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DOE OSTI2012

Physics-Based Multi-State Models of Passive Component Degradation for the R7 Reactor Simulation Environment

Abstract: The Next Generation Systems Analysis Code - referred to as R7 - is reactor systems simulation software being developed to support the Risk-Informed Safety Margin Characterization Pathway of the U.S. Department of Energy's Light Water Reactor Sustainability Program. It will provide an integrated multi-physics environment, implemented in an uncertainty quantification (UQ) framework that can produce risk and other performance insights on long-term reactor operations. An element of this simulation environment will be the performance of passive components and materials. Conventional models of component reliability are largely parametric, relying on plant service data to estimate component lifetimes and failure rates. This type of model has limited usefulness in the R7 environment where the intent is to explicitly determine the influence of physical stressors on component degradation. In this paper, we describe a new class of multi-state physics-based component models designed to be R7-compatible. These models capture the physics of materials degradation while also incorporating the effects of interventions and component rejuvenation. The models are implemented in a cumulative damage framework that allows the impact of an evolving physical environment to be addressed without recourse to resampling within the Monte Carlo-based UQ framework. The paper describes an application to stress corrosion cracking in dissimilar metal welds - a principal contributor to potential loss of coolant accidents. So while R7 will have the more conventional capability of reactor simulation codes to model the impact of degraded components and systems on plant performance, the methodology described here allows R7 to model the inverse effect; the impact of the physical environment on component degradation and performance.

PRA, passive components, stress corrosion cracking↗

DOE OSTI2013

NETL's Hybrid Performance, or Hyper, facility

NETL's Hybrid Performance, or Hyper, facility is a one-of-a-kind laboratory built to develop control strategies for the reliable operation of fuel cell/turbine hybrids and enable the simulation, design, and implementation of commercial equipment. The Hyper facility provides a unique opportunity for researchers to explore issues related to coupling fuel cell and gas turbine technologies.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2014

Individual Differences in Human Reliability Analysis

While human reliability analysis (HRA) methods include uncertainty in quantification, the nominal model of human error in HRA typically assumes that operator performance does not vary significantly when they are given the same initiating event, indicators, procedures, and training, and that any differences in operator performance are simply aleatory (i.e., random). While this assumption generally holds true when performing routine actions, variability in operator response has been observed in multiple studies, especially in complex situations that go beyond training and procedures. As such, complexity can lead to differences in operator performance (e.g., operator understanding and decision-making). Furthermore, psychological research has shown that there are a number of known antecedents (i.e., attributable causes) that consistently contribute to observable and systematically measurable (i.e., not random) differences in behavior. This paper reviews examples of individual differences taken from operational experience and the psychological literature. The impact of these differences in human behavior and their implications for HRA are then discussed. We propose that individual differences should not be treated as aleatory, but rather as epistemic. Ultimately, by understanding the sources of individual differences, it is possible to remove some epistemic uncertainty from analyses.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2014

A Prototyping Environment for Research on Human-Machine Interfaces in Process Control: Use of Microsoft WPF for Microworld and Distributed Control System Development

Operators of critical processes, such as nuclear power production, must contend with highly complex systems, procedures, and regulations. Developing human-machine interfaces (HMIs) that better support operators is a high priority for ensuring the safe and reliable operation of critical processes. Human factors engineering (HFE) provides a rich and mature set of tools for evaluating the performance of HMIs, but the set of tools for developing and designing HMIs is still in its infancy. Here we propose that Microsoft Windows Presentation Foundation (WPF) is well suited for many roles in the research and development of HMIs for process control.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

Bridging Equipment Reliability Data and Robust Decisions in a Plant Operation Context

