor systems. Designing new experiments and generating data on specific conditions require both time and cost investment. A high-fidelity model of the experiment environment can be created using finite element analysis software to support the actual experiments, but computation time is still a concern in applying outcomes to real-time usage (e.g., a digital twin). This research proposes a machine-learning-aided approach to temperature and displacement predictions, based on the thickness of the outer gas gap on the experimental capsule used for the in-pile demonstration of a novel thermal conductivity probe in the Advanced Test Reactor. The capsule consisted of U10Zr fuel, a rodlet, sodium, and inner and outer capsules. There were gas gaps between the fuel and rodlet and between the inner and outer capsule. The learning data consisted of an experimental capsule’s radial distributions of temperature and displacement, as obtained from Abaqus and the physical features. For the first step, temperature was predicted using three positional parameters. Then the displacement was predicted using six different positional parameters. Each physical feature was normalized to be both nondimensional and standardized. The temperature and displacement predictions showed good agreement in all cases involving interpolation and extrapolation. Also, data similarity enhancement increased the similarity between training and target data increasing the predictive accuracy of machine-learning models. In some cases of extrapolation, the accuracy of the machine-learning model showed limited performance, but still data similarity enhancement improved the accuracy.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Study of 149 Sm Capture and Total Cross Sections for Burnup Credit Applications [Slides]
This presentation highlights how capture and transmission measurements were performed with the DANCE (Detector for Advanced Neutron Capture Experiments) instrument with capture data from 8 eV – 1 keV, and DICER (Device for Indirect Capture Experiments on Radionuclides) instruments with transmission data from 1meV – 1 keV. There were additional measurements of 147 Sm which had contaminant in the samples, 3.4 eV strong resonance, interesting abnormalities. The data analysis is complete and the R-Matrix analysis almost (90%) complete.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Boundary Layer Exploration of Aerosols and Clouds ON Ships (BEACONS)
BEACONS aims to demonstrate autonomous deployment of a shipborne system that routinely observes atmospheric, cloud, and aerosol properties. These observations enable scientists to study aerosol-cloud interactions, advancing energy resilience and the Department of Energy's mission to improve model prediction. Key Project Outcomes Autonomous deployment of aerosol and cloud measurement systems aboard a Pasha Hawaii Marjorie C commercial ship. Six-month campaign for continuous, high-resolution aerosol and cloud data collection with minimal crew intervention. Development and testing of instruments in two phases, advancing from essential to complex systems. AI-ready processed data on the BEACONS Data Platform for near-real-time data access. Validation of aerosol-cloud interaction hypotheses for improved modeling. Establishment of best practices for instrument deployment in marine environments. Scientific collaboration to support the U.S. Department of Energy (DOE) Biological and Environmental Research (BER) mission.
54 ENVIRONMENTAL SCIENCES↗
Versailles Project on Advanced Materials and Standards interlaboratory study on intensity calibration for x-ray photoelectron spectroscopy instruments using low-density polyethylene
We report the results of a Versailles Project on Advanced Materials and Standards interlaboratory study on the intensity scale calibration of x-ray photoelectron spectrometers using low-density polyethylene (LDPE) as an alternative material to gold, silver, and copper. An improved set of LDPE reference spectra, corrected for different instrument geometries using a quartz-monochromated Al Kα x-ray source, was developed using data provided by participants in this study. Using these new reference spectra, a transmission function was calculated for each dataset that participants provided. When compared to a similar calibration procedure using the NPL reference spectra for gold, the LDPE intensity calibration method achieves an absolute offset of ~3.0% and a systematic deviation of ±6.5% on average across all participants. For spectra recorded at high pass energies (≥90 eV), values of absolute offset and systematic deviation are ~5.8% and ±5.7%, respectively, whereas for spectra collected at lower pass energies (<90 eV), values of absolute offset and systematic deviation are ~4.9% and ±8.8%, respectively; low pass energy spectra perform worse than the global average, in terms of systematic deviations, due to diminished count rates and signal-to-noise ratio. Differences in absolute offset are attributed to the surface roughness of the LDPE induced by sample preparation. We further assess the usability of LDPE as a secondary reference material and comment on its performance in the presence of issues such as variable dark noise, x-ray warm up times, inaccuracy at low count rates, and underlying spectrometer problems. In response to participant feedback and the results of the study, we provide an updated LDPE intensity calibration protocol to address the issues highlighted in the interlaboratory study. Finally, we also comment on the lack of implementation of a consistent and traceable intensity calibration method across the community of x-ray photoelectron spectroscopy (XPS) users and, therefore, propose a route to achieving this with the assistance of instrument manufacturers, metrology laboratories, and experts leading to an international standard for XPS intensity scale calibration.
36 MATERIALS SCIENCE↗
2020 ETI Annual Summer School: Data Science and Engineering
The Consortium for Enabling Technologies & Innovation (ETI) was established in 2019 to address emerging technologies within the context of nuclear nonproliferation. ETI creates a research and education environment to support cross-cutting technologies across three core disciplines: 1) computer and engineering science research specifically in a form of machine learning and high performance computing (HPC), 2) advanced manufacturing, and 3) nuclear detection technologies. For outreach and development, ETI hosted the first of three summer schools from August 24-28, 2020 with the theme of “Data Science and Engineering”. The school was hosted in an on-line format and had over 200 participants. The recorded content is available on-line as a resource for students. The summer school had four modules: 1) Fundamentals of data Applications, 2) Computational Machine Learning, 3) Bayesian Modeling and Inference, and 4) Data Science for Safeguards. Modules contained both lectures as well as student exercises. Poll Everywhere was utilized in some modules as an on-line method to engage large groups of students. Upcoming ETI Summer Schools include Novel Instrumentation in 2021 and Advanced Manufacturing in 2022.
Biegalski, Steven R.↗
Icy Moon Project (Final Report)
For this Icy Moon project, we designed, procured, built, tested and used a compact ICI instrument. This instrument has several technical advances, including a compact form factor that uses fiber arrays to relay light to six APDs. This enables a single data acquisition to measure eleven unique baselines simultaneously. The DAQ is well-synchronized and provides a path forward to even more channels. The combination of a bright LED, a sinusoidal target and cylindrical optics enables high-SNR measurements based on a known formula: a 25-ms measurement should have an SNR of 188 on the baseline zero signal. This rapid measurement time should enable two-dimensional operation and obviate the impact of temperature drifts. However, the experiment did not detect a coherence signal on any baseline (see Section Single-shot measurement results).
47 OTHER INSTRUMENTATION↗