Results for “sensor performance” — Page 24

DOE OSTI2025

Reinforcement Learning for Anomaly Detection in Nuclear Power Plant Operation and Maintenance

In nuclear power plants (NPPs), timely identification of sensor and human errors is critical to ensure safe and efficient plant operations. Anomaly detection models can be employed for this task. However, traditional anomaly detection approaches may have high dependency on labeled datasets and struggle with adaptability in complex, dynamic environments. Reinforcement learning (RL) has demonstrated significant potential in fault diagnosis and anomaly detection; however, its application to anomaly detection in NPPs remains a relatively underexplored research direction. Hence, to address this gap, in this study, we present a novel physics-informed reinforcement learning model, PIRL-AD: Physics-Informed Reinforcement Learning for Anomaly Detection, that integrates domain knowledge from calorimetric equations into the RL framework for enhanced sensor and human error anomaly detection. We evaluate the performance of PIRL-AD against a non-physics informed RL benchmark and a support vector machine (SVM) on data collected from a forced flow loop testbed. Experimental results suggest that PIRL-AD outperforms other baselines on a range of anomalous datasets that include both sensor and human-induced anomalies across key performance metrics, statistically outperforming the RL and SVM benchmarks with respect to geometric mean (respectively, 92.96% vs. 91.06% vs. 83.01%) and F1-score (respectively, 89.23% vs. 86.98% vs. 77.01%). Furthermore, the findings suggest the potential of physics-integrated reinforcement learning models for enhanced anomaly detection performance in NPPs.

Reinforcement learning↗

DOE OSTI2020

Sensor Impacts Evaluation and Verification: Expert Interview Responses

The sensor configuration/deployment method has critical impacts on energy efficient building control and thermal comfort. However, traditional sensor techniques for building operation and fault detection and diagnostics (FDD) are not necessarily optimal in terms of energy efficiency and thermal comfort, and their global effects are not thoroughly investigated. In an effort to address and overcome this limitation, a multilaboratory and multiyear project, “Sensor Impact Evaluation and Verification,” was proposed. Its purpose is to develop a framework to investigate the impacts of sensor deployment and configuration on building energy optimization, FDD, occupant thermal comfort, and potentially grid efficiency. The first project task was a literature review to establish a solid knowledge of and a background related to sensor technologies and placement. To accomplish this task, an extensive review of previous research literature was performed. A series of expert interviews were conducted to augment the findings of the literature review. This report summarizes the interview design and interview results and findings. The interview was designed and performed to (1) investigate the current status and limitations of sensor configuration, (2) identify the research gaps and expectations for potential improvements in sensor configuration and deployment, and (3) integrate expert (e.g., researcher, building operation practitioner) knowledge and experience to develop use-case scenarios.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Deliberate Motion Analytics Fused Radar and Video Test Results Deployed Beyond the Perimeter Fence in a High Noise Environment

Security systems that protect the nation’s critical facilities must be capable of detecting physical intrusions in all weather conditions. Intrusion detection sensors in a perimeter with a high nuisance alarm rate (NAR) significantly undermine detection performance and degrade security system effectiveness. This research demonstrated a fused sensor system that can differentiate foliage and weather-induced nuisance alarms from those caused by intruders, providing reliable detection within a two-fence perimeter or beyond the fence. A key element of this work is the creation and application of a “deliberate motion algorithm” that fuses alarm data from radar and video analytics to create video motion detection fused radar system. The two-layer architecture of the algorithm uses machine learning, multi-hypothesis tracking, and Dynamic Bayes Nets to differentiate intruder alarms from weather induced alarms.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

A machine learning approach to water quality forecasts and sensor network expansion: Case study in the Wabash River Basin, United States