In order to reduce operation and maintenance (O&M) costs, nuclear power plants (NPPs) are moving from corrective and periodic maintenance to predictive maintenance strategies. Such transition requires changes on the data that needs to be retrieved and on the type of decision processes to be employed. Advanced monitoring and data analysis technologies are essential to support predictive strategies. They can in fact provide precise information about health of a component, track its degradation trends, and provide information of its expected failure time. With such information, maintenance operations for a component can be performed right before its expected failure time. This dynamic context of O&M operations requires new methods to analyze data, propagate component health information from the component to the system level, and optimize plant resources. In this respect, the risk informed asset management (RIAM) project has been tasked to develop and test this new class of methods into a risk analytics toolset. This toolset consists of data analytics tools coupled with reliability methods designed to manage plant assets and performances in a predictive maintenance context. This report shows the latest improvements on such development and the initial testing of our methods on the three main research areas that the RIAM project is focusing on. These areas are the following: equipment reliability data analytics, system reliability modeling, and plant resources optimization methods. We show how the methods developed in these areas can support predictive maintenance strategies by: 1) analyzing equipment reliability data (either in numeric and textual form), 2) assessing component and system health through an innovative margin-based reliability approach, and 3) identifying the most critical components and set optimal maintenance schedule based on plant economic and operational constraints.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Identification and mitigation of memory block timing issue in ITk ABCStar during ASIC production

The ABCStar is a mixed-signal front-end readout ASIC for the strips sensor portion of the ATLAS ITk detector being developed as part of the High-Luminosity LHC upgrade. In pre-production testing, a subtle design flaw was uncovered in the ABCStar that was reducing wafer yields in some manufactured lots from the expected 90% to as low as 2%. The root cause was determined to be a timing issue in the logic synthesized to control previously silicon proven memory blocks re-used for this ASIC. The solutions proposed included manufacturing process changes by the wafer foundry, changes to the operating parameters for the ABCStar in the detector, and the possibility that a redesign might be required. The two mitigation efforts were undertaken in parallel, with the process modification route a less desirable solution since already manufactured wafers would need to be scrapped in favour of the new ones. Based on a knowledge of the existing process, and testing done on the worst performing wafers, it was proposed that raising the core operating voltage of the ABCStar from 1.20V to 1.25V could address the timing issue by sufficiently speeding up its transistors. An extensive testing program that included the effects of temperature and radiation expected over the lifetime of the ITk detector was conducted to validate that approach. Those tests and studies proved that even the worst performing wafers would have yields over 80% with the 1.25V core voltage, and neither the modified process nor redesign would be required for ensuring reliable operation of the ITk. Based on testing, a further timing mitigation was implemented to provide an additional margin of reliability by increasing the duty cycle of the clock to the ABCStar. Testing of all ABCStar wafers has been completed and the production of the detector modules using these ASICs is now well underway as a result of the efforts detailed herein.

FOS: Physical sciences↗

DOE OSTI2011

Southwest Region Experiment Station - Final Technical Report

Southwest Technology Development Institute (SWTDI), an independent, university-based research institute, has been the operator of the Southwest Region Photovoltaic Experiment Station (SWRES) for almost 30 years. The overarching mission of SWTDI is to position PV systems and solar technologies to become cost-effective, major sources of energy for the United States. Embedded in SWTDI's general mission has been the more-focused mission of the SWRES: to provide value added technical support to the DOE Solar Energy Technologies Program (SETP) to effectively and efficiently meet the R&D needs and targets specified in the SETP Multi-Year Technical Plan. : The DOE/SETP goals of growing U.S. PV manufacturing into giga-watt capacities and seeing tera-watt-hours of solar energy production in the U.S. require an infrastructure that is under development. The staff of the SWRES has supported DOE/SETP through a coherent, integrated program to address infrastructural needs inhibiting wide-scale PV deployment in three major technical categories: specialized engineering services, workforce development, and deployment facilitation. The SWRES contract underwent three major revisions during its five year period-of- performance, but all tasks and deliverables fell within the following task areas: Task 1: PV Systems Assistance Center 1. Develop a Comprehensive multi-year plan 2. Provide technical workforce development materials and workshops for PV stakeholder groups including university, professional installers, inspectors, state energy offices, Federal agencies 3. Serve on the NABCEP exam committee 4. Provide on-demand technical PV system design reviews for U.S. PV stakeholders 5. Provide PV system field testing and instrumentation, technical outreach (including extensive support for the DOE Market Transformation program) Task 2: Design-for-Manufacture PV Systems 1. Develop and install 18 kW parking carport (cost share) and PV-thermal carport (Albuquerque) deriving and publishing lessons learned Task 3: PV Codes and Standards 1. Serve as the national lead for development and preparation of all proposals (related to PV) to the National Electrical Code 2. Participate in the Standards Technical Panels for modules (UL1703) and inverters (UL1741) Task 4: Assess Inverter Long Term Reliability 1. Install and monitor identical inverters at SWRES and SERES 2. Operate and monitor all inverters for 5 years, characterizing all failures and performance trends Task 5: Test and Evaluation Support for Solar America Initiative 1. Provide test and evaluation services to the National Laboratories for stage gate and progress measurements of SAI TPP winners

