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DOE OSTI2021

Experimental validation of the Finite Element-Multibody model of a cryomodule during road transportation

The present activity aims to test the Transportation Tooling (TT) designed for the Cryomodule SSR1 at Fermilab facilities. The tests consisted of road transportation trials of a cryomodule sub-assembly, suspended on a truck using the designed TT. The truck drove along a path inside Fermilab facilities. The trial was performed at an approximate speed of about 25 km/h, which is close to the transportation conditions of the complete Cryomodule. Several accelerometers were mounted on the Transportation Tooling frame and the cryomodule sub-assembly. This allowed comparing the vibration behavior of the inner frame of the TT with the solicitation coming from the truck loading bed, to assess the filtering capabilities of the tooling. Also, the acceleration of the main components could be monitored during the whole transportation, to obtain a validation of the developed numerical model.

43 PARTICLE ACCELERATORS↗

DOE OSTI2022

Changes in active-site geometry on X-ray photoreduction of a lytic polysaccharide monooxygenase active-site copper and saccharide binding

The recently discovered lytic polysaccharide monooxygenases (LPMOs) are Cu-containing enzymes capable of degrading polysaccharide substrates oxidatively. The generally accepted first step in the LPMO reaction is the reduction of the active-site metal ion from Cu 2+ to Cu + . Here we have used a systematic diffraction data collection method to monitor structural changes in two AA9 LPMOs, one from Lentinus similis (LsAA9_A) and one from Thermoascus aurantiacus (TaAA9_A), as the active-site Cu is photoreduced in the X-ray beam. For LsAA9_A, the protein produced in two different recombinant systems was crystallized to probe the effect of post-translational modifications and different crystallization conditions on the active site and metal photoreduction. We can recommend that crystallographic studies of AA9 LPMOs wishing to address the Cu 2+ form use a total X-ray dose below 3 × 10 4 Gy, while the Cu + form can be attained using 1 × 10 6 Gy. In all cases, we observe the transition from a hexacoordinated Cu site with two solvent-facing ligands to a T-shaped geometry with no exogenous ligands, and a clear increase of the θ 2 parameter and a decrease of the θ 3 parameter by averages of 9.2° and 8.4°, respectively, but also a slight increase in θ T . Thus, the θ 2 and θ 3 parameters are helpful diagnostics for the oxidation state of the metal in a His-brace protein. On binding of cello-oligosaccharides to LsAA9_A, regardless of the production source, the θ T parameter increases, making the Cu site less planar, while the active-site Tyr—Cu distance decreases reproducibly for the Cu 2+ form. Thus, the θ T increase found on copper reduction may bring LsAA9_A closer to an oligosaccharide-bound state and contribute to the observed higher affinity of reduced LsAA9_A for cellulosic substrates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Utah FORGE: GES Well 16A(78)-32 and Well 16B(78)-32 Stimulation Seismic Event Catalogs

This dataset contains seismic event catalogs from the hydraulic stimulation of wells 16A(78)-32 and 16B(78)-32 at the Utah FORGE site in April 2024. The data was collected by Geo Energy Suisse (GES) using a variety of seismic monitoring technologies, including 3-component (3C) geophones and distributed acoustic sensing (DAS) systems. These technologies were deployed across several locations, including wells 16A, 16B, and Delano-1, with sensor arrays at multiple depths to capture microseismic activity during the stimulations. The catalogs provide both real-time and manually checked seismic event locations, with detailed parameters such as trigger conditions, velocity models, and data acquisition settings. The dataset includes information on the stimulation stages, event rates, and hydraulic injection conditions for each well, with a report detailing the data acquisition configuration and seismic event location methodologies. Users will need to reference the included report for a complete understanding of the sensor network, data processing techniques, and accuracy considerations.

15 GEOTHERMAL ENERGY↗

DOE OSTI2020

Machine-Learning Enabled Evaluation of Probability of Piping Degradation In Secondary Systems of Nuclear Power Plants

