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At least 199 records · Page 11

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DOE OSTI2017

A review of human-system interface design issues observed during analog-to-digital and digital-to-digital migrations in U.S. nuclear power plants - 312

The United States (U.S.) Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) program is developing a scientific basis through targeted research and development (R and D) to support the U.S. nuclear power plant (NPP) fleet in extending their existing licensing period and ensuring their long-term reliability, productivity, safety, and security. Over the last several years, human factors engineering (HFE) professionals at the Idaho National Laboratory (INL) have supported the LWRS Advanced Instrumentation, Information, and Control (II and C) System Technologies pathway across several U.S. commercial NPPs in analog-to-digital migrations (i.e., turbine control systems) and digital-to-digital migrations (i.e., Safety Parameter Display System). These efforts have included in-depth human factors evaluation of proposed human-system interface (HSI) design concepts against established U.S. Nuclear Regulatory Commission (NRC) design guidelines from NUREG-0700, Rev 2 to inform subsequent HSI design prior to transitioning into Verification and Validation. This paper discusses some of the overarching design issues observed from these past HFE evaluations. In addition, this work presents some observed challenges such as common tradeoffs utilities are likely to face when introducing new HSI technologies into NPP hybrid control rooms. The primary purpose of this work is to distill these observed design issues into general HSI design guidance that industry can use in early stages of HSI design. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2017

Study on main control room and human-system interface of generation III in China - 313

This paper focuses on the study on Main Control Room (MCR) and Human-System Interface (HSI) of Generation III in China. An intelligent HSI has been developed to enhance the safety and availability of Nuclear Power Plant (NPP) by improving operational reliability. The key elements of the proposed HSI such as Advanced Alarm System, State Oriented Procedures (SOP), Large Display Panel (LDP), Computerized-base procedures..., that affects what information the operator sees or the system's response to a control input must be empirically evaluated to ensure that the new design does not compromise HSI effectiveness. Based on CPR1000's experience in China, this paper presents the approach used as well as the most relevant aspects of this kind of project for the next generation. This approach will be used in new Generation III nuclear power plant, and also used in modernizing I and C system in currently operative nuclear power plants, in addition to meeting safety requirements and the plant's operational requirements, to improve cost-effective plant and human performance and to reduce likelihood of human errors, to gain maximum benefit of the implemented technology and to increase the performance, resulting in improved plant safety, availability, reliability, and cost-effective operation. The proposed HSI has been validated and demonstrated using the CPR1000 simulator of National Key Laboratory. After sufficient validation, the characteristic design features of the proposed HSI will be reflected in the design of the main control room of the next generation reactors in China. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2017

Training and certification of human organizational factors designers in nuclear plant engineering - 344

Performing an increasing number of small and large-scope nuclear Human Factors Programs, AREVA NP is facing the challenging situation that (1) well-trained experts in Human Factors or Usability Engineering are currently hard to find and (2) newly hired ones need at least two years 'on the job' to become familiar with the nuclear field and the complexity of engineering Human Factors in this field. To innovatively meet this challenge we focus instead on company-internal engineers. Demonstrated nuclear engineering knowledge and skills and pronounced interest to extend both renders them optimal applicants to the AREVA NP two years Human Organizational Factors Training and Certification program. The AREVA NP training shall convey Human Factors knowledge and skills within the limited time of two years. We also stress the need to change participants' mindset from a technically-biased to a human-centered approach, committed to involve end-users throughout design. Next to psychology principles, user testing, user-centered design, plant operation principles, and the acquisition and documentation of users' requirements, design constraints, and inherent tradeoffs, participants will be trained to apply learned methods in a real environment and meet the challenges of real projects. The following sections provide insights in the training conception and preparation phase, the identification of appropriate candidates, the development of course syllabuses, and the schedule of lectures and tests. The promising experiences made thus far are reported as well. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Integrated Operations for Nuclear: Work Reduction Opportunity Demonstration

