Results for “Operator learning” — Page 37

DOE OSTI2026

Co-Simulation Meets AI: MCP-Driven Power System Analysis

GridGPT, a fine-tuned Generative AI model is designed for on-premise use in grid control rooms. This presentation will demonstrate how eGridGPT can seamlessly integrate with control room solutions to offer operators, engineers, and corporate users enhanced guidance and decision support. It is to show how this innovative AI solution can improve state estimation, boost variable energy forecasting, and optimize grid operations. By leveraging eGridGPT's unique features, audience will learn to unlock new levels of automation, predictive analytics, and reliability within their power systems, ultimately leading to reduced downtime and improved operational efficiency.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2016

MO-AB-206-00: Nuclear Medicine Physics and Testing

This education session will cover the physics and operation principles of gamma cameras and PET scanners. The first talk will focus on PET imaging. An overview of the principles of PET imaging will be provided, including positron decay physics, and the transition from 2D to 3D imaging. More recent advances in hardware and software will be discussed, such as time-of-flight imaging, and improvements in reconstruction algorithms that provide for options such as depth-of-interaction corrections. Quantitative applications of PET will be discussed, as well as the requirements for doing accurate quantitation. Relevant performance tests will also be described. Learning Objectives: Be able to describe basic physics principles of PET and operation of PET scanners. Learn about recent advances in PET scanner hardware technology. Be able to describe advances in reconstruction techniques and improvements Be able to list relevant performance tests. The second talk will focus on gamma cameras. The Nuclear Medicine subcommittee has charged a task group (TG177) to develop a report on the current state of physics testing of gamma cameras, SPECT, and SPECT/CT systems. The report makes recommendations for performance tests to be done for routine quality assurance, annual physics testing, and acceptance tests, and identifies those needed satisfy the ACR accreditation program and The Joint Commission imaging standards. The report is also intended to be used as a manual with detailed instructions on how to perform tests under widely varying conditions. Learning Objectives: At the end of the presentation members of the audience will: Be familiar with the tests recommended for routine quality assurance, annual physics testing, and acceptance tests of gamma cameras for planar imaging. Be familiar with the tests recommended for routine quality assurance, annual physics testing, and acceptance tests of SPECT systems. Be familiar with the tests of a SPECT/CT system that include the CT images for SPECT reconstructions. Become knowledgeable of items to be included in annual acceptance testing reports including CT dosimetry and PACS monitor measurements. T. Turkington, GE Healthcare.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2016

MO-AB-206-01: PET Physics

This education session will cover the physics and operation principles of gamma cameras and PET scanners. The first talk will focus on PET imaging. An overview of the principles of PET imaging will be provided, including positron decay physics, and the transition from 2D to 3D imaging. More recent advances in hardware and software will be discussed, such as time-of-flight imaging, and improvements in reconstruction algorithms that provide for options such as depth-of-interaction corrections. Quantitative applications of PET will be discussed, as well as the requirements for doing accurate quantitation. Relevant performance tests will also be described. Learning Objectives: Be able to describe basic physics principles of PET and operation of PET scanners. Learn about recent advances in PET scanner hardware technology. Be able to describe advances in reconstruction techniques and improvements Be able to list relevant performance tests. The second talk will focus on gamma cameras. The Nuclear Medicine subcommittee has charged a task group (TG177) to develop a report on the current state of physics testing of gamma cameras, SPECT, and SPECT/CT systems. The report makes recommendations for performance tests to be done for routine quality assurance, annual physics testing, and acceptance tests, and identifies those needed satisfy the ACR accreditation program and The Joint Commission imaging standards. The report is also intended to be used as a manual with detailed instructions on how to perform tests under widely varying conditions. Learning Objectives: At the end of the presentation members of the audience will: Be familiar with the tests recommended for routine quality assurance, annual physics testing, and acceptance tests of gamma cameras for planar imaging. Be familiar with the tests recommended for routine quality assurance, annual physics testing, and acceptance tests of SPECT systems. Be familiar with the tests of a SPECT/CT system that include the CT images for SPECT reconstructions. Become knowledgeable of items to be included in annual acceptance testing reports including CT dosimetry and PACS monitor measurements. T. Turkington, GE Healthcare.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2016

