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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Joint tests at INL and CEA of a transient hot wire needle probe for in-pile thermal conductivity measurement

Thermal conductivity is a key property that must be known for proper design, testing, and deployment of new fuels and structural materials in nuclear reactors. Thermal conductivity is highly dependent on the physical structure, chemical composition, and the state of the material. Typically, thermal conductivity changes that occur during irradiation are currently measured out-of-pile using a 'cook and look' approach. But repeatedly removing samples from a test reactor to make measurements is expensive, has the potential to disturb phenomena of interest, and only provides understanding of the sample's end state when each measurement is made. There are also limited thermo-physical property data available for advanced fuels; and such data are needed for simulation codes, the development of next generation reactors, and advanced fuels for existing nuclear plants. Being able to quickly characterize fuel thermal conductivity during irradiation can improve the fidelity of data, reduce costs of post-irradiation examinations, increase understanding of how fuels behave under irradiation, and confirm or improve existing thermal conductivity measurement techniques. This paper discusses efforts to develop and evaluate an innovative in-pile thermal conductivity sensor based on the transient hot wire thermal conductivity method (THWM), using a single needle probe (NP) containing a line heat source and thermocouple embedded in the fuel. The sensor that has been designed and manufactured by the Idaho National Laboratory (INL) includes a unique combination of materials, geometry, and fabrication techniques that make the hot wire method suitable for in-pile applications. In particular, efforts were made to minimize the influence of the sensor and maximize fuel hot-wire heating. The probe has a thermocouple-like construction with high temperature resistant materials that remain ductile while resisting transmutation and materials interactions. THWM-NP prototypes were fabricated for both room temperature proof-of-concept evaluations and high temperature testing. Evaluations have been performed jointly by the INL and the French Alternative Energies and Atomic Energy Commission (CEA), both in Idaho Falls (USA) and in Cadarache (France), in the framework of a collaborative program for instrumentation of Material Testing Reactors. Initial tests were conducted on samples with a large range of thermal conductivities and temperatures ranging from 20 deg. C to 600 deg. C. Particularly, tests were recently performed on a sample having thermal conductivity and dimensions similar to UO{sub 2} and MOX nuclear fuels, in order to validate the ability of this sensor to operate for in-pile characterization of Light Water Reactors fuels. The results of the tests already completed at INL and CEA indicate that the Transient Hot Wire Needle Probe offers an enhanced method for in-pile detection of thermal conductivity. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2018

Performance of low-cost monitors to assess household air pollution

Highlights: • We assessed PM{sub 2.5} and CO low-cost monitors in semi-controlled and field studies. • Devices experienced high failure rate and susceptibility to environmental changes. • Low-cost monitors tested may not be ready to replace more established methods. Exposure to household air pollution is a leading cause of morbidity and mortality globally. However, due to the lack of validated low-cost monitors with long-lasting batteries in indoor environments, most epidemiologic studies use self-reported data or short-term household air pollution assessments as proxies of long-term exposure. We evaluated the performance of three low-cost monitors measuring fine particulate matter (PM{sub 2.5}) and carbon monoxide (CO) in a wood-combustion experiment conducted in one household of Spain for 5 days (including the co-location of 2 units of HAPEX and 3 units of TZOA-R for PM{sub 2.5} and 3 units of EL-USB-CO for CO; a total of 40 unit-days). We used Spearman correlation (ρ) and Concordance Correlation Coefficient (CCC) to assess accuracy of low-cost monitors versus equivalent research-grade devices. We also conducted a field study in India for 1 week (including HAPEX in 3 households and EL-USB-CO in 4 households; a total of 49 unit-days). Correlation and agreement at 5-min were moderate-high for one unit of HAPEX (ρ = 0.73 / CCC = 0.59), for one unit of TZOA-R (ρ = 0.89 / CCC = 0.62) and for three units of EL-USB-CO (ρ = 0.82–0.89 / CCC = 0.66–0.91) in Spain, although the failure or malfunction rate among low-cost units was high in both settings (60% of unit-days in Spain and 43% in India). Low-cost monitors tested here are not yet ready to replace more established exposure assessment methods in long-term household air pollution epidemiologic studies. More field validation is needed to assess evolving sensors and monitors with application to health studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

TRANSENSOR: Transformer Real-time Assessment INtelligent System with Embedded Network of Sensors and Optical Readout. Final Report

