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At least 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Crowdsourced reliable labeling of safety-rule violations on images of complex construction scenes for advanced vision-based workplace safety

Construction workplace hazard detection requires engineers to analyze scenes manually against many safety rules, which is time-consuming, labor-intensive, and error-prone. Computer vision algorithms are yet to achieve reliable discrimination of anomalous and benign object relations underpinning safety violation detections. Recently developed deep learning-based computer vision algorithms need tens of thousands of images, including labels of the safety rules violated, in order to train deep-learning networks for acquiring spatiotemporal reasoning capacity in complex workplaces. Such training processes need human experts to label images and indicate whether the relationship between the worker, resource, and equipment in the scenes violate spatiotemporal arrangement rules for safe and productive operations. False alarms in those manual labels (labeling no-violation images as having violations) can significantly mislead the machine learning process and result in computer vision models that produce inaccurate hazard detections. Compared with false alarms, another type of mislabels, false negatives (labeling images having violations as “no violations”), seem to have fewer impacts on the reliability of the trained computer vision models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

SMART – A Comprehensive Research and Development Program to Demonstrate Application of Machine Learning for Supporting CCS Deployment

Presentation material for a paper presented at the GHGT-17 conference, Calgary, Canada, October 20-24, 2024. The objective of the US Department of Energy’s SMART Initiative, i.e., Science-informed Machine Learning (ML) for Accelerating Real-Time Decisions in Subsurface Applications, is to showcase how the utilization of ML can significantly improve efficiency and effectiveness of field-scale commercial carbon storage operations. This paper will present the results from the current phase of SMART (field deployment) for demonstrating the applicability of ML-based tools and workflows for: (a) virtual learning during the pre-injection permitting phase, (b) advanced storage reservoir imaging to better characterize fractures and faults, and (c) dynamic storage reservoir modelling and optimization to inform operational decision making and visualization of system evolution.

CO2 geologic storage↗
DOE OSTI2024

SMART – A Comprehensive Research and Development Program to Demonstrate Application of Machine Learning for Supporting CCS Deployment

The objective of the US Department of Energy’s SMART Initiative, i.e., Science-informed Machine Learning (ML) for Accelerating Real-Time Decisions in Subsurface Applications, is to showcase how the utilization of ML can significantly improve efficiency and effectiveness of field-scale commercial carbon storage operations. This paper will present the results from the current phase of SMART (field deployment) for demonstrating the applicability of ML-based tools and workflows for: (a) virtual learning during the pre-injection permitting phase, (b) advanced storage reservoir imaging to better characterize fractures and faults, and (c) dynamic storage reservoir modelling and optimization to inform operational decision making and visualization of system evolution.

Siriwardane, Hema↗
DOE OSTI2024

Visibility-enhanced model-free deep reinforcement learning algorithm for voltage control in realistic distribution systems using smart inverters

Increasing integration of distributed solar photovoltaic (PV) into distribution networks could result in adverse effects on grid operation. Traditional model-based control algorithms require accurate model information that is difficult to acquire and thus are challenging to implement in practice. Here, this paper proposes a surrogate model-enabled grid visibility scheme to empower deep reinforcement learning (DRL) approach for distribution network voltage regulation using PV inverters with minimal system knowledge. In contrast to existing DRL methods, this paper presents and corroborates the adverse impact of missing load information on DRL performance and, based on this finding, proposes a surrogate model methodology to impute load information utilizing observable data. Additionally, a multi-fidelity neural network is utilized to construct the DRL training environment, chosen for its efficient data utilization and enhanced robustness to data uncertainty. The feasibility and effectiveness of the proposed algorithm are assessed by considering DRL testing across varying degrees of observable load information and diverse training environments on a realistic power system.