MO-AB-206-02: Testing Gamma Cameras Based On TG177 WG Report

This education session will cover the physics and operation principles of gamma cameras and PET scanners. The first talk will focus on PET imaging. An overview of the principles of PET imaging will be provided, including positron decay physics, and the transition from 2D to 3D imaging. More recent advances in hardware and software will be discussed, such as time-of-flight imaging, and improvements in reconstruction algorithms that provide for options such as depth-of-interaction corrections. Quantitative applications of PET will be discussed, as well as the requirements for doing accurate quantitation. Relevant performance tests will also be described. Learning Objectives: Be able to describe basic physics principles of PET and operation of PET scanners. Learn about recent advances in PET scanner hardware technology. Be able to describe advances in reconstruction techniques and improvements Be able to list relevant performance tests. The second talk will focus on gamma cameras. The Nuclear Medicine subcommittee has charged a task group (TG177) to develop a report on the current state of physics testing of gamma cameras, SPECT, and SPECT/CT systems. The report makes recommendations for performance tests to be done for routine quality assurance, annual physics testing, and acceptance tests, and identifies those needed satisfy the ACR accreditation program and The Joint Commission imaging standards. The report is also intended to be used as a manual with detailed instructions on how to perform tests under widely varying conditions. Learning Objectives: At the end of the presentation members of the audience will: Be familiar with the tests recommended for routine quality assurance, annual physics testing, and acceptance tests of gamma cameras for planar imaging. Be familiar with the tests recommended for routine quality assurance, annual physics testing, and acceptance tests of SPECT systems. Be familiar with the tests of a SPECT/CT system that include the CT images for SPECT reconstructions. Become knowledgeable of items to be included in annual acceptance testing reports including CT dosimetry and PACS monitor measurements. T. Turkington, GE Healthcare.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2026

Data-Driven Modeling and Correction of Vehicle Dynamics

We develop a data-driven framework for learning and correcting nonautonomous vehicle dynamics. Physics-based vehicle models are often simplified for tractability and therefore exhibit inherent model-form uncertainty, motivating the need for data-driven correction. Moreover, nonautonomous dynamics are governed by time-dependent control inputs, which pose challenges in learning predictive models directly from temporal snapshot data. To address these, we reformulate the vehicle dynamics via a local parameterization of the time-dependent inputs, yielding a modified system composed ofa sequence of local parametric dynamical systems. Here, we approximate these parametric systems using two complementary approaches. First, we employ the dimension reduction and interpolation in parameter space (DRIPS) methodology to construct efficient linear surrogate models, equipped with lifted observable spaces and manifold-based operator interpolation. This enables data-efficient learning of vehicle models whose dynamics admit accurate linear representations in the lifted spaces. Second, for more strongly nonlinear systems, we employ flow map learning (FML), a deep neural network (DNN) approach that approximates the parametric evolution map without requiring special treatment of nonlinearities. We further extend FML with a transfer-learning-based model correction procedure, enabling the correction of misspecified prior models using only a sparse set of high-fidelity or experimental measurements, without assuming a prescribed form for the correction term. Through a suite of numerical experiments on unicycle, simplified bicycle, and slip-based bicycle models, we demonstrate that DRIPS offers robust and highly data-efficient learning of nonautonomous vehicle dynamics, while FML provides expressive nonlinear modeling and effective correction of model-form errors under severe data scarcity.