Utilities across the world are wrestling with evolving market dynamics, population growth and climate change. Distributed Energy Resources (DERs) such as solar photovoltaics, distributed generators and energy storage systems are becoming important parts of the U.S. energy mix. These factors are driving an increasing need for low-cost grid asset monitoring. Aside from being costly, traditional utility monitoring systems are not sufficiently robust and do not provide real-time visibility into the condition of grid assets such as transformers. Lack of accurate real-time measurements on performance has resulted in the use of lagging indicators, such as oil sample analysis. To address this need, an innovative embedded optical sensing technology, Transformer Real-time Assessment Intelligent System (TRANSENSOR) was developed, validated and demonstrated in this project. To date, TRANSENSOR has focused on transformers but is extendable to other grid assets. In addition to the technology being embedded into new transformers during manufacturing, a retrofit configuration that can be installed on existing transformers in the field was also developed. It is anticipated that the ability to retrofit existing transformers will help accelerate adoption of the underlying TRANSENSOR technology. Phase 1 of the project focused on laboratory development and qualification of the technology. Following iterations and exploration of relevant optical sensing modalities and multiplexed configurations of interest for the transformer environment, an effective candidate configuration was agreed upon, down-selected and custom-designed for embedding into General Electric (GE) network transformers. Two new GE 500 kilovolt ampere (kVA), 27 kilovolt (kV) distribution network transformers were built with embedded fiber-optic (FO) sensors and successfully qualified per industry standards at GE’s Shreveport, Louisiana facility during Phase 1 of the project. Following the successful completion of Phase 1, the team proceeded to Phase 2, which focused on a field demonstration of the technology. Over Phase 2, the first new GE transformer built with embedded fiber-optic sensors was installed in an above-ground cage and connected to the grid at Con Edison’s Astoria facility. A second transformer equipped with TRANSENSOR was installed in an underground vault. Additionally, an older (1982 year model) GE transformer in an above-ground cage was retrofitted with fiber- optic sensors and reconnected to the grid at ConEd’s facility. Analysis of data acquired from the sensors showed interesting correlations with transformer loading. Additionally, key events such as transformer low-voltage network connection, primary-side energizing, and a pressure loss event from an oil sampling were detected by the TRANSENSOR system. Online data processing/feature extraction algorithms for the second transformer in the underground vault detected key features and event alerts that were transmitted through a wireless 4G connection. The remote deployment concept showed promising results with data collection running for a total of 8 months across the three (3) GE transformers instrumented for the Phase 2 field demonstration. This sets the stage well for further development and commercialization.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

An Applied Strategy for Using Empirical and Hybrid Models in Online Monitoring

The monitoring of plant equipment for failure prediction is one of the key contributors to operation and maintenance (O&M) costs for a nuclear power plant (NPP) because O&M monitoring depends on labor-intensive activities that are required to meet high equipment reliability standards. These activities rely primarily on humans for information gathering, condition diagnosis, and predictive analysis. Online monitoring aims to automate these activities by relying on sensors to replace human information gathering and machine learning to replace human analysis and decision making. To facilitate automated monitoring, a systematic strategy for anomaly detection is needed to optimally use the available sensor data, empirical models, and physics-supported models. This strategy is essential to provide credible reasoning on why and when an empirical (i.e., purely data-driven) versus hybrid (i.e., physics-supported) approach should be used and to determine the ideal mix of these two approaches for a defined anomaly detection scope. The extant methods usually adopt an ad hoc trial-and-error approach that, in addition to being time-consuming and costly, is also highly subjective; it is impacted by the background and the skill set of the personnel making the decisions. Thus, such an approach cannot guarantee an optimum outcome. This represents the motivation of the current research effort, which is focused on devising a scientifically supported strategy for the optimum selection of anomaly detection methods. This report presents a detailed assessment of the main anomaly detection techniques within the empirical or hybrid method streams. Empirical methods include pattern, statistical, and causal inference. Hybrid methods include the use of physics models to train and test data methods, reduce data dimensionality, reduce data-model complexity, augment data, and reduce empirical uncertainty; hybrid methods also include the use of data to tune physics models. The listed techniques within these two streams represent the vast majority of techniques performed for anomaly detection. Using the techniques as outcomes, a strategy was developed to enable a systematic decision-making process to lead to one of these techniques. The strategy is driven by key decision points related to data relevance, simple modeling feasibility, data inference, physics-modeling value, data dimensionality, physics knowledge, method of validation, performance, data availability and suitability for training and testing, cause-effect, entropy inference, and model fitting. Each of these decision points in the strategy is explained in detail in this report with examples, along with the scientific basis behind the decisions and outcomes in common and simplified terminology. The strategy is developed for use by any NPP staff with basic engineering or science knowledge. A user-friendly graphical state flow diagram was also developed as a visual presentation of the strategy. The strategy was tested and demonstrated through two pilot projects for the application of anomaly detection at an NPP. Each pilot had two use cases: an initial case in which certain decisions were made that resulted in one or more empirical techniques and a revised use case where one or more key decisions were modified resulting in using a set of hybrid methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Phylogenetic and amino acid conservation analyses of bacterial l-aspartate-α-decarboxylase and of its zymogen-maturation protein reveal a putative interaction domain

