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DOE OSTI2025

Preliminary Plan to Inform Testing of a Heat Exchanger Test Article

This report presents a preliminary plan to guide the qualification testing of advanced heat exchanger (HX) components for nuclear-to-industrial heat transfer applications. The objective is to establish a defensible, physics-based methodology that integrates computational modeling, targeted experimentation, and in-service inspection considerations to demonstrate component performance and reliability under representative reactor conditions. The analysis identifies Sodium-cooled Fast Reactor (SFR) and High-Temperature Gas-cooled Reactor (HTGR) systems as reference configurations in terms of temperature, pressure, and chemical environment. Within these operating envelopes, dominant degradation mechanisms— including creep–fatigue interaction, flow-induced vibration, corrosion, and diffusion-bond deterioration—were evaluated to define test requirements. A comprehensive computationalexperimental framework is proposed to support life prediction and qualification activities. The framework couples high-fidelity structural-mechanics, thermal-hydraulic, and fluid-structure interaction models with accelerated degradation testing to produce a traceable linkage between microstructural evolution, mechanical performance, and remaining useful life (RUL). The approach adheres to established Verification, Validation, and Uncertainty Quantification (VVUQ) standards (ASME V&V 10/20; NUREG-2152) and incorporates a digital-twin architecture for continuous model refinement through data assimilation. The plan further outlines testing methodologies, including pre-test analyses, test-loop design parameters, and sensor placement strategies that maximize information yield while maintaining mechanistic fidelity. Complementary sections describe in-service inspection (ISI), on-line monitoring (OLM), and structural-health-monitoring (SHM) techniques applicable to compact HX geometries typical of advanced reactors. Collectively, these activities establish the technical foundation for demonstrating 40-60-year equivalent service life of advanced heat exchangers in support of the U.S. Department of Energy’s Advanced Reactor and Integrated Energy Systems programs. The forthcoming phase will execute the defined pre-test analyses, initiate hardware fabrication, and implement the integrated testing campaign to validate the proposed qualification methodology.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Grid-Ready Flexible Transformers for Resilient Transmission Networks

Today’s transmission networks extend for hundreds of kilometers, transferring significant amounts of electrical energy. For every country, there are sort of “back- bone” lines, where a massive failure of transformers would create serious challenges to sustain a reliable energy supply. Natural phenomena and human driven actions could be triggers of such catastrophic events. For large utilities, or groups of utilities, it would be highly convenient to count with “flexible transformers”, which could be used as replacements for different voltage levels and with impedance adjustable to match optimum local requirements of impacted substations. Currently applied “emergency transformers” are fixed-impedance devices. In this contribution, a new concept of flexible, “future-grid ready” type of power transformers, specially devised for transmission networks is explained. This transformer has adjustable impedance features, which could be on-line operated. The concept has been proven to be functional and stable in the factory lab and in the field, by means of a prototype autotransformer of of 100 MVA, 165/69 Kv.

impedance of transformers↗

DOE OSTI2023

Real-time infrared spectroscopy coupled with blind source separation for nuclear waste process monitoring

On-line infrared absorbance spectroscopy enables rapid measurement of solution-phase molecular species. Many spectra-to-concentration models exist for spectral data, with some models able to handle overlapping spectral bands and nonlinearities. However, model accuracy is limited by the quality of training data used in model fitting. The process spectra of nuclear waste simulants at the Savannah River Site display incongruity between training and process spectra; the glycolate spectral signature in the training data does not match the glycolate signature in Savannah River National Laboratory process data. A novel blind source separation algorithm is proposed that preprocesses spectral data so that process spectra more closely resemble training spectra, thereby improving model quantification accuracy when unexpected sources of variation appear in process spectra. The novel blind source separation preprocessing algorithm is shown to improve nitrate quantification from an R 2 of 0.934 to 0.988 and from 0.267 to 0.978 in two instances analyzing nuclear waste simulants from the Slurry Receipt Adjustment Tank and Slurry Mix Evaporator cycle at the Savannah River Site.