Integrated Operations for Nuclear: Work Reduction Opportunity Demonstration The DI BCA document also identifies specific, digitally enabled WRO categories for further study. These were selected as most relevant by Reference Plant personnel from a larger list of WRO areas identified across the nuclear industry as captured INL/RPT-21-64134, “Process for Significant Nuclear Work Function Innovation Based on Integrated Operations Concepts.” This ION WRO demonstration report was developed to provide illustrative, specific, and actionable direction for intertwined PTPG changes associated with digital modernization efforts. The coordinated changes in these areas are intended to maximize safe plant operational and economic performance. This includes enabling WROs associated with detailed configuration, implementation, and use of digital systems and how they are supported over their lifecycle. Illustrating this direction through a minimum set of advanced technology examples establishes a model PTPG framework that can be leveraged across the spectrum of nuclear plant digital modernization efforts going forward. This document addresses many related concepts. To promote an integrated understanding of the topics that make up this work, this document contains an extensive set of internal hyperlinks. This set includes hyperlinks to page numbers in the table of contents, section numbers, items in lists, figures, tables, and references to other documents within the report. When hovering the cursor above hyperlinked text in Adobe, the cursor will change from “ ” to “ .” When the “ ” appears, a left mouse click will take the reader to the referenced location in the document. To return to the original location in the document, the reader need only press and hold the “alt” button on the keyboard and then simultaneously press the “<” directional key on the keyboard.

42 ENGINEERING↗

DOE OSTI2016

HPSim, Version 1.0

HPSim is a GPU-accelerated online multi-particle beam dynamics simulation tool for ion linacs. It was originally developed for use on the Los Alamos 800-MeV proton linac. It is a “z-code” that contains typical linac beam transport elements. The linac RF-gap transformation utilizes transit-time-factors to calculate the beam acceleration therein. The space-charge effects are computed using the 2D SCHEFF (Space CHarge EFFect) algorithm, which calculates the radial and longitudinal space charge forces for cylindrically symmetric beam distributions. Other space- charge routines to be incorporated include the 3D PICNIC and a 3D Poisson solver. HPSim can simulate beam dynamics in drift tube linacs (DTLs) and coupled cavity linacs (CCLs). Elliptical superconducting cavity (SC) structures will also be incorporated into the code. The computational core of the code is written in C++ and accelerated using the NVIDIA CUDA technology. Users access the core code, which is wrapped in Python/C APIs, via Pythons scripts that enable ease-of-use and automation of the simulations. The overall linac description including the EPICS PV machine control parameters is kept in an SQLite database that also contains calibration and conversion factors required to transform the machine set points into model values used in the simulation.

Pang, Xiaoying↗

DOE OSTI2016

HPSim, Version 1.0

Pang, Xiaoying↗

DOE OSTI2024

Machine learning-guided design of direct methanol fuel cells with a platinum group metal-free cathode

Direct methanol fuel cells (DMFCs) offer a promising solution for clean electricity generation, particularly in small electronics and remote auxiliary power units. However, optimizing their efficiency and performance is challenging due to the complex interactions between various factors. Here, we present a novel approach that integrates experiments with machine learning to model and predict the performance of these fuel cells using atomically dispersed platinum group metal (PGM)-free catalysts at the cathode. Further, our machine learning models, trained on diverse input parameters, allow for the comprehensive optimization of DMFC performance prior to fabrication and testing. Through extensive experimental validation, we demonstrate that this data-driven approach accurately predicts key performance metrics, such as maximum power output and polarization curves. By combining our models with interpretable game-theory methods, we provide deep insights into the factors governing fuel cell performance, ultimately paving the way for the design of scalable and efficient DMFC technologies.

25 ENERGY STORAGE↗

DOE OSTI2026

Steady Spills, Stronger Signals: Machine Learning for Slow Spill Analysis

Particle accelerator experiments rely on stable, consistent proton beams to maximize scientific discovery. This presentation introduces beam spills, duty factor, and beam stability using a meteor shower analogy before exploring how feedback control and machine learning, including recurrent neural networks (RNNs), can analyze spill data, identify patterns, and predict beam behavior. Together, these approaches support beam optimization and improve our understanding of accelerator performance for experiments such as Mu2e.