data-driven modeling↗

DOE OSTI2016

SU-F-T-462: Lessons Learned From a Machine Incident Reporting System

Purpose: Linear accelerators must operate with minimal downtime. Machine incident logs are a crucial tool to meet this requirement. They providing a history of service and demonstrate whether a fix is working. This study investigates the information content of a large department linear accelerator incident log. Methods: Our department uses an electronic reporting system to provide immediate information to both key department staff and the field service department. This study examines reports for five linac logs during 2015. The report attributes for analysis include frequency, level of documentation, who solved the problem, and type of fix used. Results: Of the reports, 36% were documented as resolved. In another 25% the resolution allowed treatment to proceed although the reported problem recurred within days. In 5% only intermediate troubleshooting was documented. The remainder lacked documentation. In 60% of the reports, radiation therapists resolved the problem, often by clearing the appropriate faults or reinitializing a software or hardware service. 22% were resolved by physics and 10% by field service engineers. The remaining 8% were resolved by IT, Facilities, or resolved spontaneously. Typical fixes, in order of scope, included clearing the fault and moving on, closing and re-opening the patient session or software, cycling power to a sub-unit, recalibrating a device (e.g., optical surface imaging), and calling in Field Service (usually resolving the problem through maintenance or component replacement). Conclusion: The reports with undocumented resolution represent a missed opportunity for learning. Frequency of who resolves a problem scales with the proximity of the person’s role (therapist, physicist, or service engineer), which is inversely related to the permanence of the resolution. Review of lessons learned from machine incident logs can form the basis for guidance to radiation therapists and medical physicists to minimize equipment downtime and ensure safe operation.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2021

Lowering post‐construction yield assessment uncertainty through better wind plant power curves

Abstract Many operational analyses of wind power plants require a statistical relationship, which can be called the wind plant power curve, to be developed between wind plant energy production and concurrent atmospheric variables. Currently, a univariate linear regression at monthly resolution is the industry standard for post‐construction yield assessments. Here, we evaluate the benefits in augmenting this conventional approach by testing alternative regressions performed with multiple inputs, at a finer time resolution, and using nonlinear machine‐learning algorithms. We utilize the National Renewable Energy Laboratory's open‐source software package OpenOA to assess wind plant power curves for 10 wind plants. When a univariate generalized additive model at daily or hourly resolution is used, regression uncertainty is reduced, in absolute terms, by up to 1.0 % and 1.2 % (corresponding to a −59 % and −80 % relative change), respectively, compared to a univariate linear regression at monthly resolution; also, a more accurate assessment of the mean long‐term wind plant production is achieved. Additional input variables also reduce the regression uncertainty: when temperature is added as an input to the conventional monthly linear regression, the operational analysis uncertainty connected to regression is reduced, in absolute terms, by up to 0.5 % (−43 % relative change) for wind power plants with strong seasonal variability. Adding input variables to the machine‐learning model at daily resolution can further reduce regression uncertainty, with up to a −10 % relative change. Based on these results, we conclude that a multivariate nonlinear regression at daily or hourly resolution should be recommended for assessing wind plant power curves.

17 WIND ENERGY↗

DOE OSTI2023

Model-Free Probabilistic Forecasting of Nodal Voltages in Distribution Systems

As the penetration of distributed energy resources (DERs) into distribution systems increases, so does the interest in forecasting relevant system variables to help mitigate the associated challenges. One such challenge is the more frequent occurrence of excessive voltages in distribution systems with higher shares of DERs. Accurate and reliable estimates together with forecasts of system states (i.e., nodal voltages) will therefore play a key role in improving the utilization of these variable and uncertain sources while mitigating potential operational risks. Whilst recent literature has explored machine learning (ML) methods for voltage estimation and their extrapolation for a short-time period into the future, few have taken uncertainty quantification into account, and these methods have not yet been translated into operations. This paper discusses the advantages offered by probabilistic voltage forecasts and proposes a non-parametric Bayesian method suitable for forecasting nodal voltages at short-term time horizons while accounting for uncertainties in load and distributed photovoltaic (PV) generation. We demonstrate the value of the proposed Gaussian process (GP) model for a case study using historical forecasts and observation data.

distribution system↗

DOE OSTI2017

Considerations and Lessons Learned from the Use of Gamma Spectroscopy Systems at FUSRAP Project Sites - 17459