The transition to condition-based, risk-informed automated maintenance will contribute to a significant reduction of operations and maintenance costs that account for the majority of nuclear power generation costs. Furthermore, of the operations and maintenance costs in U.S. plants, approximately 80% are labor costs. To address the issue of rising operating costs and economic viability, technologies used to perform online monitoring of piping and other secondary system structural components in commercial nuclear power plants (NPPs) are under evaluation. These online monitoring systems have the potential to identify when a more detailed inspection is needed using real time measurements, rather than at a pre-determined inspection interval thus reducing the maintenance cost. This paper describes distributed high-temperature stable fiber sensors fabricated in optical fibers through a roll-to-roll laser direct writing process using femtosecond lasers. Using phase-sensitive optical time domain reflectometry, distributed acoustic and vibration sensors can be developed and deployed to critical components and systems in NPPs to perform active measurements with spatial resolution down to 0.5-meter throughout the piping systems. Complex acoustic and vibration signatures harnessed by distributed sensors are registered and analyzed by artificial intelligence algorithms for degradation detection and flaw identification. Piping elbows with machined-in flaws were instrumented with fiber sensors. High-spatial-resolution data were used to develop and validate machine learning algorithms, including both linear and nonlinear regression, and classification. Additionally, classification and sensor analysis were also performed for data analysis. The paper concludes with recommendations and future work on applications of machine learning enabled high-resolution fiber sensors for piping degradation monitoring in current or future NPPs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Best Practices for Resilience in Smart Grid-Interactive Efficient Buildings

The Federal Energy Management Program (FEMP) supports federal agencies' energy decisions with information and guidance on design, funding, and operations to ensure federal buildings are efficient and resilient. The modernization of building infrastructure and the evolution of buildings to support decarbonization involves complex implementation of multiple components across several systems. This includes energy-efficient equipment, on-site energy generation and storage systems, and control systems. These systems have operational modes that can operate more efficiently if they are able to behave responsively to the conditions of the electrical grid. These grid-interactive efficient buildings (GEB) allow facilities to manage power demand according to operational constraints and market signals issued by grid operators. With proper design and planning, these same capabilities have the potential to enable building and facility resilience - coordinating with microgrids, maintaining power on critical circuits to sustain essential operations, and monitoring building health and safety status during an outage. By managing the load of buildings, GEBs can also reduce the cost of backup generation and make better use of renewable power sources on site. This document outlines some of the processes and considerations to guide the design and operation of GEBs in ways that promote facility resilience.

building decarbonization↗

DOE OSTI2021

A Heterogeneous System for Eagle Detection, Deterrent, and Wildlife Collision Detection for Wind Turbines (Final Technical Report)

This report summarizes the design, implementation, and test of an integrated system for automated detection and deterrence of eagles, with included wind turbine blade strike detection and imaging functionality. A machine learning approach was used in conjunction with a 360° camera system for automated detection and classification of golden eagles. This was developed using footage obtained from trained golden eagles and other raptors, in collaboration with wildlife biologists and professional bird handlers. Oregon State University developed a visual deterrent system, which uses inflatable anthropomorphic sculptures with random, kinetic motion to deter eagles, and conducted limited field testing on live eagles; the deterrent can be triggered by the visual detection of eagles using the vision system. Finally, a multi-sensor module was developed that is mounted at the turbine blade root. This module measures vibration and other motions to detect blade strikes, and an integrated on-blade camera captures an image of any impacting objects. Long-term, this blade strike detection system is intended to support an automatic monitoring and certification system for the eagle detection and deterent system. Independent field testing of each system component is described. Testing of the integrated system on an operational wind turbine was conducted across three separate field tests. This includes multi-day fields tests on a General Electric 1.5MW wind turbine at the National Renewable Energy Laboratory (NREL) National Wind Technology Center (NWTC) in Boulder, CO in October 2018 and July 2019; installation procedures, test procedures, and a summary of collected data are presented. A third multi-day on-turbine field test is also presented, which was performed using a General Electric 1.5MW wind turbine at the North American Wind Research and Training Center (NAWRTC) at Mesalands Community College, Tucumcari, NM in April 2019. Across these field tests, the vision system was demonstrated using unmanned aerial vehicles (UAV), and the eagle classification algorithm was not tested; the visual deterrent system was demonstrated, including automatic, remote deployment following surrogate visual detections; and, multi-sensor on-blade data was recorded across multiple wind turbine operational conditions and through more than 100 surrogate blade strikes using soft projectiles, including the successful demonstration of automatic image capture of striking objects. This data set was also used for offline development and validation of enhanced collision detection algorithms. As summarized in this report, the development and field validation of an integrated detection, deterrent, and blade collision detection system represents a critical proof of concept for future technology development of related detection and deterrent technologies, where both deterrent as well as collision detection recording devices are needed for future siting, monitoring, and operation of wind turbine installations, both onshore and offshore.