14 SOLAR ENERGY↗
DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy's National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real-time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI's Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: Run in real-time on the edge (≥ 12 Hz), Classification: Achieve less than 5% false positive detection, Classification: Achieve ≥ 95% methane plume detection rate, Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time. In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗
DOE OSTI2016

From Organized High-Throughput Data to Phenomenological Theory using Machine Learning: The Example of Dielectric Breakdown

Understanding the behavior (and failure) of dielectric insulators experiencing extreme electric fields is critical to the operation of present and emerging electrical and electronic devices. Despite its importance, the development of a predictive theory of dielectric breakdown has remained a challenge, owing to the complex multiscale nature of this process. We focus on the intrinsic dielectric breakdown field of insulators—the theoretical limit of breakdown determined purely by the chemistry of the material, i.e., the elements the material is composed of, the atomic-level structure, and the bonding. Starting from a benchmark dataset (generated from laborious first principles computations) of the intrinsic dielectric breakdown field of a variety of model insulators, simple predictive phenomenological models of dielectric breakdown are distilled using advanced statistical or machine learning schemes, revealing key correlations and analytical relationships between the breakdown field and easily accessible material properties. Lastly, the models are shown to be general, and can hence guide the screening and systematic identification of high electric field tolerant materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Medium Energy Electron Flux in Earth's Outer Radiation Belt (MERLIN): A Machine Learning Model

The radiation belts of the Earth, filled with energetic electrons, comprise complex and dynamic systems that pose a significant threat to satellite operation. While various models of electron flux both for low and relativistic energies have been developed, the behavior of medium energy (120–600 keV) electrons, especially in the MEO region, remains poorly quantified. At these energies, electrons are driven by both convective and diffusive transport, and their prediction usually requires sophisticated 4D modeling codes. In this paper, we present an alternative approach using the Light Gradient Boosting (LightGBM) machine learning algorithm. The Medium Energy electRon fLux In Earth's outer radiatioN belt (MERLIN) model takes as input the satellite position, a combination of geomagnetic indices and solar wind parameters including the time history of velocity, and does not use persistence. MERLIN is trained on >15 years of the GPS electron flux data and tested on more than 1.5 years of measurements. Tenfold cross validation yields that the model predicts the MEO radiation environment well, both in terms of dynamics and amplitudes o f flux. Evaluation on the test set shows high correlation between the predicted and observed electron flux (0.8) and low values of absolute error. The MERLIN model can have wide space weather applications, providing information for the scientific community in the form of radiation belts reconstructions, as well as industry for satellite mission design, nowcast of the MEO environment, and surface charging analysis.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

PowerModel-AI: A First On-the-Fly Machine-Learning Predictor for AC Power Flow Solutions

The real-time creation of machine-learning models via active or on-the-fly learning has attracted considerable interest across various scientific and engineering disciplines. These algorithms enable machines to build models autonomously while remaining operational. Through a series of query strategies, the machine can evaluate whether newly encountered data fall outside the scope of the existing training set. In this study, we introduce PowerModel-AI, an end-to-end machine learning software designed to accurately predict AC power flow solutions. We present detailed justifications for our model design choices and demonstrate that selecting the right input features effectively captures load flow decoupling inherent in power flow equations. Our approach incorporates on-the-fly learning, where power flow calculations are initiated only when the machine detects a need to improve the dataset in regions where the model’s suboptimal performance is based on specific criteria. Otherwise, the existing model is used for power flow predictions. This study includes analyses of five Texas A&M synthetic power grid cases, encompassing the 14-, 30-, 37-, 200-, and 500-bus systems. The training and test datasets were generated using PowerModels.jl, an open-source power flow solver/optimizer developed at Los Alamos National Laboratory, NM, USA.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2011

RF Test Results from Cryomodule 1 at the Fermilab SRF Beam Test Facility

Powered operation of Cryomodule 1 (CM-1) at the Fermilab SRF Beam Test Facility began in late 2010. Since then a series of tests first on the eight individual cavities and then the full cryomodule have been performed. We report on the results of these tests and lessons learned which will have an impact on future module testing at Fermilab. Since November 2010 Cryomodule 1 has been operating at 2 Kelvin. After evaluating each of the eight cavities while individually powered, the entire module has recently been powered and peak operation determined as shown in Figure 4. Several more weeks of measurements are planned before the module is warmed up, removed and replaced with Cryomodule 2 now under assembly at Fermilab.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Separating Physically Distinct Mechanisms in Complex Infrared Plasmonic Nanostructures via Machine Learning Enhanced Electron Energy Loss Spectroscopy