Abstract Midwestern cities require forecasts of surface nitrate loads to bring additional treatment processes online or activate alternative water supplies. Concurrently, networks of nitrate monitoring stations are being deployed in river basins, co‐locating water quality observations with established stream gauges. However, tools to evaluate the future value of expanded networks to improve water quality forecasts remains challenging. Here, we construct a synthetic data set of stream discharge and nitrate for the Wabash River Basin—one of the United States’ most nutrient polluted basins—using the established Agro‐IBIS and THMB models. Synthetic data enables rapid, unbiased and low‐cost assessment of potential sensor placements to support management objectives, such as near‐term forecasting. Using the synthetic data, we established baseline 1‐day forecasts for surface water nitrate at 12 cities in the basin using support vector machine regression (SVMR; RMSE 0.48–3.3 ppm). Next, we used the SVMRs to evaluate the improvement in forecast performance associated with deployment of additional nitrate sensors. We identified the optimal sensor placement to improve forecasts at each city, and the relative value of sensors at each candidate location. Finally, we assessed the co‐benefit realized by other cities when a sensor is deployed to optimize a forecast at one city, finding significant positive externalities in all cases. Ultimately, our study explores the potential for machine learning to make near‐term predictions and critically evaluate the improvement realized by expanding a monitoring network. While we use nitrate pollution in the Wabash River Basin as a case study, this approach could be readily applied to any problem where the future value of sensors and network design are being evaluated.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Multimodal sensor fusion framework for residential building occupancy detection

For several years now, smart building energy systems have been a research area of intensive activity. In light of the increasing need for sustainable buildings and energy systems, this trend motivates an increasing need for a solution to reduce carbon dioxide emissions and improve energy efficiency. This work proposes a high-performing and transferable occupancy detection framework that combines sensor data from different data modalities, including time series environmental data (temperature, humidity, and illuminance), image data, and acoustic energy data using ensemble method. To draw out the best prediction performance in each modality, the proposed framework was developed, including various models that were designed to learn the occupancy patterns reflected in the physical data streams. To tackle the time series environmental data, we designed two variants of an occupancy detection spatiotemporal pattern network (Occ-STPN) that performs both feature level and decision level fusion, respectively. We also propose a new metric; the fading memory mean square error (FMMSE), that provides a fair evaluation and penalization of delayed occupancy predictions. Multiple open-sourced datasets, including the Electricity Consumption and Occupancy and the University of California, Irvine's (UCI) building occupancy detection dataset, along with our own real data collected from six different houses, were used to validate the algorithms' performance. The experimental results presented herein break down the performance for each sensing modality, and a detailed analysis of the performance is also discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Testing and Characterization to Develop a Mechanistic Explanation for Unsaturated Drift of Fiber Optic Sensors during High-Dose Irradiation

The primary limitation for any optical fiber-based sensor for nuclear reactor applications is radiation-induced attenuation (RIA) of the transmitted and/or reflected signals. Based on several recent studies, RIA is tolerable for some fused silica optical fibers with the proper choice of sensing wavelength and fiber dopants. For extreme temperature applications (> 1000°C), sapphire optical fibers have been proposed; however, recent optical transmission measurements performed on bulk sapphire samples showed prohibitively large RIA. For some sensors, radiation-induced dimensional changes in the fiber materials can also cause significant drift. Moreover, the drift that was observed in numerous experiments performed in the High Flux Isotope Reactor (HFIR), the Advanced Test Reactor, the Massachusetts Institute of Technology Reactor, and other international facilities far exceeded what would be expected based on compaction of fused silica glass. Clearly, additional work is needed to better understand the origins of both RIA and radiation-induced drift in both silica and sapphire optical fiber-based sensors before these sensors can be reliably deployed for nuclear applications. This work evaluated the underlying mechanisms that may be responsible for RIA and drift in silica and sapphire materials. First, detailed characterization was performed on bulk fused silica glass samples that were previously irradiated to different neutron fluences at different temperatures to better understand the structural changes that drive radiation-induced drift in the absence of coating effects that are discussed later. Results show that the non-monotonic compaction that occurs with increasing neutron fluence continues up to fast neutron fluences approaching 10 22 n/cm 2 , which has important implications for physics-based models that may be used to compensate for the sensor drift. Initial Raman spectroscopy and synchrotron x-ray diffraction provide insights into the nature of the structural changes. Next, detailed characterizations were performed on silica fibers with various coatings that were subjected to several different thermal treatments. The hypothesis is that the coatings convert to carbon-rich materials that compact under irradiation, putting a large compressive strain on the fiber. Out-of-pile testing confirms that both polyimide and acrylate fiber coatings convert to glassy carbon (GC) materials when heated under inert conditions, and the degree of order (i.e., graphitization) increases with increasing temperature. The results provide increasingly strong evidence that the combination of polymeric coatings and inert (or vacuum) conditions render fiber optic sensors susceptible to significant radiation-induced drift that would not otherwise exist in uncoated fibers. Finally, transmission electron microscopy was performed on bulk sapphire samples that were irradiated to two neutron fluences at different temperatures to gain insights into the potential mechanisms driving the prohibitive RIA at higher neutron fluences and temperatures. Contrary to previous hypotheses, results show that scattering from radiation-induced voids cannot explain the observed RIA. Similarly, models for scattering losses from dislocation loops also do not agree with the experimental results. Instead, fitting to the experimental data shows that increased absorption from aluminum vacancy centers is the most likely explanation for the the prohibitively large RIA that was observed at high irradiation temperature and dose. In addition, the voids that formed in these single-crystal samples were found to align along the basal plane (a-axis) as opposed to that seen in previous observations of c-axis alignment in polycrystalline samples, which could have important implications for anisotropic swelling and other phenomena that could affect sensor performance at high neutron fluence.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Testing and Characterization to Develop a Mechanistic Explanation for Unsaturated Drift of Fiber Optic Sensors during High-Dose Irradiation