Prescott, Matthew J. [Fermilab]↗

DOE OSTI2023

Machine-learning-based investigation of the variables affecting summertime lightning occurrence over the Southern Great Plains

Lightning is affected by many factors, many of which are not routinely measured, well understood, or accounted for in physical models. Several commonly used machine learning (ML) models have been applied to analyze the relationship between Atmospheric Radiation Measurement (ARM) data and lightning data from the Earth Networks Total Lightning Network (ENTLN) in order to identify important variables affecting lightning occurrence in the vicinity of the Southern Great Plains (SGP) ARM site during the summer months (June, July, August and September) of 2012 to 2020. Testing various ML models, we found that the random forest model is the best predictor among common classifiers. When convective clouds were detected, it predicts lightning occurrence with an accuracy of 76.9 % and an area under the curve (AUC) of 0.850. Using this model, we further ranked the variables in terms of their effectiveness in nowcasting lightning and identified geometric cloud thickness, rain rate and convective available potential energy (CAPE) as the most effective predictors. The contrast in meteorological variables between no-lightning and frequent-lightning periods was examined for hours with CAPE values conducive to thunderstorm formation. Besides the variables considered for the ML models, surface variables and mid-altitude variables (e.g., equivalent potential temperature and minimum equivalent potential temperature, respectively) have statistically significant contrasts between no-lightning and frequent-lightning hours. For example, the minimum equivalent potential temperature from 700 to 500 hPa is significantly lower during frequent-lightning hours compared with no-lightning hours. Finally, a notable positive relationship between the intracloud (IC) flash fraction and the square root of CAPE ($\sqrt{CAPE}$) was found, suggesting that stronger updrafts increase the height of the electrification zone, resulting in fewer flashes reaching the surface and consequently a greater IC flash fraction.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Technical Report: Reference photon dosimetry data for Varian accelerators based on IROC-Houston site visit data

Purpose: Accurate data regarding linear accelerator (Linac) radiation characteristics are important for treatment planning system modeling as well as regular quality assurance of the machine. The Imaging and Radiation Oncology Core-Houston (IROC-H) has measured the dosimetric characteristics of numerous machines through their on-site dosimetry review protocols. Photon data are presented and can be used as a secondary check of acquired values, as a means to verify commissioning a new machine, or in preparation for an IROC-H site visit. Methods: Photon data from IROC-H on-site reviews from 2000 to 2014 were compiled and analyzed. Specifically, data from approximately 500 Varian machines were analyzed. Each dataset consisted of point measurements of several dosimetric parameters at various locations in a water phantom to assess the percentage depth dose, jaw output factors, multileaf collimator small field output factors, off-axis factors, and wedge factors. The data were analyzed by energy and parameter, with similarly performing machine models being assimilated into classes. Common statistical metrics are presented for each machine class. Measurement data were compared against other reference data where applicable. Results: Distributions of the parameter data were shown to be robust and derive from a student’s t distribution. Based on statistical and clinical criteria, all machine models were able to be classified into two or three classes for each energy, except for 6 MV for which there were eight classes. Quantitative analysis of the measurements for 6, 10, 15, and 18 MV photon beams is presented for each parameter; supplementary material has also been made available which contains further statistical information. Conclusions: IROC-H has collected numerous data on Varian Linacs and the results of photon measurements from the past 15 years are presented. The data can be used as a comparison check of a physicist’s acquired values. Acquired values that are well outside the expected distribution should be verified by the physicist to identify whether the measurements are valid. Comparison of values to this reference data provides a redundant check to help prevent gross dosimetric treatment errors.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Pd–Methyl Bond Energy─Property Correlations, Noncorrelations, Machine Learning Models, and Application to Polymerization Catalysis

Metal–carbon bonds are a key intermediate in a variety of homogeneous organometallic transformations and often determine the critical thermodynamics and kinetics of catalytic processes. Surprisingly, the influence of different ligands on metal–carbon bond strengths has been largely overlooked. Here, in this study, we evaluated nearly 700 experimental Pd–methyl complexes by calculating their bond dissociation energies using density functional theory (DFT) and compared these bond strengths to several fundamental molecular properties, and this revealed several surprising correlations and noncorrelations. Most surprising was that several fundamental properties, such as the bond length, bond force constant, and bond electron density, have no correlation with bond strength, despite these correlations often holding for main-group compounds. We were indeed able to identify key ligand-dependent chemical features/descriptors that provided a highly accurate machine learning model and provided insight into the general factors that control the Pd–carbon bond strength, such as radical delocalization and nucleophilicity. Insights gained from the Pd–Me bond energy analysis were then applied to CO migratory insertion steps that are part of copolymerization reactions.

binding energy↗

DOE OSTI2024

Predictive Modeling of NOx Emissions from Lean Direct Injection of Hydrogen and Hydrogen/Natural Gas Blends Using Flame Imaging and Machine Learning