Background: All organisms must synthesize the enzymatic cofactor coenzyme A (CoA) from the precursor pantothenate. Most bacteria can synthesize pantothenate de novo by the condensation of pantoate and β-alanine. The synthesis of β-alanine is catalyzed by l-aspartate-α-decarboxylase (PanD), a pyruvoyl enzyme that is initially synthesized as a zymogen (pro-PanD). Active PanD is generated by self-cleavage of pro-PanD at Gly24-Ser25 creating the activesite pyruvoyl moiety. In Salmonella enterica, this cleavage requires PanM, an acetyl-CoA sensor related to the Gcn5-like N-acetyltransferases. PanM does not acetylate pro-PanD, but the recent publication of the three-dimensional crystal structure of the PanM homologue PanZ in complex with the PanD zymogen of Escherichia coli provides validation to our predictions and provides a framework in which to further examine the cleavage mechanism. In contrast, PanD from bacteria lacking PanM efficiently cleaved in the absence of PanM in vivo. Results: Using phylogenetic analyses combined with in vivo phenotypic investigations, we showed that two classes of bacterial l-aspartate-α-decarboxylases exist. This classification is based on their posttranslational activation by self-cleavage of its zymogen. Class I l-aspartate-α-decarboxylase zymogens require the acetyl-CoA sensor PanM to be cleaved into active PanD. This class is found exclusively in the Gammaproteobacteria. Class II l-aspartate-αdecarboxylase zymogens self cleave efficiently in the absence of PanM, and are found in a wide number of bacterial phyla. Several members of the Euryarchaeota and Crenarchaeota also contain Class II l-aspartate-α-decarboxylases. Phylogenetic and amino acid conservation analyses of PanM revealed a conserved region of PanM distinct from conserved regions found in related Gcn5-related acetyltransferase enzymes (Pfam00583). This conserved region represents a putative domain for interactions with l-aspartate-α-decarboxylase zymogens. This work may inform future biochemical and structural studies of pro-PanD-PanM interactions. Conclusions: Experimental results indicate that S. enterica and C. glutamicum l-aspartate-α-decarboxylases represent two different classes of homologues of these enzymes. Class I homologues require PanM for activation, while Class II self cleave in the absence of PanM. Computer modeling of conserved amino acids using structure coordinates of PanM and l-aspartate-α-decarboxylase available in the protein data bank (RCSB PDB) revealed a putative site of interactions, which may help generate models to help understand the molecular details of the self-cleavage mechanism of l-aspartate-α-decarboxylases.

Aspartate decarboxylase↗
DOE OSTI2018

Development and Optimization of Voltammetric Methods for Real Time Analysis of Electrorefiner Salt with High Concentrations of Actinides and Fission Products

This project addresses the problem of achieving accurate material control and accountability (MC&A) around pyroprocessing electrorefiner systems. Spent nuclear fuel pyroprocessing poses a unique challenge with respect to reprocessing technology in that the fuel is never fully dissolved in the process fluid. In this case, the process fluid is molten, anhydrous LiCl-KCl salt. Therefore, there is no traditional input accountability tank. However, electrorefiners (ER) accumulate very large quantities of fissile nuclear material (including plutonium) and should be well safeguarded in a commercial facility. Idaho National Laboratory (INL) currently operates a pyroprocessing facility for treatment of spent fuel from Experimental Breeder Reactor-II with two such ER systems. INL implements MC&A via a mass tracking model in combination with periodic sampling of the salt and other materials followed by destructive analysis. This approach is projected to be insufficient to meet international safeguards timeliness requirements. A real time or near real time monitoring method is, thus, direly needed to support commercialization of pyroprocessing. A variety of approaches to achieving real time monitoring for ER salt have been proposed and studied to date—including a potentiometric actinide sensor for concentration measurements, a double bubbler for salt depth and density measurements, and laser induced breakdown spectroscopy (LIBS) for concentration measurements. While each of these methods shows some promise, each also involves substantial technical complexity that may ultimately limit their implementation. Yet another alternative is voltammetry —a very simple method in theory that has previously been tested for this application to a limited extent. The equipment for a voltammetry system consists of off-the-shelf components (three electrodes and a potentiostat), which results in substantial benefits relative to cost and robustness. Based on prior knowledge of electrochemical reduction potentials for each of the species of interest, voltammetry can be used to quantify concentrations of a variety of elemental species—including uranium, plutonium, minor actinides, and rare earths. Various methods have been tested by other researchers to date—including cyclic voltammetry, square wave voltammetry, normal pulse voltammetry, etc. In most cases, it has been observed that there is a very limited concentration range for which the output can be readily correlated with concentration in the salt. Furthermore, testing to date has been limited to simple ternary salts with only a single element being quantified. While incomplete for application to MC&A for pyroprocessing, these results lead us to believe that voltammetry can be optimized based on salt properties and fundamental electrochemical rate processes to yield a highly accurate and robust method. This project is divided into four tasks jointly executed by three university research groups. This includes experimental measurement of key physical data on the systems of interest, development of a predictive voltammetry model, experimental validation of the voltammetry model, and design/verification of an optimized measurement method. This project supports the goals of the US-ROK Joint Fuel Cycle Study in addition to the NA-24 Office of the National Nuclear Security Agency and the International Atomic Energy Agency (IAEA).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Seamless Wireless Communication Platform for Internet of Things Applications