The primary limitation for any optical fiber-based sensor for nuclear reactor applications is radiation-induced attenuation (RIA) of the transmitted and/or reflected signals. Based on several recent studies, RIA is tolerable for some fused silica optical fibers with the proper choice of sensing wavelength and fiber dopants. For extreme temperature applications (> 1000°C), sapphire optical fibers have been proposed; however, recent optical transmission measurements performed on bulk sapphire samples showed prohibitively large RIA. For some sensors, radiation-induced dimensional changes in the fiber materials can also cause significant drift. Moreover, the drift that was observed in numerous experiments performed in the High Flux Isotope Reactor (HFIR), the Advanced Test Reactor, the Massachusetts Institute of Technology Reactor, and other international facilities far exceeded what would be expected based on compaction of fused silica glass. Clearly, additional work is needed to better understand the origins of both RIA and radiation-induced drift in both silica and sapphire optical fiber-based sensors before these sensors can be reliably deployed for nuclear applications. This work evaluated the underlying mechanisms that may be responsible for RIA and drift in silica and sapphire materials. First, detailed characterization was performed on bulk fused silica glass samples that were previously irradiated to different neutron fluences at different temperatures to better understand the structural changes that drive radiation-induced drift in the absence of coating effects that are discussed later. Results show that the non-monotonic compaction that occurs with increasing neutron fluence continues up to fast neutron fluences approaching 10 22 n/cm 2 , which has important implications for physics-based models that may be used to compensate for the sensor drift. Initial Raman spectroscopy and synchrotron x-ray diffraction provide insights into the nature of the structural changes. Next, detailed characterizations were performed on silica fibers with various coatings that were subjected to several different thermal treatments. The hypothesis is that the coatings convert to carbon-rich materials that compact under irradiation, putting a large compressive strain on the fiber. Out-of-pile testing confirms that both polyimide and acrylate fiber coatings convert to glassy carbon (GC) materials when heated under inert conditions, and the degree of order (i.e., graphitization) increases with increasing temperature. The results provide increasingly strong evidence that the combination of polymeric coatings and inert (or vacuum) conditions render fiber optic sensors susceptible to significant radiation-induced drift that would not otherwise exist in uncoated fibers. Finally, transmission electron microscopy was performed on bulk sapphire samples that were irradiated to two neutron fluences at different temperatures to gain insights into the potential mechanisms driving the prohibitive RIA at higher neutron fluences and temperatures. Contrary to previous hypotheses, results show that scattering from radiation-induced voids cannot explain the observed RIA. Similarly, models for scattering losses from dislocation loops also do not agree with the experimental results. Instead, fitting to the experimental data shows that increased absorption from aluminum vacancy centers is the most likely explanation for the the prohibitively large RIA that was observed at high irradiation temperature and dose. In addition, the voids that formed in these single-crystal samples were found to align along the basal plane (a-axis) as opposed to that seen in previous observations of c-axis alignment in polycrystalline samples, which could have important implications for anisotropic swelling and other phenomena that could affect sensor performance at high neutron fluence.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Medium Voltage Testbed for Comparing Advanced Power Line Sensors vs. Transformers with Electrical Grid Events