14 SOLAR ENERGY↗

DOE OSTI2012

Methodology to Determine the Technical Performance and Value Proposition for Grid-Scale Energy Storage Systems: A Study for the DOE Energy Storage Systems Program

As the amount of renewable generation increases, the inherent variability of wind and photovoltaic systems must be addressed in order to ensure the continued safe and reliable operation of the nation’s electricity grid. Grid-scale energy storage systems are uniquely suited to address the variability of renewable generation and to provide other valuable grid services. The goal of this report is to quantify the technical performance required to provide different grid benefits and to specify the proper techniques for estimating the value of grid-scale energy storage systems.

Byrne, Raymond Harry↗

NASA NTRS1986

Design of an oxygen sensor with automatic self-testing and calibration capability

Future long duration manned space missions require a reliable sensor to monitor cabin oxygen (O2) pressure. This paper presents a concept for a solid zirconia O2 sensor capable of automatic self-test and calibration without the need for consumable materials. The operation and the techniques used to verify proper performance and to calibrate the sensor are described. Coulometric measurements are used to calculate the cabin O2 pressure without comparison to any reference state; potentiometric measurements are simple and reliable. The use of independent measurements permits the verification of results obtained with either technique. The design includes fault diagnostics which takes corrective action if a fault occurs.

Cusick, Robert J.↗

NASA NTRS2019

Advancing the Standards for Unmanned Air System Communications, Navigation and Surveillance

Under NASA program NNA16BD84C, new architectures were identified and developed for supporting reliable and secure Communications, Navigation and Surveillance (CNS) needs for Unmanned Air Systems (UAS) operating in both controlled and uncontrolled airspace. An analysis of architectures for the two categories of airspace and an implementation technology readiness analysis were performed. These studies produced NASA reports that have been made available in the public domain and have been briefed in previous conferences. We now consider how the products of the study are influencing emerging directions in the aviation standards communities. The International Civil Aviation Organization (ICAO) Communications Panel (CP), Working Group I (WG-I) is currently developing a communications network architecture known as the Aeronautical Telecommunications Network with Internet Protocol Services (ATN/IPS). The target use case for this service is secure and reliable Air Traffic Management (ATM) for manned aircraft operating in controlled airspace. However, the work is more and more also considering the emerging class of airspace users known as Remotely Piloted Aircraft Systems (RPAS), which refers to certain UAS classes. In addition, two Special Committees (SCs) in the Radio Technical Commission for Aeronautics (RTCA) are developing Minimum Aviation System Performance Standards (MASPS) and Minimum Operational Performance Standards (MOPS) for UAS. RTCA SC-223 is investigating an Internet Protocol Suite (IPS) and AeroMACS aviation data link for interoperable (INTEROP) UAS communications. Meanwhile, RTCA SC-228 is working to develop Detect And Avoid (DAA) equipment and a Command and Control (C2) Data Link MOPS establishing LBand and C-Band solutions. These RTCA Special Committees along with ICAO CP WG/I are therefore overlapping in terms of the Communication, Navigation and Surveillance (CNS) alternatives they are seeking to provide for an integrated manned- and unmanned air traffic management service as well as remote pilot command and control. This paper presents UAS CNS architecture concepts developed under the NASA program that apply to all three of the aforementioned committees. It discusses the similarities and differences in the problem spaces under consideration in each committee, and considers the application of a common set of CNS alternatives that can be widely applied. As the works of these committees progress, it is clear that the overlap will need to be addressed to ensure a consistent and safe framework for worldwide aviation. In this study, we discuss similarities and differences in the various operational models and show how the CNS architectures developed under the NASA program apply.