The rapid growth of the Internet of Things (IoT) devices resulted in the proliferation of wireless technologies to cater to their increasing data rate requirements and support multiple applications. However, such ever-increasing wireless technologies present numerous challenges such as incompatible wireless standards, increased energy consumption, and insecure communication. The traditional gateways proposed in the literature suffers from limitations such as computational complexity, resource requirements, increased cost, and device size. We vision an era of seamless wireless communication to alleviate the aforementioned challenges in IoT applications. through three inter-dependent functionalities namely detection and identification of wireless technologies, energy-efficient transmit power control, and secure end-to-end communication. To prove the concept, a new gateway is proposed to achieve these three functionalities with only physical layer measurements so that the different communication protocols in the higher layers can be avoided. Novel schemes are conceptualized for resource-limited seamless IoT applications. Moreover, the conceptual seamless IoT platform is validated through software-based computer simulation and software-defined radio-based testbed implementation. Finally, the preliminary analysis demonstrates that the proposed platform has great potential in advancing seamless IoT applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Hawaii Wave Surge Energy Converter (HAWSEC) OSU O.H. Hinsdale Basin

The following information and metadata applies to both the Phase I (Hydrodynamics) and Phase II (Full System Power Take-Off) zip folders which contain testing data from the OSU (Oregon State University) O.H. Hinsdale Wave Research Laboratory, from both OSU and the University of Hawaii at Manoa (UH). See zip folders provided further below in the downloads section. For experimental data of the full system, including PTO, see Phase II dataset. There are two main directories in each Phases's zip folder: "OSU_data" and "UH_data". The "OSU_data" directory contains data collected from their DAQ (data acquisition system), which includes all wave gauge observations, as well as body motions derived from their Qualisys motion tracking system. The organization of the directory follows OSU's convention. Detailed information on the instrument setup can be found under "OSU_data/docs/setup/instm_locations". The experiments conducted are documented in the "OSU_data/docs/daq_logs", which provides the trial number to the corresponding data located under "OSU_data/data" in several formats (e.g., ".mat" and ".txt"). Inside the trial directory, data is provided for each of the instruments defined in "OSU_data/docs/setup/instm_locations". The "UH_data" directory contains data collected from their DAQ. The data is stored in a ".tdms" file format. There are free plug-ins for Microsoft Excel and MathWorks MATLAB to read the ".tdms" format. Below are a few links providing methods to read in the data, but a Google search should identify alternatives sources if these no longer exist (valid as of January 2024): Excel: http://www.ni.com/example/27944/en/ MATLAB: https://www.mathworks.com/matlabcentral/fileexchange/30023-tdms-reader The Excel plugin is recommend to get a quick overview of the data. The UH data is organized by directory name, in which the sub-directories for each experiment contains a directory whose name defines the wave height and period for the experimental data within. For example, a directory name "H02_T0275" corresponds to an experiment with wave height 0.1m and a period of 2.75s. For random wave data, the gamma value is also included in the directory name. For example, a directory name "H02_T0225_G18" corresponds to an experiment with a significant wave height of 0.2m, a peak period of 2.25s, and a gamma value of 1.8, with each spectra being a TMA spectrum. For the free decay experiments, the directory name is defined by the initial angular displacement. For example, a directory name "ang05_run01" corresponds to an experiment with an initial angular displacement of 5 degrees. There is a dataset in the UH data for each corresponding experiment defined in the OSU DAQ logs. The ".tdms" data is output from the DAQ at fixed intervals. Therefore, if multiple files are contained within the folder, the data will need to be stitched together. Within the UH dataset, there are two input channels from the OSU DAQ providing a random square wave signal for time synchronization ("ENV-WHT-0010") and a high/low signal ("ENV-WHT-0012") to identify when the wave maker is active (+5V). The UH data is logged as a collection of channel outputs. Channels not in use for the OSU testing (either Phase I or Phase II) are marked "nan" below. If the sensor is disconnected, it will record noise throughout the experiment. Below are the channel definitions in terms of what they measure: GPS Time = time CYL-POS-0001 = position between flap and fixed reference CYL-LCA-0001 = force between flap and hydraulic cylinder REC-LPT-0001 = nan REC-HPT-0001 = nan REC-HPT-0002 = nan REC-HPT-0003 = nan HHT-HPT-0001 = pressure at exhaust ("head" only) REC-FQC-0001 = nan REC-FQC-0002 = nan HHT-FQC-0001 = flow at exhaust ("head" only) ENV-WHT-0001 = nan ENV-WHT-0002 = nan ENV-WHT-0003 = nan ENV-WHT-0010 = random signal from OSU DAQ ENV-WHT-0012 = high/low signal from OSU DAQ Also included is a calibration curve to convert the string pot data to flap pi...

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

A Multi-Sensor Approach for Measuring Bird and Bat Collisions with Offshore Wind Turbines (Final Technical Report)