This research paper explores the use of machine learning to relate images of flame structure and luminosity to measured NOx emissions. Images of reactions produced by 16 aero-engine derived injectors for a ground-based turbine operated on a range of fuel compositions, air pressure drops, preheat temperatures and adiabatic flame temperatures were captured and postprocessed. The experimental investigations were conducted under atmospheric conditions, capturing CO, NO and NOx emissions data and OH* chemiluminescence images from 27 test conditions. The injector geometry and test conditions were based on a statistically designed test plan. These results were first analyzed using the traditional analysis approach of analysis of variance (ANOVA). The statistically based test plan yielded 432 data points, leading to a correlation for NOx emissions as a function of injector geometry, test conditions and imaging responses, with 70.2% accuracy. As an alternative approach to predicting emissions using imaging diagnostics as well as injector geometry and test conditions, a random forest machine learning algorithm was also applied to the data and was able to achieve an accuracy of 82.6%. This study offers insights into the factors influencing emissions in ground-based turbines while emphasizing the potential of machine learning algorithms in constructing predictive models for complex systems.

08 HYDROGEN↗

DOE OSTI2022

General-Purpose Unsupervised Cyber Anomaly Detection via Non-Negative Tensor Factorization

Distinguishing malicious anomalous activities from unusual but benign activities is a fundamental challenge for cyber defenders. Prior studies have shown that statistical user behavior analysis yields accurate detections by learning behavior profiles from observed user activity. These unsupervised models are able to generalize to unseen types of attacks by detecting deviations from normal behavior, without knowledge of specific attack signatures. However, approaches proposed to date based on probabilistic matrix factorization are limited by the information conveyed in a two-dimensional space. Non-negative tensor factorization, on the other hand, is a powerful unsupervised machine learning method that naturally models multi-dimensional data, capturing complex and multi-faceted details of behavior profiles. Herein, our new unsupervised statistical anomaly detection methodology matches or surpasses state-of-the-art supervised learning baselines across several challenging and diverse cyber application areas, including detection of compromised user credentials, botnets, spam e-mails, and fraudulent credit card transactions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Materials structure–property factorization for identification of synergistic phase interactions in complex solar fuels photoanodes

Abstract Properties can be tailored by tuning composition in high-order composition spaces. For spaces with complex phase behavior, modeling the properties as a function of composition and phase distribution remains a formidable challenge. We present materials structure–property factorization (MSPF) as an approach to automate modeling of such data and identify synergistic phase interactions. MSPF is an interpretable machine learning algorithm that couples phase mapping via Deep Reasoning Networks (DRNets) to matrix factorization-based modeling of the representative properties of each phase in a dataset. MSPF is demonstrated for Bi–Cu–V oxide photoanodes for solar fuel generation, which contains 25 different phase combinations and correspondingly exhibits complex composition-structure-photoactivity relationships. Comparing the measured photoactivity to a learned model for non-interacting phases, synergistic phase interactions are identified to guide further photoactivity optimization and understanding. MSPF identifies synergistic interactions of a BiVO 4 -like phase with both Cu 2 V 2 O 7 -like and CuV 2 O 6 -like phases, creating avenues for understanding complex photoelectrocatalysts.

36 MATERIALS SCIENCE↗

DOE OSTI2023

QASMTrans: A QASM Quantum Transpiler Framework for NISQ Devices

In quantum computing, transpilation plays a crucial role in converting high-level, machine-independent quantum circuits into circuits specially for a quantum device, considering factors such as basis gate set, topology, error profile, etc. Yet, the efficiency of transpilation remains a significant bottleneck, particularly when dealing with very large QASM level input files. In this paper, we present QASMTrans, a C++ based high-performance quantum transpiler framework that can demonstrate on average 50-100× speedups compared to the internal transpiler of Qiskit. Particularly, for large dense circuits such as ’uccsd n24’ and ’qft n320’ incorporating millions of gates, QASMTrans can successfully transpile in 69s and 31s, respectively, while Qiskit failed to finish in one hour. Using QASMTrans as the baseline, it becomes more feasible to explore much larger design space and impose more comprehensive compiler optimizations.