Electrical utilities have relied upon potential transformers (PTs) and current transformers (CTs) for very accurate metering and to provide reliable signals for protective relays. These devices measure phase voltages and currents and are commissioned by electrical engineers. PTs/CTs can detect and react to various electrical anomalies that could adversely affect electrical grid operations. Less expensive alternative sensing technologies offer the possibility of wider deployment, particularly in grids that employ distributed energy resources. In this work, the performance of an advanced medium-voltage sensor is compared with that of a reference PT and a CT and experimentally evaluated for different power grid scenarios on an advanced outdoor power line sensor testbed at the U.S. Department of Energy’s Oak Ridge National Laboratory. The sensor is based on a capacitive divider for voltage monitoring and a Rogowski coil with an integrator for current monitoring. This study simulated a power grid model based on a utility circuit at the Riverside EPB of Chattanooga. The simulation circuit was created with MATLAB/Simulink software and was integrated into an RT-LAB project to run with the OP4510 real-time simulator at the OPLST. During the tests, the real-time simulations were run for 40 s, and the signal to record the test event with the power meter was set at 30 s for the event-trigger circuit. The advanced outdoor power line sensor testbed has a real-time simulator that is used to generate transient scenarios (e.g., electrical faults, capacitor bank operation, and service restoration), while the analog signals are recorded by the same high-resolution power meter. The behaviors of analog signals, harmonic components, total harmonic distortion, and crest factors are assessed for this power line sensor and compared with those of the reference PT/CT because of the absence of testing standards for advanced outdoor power line sensors. The results showed that this OPLS technology responded identically to the PT and CT under all conditions.

Piesciorovsky, Emilio [ORNL]↗

DOE OSTI2022

Additively manufactured strain sensors for in-pile applications

Accurate, real-time monitoring of strain in fuel, cladding, and structural components of nuclear reactors is critical to better understand radiation induced phenomena during reactor tests and operations. The data provided is crucial to verify physics-based, multiscale modeling and simulation efforts, which aim to shorten the timeline for the development of new nuclear materials. Resistive strain gauges have limited performance during in-pile experiments due to the harsh operating conditions and limited physical space between fuel and cladding components. In this work, aerosol jet printing using silver nanoparticle inks was used to fabricate interdigitated electrode capacitive strain gauges on aluminum alloy 6061 tensile specimens. Here to simulate the temperatures of a traditional light water reactor, the capacitive strain gauges were tested with a mechanical test frame up to 300 °C and compared to commercially available bondable resistive strain gauges. The printed capacitive strain gauges exhibited a gauge factor of 1.0 and showed higher reproducibility and predictability of strain sensing performance than the resistive strain gauge. The results demonstrate the potential of aerosol jet printing to fabricate strain sensors with predictable performance and reduced invasiveness for high-temperature applications with confined spacing.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Data and Scripts associated with: “Laboratory evaluation of open source and commercial electrical conductivity sensor precision and accuracy”

This data package is associated with the publication “Laboratory evaluation of open source and commercial electrical conductivity sensor precision and accuracy: How do they compare?” submitted to PLOS ONE and accepted for publication (Fulton, S.G. et al. 2023; doi not yet available).Variation in electrical conductivity (EC) of water is important to reveal environmental disturbance and natural dynamics, including factors such as anthropogenic salinization. This data package supports a study addressing the need for a robust performance assessment of open source (OS) EC sensors. We evaluated the accuracy (mean error, %) and precision (sample standard deviation) of OS EC sensors in the laboratory via comparison to EC calibration standards using three different OS and OS/commercial-hybrid (OS/C) EC sensors and data logger configurations and two commercial (C) EC sensors and data logger configurations. We also evaluated the effect of cable length (7.5 m and 30 m) and sensor calibration on OS sensor accuracy and precision. This data package presents the results from the different testing laboratory configurations. It also includes an R script for statistical analysis of the data and an Arduino IDE file used to calibrate and collect data with the OS Atlas EC sensor. File types are .csv, .pdf, .R, and .ino.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Numerical Investigation of Butterfly Valve Performance in Variable Valve Sizes, Positions and Flow Regimes