Collision of birds and bats with wind turbines is a conservation concern for both land-based and offshore wind projects. The fatality rates of birds and bats at land-based turbines are well documented. The measurement strategies on land focus on finding carcasses following collision, estimating the number of carcasses missed through searcher efficiency, carcass persistence trials and carcass fall distributions, and modeling statistically robust fatality rates. Few technologies have been developed to monitor offshore bird and bat collisions, and many that have been developed focused on detecting collisions with large birds. The few studies that have attempted to document collisions at offshore turbines do not account for smaller bodied animals or for collisions that might be missed, which prevents the calculation of statistically robust fatality rates. The overall goal of this report, A Multi-Sensor Approach for Measuring Bird and Bat Collisions with Offshore Wind Turbines (Project), was to develop an effective multi-sensor system for quantifying bird and bat collision rates, specifically for offshore wind facilities. The Project goal and resulting automated collision detection system was achieved through two major technological advancements: 1) refining The Netherlands Organisation for Applied Scientific Research’s (TNO’s) existing WT-Bird® vibration sensing system, that had successfully detected large bird collisions during daytime, to allow for improved detection of smaller birds and bats during both daytime and nighttime hours and 2) improving image processing systems and developing and integrating machine learning algorithms to automatically detect and classify small and large bird and bat collisions with offshore turbines. This final technical report (FTR) summarizes Methods , Results , Conclusions , and Lessons Learned during each of the five Tasks identified for this research and development effort. This FTR includes summaries of the following: Task 1. Initial Engineering Tests to Improve WT-Bird® Task 2. Installation of WT‐Bird® on a Utility-scale Turbine at the National Wind Technology Center – National Renewable Energy Laboratory Task 3. Field Tests and Refinement of the Object Detection System Task 4. Validation of WT-Bird® on a Land-based Turbine Task 5. Preparation for the Implementation of WT-Bird® on an Offshore Turbine. This research and development effort documented successful improvement of the WT Bird® collision detection system to detect small birds and bats, and WT-Bird® is the first collision detection system to validate results compared to land-based post-construction monitoring. The collision trials provide estimates of missed targets that can be used to estimate fatality rates, a significant improvement relative to other offshore collision monitoring systems. Advances were made in developing an edge-processing solution to reduce data storage requirements, which is important if the system is deployed for long periods of time at offshore turbines. The improved WT-Bird® system also provides an important option for wind operators on land or offshore who need to document specific details about when collisions occur, particularly efforts to further research on bat impact minimization, or when standard fatality searches are impractical (e.g. offshore) or inadequate (e.g. challenging locations on land).

17 WIND ENERGY↗
DOE OSTI2018

Efficient Full-Field Vibration Measurements and Operational Modal Analysis Using Neuromorphic Event-Based Imaging

Traditional vibration measurement typically requires physically attached sensors, such as accelerometers and strain gauges. However, these discrete-point sensors provide only low spatial resolution vibration measurements, potentially foregoing valuable structural information such as localized damage. Noncontact optical measurement methods such as laser vibrometers can achieve high spatial resolution vibration measurements but only through time-consuming sequential measurements and are not cost-effective. As an alternative to traditional vibration measurement methods, digital video cameras are relatively low-cost, agile, and offer noncontact, simultaneous high spatial resolution measurements where every pixel on the structure becomes a measurement point. However, regular digital video cameras are frame-based where each pixel simultaneously performs temporally uniform (synchronous) measurements containing large amounts of redundant (background) data, which consumes considerable resources for video data measurement, management, and processing. To alleviate such a challenge, this work explores the use of event-based neuromorphic imagers, specifically silicon retinas, an efficient alternative to traditional frame-based video cameras, to perform full-field vibration measurements and operational modal analysis. By imitating biological vision, each silicon retina pixel independently and asynchronously records only intensity change events that contain structural motion information while excluding redundant (background) information. Such an asynchronous event-based data measurement mechanism allows for structural motion to be captured on the microsecond scale in an extremely data-efficient manner, which could benefit real-time vibration measurement and control applications. This study takes the first step toward these applications by formulating an existing video frame-based full-field operational modal analysis technique in the event-based, asynchronous silicon retina measurement framework. Specifically, local phase-based motion extraction and blind source separation are used to automatically and efficiently extract full-field vibration and dynamics parameters from silicon retina measurements. The developed method is validated by laboratory experiments on a bench-scale cantilever beam.

42 ENGINEERING↗
DOE OSTI2024

A multiscale anisotropic polymer network model coupled with phase field fracture

The study of polymers has continued to gain substantial attention due to their expanding range of applications, spanning essential engineering fields to emerging domains like stretchable electronics, soft robotics, and implantable sensors. These materials exhibit remarkable properties, primarily stemming from their intricate polymer chain network, which, in turn, increases the complexity of precisely modeling their behavior. Especially for modeling elastomers and their fracture behavior, accurately accounting for the deformations of the polymer chains is vital for predicting the rupture in highly stretched chains. Despite the importance, many robust multiscale continuum frameworks for modeling elastomer fracture tend to simplify network deformations by assuming uniform behavior among chains in all directions. Recognizing this limitation, our study proposes a multiscale fracture model that accounts for the anisotropic nature of elastomer network responses. At the microscale, damage in the chains is assumed to be driven by both the chain's entropy and the internal energy due to molecular bond distortions. In order to bridge the stretching in the chains to the macroscale deformation, we employ the maximal advance path constraint network model, inherently accommodating anisotropic network responses. As a result, chains oriented differently can be predicted to exhibit varying stretch and, consequently, different damage levels. To drive macroscale fracture based on damages in these chains, we utilize the micromorphic regularization theory, which involves the introduction of dual local-global damage variables at the macroscale. The macroscale local damage variable is obtained through the homogenization of the chain damage values, resulting in the prediction of an isotropic material response. The macroscale global damage variable is subjected to nonlocal effects and boundary conditions in a thermodynamically consistent phase field continuum formulation. Moreover, the total dissipation in the system is considered to be mainly due to the breaking of the molecular bonds at the microscale. To validate our model, we employ the double-edge notched tensile test as a benchmark, comparing simulation predictions with existing experimental data. Additionally, to enhance our understanding of the fracturing process, we conduct uniaxial tensile experiments on a square film made up of polydimethylsiloxane (PDMS) rubber embedded with a hole and notches and then compare our simulation predictions with the experimental observations. Furthermore, we visualize the evolution of stretch and damage values in chains oriented along different directions to assess the predictive capacity of the model. In conclusion, the results are also compared with another existing model to evaluate the utility of our model in accurately simulating the fracture behavior of rubber-like materials.