17 WIND ENERGY↗

DOE OSTI2022

Chemostatic concentration–discharge behaviour observed in a headwater catchment underlain with discontinuous permafrost

Concentration–discharge dynamics were evaluated in a small (~ 2.25 km2) headwater catchment underlain with discontinuous permafrost on the Seward Peninsula of western Alaska. A large storm, during which 48 mm of rain fell over a 24-h period, enabled the evaluation of solute concentration–discharge response to a sizeable hydrological event, while water stable isotopes enabled an appraisal of the contributions of event water. Under normal catchment conditions, chemostatic behaviour was observed for solutes typically derived from mineral weathering (e.g. calcium, magnesium, sodium and silica). The chemostatic behaviour observed for most solutes under normal catchment conditions indicated that catchment storage and residence times are sufficiently long for many solute generating reactions to approach equilibrium. Following the storm however, most solutes exhibited dilutive and highly variable behaviour. This likely indicated the exceedance of a discharge threshold where chemostatic behaviour could no longer be maintained for most solutes. Dissolved organic carbon and silica were the only solutes monitored to exhibit chemostatic behaviour during all time periods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Effect of decades of corrosion on the microstructure of altered glasses and their radiation stability

Understanding the microstructural evolution of glasses during their interaction with water and radiation is of fundamental importance in addressing the corrosion of nuclear waste forms under geological disposal conditions. Here we report the results of more than 21 years of corrosion of two borosilicate glasses showing the formation of mesoporous C–S–H gels in Ca-bearing glasses and a mainly microporous microstructure in Al-bearing glasses. These porous corroded glasses were then irradiated with heavy ions to simulate the effects of recoil nucleus damage and monitored in real time using transmission electron microscopy with in situ ion irradiation. The ballistic collisions remarkably healed the porous corroded glasses to a pore-free homogeneous microstructure. Besides providing new insights and predictions about how doped glasses and actual waste forms may evolve under corrosion and irradiation, the results highlight the non-universal nature of the existing corrosion models and the important role that the glass composition and radiation damage play in the evolution of the microstructure during corrosion.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Statistical Performance of Forced Oscillation Detectors in the Presence of Missing Measurements

In bulk power systems, measurement-based monitoring for large oscillations can help maintain system reliability. One of the challenges encountered in a recent field demonstration was the unavailability of measurements due to underlying measurement quality or communication problems. During the demonstration, the oscillation detector ignored a measurement location if even 10 seconds of data was missing. To extend the detector's ability to operate in these conditions, this paper evaluates the impact of three methods for addressing missing data. The strengths and weaknesses of each approach are evaluated using theoretical expressions for the probability of detection along with results from simulated data and publicly available field measurements. Based on these results, a suitable approach is identified that can extend the oscillation detector's performance when large segments of data are missing.

Follum, James D.↗

DOE OSTI2021

The physiological acclimation and growth response of Populus trichocarpa to warming

Plant metabolic acclimation to thermal stress remains underrepresented in current global climate models. Gaps exist in our understanding of how metabolic processes (i.e., photosynthesis, respiration) acclimate over time and how aboveground versus belowground acclimation differs. We measured the thermal acclimation of Populus trichocarpa, comparing aboveground versus belowground physiology over time. Ninety genetically identical ramets were propagated in mesocosms that separated root and microbial components. After establishment at 25°C for 6 weeks, 60 clones were warmed +4 or +8°C and monitored for 10 weeks, measuring photosynthesis (A), leaf respiration (R), soil respiration (R s ), root plus soil respiration (R s+r ), and root respiration (R r ). We observed thermal acclimation in both A and R, with rates initially increasing, then declining as the thermal photosynthetic optimum (T opt ) and the temperature-sensitivity (Q 10 ) of respiration adjusted to warmer conditions. Photosynthetic acclimation was constructive, based on an increase in both T opt and peak A. Belowground, R s+r decreased linearly with warming, while R s rates declined abruptly, then remained constant with additional warming. Plant biomass was greatest at +4°C, with 30% allocated belowground. Rates of mass-based R r were similar among treatments; however, root nitrogen declined at +8°C leading to less mass nitrogen-based R r in that treatment. The Q 10 -temperature relationship of R r was affected by warming, leading to differing values among treatments. Aboveground acclimation exceeded belowground acclimation, and plant nitrogen-use mediated the acclimatory response. Results suggest that moderate climate warming (+4°C) may lead to acclimation and increased plant biomass production but increases in production could be limited with severe warming (+8°C).