Ponchak, Denise S.↗

DOE OSTI2020

What Is the Value of Alternative Methods for Estimating Ramping Needs?

Power system operators procure and deploy flexibility reserves or ramping products to address balancing needs caused by uncertainty and variability of load and generation. Existing methods estimate ramping needs using calendar information and historical forecast errors. Novel methods investigate if real-time weather information could inform ramping and other balancing requirements. This paper compares estimation methods for ramping requirements in theory and practice. The theoretical framework indicates when an alternative method could yield improved economic or reliability performance than existing methods by requiring lower or higher levels of ramping products. Preliminary simulations on a 118-bus test system for 4 days in May 2019 illustrate how system performance improves or deteriorates when ramping requirements are weather-informed (alternative) instead of calendar-based (baseline). Preliminary results suggest high variability in change of performance and underline the impact of additional factors, such as system conditions, on the realized performance change.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2020

Physics-Informed Deep Neural Network Method for Limited Observability State Estimation

The precise knowledge regarding the state of the power grid is important in order to ensure optimal and reliable grid operation. Specifically, knowing the state of the distribution grid becomes increasingly important as more renewable energy sources are connected directly into the distribution network, increasing the fluctuations of the injected power. In this paper, we consider the case when the distribution grid becomes partially observable, due to for example cyber attacks, and the state estimation problem is under-determined. We present a new methodology that leverages a deep neural network (DNN) to estimate the grid state. The standard DNN training method is modified to explicitly incorporate the physical information of the grid topology and line/shunt admittance. We show that our method leads to a superior accuracy of the estimation when compared to the case when no physical information is provided. Finally, we compare the performance of our method to the standard state estimation approach, which is based on the weighted least squares with pseudo-measurements, and show that our method performs significantly better with respect to the estimation accuracy.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Power Electronics Materials and Bonded Interfaces - Reliability and Lifetime

The need for reliable bonded interface materials is critical to realize the performance benefits of wide-bandgap devices in power electronic modules, especially in operating temperatures greater than 150 degrees Celsius. In this paper, we investigate the thermomechanical performance of sintered copper (Cu) as a large-area attachment, bonded between Cu baseplates and active-metal-bonded substrates, under accelerated thermal shock (-40 degrees Celsius to 200 degrees Celsius) conditions. In the fabrication phase of the samples, we used different stencil patterns and found out that the grid and stripe patterns resulted in a better outgassing of the residual organics during the sintering process, thereby ensuring a substantially improved bond quality than a full-area print. The paste consisted of Cu microflakes and we performed sintering using a Budatec SP300 sintering press at 275 degrees Celsius with 15 MPa of bonding pressure for 5 minutes in a nitrogen atmosphere. Under accelerated experiments, we monitored the degradation of the sintered Cu bond in the samples through C-mode scanning acoustic microscope (C-SAM) images. To quantify the defect percentage in C-SAM images, we investigated image denoising techniques to exclude the pattern prints. Finally, we cross-sectioned a sample and obtained digital microscope images, which revealed adhesive fracture as the dominant failure mechanism.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2016

Development of Real-Time Quantitative Safety Evaluation Method for Nuclear Power Plants