42 ENGINEERING↗
DOE OSTI2023

Scaling Containment on a Large Centrifuge

Containment science concerns the trapping or leakage of radioactive cavity gases from underground nuclear explosions (UNEs). The most physically relevant data for validation of containment science comes from legacy UNE testing or large field-scale chemical explosions. Field-scale tests can inform on the degree of residual stress imparted to host geology and possible formation of an accompanying gas-containing “stress cage”, for example, as a function of chemical explosion yield. However, field testing can be expensive, difficult to conduct, and challenging to instrument with limitations on data coverage (i.e., the number of boreholes; difficult coring through damage zones). Small-scale laboratory tests are typically simpler to conduct and more thoroughly characterize, but may not include the right scale of containment processes for stress-cage or chimney formation. This report presents the theory and fundamentals of large centrifuge physical modeling, including a history of explosive or similar testing relevant to containment science investigations on centrifuges. Subscale models with embedded chemical explosives in the enhanced gravity of a large centrifuge can represent hundreds of meters of depth and large explosions not otherwise attainable in the laboratory as based on scaling of length, energy, and other processes by the g-factor of the centrifuge (i.e., the number of times larger the centrifugal force is than the gravitational force on the Earth’s surface). The centrifuge uniquely joins the simplicity and exhaustiveness of laboratory-scale characterization with the physics of field-scale processes. This report is part of Sandia National Laboratories’ (SNL’s) effort to prepare for physical modeling with approximately one-meter tall geologic models of stress cage and/or chimney formation on its 29-foot [8.84 m] radius centrifuge with a load capacity of 1.6 million 𝑔-pounds [726 𝑔-ton]. The scaling relationships herein will inform model design decisions and performance requirements for in-flight sensors.

42 ENGINEERING↗
DOE OSTI2013

Long-term monitoring experiences at the HADES underground lab and its relevance for radwaste repository monitoring

In the frame of its radwaste disposal research programme, SCK-CEN started the construction of the HADES underground research facility in 1980. Including several extensions and a comprehensive experimental programme, it has provided a lot of experience on monitoring. Monitoring is performed for many reasons: construction follow-up, field characterisation, investigation of phenomena, and model validations - in which the underground lab offers the opportunity for up-scaling conventional laboratory set-ups. Construction monitoring has allowed to develop and optimise the underground construction techniques in a previously poorly known environment, resulting in a well-mastered application of mechanised methods for gallery construction with minimal damage to the host formation. Access to this formation also allows its characterisation, both geotechnical, geological and geochemical, and the detailed investigation of phenomena such as fracturing and oxidation. Finally, instrumented set-ups allow to test various numerical models by comparing the observations with the predicted behaviour. The specific conditions of the underground laboratory put particular requirements to the sensors. These conditions include the long-term nature of many set-ups - typically several years to decades, the inaccessibility of many sensors after installation, high mechanical and water pressures, and corrosion. Combined with the fact that many sensors are custom made, obtaining and maintaining a fully functional instrumented set-up can be challenging. A lot of experience has therefore been gained which is very valuable when designing the monitoring of radwaste repositories - and it has allowed us to determine the critical success factors for monitoring. Engineers tend to look at this first from a technical viewpoint - and there are many technical aspects indeed that determine the reliability of monitoring. A first one is the combination of different observations ('redundancy') which can be implemented by the use of several sensors, different sensor principles, different (coupled) parameters, and the combination of point measurements with geophysical techniques. Cabling is also a critical issue as it is often considered as the primary enemy of barrier integrity. Minimal cabling techniques, such as distributed fibre optic monitoring and wireless signal transmission, therefore get increasing attention. Also the interpretation of the monitoring data - in particular those that are perceived as 'wrong' or 'unexpected', needs sufficient attention. The long-term experience has however also shown that the design of a monitoring programme must look beyond the technical part. In particular for long-term applications, issues such as data management and record keeping are vital to guarantee success in this. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Analytics-at-scale of Sensor Data for Digital Monitoring in Nuclear Plants (3 rd Annual Report)

Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power. This report primarily focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances of 0.036 ± 0.014, 0.0026 ± 0, and 0.063 ± 0.004 RMSE, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure. This report summarizes the Fiscal Year 2021 research progress encompassing the (1) data cleaning and feature selection necessary for ML applications; (2) development of short-term forecasting models to predict future plant process parameters for both single and multiple time steps ahead; and (3) validation of the feature selection methods and short-term forecasting models given new data from different systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