Electron energy loss spectroscopy (EELS) enables direct exploration of plasmonic phenomena at the nanometer level. To isolate individual plasmon modes, linear unmixing methods can be used to separate different physical mechanisms, but in larger and more complex systems the interpretability of the components becomes uncertain. Here, infrared plasmonic resonances in self-assembled heterogeneous monolayer films of doped-semiconductor nanoparticles are examined beyond linear unmixing techniques, and both supervised and unsupervised machine-learning-based analyses of hyperspectral EELS datasets are demonstrated. Additionally, in the supervised approach, a human operator labels a small number of pixels in the hyperspectral dataset corresponding to features of interest which are then propagated across the entire dataset. In the unsupervised approach, non-linear autoencoders are used to create a highly-reduced latent-space representation of the dataset, within which insight into the relevant physics can be gleaned from straightforward distance metrics that do not depend on operator input and bias. The advantage of these approaches is that the labeling separates physical mechanisms without altering the data, enabling robust analyses of the influence of heterogeneities in mesoscale complex systems.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Machine learning for geophysical characterization of brittleness: Tuscaloosa Marine Shale case study

Brittleness is one of the most important reservoir properties for unconventional reservoir exploration and production. Better knowledge about the brittleness distribution can help to optimize the hydraulic fracturing operation and lower costs. However, there are very few reliable and effective physical models to predict the spatial distribution of brittleness. We have developed a machine learning-based method to predict subsurface brittleness by using multidiscipline data sets, such as seismic attributes, rock physics, and petrophysics information, which allows us to implement the prediction without using a physical model. The method is applied on a data set from Tuscaloosa Marine Shale, and the predicted rock physics template is close to the calculated value from conventional inverted elastic parameters. Therefore, the proposed method helps determine areas of the reservoir that have optimal geomechanical properties for successful hydraulic fracturing.

Geochemistry & Geophysics↗
DOE OSTI2021

Accurate prediction of mega-electron-volt electron beam properties from UED using machine learning

To harness the full potential of the ultrafast electron diffraction (UED) and microscopy (UEM), we must know accurately the electron beam properties, such as emittance, energy spread, spatial-pointing jitter, and shot-to-shot energy fluctuation. Owing to the inherent fluctuations in UED/UEM instruments, obtaining such detailed knowledge requires real-time characterization of the beam properties for each electron bunch. While diagnostics of these properties exist, they are often invasive, and many of them cannot operate at a high repetition rate. Here, we present a technique to overcome such limitations. Employing a machine learning (ML) strategy, we can accurately predict electron beam properties for every shot using only parameters that are easily recorded at high repetition rate by the detector while the experiments are ongoing, by training a model on a small set of fully diagnosed bunches. Applying ML as real-time noninvasive diagnostics could enable some new capabilities, e.g., online optimization of the long-term stability and fine single-shot quality of the electron beam, filtering the events and making online corrections of the data for time-resolved UED, otherwise impossible. This opens the possibility of fully realizing the potential of high repetition rate UED and UEM for life science and condensed matter physics applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

On the Formalization of Development and Assessment Process for Digital Twins in the Nearly Autonomous Management and Control System