Crouse, Steven H.↗

DOE OSTI2023

Considerations about future hard x-ray area detectors

X-ray sources continue to advance in both intensity and temporal domains, thereby opening new ways to analyze the structure and properties of matter, provided that the resultant x-ray images can be efficiently and quantitatively recorded. In this perspective we focus on specific limitations of pixel area x-ray detectors. Although pixel area x-ray detectors have also advanced in recent years, many experiments are still detector limited. Specifically, there is need for detectors that can acquire successive images at GHz rates; detectors that can accurately measure both single photon and millions of photons per pixel in the same image at frame rates of hundreds of kHz; and detectors that efficiently capture images of very hard x-rays (20 keV to several hundred keV). The data volumes and data rates of state-of-the-art detection exceeds most practical data storage options and readout bandwidths, thereby necessitating on-line processing of data prior to, or in lieu of full frame readouts.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Lithium-Ion Battery Diagnostics Using Electrochemical Impedance via Machine-Learning

Diagnosing battery states such as health, state-of-charge, or temperature is crucial for ensuring the safety and reliability of electrochemical energy storage systems. While some states, such as temperature, may be measured using cheap sensors, accurate diagnosis of battery health metrics usually requires time-consuming performance measurements, making them infeasible for use in real-world operation. These health metrics can be measured during lab-testing and then estimated on-line using predictive life models or via state observer algorithms such as Kalman filters, but these predictive methods should be supplemented by actual measurement of battery health whenever possible to ensure reliability. Rapid measurement of battery health may be done by various types of fast diagnostic techniques such as electrochemical impedance spectroscopy (EIS), which can be performed in only a few minutes and require only a fraction of the energy and power needed for a full charge and discharge measurement. But there is a substantial challenge for estimating battery health using EIS data, as EIS is sensitive to cell temperature, state-of-charge, current, and resting time in addition to health. Thus, utilizing EIS data to predict battery capacity requires correcting for all these additional variables, a task that is extremely difficult to handle analytically. This talk utilizes machine-learning methods to estimate the effectiveness of battery capacity prediction from EIS data, leveraging a data set of hundreds of EIS measurements recorded at varying temperature and state-of-charge throughout a 500-day aging study of 32 commercial, large-format NMC-Graphite lithium-ion batteries. Using EIS as input to machine-learning models is complicated by the nonlinear response of impedance to battery health, temperature, and state-of-charge, as well as the collinearity between the impedance response at neighboring frequencies, which can easily lead to overfit models. To train robust models, features from EIS data need to be extracted from the data or some subset of critical frequencies selected. Many approaches for extracting and selecting features from EIS data from electrochemical analysis and machine-learning fields were identified for analysis: using the entire raw spectra; selection of one, two, or many frequencies from the entire spectra; selecting interesting points from the EIS measurement using domain knowledge; fitting EIS with an equivalent-circuit model; calculating statistics on the raw impedance values; and reducing the dimensionality of the data using unsupervised linear (principal component analysis) and non-linear (uniform manifold approximation and projection) methods. These approaches were rigorously compared using a machine-learning pipeline approach, training linear, Gaussian process, and random forest regression models and quantifying performance using cross-validation as well as a held-out test set. An artificial neural network model trained on the raw spectra was also tested. Promising pipelines were fine-tuned via Bayesian hyperparameter optimization using cross-validation loss and training with class-specific weights to counter data set imbalance. The most reliable method for utilizing impedance in this work was the selection of two optimal frequencies through an exhaustive search, resulting in about 2% mean absolute error on test data for both Gaussian process and random forest model architectures. Interrogation of a variety of models reveals critical frequencies of 100 Hz and 103 Hz for this data set, though the optimal set of frequencies is not necessarily intuitive, i.e., the best performing models are not simply those that use impedance at frequencies that have the highest correlation to the relative discharge capacity. The best performing model is an ensemble model, which is able to predict battery capacity with 1.9% mean absolute error for unseen cells using impedance recorded at a variety of temperatures and states-of-charge.