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Virtual Metering for Monitoring Building Energy Consumption

The United States Department of Energy (DOE) has standard metering requirements of commercial buildings for optimizing energy performance. The guiding principles are to continuously track and optimize energy performance and install building-level meters for electricity, natural gas, and steam. Some buildings at Los Alamos National Laboratory (LANL) have physical submeters monitoring their energy consumption, but these meters have proven to be unreliable. And, in most cases, replacing them has proven to be a slow process. Installing new submeters also requires a temporary lockout of the circuit on which they are being installed. Many buildings at LANL contain laboratories with ongoing experiments or data centers, which makes an equipment power outage nearly impossible to plan. This inability to plan power outages results in long-term submeter failures. Although most submeters are eventually replaced, failures lead to missing consumption data for some unpredictable, extended time. A building automation system (BAS) is a system that provides control and monitoring on a building to maintain the operational performance of the building and occupancy comfort. Many buildings at LANL currently have a BAS, and all new renovations and installs will include installing a BAS if one does not already exist. The intended purpose for a BAS is primarily to monitor the health and efficiency of a building; however, it is also possible to calculate equipment power and energy consumption using BAS information. This project aims to use virtual meters to monitor building energy consumption as a cost-effective and minimally labor-intensive alternative to installing physical submeters. The fault detection and diagnostics tool, SkySpark, provides a centralized database for all the data from the various BAS that are active at LANL. This data includes the information that is needed to create virtual meters for heating, ventilating, and air conditioning (HVAC) systems in most buildings, including heating and cooling loads. 9 This report begins with a detailed summary of the project, including the reasoning, procedure, and results. The specific processes of creating the various virtual meters are then identified. Then the limitations are discussed. And, lastly, the results and future potential are presented.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Environmental Testing of SolarSCADA's SSI Data Acquisition System

In April of 2022, SolarSCADA, Inc., was awarded voucher funding to conduct research and analysis at the National Renewable Energy Laboratory (NREL) per the American-Made Solar Prize. SolarSCADA produces a fully integrated data acquisition system that is designed specifically for PV fields and includes a data monitoring platform. SolarSCADA is focused providing the SSI unit with all sensors/measurements per requirements in IEC-61724-1 [1] and with utility scale bankability. The objective of the SolarSCADA voucher funding was established as follows: Leverage NREL's existing calibration and environmental testing capabilities to evaluate the stability of the SSI channels and overall systeme in response to extreme environmental conditions associated with fielded PV.

14 SOLAR ENERGY↗

DOE OSTI2020

Checkpoint triggering in a computer system

According to an aspect, a method for triggering creation of a checkpoint in a computer system includes executing a task in a processing node of the computer system. A monitoring block size is determined for the checkpoint. A checkpoint interval is determined based on the monitoring block size, a checkpoint bandwidth, and a failure rate of the computer system. Based on determining that the checkpoint interval has elapsed, the checkpoint including state data of the task is created to enable restarting execution of the task upon a restart operation. The state data of the checkpoint is restored from a memory responsive to detecting an error condition at the processing node. Execution of the task is restarted in the processing node based on the state data restored from the memory.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

The Impact of Behind-the-Meter Heterogeneous Distributed Energy Resources on Distribution Grids

The increasing integration of distributed energy resources (DERs) on the electric grid brings new challenges and opportunities for utility grid operations. With the rapid deployment of DERs, there is emerging interest in integrating these controllable devices with utility operations at all levels for monitoring and management. To understand the challenges with increasing behind-the-meter (BTM) DERs and to identify the needs in deploying advanced controls, a comprehensive grid impact study is indispensable. This paper presents the analysis which help visualize the DER impact on the grid, identify the challenges and provides an insight into the new distribution management and control needs to enable reliable and resilient grid operations.

behind-the-meter↗

DOE OSTI2023

Amplified drought trends in Nepal increase the potential for Himalayan wildfires

Here we report in the spring 2021, Nepal underwent a record wildfire season in which active fires were detected at a rate 10 times greater than the 2002–2020 average. Prior to these major wildfire events, the country experienced a prolonged precipitation deficit and extreme drought during the post-monsoon period (starting in October 2020). An analysis using observational, reanalysis, and climate model ensemble data indicates that both climate variability and climate change-induced severe drought conditions were at play. Further analysis of climate model outputs suggests the likely reoccurrence of drought conditions, thus favoring active wildfire seasons in Nepal throughout the twenty-first century. While the inter-model uncertainty is large and direct modeling of wildfire spread and suppression has not been completed, the demonstrated relationship between a drought index (the standardized precipitation and evapotranspiration index) and subsequent fire activity may offer actionable opportunities for forest managers to employ the monitoring and projection of climate anomalies at sub-seasonal to decadal timescales to inform their management strategies for Nepal’s wildlands.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