In recent years, the autonomous control system has been encouraged in advanced reactors for restoring economic viability, simplifying the operation and maintenance, and enabling remote-site power generations [1]. Since the reactor is expected to be operated for a long period of time with a limited number of individuals onsite, it is recommended that the autonomous control system should have access to very realistic models of the state of processes in the whole lifecycle, together with these process behaviors in interaction with their environment in the real world. As a result, digital twin (DT) technology is suggested in autonomous control systems. DT is defined as a digital representation of a physical object or system, which contains a record for the histories of loads, operation and maintenance status, predictions for the near-term transient of important state variables, and decision-making process [2]. Since machine learning (ML) can recognize patterns within a complex system in real-time applications, it has been used to build DTs in the autonomous control systems for advanced reactors. Meanwhile, due to the rareness of operation data in accident scenarios, the development and assessment of DTs is expected to be mainly driven by simulations. Although the capability and feasibility of ML-based DTs are recognized in improving the safety and efficiency of reactor control, a major concern from the regulatory commission and the nuclear industry is whether the information from a DT is developed and assessed in accordance with expectation and requirements by the target decision. Such concerns not only affect the acceptance criteria for DTs, but also values that can be extracted from DTs and autonomous control system during operations. Inspired by the success of formal methods in improving the reliability and robustness of computer programming and software development, it is suggested that the development and assessment process (DAP) for both separate DTs and integral control system should be formalized in a transparent, consistent, and improvable manner. In this study, a digital-twin development and assessment process (DT-DAP) is proposed by adapting the evaluation model development and assessment process (EMDAP) [3] to requirements by the autonomous control system, ML algorithms, and DT technology. To demonstrate the framework, a baseline nearly autonomous management and control (NAMAC) system with ML-based DTs for diagnosis and prognosis is developed and assessed based on the framework. It is found that with selected testing methods and techniques, the DT-DAP can help identify errors in DTs and NAMAC which would otherwise be left unverified. Meanwhile, it is found that the DT-DAP can improve the DTs and NAMAC by continuously learning and iterating through different elements.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

A Better Method to Calculate Fuel Burnup in Pebble Bed Reactors Using Machine Learning

Burnup measurement is an important step in material control and accountancy (MC&A) at nuclear reactors, and may be done by examining gamma spectra of fuel samples. Traditional approaches rely on known correlations to specific photopeaks (e.g. 137 Cs) and operate via a standard linear regression method. However, the quality of these regression methods is limited even in the best case, and is significantly poorer at short fuel cool-down times, due to the elevated radiation background by short life-time isotopes, and self-shielding effect of the fuel. For practical operation of pebble bed reactors (PBRs), quick measurements (in minutes) and short cooling times (in hours) are required from a safety and security perspective. We investigated the efficacy and performance of machine learning (ML) methods to predict the burnup of the pebble fuel from full gamma spectra (rather than specific discrete photopeaks) and found a full-spectrum ML approach to far outperform baseline regression predictions in all measurement and cooling conditions - including in operational-like measurement conditions. We also performed model and data ablation experiments to determine the relative performance impact of our ML methods' capacity to model data nonlinearities and the inherent additional information in full spectra. Applying our ML methods, we found a number of surprising results, including improved accuracy at shorter fuel cooling times (the opposite of the norm), remarkable robustness to spectrum compression (via rebinning), and competitive burnup predictions even when using background signal only (i.e. explicitly omitting known isotope photopeaks).

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2016

Direct Feed Low Activity Waste Program Integration - 16638

The mission of Program Integration is to champion and steer the development and implementation of common and consistent programs between the Tank Operating Contract (TOC) contractor and the Waste Treatment Plant (WTP) contractor. TOC is managed by Washington River Protection Solutions LLC (WRPS) and WTP is managed by Bechtel National, Inc. (BNI). The DOE Office of River Protection (DOE ORP) is the Program Owner. WRPS has been actively managing the TOC at the Hanford Site Tank Farms (TF) since 2008. Programs are already developed, verified, and in use for the TOC, whereas WTP is still in the construction phase. WTP Programs are established for construction only, but not developed for commissioning and future operations. An extensive effort is underway at WTP to develop commissioning programs. In order to drive consistency and minimize differences in program requirements between TF, which will feed the tank waste, and WTP, which will receive and treat the tank waste, DOE-ORP requires the two contractors to integrate and collaborate in program development. Advantages of integration are: - Achieves cost and time savings as WTP is able to review existing TOC programs as starting points in development of its programs. - Promotes interfaces with industry counterparts to share experiences, issues, and lessons learned. ? Minimizes difference in program requirements between the companies. - Helps prepare for and ultimately succeed in Operational Readiness Reviews (ORR). Program Integration is a priority for TOC and WTP, and in order to maintain the focus and drive to this effort, a unified One System Program Integration Council (OSPIC) was created in 2012. Membership of the OSPIC consists of senior managers from both companies and the OSPIC chair's report directly to their respective President. Thirty (30) programs are subject to integration, including but not limited to Conduct of Operations, Worker Safety and Health, Quality Assurance, Contractor Assurance, and Environmental Management. Progress of program development and integration efforts are tracked and monitored, and barriers to integration are resolved in a collaborative fashion within both companies. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Nominal and adversarial synthetic PMU data for standard IEEE test systems