Reliability and efficiency of valves are necessary for precise control and sufficient heat-flow to heat application plants for the integrated energy systems of nuclear power plants (NPPs). Strategic Management Analysis Requirement and Technology (SMART) valves’ ability to control flow and assess environmental parameters stands out for these requirements. Their ability to sustain the downstream flow rate, prevent reverse flow, and maintain pressure in the heat transport loop is much more efficient with the integration of sensors and intelligent algorithms. For assessing valve performance and monitoring, mechanical design and operating conditions are two important parameters. In this study, the butterfly valves of three different sizes are simulated with water and steam using STAR-CCM+ in various flow regimes and positions to analyze performance parameters to strategize an automated control system for efficiently balancing the heat–transport network. Also, flow behavior is studied using velocity and pressure fields for valve–body geometry optimization. It can be observed, through performance parameters, that the valves are suitable for operation between 30° and 90° positions with significantly low loss coefficients and high flow coefficients, and the performance parameters follow a certain pattern in both water and steam flow in each scenario.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2026

Geothermal Heat Pump System Showcase: Short-Term Validation of Borehole Heat Exchanger Performance from Field Data to Numerical Modeling: Preprint

Since 2011, a geothermal heat pump (GHP) system has been operating to provide space heating and cooling for the Solar Radiation and Research Laboratory building at the National Laboratory of the Rockies (NLR) in Golden, Colorado. The system consists of 23 vertical boreholes, each extending to a depth of 300 ft (91 m), connected to 11 water-to-air heat pump units and four circulation pumps. Between fiscal years 2023 and 2025, additional power meters and temperature sensors were retrofitted to support detailed system performance assessment and model development. This study presents preliminary monitoring results and the development of an initial numerical model of the borehole heat exchanger field. The model incorporated site-specific geometry, ground thermal properties derived from thermal response tests, and ambient temperatures, and simulated system behavior over a representative operating day in September. Model predictions of outlet temperatures were compared against corresponding field measurements. Results showed that modeling initialized with a simplified linear subsurface temperature gradient presents systematic discrepancies in outlet temperature, whereas incorporating depth-resolved borehole temperature measurements for initialization yields substantially improved agreement with observations. The findings highlight the sensitivity of short-term predictive modeling to the representation of initial subsurface thermal conditions and underscore the value of high-resolution field measurements for model calibration and validation. These preliminary results inform ongoing efforts to extend the modeling framework to longer time horizons and to refine monitoring and modeling strategies that support the design guidance and operational optimization of GHP systems in research and commercial buildings.

15 GEOTHERMAL ENERGY↗

DOE OSTI2020

Strategies for High-Performance Solid-State Triplet–Triplet-Annihilation-Based Photon Upconversion

Not provided.

Chemistry↗

DOE OSTI2024

Convergence of Emerging Technologies - EAGL Test Information

The Emergency Automatic Gunshot Detection and Lockdown (EAGL) system provides automatic, autonomous, and timely gunshot detection in both indoor and outdoor environments. This system uses both wired and wireless devices. Self-contained wireless EAGL sensors passively “listen” for gunshot events. These devices also perform a single, daily supervisory heartbeat (HB) function to include a device self-check with reporting capability. Transmissions are received by an assigned EAGL Gateway, which translates the RF sensor data to a PoE network format solely for use by the EAGL system server. The server then performs additional processes after data receipt, which include but are not limited to: event validation and logging, GUI presentation, notifications, and other independent operations.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Beam test performance of prototype silicon detectors for the Outer Tracker for the Phase-2 Upgrade of CMS

A new CMS tracker detector will be installed for operation at the High Luminosity LHC (HL-LHC). This detector comprises modules with two closely spaced parallel sensor plates and front-end ASICs capable of transmitting tracking information to the CMS Level-1 (L1) trigger at the 40 MHz beam crossing rate. The inclusion of tracking information in the L1 trigger decision will be essential for selecting events of interest efficiently at the HL-LHC. The CMS Binary Chip (CBC)has been designed to read out and correlate hits from pairs of tracker sensors, forming so called track stubs. For the first time, a prototype irradiated module and a full-sized module, both equipped with the version 2 of the CBC, have been operated in test beam facilities. The efficiency of the stub finding logic of the modules for various angles of incidence has been studied. The ability of the modules to reject tracks with transverse momentum less than 2 GeV has been demonstrated. For modules built with irradiated sensors, no significant drop in the stub finding performance has been observed. Results from the beam tests are described in this paper.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Low-Power, Flexible Sensor Arrays with Solderless Board-to-Board Connectors for Monitoring Soil Deformation and Temperature: Supporting Data