This paper will discuss the application of gamma spectroscopy systems for analysis of environmental media samples for parents and daughter progeny of the Natural Uranium and Thorium Decay Series radionuclides. The concentrations and relative ratios of naturally occurring isotopes are used to confirm the presence of FUSRAP-related wastes and for understanding the associated potential dose and health risks. Focus will be given to lessons learned from the set-up and operation of onsite gamma spectroscopy laboratories used to provide stakeholders with defensible and timely results to support FUSRAP site investigations and remediation including adaptive survey design implementation, physical remediation and waste transport, and confirmatory final status survey activities. This paper focuses on the potential impacts to gamma spectroscopy measurement accuracy from the following: - Bias caused by sample matrix effects as well as use of different library reference values between performance evaluation study providers and testing laboratories; - Use of calibration sources with limited energy ranges, and; - Selection of available alternative isotopic decay reference data for the input of line energies and their intensities. These lessons learned include discussing how inter-laboratory measurement differences from blind performance evaluation samples are affected by the manufacturer's measurement method, including equilibrium assumptions; the isotope decay information source library used by the study provider and participating laboratories; and calibration source geometry/density variances from actual sample conditions. Any of these factors can significantly impact the accuracy of reported results data. Deepening the reader's understanding of the various factors that impact the accuracy, interpretation, and overall usability of gamma spectroscopy results data from laboratories will help ensure defensible decision-making and cost-effective mission implementation within FUSRAP and similar remediation programs. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2017

Using a critical infrastructure game to provide realistic observation of the human in the loop by criminal justice students

Understanding human behavior is crucial in anticipating adversarial actions during cyberattacks. The Criminal Justice (CJ) discipline offers the necessary frameworks to unpack the complex facets of adversarial behavior and movement, and should therefore be leveraged for their possible contributions to the area of proactive cybersecurity. Yet the discipline remains weak at training current and future CJ workforce on these matters in a hands-on manner. This paper presents a cybersecurity training exercise where a power grid simulator is used to educate CJ students via experiential learning about concepts of cyberattacks and cybersecurity as well as exposing them to doing hands-on cybersecurity field research. The paper reports on Game use as an important opportunity to observe humans put under additional stress in operating conditions. The paper discusses what CJ students learn from multidisciplinary simulation-based exercises, the challenges and limitations they face, and how training this workforce could help contribute towards proactive cyberdefense of critical infrastructure.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Deep Learning Experiments for Tropical Cyclone Intensity Forecasts

Reducing tropical cyclone (TC) intensity forecast errors is a challenging task that has interested the operational forecasting and research community for decades. To address this, we developed a deep learning (DL)-based multilayer perceptron (MLP) TC intensity prediction model. The model was trained using the global Statistical Hurricane Intensity Prediction Scheme (SHIPS) predictors to forecast the change in TC maximum wind speed for the Atlantic basin. In the first experiment, a 24-h forecast period was considered. To overcome sample size limitations, we adopted a leave one year out (LOYO) testing scheme, where a model is trained using data from all years except one and then evaluated on the year that is left out. When tested on 2010–18 operational data using the LOYO scheme, the MLP outperformed other statistical–dynamical models by 9%–20%. Additional independent tests in 2019 and 2020 were conducted to simulate real-time operational forecasts, where the MLP model again outperformed the statistical–dynamical models by 5%–22% and achieved comparable results as HWFI. The MLP model also correctly predicted more rapid intensification events than all the four operational TC intensity models compared. In the second experiment, we developed a lightweight MLP for 6-h intensity predictions. When coupled with a synthetic TC track model, the lightweight MLP generated realistic TC intensity distribution in the Atlantic basin. Therefore, the MLP-based approach has the potential to improve operational TC intensity forecasts, and will also be a viable option for generating synthetic TCs for climate studies.