battery↗

DOE OSTI2023

The Fermilab SSR1 and HB650 Synoptic and EPICS remote cryogenic control system

The 325MHz Single Spoke Resonator 1 (SSR1) Cryomodule (CM) has a 5.3-meter length, and it has 288-liter liquid helium @ 4.5K mode. The High Beta 650 MHz (HB650) CM contains six elliptical cavity cryomodules, has a 9.9-meter overall length and it has 530-liter liquid helium. The SSR1 and HB650 CM had been tested at Fermilab Cryomodule Test Facility (CMTF). Their cryogenic control system includes the Siemens Process Control System S7-400, Automation Direct DL205 PLC, and remote synoptic HMI/EPICS Phoebus GUI. This paper presents a method which has been successfully used by Fermilab PIP2 IT cryogenic remote on-line, real-time control systems.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Process for ultra-sensitive quantification of target analytes in complex biological systems

Antibody-free processes are disclosed that provide accurate quantification of a wide variety of low-abundance target analytes in complex samples. The processes can employ high-pressure, high-resolution chromatographic separations for analyte enrichment. Intelligent selection of target fractions may be performed via on-line Selected Reaction Monitoring (SRM) or off-line rapid screening of internal standards. Quantification may be performed on individual or multiplexed fractions. Applications include analyses of, e.g., very low abundance proteins or candidate biomarkers in plasma, cell, or tissue samples without the need for affinity-specific reagents.

Shi, Tujin↗

DOE OSTI2023

In Silico Versus In Situ: The Challenging Landscape of Nuclear Criticality Safety Training

Nuclear criticality safety grew out of the ranks of experimentalists studying the physics of chain-reacting systems at critical experiment facilities. Consequently, critical experiment facilities provide the best forum for conducting training in nuclear criticality safety. As the nuclear renaissance gains traction, there is an increased demand to train personnel in nuclear criticality safety (NCS). Strides have been made in both on-line and virtual reality based NCS training. While these are perhaps a necessary component of NCS training, hands-on training at critical experiments facilities remains the most effective means of developing competency for fissionable material handlers, managers of fissionable material operations, criticality safety analysts, and experimentalists. This paper explores the challenges of developing and maintaining workforce competency in nuclear criticality safety to support safe and efficient fissionable material operations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Centrifugal contactor including central dynamic examination device

Centrifugal contactors that can be used for mixing or separating materials are described. The contactors include a sensing system including a communications fiber within the shaft of the contactors and access ports providing access from the communications fiber to the mixing/separating zone of the contactor. The sensing system can be utilized during operation of the contactor and can provide for detailed and accurate on-line characterization of a protocol, as well as process control and system modification as necessary during operation.

Restivo, Michael L.↗

DOE OSTI2023

Electrochemical Characterization of Molten Salt Chemistry During Atmospheric Ingressions

This report describes the salt chemistry and sedimentation studies that were completed in FY23 as part of the Pyrochemical Fuel Cycles – ANL project. The primary objective of these activities was to employ electrochemical methods in order to quantify and gain insights into the reaction mechanisms associated with the interaction of O2 and moisture impurities with the molten chloride salts used for the pyrochemical processing of nuclear materials. If not monitored and controlled, the ingression of these atmospheric impurities can lead to changes in the salt redox conditions and initiate the formation of oxide particles, potentially resulting in operational and safeguards challenges. By elucidating the mechanisms underlying the formation of oxide particles, we can effectively monitor the conditions of the salt and design systems to mitigate the influence of atmospheric O2 and H2O. To achieve these goals, we designed a test system that allowed for precisely controlled gas ingressions into the molten salt vessel. This system included on-line monitoring provided by electrochemical probes that enable near real-time measurements of the salt conditions throughout the course of the experiment. Using this system, we systematically varied experimental conditions, including O2/Ar flow rates and O2 concentrations, to comprehensively understand their impact on solid particle formation. We also used a particle size and shape analyzer to characterize the particles that were generated. Tests in FY23 concentrated on the formation of CeO2 particles, but we also prepared a separate apparatus targeting UO2 particles for use in FY24. We additionally conducted extensive modeling activities in support of this work. This included multiphysics simulations of the gas ingressions along with the development of a machine learning workflow to enable accelerated molecular dynamics modeling of the molten salt chemistry of LiCl-KCl-UCl3. The combination of experimental and modeling tools has allowed us to begin to get a more complete understanding of the reactions that occur when atmospheric ingressions occur in pyroprocessing systems.