The EGS Collab Project: Status and Accomplishments

The EGS Collab project, supported by the US Department of Energy, is addressing challenges in implementing enhanced geothermal systems (EGS). This includes improving understanding of the stimulation of crystalline rock to create appropriate flow pathways, and the ability to effectively simulate both the stimulation and the flow and transport processes in the resulting fracture network. The project is performing intensively monitored rock stimulation and flow tests at the 10-m scale in an underground research laboratory. Data and observations from the field test are compared to simulations to understand processes and to build confidence in numerical modeling of the processes. In Experiment 1, we examined hydraulic fracturing an underground test bed at the Sanford Underground Research Facility (SURF) in Lead, South Dakota, at a depth of approximately 1.5 km. We drilled eight sub-horizontal boreholes in a well-characterized phyllite. Six of the boreholes were instrumented with many sensor types to allow careful monitoring of stimulation events and flow tests, and the other two boreholes were used for water injection and production. We performed a number of stimulations and flow tests in the testbed. Our monitoring systems allowed detailed observations and collection of numerous data sets of processes occurring during stimulation and during dynamic flow tests. Long-term ambient temperature and chilled water flow tests were performed in addition to many tracer tests to examine system behavior. Data were rapidly analyzed, allowing adaptive control of the tests. Numerical simulation was used to answer key experimental design questions, to forecast fracture propagation trajectories and extents, and to analyze and evaluate results. Many simulations were performed in near-real-time in conjunction with the field experiments, with more detailed process study simulations performed on a longer timeframe. Experiment 2 will examine hydraulic shearing in a test bed being built at the SURF at a depth of about 1.25 km in amphibolite under a different set of stress and fracture conditions than Experiment 1. Five sets of fracture orientations were considered in design, and three orientations seem to be consistently observed.

EGS Collab↗
DOE OSTI2026

Intelligent Experiments through Real-Time AI: Fast Data Processing and Autonomous Detector Control for High-Energy Nuclear Experiments

The aim of this project is to develop software and hardware for fast real-time data processing and autonomous detector control and calibration for the sPHENIX and the future EIC experiments. Below summarizes Georgia Tech team efforts in the past year: 1. We developed a real-time clustering algorithm and FPGA-based pipeline architecture for processing fired pixel data from ALPIDE sensors in sPHENIX experiments. Our Columnar Clustering Co-Design introduces a hardware-aware, stream-friendly approach that segments pixel data by column pairs using a Column Pair Clustering (CPC) strategy, followed by Cluster Stitching to merge adjacent subclusters. Implemented in Vitis HLS, the pipeline comprises five stages—read-in, subclustering, stitching, analysis, and write-out—connected by tagged HLS streams with custom end-of-event signaling for robust synchronization. We designed a pipelined dataflow model optimized for throughput, low latency, and minimal buffering, enabling scalable clustering across events of arbitrary size. Our system maintains spatial precision via center-of-mass and shape key extraction and efficiently handles edge cases such as fragmented or nested clusters. Compared against DBSCAN in both software and hardware, our approach demonstrates competitive performance under FPGA constraints. 2. We also conducted a comprehensive algorithm-to-hardware co-design of connected component analysis tailored for sPHENIX experiments, focusing on real-time, low-latency processing using FPGAs and High-Level Synthesis (HLS). Starting from a Python-based particle tracking pipeline, the team translated the core logic—graph traversal via DFS and Union-Find—into an HLS-compatible C++ model, replacing dynamic memory and recursion with static arrays and pipelined control flow. The final design includes a fully streamed and dataflow-compatible Union-Find kernel optimized across five iterations, incorporating loop pipelining, array partitioning, AXI/FIFO interface tuning, and function flattening. Experimental results show up to 14.8× speedup over the CPU baseline, reducing per-graph latency to 1.58 μs and demonstrating strong resource efficiency with only ~7k LUTs and zero BRAM usage. The design maintains functional correctness against the Python reference using a Python-based C-simulation framework and Mean Squared Error metrics. This work validates the potential of HLS-driven FPGA designs for edge-level HEP data acquisition, laying a scalable foundation for future integration with real-time detector pipelines and multi-graph processing systems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Root genetics in the field to understand drought adaptation and carbon sequestration (Final Scientific/Technical Report)