This dataset was used to assess the potential of a Soil Deformation and Temperature Monitoring System developed and presented in the article named "Low-Power, Flexible Sensor Arrays with Solderless Board-to-Board Connectors for Monitoring Soil Deformation and Temperature" and published in Sensors. There are 21 comma-delimited data files (.csv). 14 files contain current measurements performed in a lab setting, enabling the electrical evaluation of the sensor probe. These files are generated by the Keithley DMM6500 multimeter, and list the measured supply current (first column) as a function of time (3rd column). Another set of 6 files is also acquired in lab experiments, but contain soil temperature an deformation measurements, enabling an assessment of the developed device's accuracy. In these files, the first column contains the time (UTC), followed by the sensor's battery voltage and temperature and acceleration values (X, Y, Z) in subsequent columns. The measurements were acquired every 5 seconds. Another .csv file contains field data in a similar format (time, temperature, acceleration) collected at the Teller road (mile 27) site near Nome, Alaska with one probe. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Boron-Based Neutron Scintillator Screens for Neutron Imaging

In digital neutron imaging, the neutron scintillator screen is a limiting factor of spatial resolution and neutron capture efficiency and must be improved to enhance the capabilities of digital neutron imaging systems. Commonly used neutron scintillators are based on 6 LiF and gadolinium oxysulfide neutron converters. This work explores boron-based neutron scintillators because 10 B has a neutron absorption cross-section four times greater than 6Li, less energetic daughter products than Gd and 6 Li, and lower γ-ray sensitivity than Gd. These factors all suggest that, although borated neutron scintillators may not produce as much light as 6 Li-based screens, they may offer improved neutron statistics and spatial resolution. This work conducts a parametric study to determine the effects of various boron neutron converters, scintillator and converter particle sizes, converter-to-scintillator mix ratio, substrate materials, and sensor construction on image quality. The best performing boron-based scintillator screens demonstrated an improvement in neutron detection efficiency when compared with a common 6 LiF/ZnS scintillator, with a 125% increase in thermal neutron detection efficiency and 67% increase in epithermal neutron detection efficiency. The spatial resolution of high-resolution borated scintillators was measured, and the neutron tomography of a test object was successfully performed using some of the boron-based screens that exhibited the highest spatial resolution. For some applications, boron-based scintillators can be utilized to increase the performance of a digital neutron imaging system by reducing acquisition times and improving neutron statistics.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Uncertainty-Guided Prediction Horizon of Phase-Resolved Ocean Wave Forecasting Under Data Sparsity: Experimental and Numerical Evaluation

Accurate short-term wave forecasting is critical for the safe and efficient operation of marine structures that rely on real-time, phase-resolved ocean wave information for control and monitoring purposes (e.g., digital twins). These systems often depend on environmental sensors (e.g., waverider buoys, wave-sensing LIDAR). Challenges arise when upstream sensor data are missing, sparse, or phase-shifted due to drift. This study investigates the performance of two machine learning models, time-series dense encoder (TiDE) and long short-term memory (LSTM), for forecasting phase-resolved ocean surface elevations under varying degrees of data degradation. We introduce the τ-trimming algorithm, which adapts the prediction horizon based on uncertainty thresholds derived from historical forecasts. Numerical wave tank (NWT) and wave basin experiments are used to benchmark model performance under short- and long-term data masking, spatially coarse sensor grids, and upstream phase shifts. Results show under a 50% probability of upstream data loss, the τ-trimmed TiDE model achieves a 46% reduction in error at the most upstream target, compared to 22% for LSTM. Furthermore, phase misalignment in upstream data introduces a near-linear increase in forecast error. Under moderate model settings, a ±3 s misalignment increases the mean absolute error by approximately 0.5 m, while the same error is accumulated at ±4 s using the more conservative approach. These findings inform the design of resilient, uncertainty-aware wave forecasting systems suited for realistic offshore sensing environments.

42 ENGINEERING↗