58 GEOSCIENCES↗

DOE OSTI2020

Autotuning of Double-Dot Devices In Situ with Machine Learning

The current practice of manually tuning quantum dots (QDs) for qubit operation is a relatively time-consuming procedure that is inherently impractical for scaling up and applications. In this work, we report on the in situ implementation of a recently proposed autotuning protocol that combines machine learning (ML) with an optimization routine to navigate the parameter space. In particular, we show that a ML algorithm trained using exclusively simulated data to quantitatively classify the state of a double-QD device can be used to replace human heuristics in the tuning of gate voltages in real devices. Here, we demonstrate active feedback of a functional double-dot device operated at millikelvin temperatures and discuss success rates as a function of the initial conditions and the device performance. Modifications to the training network, fitness function, and optimizer are discussed as a path toward further improvement in the success rate when starting both near and far detuned from the target double-dot range.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Overview of SMART Initiative

The objective of the SMART Initiative, i.e., Science-informed Machine Learning (ML) for Accelerating Real-Time Decisions in Subsurface Applications, is to show how the utilization of ML can significantly improve efficiency and effectiveness of field-scale commercial carbon storage operations in three main areas: real-time visualization, virtual learning, and real-time forecasting. This presentation reports the status of SMART initiative for demonstrating: (a) virtual learning during the pre-injection permitting phase, and (b) ML-assisted operational decision making and visualization.

Siriwardane, Hema↗

DOE OSTI2022

Noise-Resilient Quantum Machine Learning for Stability Assessment of Power Systems

Transient stability assessment (TSA) is a cornerstone for resilient operations of todays interconnected power grids. This paper is a confluence of quantum computing, data science and machine learning to potentially address the power system TSA issue. Here, we devise a quantum TSA (QTSA) method to enable scalable and efficient data-driven transient stability prediction for bulk power systems, which is the first attempt to tackle the TSA issue with quantum computing. Our contributions are three-fold: 1) A high expressibility, low-depth (HELD) quantum circuit is designed for accurate and noise-resilient TSA; 2) A quantum natural gradient descent algorithm is developed for efficient HELD circuit training; 3) A systematical analysis on QTSAs performance under various quantum factors is per-formed. QTSA underpins a foundation of quantum-enabled and data-driven power grid stability analytics. It renders the intractable TSA straightforward and effortless in the Hilbert space, and therefore provides stability information for power system operations. Extensive experiments on quantum simulators and real quantum computers verify the accuracy, noise-resilience, scalability and universality of QTSA.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Tactical Analysis for Calculating Contextual Risk at Boundaries: Summary of Laboratory Directed Research & Development Effort

The Tactical Analysis for Calculating Contextual Risk at Boundaries (TACCRAB) tool is an innovative digital twin (DT) platform and automated risk algorithm designed to transform operational decision-making in structured screening environments, with an initial focus on Southern Border Land Ports of Entry (POEs). The invention provides integration points for advanced artificial intelligence, predictive modeling, and real-time data analysis to produce a comprehensive risk management tool that enables proactive, data-informed security strategies. The core inventive features of TACCRAB center on its unique risk algorithm, which dynamically calculates contextual risk by synthesizing historical data, near real-time streaming data from the checkpoints themselves, and AI-generated predictions. Unlike traditional risk assessment methods, TACCRAB utilizes a DT to provide comprehensive operational insights, allowing stakeholders to visualize, simulate, and optimize checkpoint configurations with unprecedented speed and contextual awareness. TACCRAB's key innovation lies in its ability to combine multiple complex inputs - including technology detection probabilities, resource availability, screening pathway characteristics, and threat actor behavioral patterns - into a unified risk calculation and update these inputs based on changing operational and environmental conditions. By leveraging a DT that continuously updates and learns from linked data, TACCRAB can suggest adaptive mitigation strategies that minimize risk while maintaining operational efficiency. Particularly novel is the platform's approach to decision support, which goes beyond static risk assessment. The DT provides dynamic metrics such as wait times, resource allocation effectiveness, and potential emerging threat scenarios, enabling users to view sophisticated, relevant what-if simulations and optimize checkpoint operations in near real-time. The system's architecture allows for generalized application across different screening environments, such as secure facilities, ports of entry, and soft targets, making it a versatile tool for security and operational management. The invention distinguishes itself through its comprehensive integration of predictive modeling, AI-driven pattern discovery, and user-friendly interface design. By combining these elements, TACCRAB transforms complex risk data into actionable insights, supporting decision-makers at various organizational levels - from booth agents making split-second screening decisions to checkpoint managers optimizing the day's resource allocation to strategic planners managing long-term investments.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2020