For all crop plants, roots play a critical role in growth. Roots anchor the plants, and are the primary site of nutrient and water uptake. Roots are also the main source of C to soil in the form of root tissues and exudates, and thus greatly influence SOM stocks. To perform these functions, primary roots extend into soil, producing a network of branching roots of characteristic form, known as its root system architecture (RSA). RSA varies among species, and among varieties within a species that are adapted to different environments. Root traits are major targets for the second green revolution because of their potential to improve crop productivity, increase drought tolerance and nutrient acquisition, and increase C capture of soil. Improving the quality of roots in maize will be particularly valuable, since this crop is planted on over 92 million acres annually in the US. The future sustainability of agricultural systems relies on their ability to enhance soil organic matter (SOM) storage and reduce GHG emissions, while maintaining or enhancing productivity. This program had two components, Sensors and Models. For the first component, we designed and built a high-throughput phenotyping platform for root pulling of maize plants. This eliminated the physical labor of manually pulling up plants and reduced the number of personnel required down to one. The standardized pulling mechanism allowed recording force curves during the pulling process, providing additional information. We validated that the maximum force for pulling the root system was well-correlated with the root system mass and provided root crowns for further RSA analysis. These root crowns identified significant correlations with 2D root area and root depth, along with 3D root volume, total root length and number of root tips. We then used this system for field-based studies in maize on the genetics of root system architecture and its relation to nitrogen-use efficiency (NUE), including using lines relevant to the Corteva breeding program. Varieties were also evaluated at Corteva sites in the cornbelt and Danforth farm in Missouri, to establish responses across sites. From these studies we have identified genetic loci associated with root traits and created mutant lines for these loci and correlations of root traits with NUE. For the Models component, we worked to incorporate root and soil characteristics into the MEMS 2.0 soil and ecosystem biogeochemical model. Existing soil C models, such as Century, are unable to represent specific root trait interactions with the soil environment and therefore to accurately forecast the potential C sequestration benefits of root breeding under different climatic and soil type conditions. We have developed the MEMS 2.0 ecosystem biogeochemical model to improve quantification of farm-scale soil carbon and greenhouse gas emissions. The new knowledge and large datasets produced by this project will be used to develop and drive an innovative model capable of forecasting the impacts on soil C stocks and nutrient dynamics. An innovation was to use the empirical data from the field studies (in 1, above) to model genetic variation in nitrogen use efficiencies and soil C input. Our work demonstrated that maize root-derived C rapidly replaces existing soil C and after 3 years of continuous maize, up to 20% of soil organic C in the topsoil (0-15cm) and 3% in the subsoil (15-30cm) was contributed by maize. However, this contribution did not entirely represent a net increase. Root C contribution to soil was affected by maize genetics. We have analyzed soils derived from the CSU field trials for C and N stocks, in the different soil physical fractions represented by the MEMS model, using both physical fractionation with elemental analyses, and Fourier transformed infrared spectroscopy. Data will be used to link crop nitrogen use efficiencies with soil C sequestration and provide data to bridge the field trials with the model development, for verification of model predictions. The project had a number of successful outcomes: we have used the new phenotyping platform to identify new genetic loci that can enhance root phenotypes; we have partnered with multiple maize seed companies phenotype varieties in their breeding programs; we have developed the MEMS model that can help inform industry on the potential for carbon sequestration in the agricultural sector, and which is now available at the CSU Soil Carbon Solutions Center for use.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Creating a Simulation Platform for Research and Development of Advanced Control Methods

Advanced nuclear reactors are essential to meet the changing energy requirements throughout both the United States and the rest of world. In addition to other features, they are designed to enable deployment in remote locations and operate in a fully (or near-fully) autonomous manner, which will require a new control paradigm. To realize autonomously operating reactors, the U.S. Department of Energy’s Nuclear Energy Enabling Technologies Advanced Sensors and Instrumentation (NEET ASI) program conducts research and development into the enabling technologies and methods needed, including digital twins, machine learning, and risk modeling, in addition to various types of control methods. These technologies and methods are the key foundations needed to achieve fully autonomous systems. To develop and evaluate the technologies and methods necessary for achieving autonomous operations, it is critical to identify a software tool capable of integrating all the required elements. In surveying the available solutions, no software platforms were identified that could accomplish what was needed without introducing drawbacks. This challenge was the motivation for the current effort: to develop a software platform that can seamlessly integrate autonomouscontrol-enabling technologies and methods, allowing for accelerated research and development and transfer of ideas. The resulting platform, known as the Control and Optimization Modular Modeling Application for Nuclear Deployment (COMMAND), is Python-based, and leverages open-source tools to provide flexibility and facilitate building upon prior research. It is designed to enable advanced reactor developers to deploy and test advanced control technologies and methods coupled with their own models, solutions, and hardware. Given the substantial undertaking of developing such a platform, the current effort focused on laying down scalable, flexible software foundations and infrastructure, then demonstrating the platform via a use case. These foundations included developing generic modules, which contain the base variable and system blocks (the information and functional building blocks, respectively, that can be used to design a simulation) and the data handling and storage blocks needed to exchange information between the various blocks; as well as enablingtechnology-specific modules. This platform was evaluated via a use case, which was to simulate and control a process for the Microreactor Automated Control System (MACS) test bed. While MACS is not currently directly coupled to any specific microreactor physics, it was initially developed in concert with the Microreactor Applications Research Validation and Evaluation (MARVEL) microreactor, and so the MARVEL physics are used here. As part of this use case, several enabling-technology-specific blocks within COMMAND were integrated, including a proportional integral derivative (PID) control block, a Reactor Excursion and Leak Analysis Program (RELAP5-3D) block, and an anomaly detection block. The COMMAND software platform was successfully demonstrated to achieve the scalability and flexibility objectives of this effort and will be leveraged by the program’s research efforts to advance state of the art control methodologies towards autonomous operations of advanced reactors. As new use cases are created and implemented, it is anticipated that COMMAND will continue to grow and evolve to meet new requirements.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