In-pile thermal conductivity of uranium dioxide at low burnup

In-pile thermal conductivity of uranium dioxide (UO 2 ) was investigated at low burnup levels (<0.2 kWd/kg-UO 2 ) to elucidate the concurrent effects of the fission damage and the thermal recovery. In-pile experiments were performed in the Halden reactor. The fuel centerline and the cladding temperatures were measured during the experiments, and the power level of specially designed test rodlets were also monitored. The uncertainty of the pellet-cladding gap's thermal resistivity was minimized with a liquid-metal bond, while comparable measurements were made in reference test rodlets without liquid metal bonding. The experimental data were analyzed using the inverse heat transfer approach. The heat conduction equation was solved by applying the measured temperature of the cladding as a boundary condition, and the calculated fuel centerline temperature was compared with the measured fuel centerline temperature to determine the relative matrix resistivity. Results showed that the in-pile matrix resistivity, the phonon-lattice interaction terms of the thermal conductivity, was ~1.5–2.5 times higher than its unirradiated value due to fission-induced damage. Finally, the in-pile annealing caused a significant recovery of the matrix resistivity, and the amount of recovery increased from ~20 to ~33%, while the annealing temperature increased from 700 to 1,000 °C.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Estimation of sensor measurement errors in reactor coolant systems using multi-sensor fusion

A nuclear power plant is typically instrumented with a variety of sensors to continually monitor its variables, and their sensor’s measurements may be used to assess the plant state and initiate safety actions, if needed. Errors in sensor measurements, due to factors such as calibration drifts, critically affect such state assessments. Here, we address a problem of estimating sensor errors using physics-informed machine learning methods that use measurements collected under known plant conditions. For a given sensor, we propose an information fusion method that uses measurements from other sensors to estimate its output assuming it is error-free and provides its difference from an actual measurement as an error estimate. We present the ensemble of trees and support vector machine fusers, and evaluate their performance using measurements collected over an emulated test loop of a pressurized water reactor. The plant variables are related to each other through the underlying physical laws under inertial constraints that place bounds on their derivatives, which analytically justify the applicability of machine learning methods for computing these fusers. Under twenty scenarios, we assess their sensor error estimates for pressure sensors of the heat exchanger of a reactor’s primary coolant system. Multiple types of errors are captured by both fusers under externally induced calibration drifts, blockages, minor leaks and air gaps in sensing lines, and electromagnetic interference; the root mean square error of the estimation of error is under 2.2% percent of the maximum measurement. We present generalization equations, in the framework of statistical learning theory, for these methods that characterize the confidence probability that the estimation error is bounded by a specified parameter in future test scenarios.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2026

Advancing Asset Management in Water Infrastructure Systems

Aging water system infrastructure, including drinking water, wastewater, and stormwater, poses a growing challenge for utilities and municipalities. These water systems have well documented challenges with respect to their age, condition, and level of service. ASCE annual report cards consistently rate these infrastructure systems in the United States as underfunded, overcapacity, or past service life (ASCE 2025 Report Card). For example, Chini and Stillwell (2017) estimated that the mean water loss in drinking water systems, i.e., non-revenue water, is approximately 16% across the United States. These concerns are not just relegated to the United States, with Courtenay, British Columbia, identifying 17% of their water main pipes as in a ‘poor’ condition state, defined as a category condition 5 out of 5 (City of Courtenay, 2024). These cases illustrate the challenges utilities are facing to manage extensive networks of infrastructure to deliver a consistent and high level of service. For buried infrastructure such as water systems, studies suggest that preventative interventions can lead to lower maintenance costs and fewer service disruptions (Mazumder et al, 2018; Li et al, 2014). The demonstrated need and benefit of appropriately applied asset management is juxtaposed against the relatively sparse literature that evaluates water systems within an asset management construct. Since 2020, just 37 papers specifically reference asset management in the Journal of Water Resources Planning and Management. Of those, only a few specifically look to develop strategies for improved asset management. Therefore, we highlight four key research areas that represent opportunities for advancement of asset management research for water systems. First, advances in condition assessment and forecasting are needed to better estimate asset deterioration using diverse datasets. Second, machine learning (ML) and artificial intelligence (AI) hold promise for predictive maintenance and investment prioritization, though questions of generalizability and model transparency remain. Third, applying a value of information framework can guide utilities in making cost-effective sensor deployment and data collection decisions, to direct monitoring strategies towards data-informed asset management decisions. Finally, integrated infrastructure management is critical, requiring coordinated planning with other infrastructure systems and stakeholder engagement to reduce costs and enhance service delivery.

Chini, Christopher M.↗