Guo, Jicheng↗

DOE OSTI2024

New U.S. Data Tools are Playing a Crucial Role in Decarbonizing Buildings at Speed, Scale, and Low Cost

Preparing buildings for retrofits traditionally requires expensive on-site audits or timeintensive simulation models. As a result, the majority of buildings fail to pursue cost-saving retrofits. To address these barriers, the U.S. Department of Energy (DOE) has introduced the Building Efficiency Targeting Tool for Energy Retrofits (BETTER)-a new, free, on-line tool that utilizes a data-driven analytical engine and user-friendly web interface to automatically analyze a building's monthly energy usage in response to weather conditions. The tool benchmarks a building's electric and fossil energy usage against peers; estimates energy, cost, and emissions reductions at the building and portfolio levels; recommends energy efficiency measures; and prioritizes buildings for net-zero energy retrofits. Thanks to interoperability with the DOE's Standard Energy Efficiency Data (SEED) platform, BETTER is supporting U.S. jurisdictions to prepare buildings for retrofit at speed, scale, and low cost to comply with energy policies. This paper discusses the use of BETTER and SEED by one of the branches of the California state government to streamline a retrofit program across 455 public non-residential buildings to align with state goals to reduce greenhouse gas emissions. It describes the organization's challenge to reduce energy consumption across a geographically diverse, aging portfolio; explores how BETTER and SEED improved workflow efficiency; presents preliminary results, including avoiding audit costs of $3.28 million and developing the groundwork for retrofit projects estimated to prevent emission of 2,271 t CO2e annually; and provides guidance for other jurisdictions seeking similar results.

BETTER↗

DOE OSTI2024

New U.S. Data Tools are Playing a Crucial Role in Decarbonizing Buildings at Speed, Scale, and Low Cost

Preparing buildings for retrofits traditionally requires expensive on-site audits or time- intensive simulation models. As a result, the majority of buildings fail to pursue cost-saving retrofits. To address these barriers, the U.S. Department of Energy (DOE) has introduced the Building Efficiency Targeting Tool for Energy Retrofits (BETTER)—a new, free, on-line tool that utilizes a data-driven analytical engine and user-friendly web interface to automatically analyze a building’s monthly energy usage in response to weather conditions. The tool benchmarks a building’s electric and fossil energy usage against peers; estimates energy, cost, and emissions reductions at the building and portfolio levels; recommends energy efficiency measures; and prioritizes buildings for net-zero energy retrofits. Thanks to interoperability with the DOE’s Standard Energy Efficiency Data (SEED) platform, BETTER is supporting U.S. jurisdictions to prepare buildings for retrofit at speed, scale, and low cost to comply with energy policies. This paper discusses the use of BETTER and SEED by one of the branches of the California state government to streamline a retrofit program across 455 public non-residential buildings to align with state goals to reduce greenhouse gas emissions. It describes the organization’s challenge to reduce energy consumption across a geographically diverse, aging portfolio; explores how BETTER and SEED improved workflow efficiency; presents preliminary results, including avoiding audit costs of $3.28 million and developing the groundwork for retrofit projects estimated to prevent emission of 2,271 t CO2e annually; and provides guidance for other jurisdictions seeking similar results.

Li, han↗

DOE OSTI2024

Methods of separating materials by use of a centrifugal contactor

Restivo, Michael L.↗

DOE OSTI2024

Systems and methods for manufacturing nano-scale materials

Methods and systems for high-speed production of nanoparticles with very high product yields are described. Systems utilize concentric micro-scale capillaries arranged to define nanoparticle formation regions that lie along predetermined length(s) of the capillaries. Flow through the formation regions can be laminar during a formation protocol. The system can include on-line analytical tools for real time characterization of products or intermediates. Systems include an additive manufacturing-type deposition at the terminus of the formation section. The deposition area includes a print head and a print bed and provides for random or patterned deposition of nanoparticles. The print head and/or the print bed can be capable of motion in one or more degrees of freedom relative to one another.