Accelerating Large-Format Metal Additive Manufacturing: How Controls R&D is Driving Speed, Scale, and Efficiency

This article highlights work at Oak Ridge National Laboratory’s Manufacturing Demonstration Facility to develop closed-loop, feedback control for laser-wire based Directed Energy Deposition, a form of metal Big Area Additive Manufacturing (m-BAAM), a process being developed in partnership with GKN Aerospace specifically for the production of Ti-6Al-4V pre-forms for aerospace components. A large-scale structural demonstrator component is presented as a case-study in which not just control, but the entire 3D printing workflow for m-BAAM is discussed in detail, including design principles for large-format metal AM, toolpath generation, parameter development, process control, and system operation, as well as post-print net-shape geometric analysis and finish machining. In terms of control, a multi-sensor approach has been utilized to measure both layer height and melt pool size, and multiple modes of closed-loop control have been developed to manipulate process parameters (laser power, print speed, deposition rate) to control these variables. Layer height control and melt pool size control have yielded excellent local (intralayer) and global (component-level) geometry control, and the impact of melt pool size control in particular on thermal gradients and material properties is the subject of continuing research. Further, these modes of control have allowed the process to advance to higher deposition rates (exceeding 7.5 lb/hr), larger parts (1-meter scale), shorter build times, and higher overall efficiency. The control modes are examined individually, highlighting their development, demonstration, and lessons learned, and it is shown how they operate concurrently to enable the printing of a large-scale, near net shape Ti-6Al-4V component.

Gibson, Brian↗

DOE OSTI2023

Demonstration of acoustic monitoring for structural health of microreactors: Through use of neural networks and resonant ultrasound spectroscopy

Nuclear microreactors prioritize modularity and portability and are intended to be a cost-effective technology for non-conventional nuclear markets. As such, the development of microreactors into a safe and feasible solution for energy security applications will necessitate the development of non-destructive technologies to monitor the integrity of inaccessible reactors components during operation. This demonstration applies linear and nonlinear acoustic techniques, in combination with machine learning, to detect and classify mechanical changes (stress and damage) in a test article which are broadly representative of potential operating challenges within a functioning microreactor. All necessary data has been collected for this demonstration, with minor experimental issues identified that can be addressed in follow-on work. Motivated by the expected conditions within a functioning microreactor, we have demonstrated our monitoring techniques on a core-block-like test article using an unstructured excitation source that approximates the noisy acoustic environment expected during reactor operation. At all stress states mechanically applied to the test article, a machine learning model using an artificial neural network was able to classify with 100% accuracy whether a 3D laser vibrometry point measurement was made on an intact or artificially defective test article. Further, model predictions about whether the defect interface was rough or smooth were 95% accurate, indicating the ability of acoustic techniques to recover defect characteristics. Resonant ultrasound spectroscopy (RUS) was also applied to the dataset to provide further quantitative insights about material properties. RUS analysis was ultimately hampered by several minor experimental and data issues, limiting results to certain cases for this demonstration. Last, analysis using nonlinear RUS exhibited sensitivity to changing levels of applied stresses for each intact and defective state. As presented in this demonstration, acoustic monitoring exhibits sensitivity to stress changes, which are of concern due to high thermal gradients expected during startup and operation. Further, our techniques distinguish between measurements made on intact and damaged test articles.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

Conceptual Design of Integrated Energy Systems with Market Interaction Surrogate Models

Most integrated energy system (IES) optimization frameworks employ the price-taker approximation, which ignores important interactions with the market and can result in overestimated economic values. In this work, we propose a machine learning surrogate-assisted optimization framework to quantify IES/market interactions and thus go beyond price-taker. We use time series clustering to generate representative IES operation profiles for the optimization problem and use machine learning surrogate models to predict the IES/market interaction. We quantify the accuracy of the time series clustering and surrogate models in a case study to optimally retrofit a nuclear power plant with a polymer electrolyte membrane electrolyzer to co-produce electricity and hydrogen.

Chen, Xinhe↗