Human operators involved with large process control systems such as nuclear power plants (NPPs) perform significant roles in the operation of systems. In this reason, deficiencies in human factors such as poor HSI (Human System Interface) design, procedure, and training, are significant contributing factors to NPPs incidents and accidents. In this regard, many kinds of studies including human reliability analysis (HRA), operation support system, automation, have been conducted in order to reduce human errors. Among these fields of studies, automation has its own specialty since it mainly focuses on eliminating the human error-inducing tasks to reduce human error, while other topics are primarily focusing on reducing human error by aiding human operators during operation. It is commonly believed that replacing human operators to the automated system would guarantee greater efficiency, lower workloads, and fewer human errors. However, these beliefs are not always true in large and complex systems such as NPP, since excessive automation can generate new roles for human operators, change human operators' activities in unexpected ways, and inducing 'out-of-the-loop' (OOTL) problem. Especially, although human error probability drastically increases during abnormal situations in NPP due to overload of information, high workload, and short time available for diagnosis, automation is not actively adopted since conventional machine learning techniques are neither capable nor reliable to handle overwhelmingly complex abnormal situations in NPP. Recently, newly developed 'deep learning' techniques have been actively applied to many fields, and some of these studies proved that the artificial intelligences (AI) based on deep learning techniques show better performance than that of human being. In 2015, deep Q-network (DQN) which is one of the deep learning techniques was developed and applied to train AI that automatically plays various Atari 2800 games, and this AI surpassed the human-level playing in many kind of games. Also in 2016, 'AlphaGo', which was developed by 'Google Deepmind' based on deep learning technique to play the game of Go, was defeated Se-dol Lee who is the World Go champion with score of 4:1. In the point of view that the game of Go is considered as one of the challenging area in machine learning field, it is verified that these new machine learning techniques can perform well, even better than the human being in high-complexity tasks. Previously, most of AI applications in nuclear field were focusing on supporting human operators' tasks such as diagnosis and decision making by rapid classification and prediction, based on fixed training data sets. However, it is expected that AI can be applied to solve more complicated problems (i.e. automation during abnormal states in NPP) by adopting advanced machine learning methodologies. In order to apply the automation system in NPP, firstly it is needed to evaluate the status of the plant quantitatively in real-time, since the information about which state is better (i.e. safe) or worse (i.e. unsafe) is essential for training properly working automation AI. Currently, there are several methodologies for NPP safety evaluation such as probabilistic safety assessment (PSA), safety performance indicator (SPI). However, existing methodologies are not feasible for development of automated system since they are not capable for the cases which are not included in the underlying model, not able to consider delicate changes in the NPP, and hard to evaluate in real-time. Therefore, development of new NPP safety evaluation methodology which can cover previously mentioned requirements is needed to conduct the study on NPP automation further. In this study, the concept of early warning score (EWS) in medical field was adopted to develop the methodology which can evaluate the safety level of the NPP quantitatively and in real-time. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2017

Comparison of ANN (MLP), ANFIS, SVM, and RF models for the online classification of heating value of burning municipal solid waste in circulating fluidized bed incinerators

Highlights: • A rapid, cost-effective, and comparative methodology was proposed to evaluate the HVs of burning MSW online. • Models ranked in descending order of performance are ANFIS, RF, SVM, MLP. • A well trained model is capable of characterizing the variation trend of HVs accurately. - Abstract: The heating values, particularly lower heating values of burning municipal solid waste are critically important parameters in operating circulating fluidized bed incineration systems. However, the heating values change widely and frequently, while there is no reliable real-time instrument to measure heating values in the process of incinerating municipal solid waste. A rapid, cost-effective, and comparative methodology was proposed to evaluate the heating values of burning MSW online based on prior knowledge, expert experience, and data-mining techniques. First, selecting the input variables of the model by analyzing the operational mechanism of circulating fluidized bed incinerators, and the corresponding heating value was classified into one of nine fuzzy expressions according to expert advice. Development of prediction models by employing four different nonlinear models was undertaken, including a multilayer perceptron neural network, a support vector machine, an adaptive neuro-fuzzy inference system, and a random forest; a series of optimization schemes were implemented simultaneously in order to improve the performance of each model. Finally, a comprehensive comparison study was carried out to evaluate the performance of the models. Results indicate that the adaptive neuro-fuzzy inference system model outperforms the other three models, with the random forest model performing second-best, and the multilayer perceptron model performing at the worst level. A model with sufficient accuracy would contribute adequately to the control of circulating fluidized bed incinerator operation and provide reliable heating value signals for an automatic combustion control system.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Forecasting Solar-Thermal Systems Performance under Transient Operation Using a Data-Driven Machine Learning Approach Based on the Deep Operator Network Architecture