Source record↗

DOE OSTI2024

Tensioned metastable fluid detector sensing technology for multifarious-multiscale applications in the nuclear fuel cycle

The nuclear fuel cycle presents formidable 10 30+ multiscale challenges for high-efficiency spectroscopic sensing of alpha-neutron-fission radiation signatures amidst extreme gamma-beta radiation backgrounds at various stages of the cycle; it involves diverse arenas ranging from continuous/pulsed power production, security/safeguards, spent fuel, environmental remediation, and personnel health-dosimetry. This paper discusses the novel tensioned metastable fluid detector (TMFD) sensor technology. It discusses the results of studies demonstrating extreme (10 4 Sv/h) gamma-beta field blind high intrinsic efficiencies (80–100%) for neutron-alpha-fission radiation spectrometric detection. Example studies discussed in this paper include Neutron detection with spectroscopy; H*10 neutron radiation dosimetry upwards from ultra-low10 −2 µSv/h levels; Air and/or liquid-borne trace (10 –3 Bq/mL) quantity, 1 keV energy resolved alpha/fission spectroscopy for Rn/Po/U/Pu/Am environmental isotope assays at 106:1 (β:α) ratios; Directional-position enabled extended standoff tracking of neutron emitting (shielded/unshielded) special nuclear materials while stationary or moving at highway speeds; and, U.S. Department of Energy nuclear complex wide nuclear waste reprocessing, criticality safety, health physics, and environmental applications.

Alpha-fission-neutron spectrometry↗

DOE OSTI2025

Real-time gross actinide monitoring in extreme (10 6 :1) beta-gamma background fluid streams using mini-ATMFDs

Real-time, trace alpha activity monitoring in extreme mixed radiation fields is a well-known challenge in fission product-rich streams containing mixtures of beta-gamma-alpha radiation-emitting isotopes, arising due to the extreme 10 6 :1 ratio of background beta-gamma vs. alpha radiation activity levels. This paper presents results from a novel approach to addressing this challenge, based on Tensioned Metastable Fluid Detector (TMFD) sensing technology. An acoustically driven technique was developed and tested at Purdue University (using dissolved Rn-Po and U-chain alpha emitters), and validated (with dissolved Pu) at Savannah River National Laboratory for real-time monitoring of alpha activity in the 100–10,000 dpm/mL range.

Environmental Monitoring↗

DOE OSTI2025

Online monitoring of lanthanide species with combined spectroscopy in flowing aqueous aerosol systems

Combined spectroscopic analysis through absorption and laser-induced breakdown spectroscopy (LIBS) was used to monitor Nd and Pr concentrations in a flowing aqueous system. A unique, online sampling approach was employed that allowed for an approximately closed analysis loop of liquids from a reservoir. Absorption spectroscopy was performed with an optical flow cell, and LIBS was performed on an aerosol stream. Multivariate calibrations based on combined absorption and LIBS signals were built for Nd and Pr and then used to monitor concentrations in mixed solutions in a series of spiking tests. In these tests, the concentrations of Nd and Pr in solution were intermittently changed while spectroscopic signals were monitored in real-time. The combined spectroscopic signals and multivariate models were successful in monitoring changing concentrations of lanthanide species with high accuracy and minimal latency. Root-mean squared error of predictions were 0.015 mol L −1 and 0.019 mol L −1 for Nd and Pr respectively, and these lanthanides were able to be monitored to an accuracy of < 0.2 wt%. This work demonstrates both the capabilities of a sampling system for online analysis of liquids and the capabilities of multimodal spectroscopic characterization for real-time, continuous tracking of species in potentially hazardous liquid systems.

absorbance spectroscopy↗

DOE OSTI2023

Demonstration of Multi-Gamma Based Sensor Technology for As-Fired Coal Property Measurement

This project demonstrated the application of the use of an advanced multigamma attenuation (MGA) sensor to accurately and precisely measure coal properties at the point of injection into burners. Accurate and precise coal property information at the burner provides the opportunity to adjust burner parameters to better follow changing load condition, optimize flame stability, and decrease nitrogen oxide formation at each burner. In addition, coal property information at the burner will assist in managing slag flow behavior, fireside ash related deposition, corrosion, and erosion issues. The advantage of having multiple gamma emitters is that the atomic/elemental interaction with the gamma energy takes place at several energies and the resulting attenuation is dependent upon the atomic number of the atom/element encountered in the coal.

01 COAL, LIGNITE, AND PEAT↗