GridSTAGE (Spatio-Temporal Adversarial scenario GEneration) is a framework for the simulation of adversarial scenarios and the generation of multivariate spatio-temporal data in cyber-physical systems. GridSTAGE is developed based on Matlab and leverages Power System Toolbox (PST) where the evolution of the power network is governed by nonlinear differential equations. Using GridSTAGE, one can create several event scenarios that correspond to several operating states of the power network by enabling or disabling any of the following: faults, AGC control, PSS control, exciter control, load changes, generation changes, and different types of cyber-attacks. Standard IEEE bus system data is used to define the power system environment. GridSTAGE emulates the data from PMU and SCADA sensors. The rate of frequency and location of the sensors can be adjusted as well. Detailed instructions on generating data scenarios with different system topologies, attack characteristics, load characteristics, sensor configuration, control parameters are available in the Github repository - https://github.com/pnnl/GridSTAGE. There is no existing adversarial data-generation framework that can incorporate several attack characteristics and yield adversarial PMU data. The GridSTAGE framework currently supports simulation of False Data Injection attacks (such as a ramp, step, random, trapezoidal, multiplicative, replay, freezing) and Denial of Service attacks (such as time-delay, packet-loss) on PMU data. Furthermore, it supports generating spatio-temporal time-series data corresponding to several random load changes across the network or corresponding to several generation changes. A Koopman mode decomposition (KMD) based algorithm to detect and identify the false data attacks in real-time is proposed in https://ieeexplore.ieee.org/document/9303022. Machine learning-based predictive models are developed to capture the dynamics of the underlying power system with a high level of accuracy under various operating conditions for IEEE 68 bus system. The corresponding machine learning models are available at https://github.com/pnnl/grid_prediction.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

LCLS Big Data Handling – How I Learned to Stop Worrying and Love the Data Deluge

Advanced data and computing systems are vital to Linac Coherent Light Source (LCLS) operations, data interpretation and overall scientific productivity. The transition to MHz-era operation marks a fundamental change in scale that requires new infrastructure and architectures to link LCLS to the required scale of computing needed for scientific interpretation. The LCLS-II Data System meets big data challenges by implementing configurable data reduction that can adapt to multiple science areas, real-time analysis frameworks to provide visualization and fast feedback, and the ability to transfer data to local and remote computational facilities for near real time analysis at the appropriate scale. Feature extracted information generated in the data analysis pipeline - at the edge, local compute, or remote High-Performance Computing (HPC) resources - can be used to steer experiments and inform user decisions during beam time. Artificial Intelligence and Machine Learning (AI/ML) techniques present new opportunities to rapidly analyse large datasets and direct experiments, but create new challenges in scaling, adaptability, complexity, and trustworthiness. We describe how the LCLS-II Data System architecture addresses its data-driven challenges in the areas of data acquisition, data processing, data management, and workflow orchestration to decrease the overall time-to-science and provide a vision for future developments.

artificial intelligence↗
DOE OSTI2023

Deep Reinforcement Learning Based HVAC Control for Reducing Carbon Footprint of Buildings

In this paper, we present our work on deep reinforcement learning (DRL) based intelligent control of Heating, Ventilation, and Air Conditioning (HVAC) with the goal of reducing carbon emission. We performed this task using 1) Marginal Operating Emission Rates (MOER), where the objective was to shift the demand to the low emission period of the day and 2) Time-Of-Use (TOU) demand-response price where the objective was to shift the demand to low price period of the day. This was achieved by learning an optimal pre-cooing strategy. We found the carbon emission reduction in the range of 6%-16% depending on the opportunity presented by the MOER signal. Similarly, we observed the carbon emission reduction in the range of 23%-29% during the peak price period when TOU price was used. The results clearly demonstrated the applicability of our approach in reducing the carbon footprint of the building.

carbon emission↗