Modeling and prediction of the dynamic behavior of thermal systems operating under intermittent energy input and variable load requirements represent one of the greatest challenges in the development of efficient and reliable renewable-based power generation technologies. In this work, a data-driven machine learning modeling framework was developed based on a modified version of the Deep Operator Network architecture where the time coordinate in the trunk net is replaced with historical data of the predicting quantity. The modeling framework can be used to accurately predict the performance of renewable-based energy conversion technologies including wind- and solar-based power plants. This novel framework was applied on a solar-thermal system that consists of a solar collection loop using a flat plate collector, a power generation loop comprising an Organic Rankine Cycle, and a thermal energy storage tank connecting both loops. Variable solar irradiance, air temperature, and power load profiles were used by the Deep Operator Network to predict the State-of-Charge and the efficiency of the thermal system for several days. The results were compared with the State-of-Charge and efficiency functions calculated using a physics-based model. For a simple operation scenario, characterized by a clear sky solar irradiance profile and constant load, the standard deviation in the State-of-Charge prediction by Deep Operator Network is below 0.9% during a seven-day prediction time horizon. For the most realistic operation scenario that considers real solar irradiance and a rough load profile, the maximum standard deviation in the predictions for the State-of-Charge and efficiency are below 6.8% and 2.5%, respectively. A comparison between Deep Operator Network and Long Short Term Memory network was also performed. In general, both networks predict very well the State-of-Charge for different data density conditions; however, a higher accuracy, with a standard deviation below 2.0%, is obtained by the Deep Operator Network during three and half days using sparser training data of 20-minute points. The same accuracy for the State-of-Charge prediction with the Long Short Term Memory network is achieved only for 14 h. Average standard deviations for the State-of-Charge prediction of 1.1% with the Deep Operator Network and 1.5% with the Long Short Term Memory network are obtained for a four-day prediction time using a denser training data of 5-minute points.

DeepONet↗

NASA NTRS2016

A Framework for Assessing the Reusability of Hardware (Reusable Rocket Engines)

Within the space flight community, reusability has taken center stage as the new buzzword. In order for reusable hardware to be competitive with its expendable counterpart, two major elements must be closely scrutinized. First, recovery and refurbishment costs must be lower than the development and acquisition costs. Additionally, the reliability for reused hardware must remain the same (or nearly the same) as "first use" hardware. Therefore, it is imperative that a systematic approach be established to enhance the development of reusable systems. However, before the decision can be made on whether it is more beneficial to reuse hardware or to replace it, the parameters that are needed to deem hardware worthy of reuse must be identified. For reusable hardware to be successful, the factors that must be considered are reliability (integrity, life, number of uses), operability (maintenance, accessibility), and cost (procurement, retrieval, refurbishment). These three factors are essential to the successful implementation of reusability while enabling the ability to meet performance goals. Past and present strategies and attempts at reuse within the space industry will be examined to identify important attributes of reusability that can be used to evaluate hardware when contemplating reusable versus expendable options. This paper will examine why reuse must be stated as an initial requirement rather than included as an afterthought in the final design. Late in the process, changes in the overall objective/purpose of components typically have adverse effects that potentially negate the benefits. A methodology for assessing the viability of reusing hardware will be presented by using the Space Shuttle Main Engine (SSME) to validate the approach. Because reliability, operability, and costs are key drivers in making this critical decision, they will be used to assess requirements for reuse as applied to components of the SSME.

Childress-Thompson, Rhonda↗

NASA NTRS2016

A Framework for Assessing the Reusability of Hardware (Reusable Rocket Engines)

Childress-Thompson, Rhonda↗