Hua, Fei↗

DOE OSTI2014

Support Vector Machine-Based Prediction of Local Tumor Control After Stereotactic Body Radiation Therapy for Early-Stage Non-Small Cell Lung Cancer

Background: Several prognostic factors for local tumor control probability (TCP) after stereotactic body radiation therapy (SBRT) for early stage non-small cell lung cancer (NSCLC) have been described, but no attempts have been undertaken to explore whether a nonlinear combination of potential factors might synergistically improve the prediction of local control. Methods and Materials: We investigated a support vector machine (SVM) for predicting TCP in a cohort of 399 patients treated at 13 German and Austrian institutions. Among 7 potential input features for the SVM we selected those most important on the basis of forward feature selection, thereby evaluating classifier performance by using 10-fold cross-validation and computing the area under the ROC curve (AUC). The final SVM classifier was built by repeating the feature selection 10 times with different splitting of the data for cross-validation and finally choosing only those features that were selected at least 5 out of 10 times. It was compared with a multivariate logistic model that was built by forward feature selection. Results: Local failure occurred in 12% of patients. Biologically effective dose (BED) at the isocenter (BED{sub ISO}) was the strongest predictor of TCP in the logistic model and also the most frequently selected input feature for the SVM. A bivariate logistic function of BED{sub ISO} and the pulmonary function indicator forced expiratory volume in 1 second (FEV1) yielded the best description of the data but resulted in a significantly smaller AUC than the final SVM classifier with the input features BED{sub ISO}, age, baseline Karnofsky index, and FEV1 (0.696 ± 0.040 vs 0.789 ± 0.001, P<.03). The final SVM resulted in sensitivity and specificity of 67.0% ± 0.5% and 78.7% ± 0.3%, respectively. Conclusions: These results confirm that machine learning techniques like SVMs can be successfully applied to predict treatment outcome after SBRT. Improvements over traditional TCP modeling are expected through a nonlinear combination of multiple features, eventually helping in the task of personalized treatment planning.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2022

Next generation experimental data access at NSLS-II

The NSLS-II network and computing infrastructure has been significantly updated recently. The re-IP process in 2020-2021 enabled the NSLS-II network to be routable to the rest of the BNL campus. Then, standardization of the operating systems and deployment procedures helped to deliver a consistent environment to workstations and servers used by all NSLS-II beamlines. In particular, the RedHat Enterprise Linux 8 was deployed to 700+ machines using the RedHat Satellite infrastructure management product, and all critical services (IOCs, databases, etc.) were migrated to the new OS. NFS users’ home directories are consistent across all of the machines, which eliminates the need for the individual configuration of the user environment on each host. The standard suite of software packages is available to the beamline staff and users, which includes the system packages (deployed via RPM) as well as the conda environments for data acquisition and analysis. Security measures were implemented to comply with the industry standards, which include multi-factor authentication (using Duo), secure screen lock for the beamline machines, and advanced access control to the experimental data that is stored in shared central storage available on all hosts. These major enhancements facilitated sharing the experimental data (currently for a number of selected beamlines, with a plan to extend it to the whole facility in the nearest future) with the users via an externally facing JupyterHub instance. The beamlines keep using the Bluesky data acquisition framework to orchestrate their experiments, and the new infrastructure enabled them to use a next-generation data access library called tiled.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Characterizing Drought Behavior in the Colorado River Basin Using Unsupervised Machine Learning

Drought is a pressing issue for the Colorado River Basin (CRB) due to the social and economic value of water resources in the region and the significant uncertainty of future drought under climate change. Here, we use climate simulations from various Earth System Models (ESMs) to force the Variable Infiltration Capacity hydrologic model and project multiple drought indicators for the sub-watersheds within the CRB. We apply an unsupervised machine learning (ML) based on Non-Negative Matrix Factorization using K-means clustering (NMFk) to synthesize the simulated historical, future, and change in drought indicators. The unsupervised ML approach can identify sub-watersheds where key changes to drought indicator behavior occur, including shifts in snowpack, snowmelt timing, precipitation, and evapotranspiration. While changes in future precipitation vary across ESMs, the results indicate that the Upper CRB will experience increasing evaporative demand and surface-water scarcity, with some locations experiencing a shift from a radiation-limited to a water-limited evaporation regime in the summer. Large shifts in peak runoff are observed in snowmelt-dominant sub-watersheds, with complete disappearance of the snowmelt signal for some sub-watersheds. The work demonstrates the utility of the NMFk algorithm to efficiently identify behavioral changes of drought indicators across space and time and to quickly analyze and interpret hydro climate model results.

54 ENVIRONMENTAL SCIENCES↗