Modeling the reliability of complex systems with multiple data sources: a case study on making statistical tools accessible to engineers
Abstract Not Provided
42 ENGINEERING↗
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Comparative Performance Evaluation of High-performance Data Transfer Tools
Data transfer in wide-area networks has been long studied in different contexts, from data sharing among data centers to online access to scientific data. Many software tools and platforms have been developed to facilitate easy, reliable, fast, and secure data transfer over wide area networks, such as GridFTP, FDT, bbcp, mdtmFTP, and XDD. However, few studies have shown the full capabilities of existing data transfer tools from the perspective of whether such tools have fully adopted state-of-the-art techniques through meticulous comparative evaluations. In this paper, we evaluate the performance of the four highperformance data transfer tools (GridFTP, FDT, mdtmFTP, and XDD) in various environments. Our evaluation suggests that each tool has strengths and weaknesses. FDT and GridFTP perform consistently in diverse environments. XDD and mdtmFTP show improved performance in limited environments and datasets during our evaluation. Unlike other studies on data transfer tools, we also evaluate the predictability of the tools' performance, an important factor for scheduling different stages of science workflows. Performance predictability also helps in (auto)tuning the configurable parameters of the data transfer tool. We apply statistical learning techniques such as linear/polynomial regression, and k-nearest neighbors (kNN), to assess the performance predictability of each tool using its control parameters. Our results show that we can achieve good prediction performance for GridFTP and mdtmFTP using linear regression and kNN, respectively.
Nadig, Deepak↗
Reliable automatic alignment of tomographic projection data by passive auto-focus
Purpose: The authors present a robust algorithm that removes the blurring and double-edge artifacts in high-resolution computed tomography (CT) images that are caused by misaligned scanner components. This alleviates the time-consuming process of physically aligning hardware, which is of particular benefit if components are moved or swapped frequently. Methods: The proposed method uses the experimental data itself for calibration. A parameterized model of the scanner geometry is constructed and the parameters are varied until the sharpest 3D reconstruction is found. The concept is similar to passive auto-focus algorithms of digital optical instruments. The parameters are used to remap the projection data from the physical detector to a virtual aligned detector. This is followed by a standard reconstruction algorithm, namely the Feldkamp algorithm. Feldkamp et al.[J. Opt. Soc. Am. A 1, 612-619 (1984)]. Results: An example implementation is given for a rabbit liver specimen that was collected with a circular trajectory. The optimal parameters were determined in less computation time than that for a full reconstruction. The example serves to demonstrate that (a) sharpness is an appropriate measure for projection alignment, (b) our parameterization is sufficient to characterize misalignments for cone-beam CT, and (c) the procedure determines parameter values with sufficient precision to remove the associated artifacts. Conclusions: The algorithm is fully tested and implemented for regular use at The Australian National University micro-CT facility for both circular and helical trajectories. It can in principle be applied to more general imaging geometries and modalities. It is as robust as manual alignment but more precise since we have quantified the effect of misalignment.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Stochastic Models, Indices & Optimization Algorithms for Pricing & Hedging Reliability Risks in Modern Power Grids: Data Plan - Princeton
We collected and cleaned the synthetic grid data produced by NREL for the Texas and New York synthetic grids. We developed a high dimensional joint stochastic model for load at the zone level, and solar and wind power productions at the asset level, capturing the spatial and temporal dependencies between all the variables, and demonstrated how such a model could be fitted to historical data. We designed and implemented a simulation engine which can produce Monte Carlo scenarios for the hourly day-ahead values of load, and solar and wind power productions at the spatial and temporal resolutions of the historical data used to fit the model. Finally we developed an open-source Python package which can, from an input grid model, efficiently use forecasts and large numbers of Monte Carlo scenarios to provide unit commitment and economic dispatch for each of these scenarios. The high dimensional stochastic model and the subsequent Monte Carlo simulation engine were implemented in the package PGscen and the corresponding UC and ED optimization programs in the package Vatic.
14 SOLAR ENERGY↗
$\overline{TKE}$ Parameterization and $\bar{v}$ Uncertainty Analysis for CGMF
Previous work was performed on tuning CGMF parameters for 235 U, 238 U, and Plutonium isotopes. Now work is being done to tune minor uranium isotopes. However, uranium isotopes like 232 U and 236 U have almost no experimental data. We are applying cross-isotope models to extrapolate and tune CGMF on isotopes that lack experimental data. There exist several internal CGMF physics quantities that affect the output of CGMF—multi-chance fission probability, excitation energy sharing, spin-cutoff factor, spin scaling, and fragment total kinetic energy to name a few. The mean fragment total kinetic energy, $\overline{TKE}$, is particularly interesting because of its strong anti-correlation with $\bar{v}$. We are most interested in the mean fragment total kinetic energy before neutron emissions. $\overline{TKE}$ is assumed to be pre-neutron emission unless otherwise stated. Currently in CGMF, the $\overline{TKE}$ model for 233,234,235,238 U are tuned independently to reproduce ν for the associated isotopes. In this report, we will tune a cross-isotope $\overline{TKE}$ model to experimental $\overline{TKE}$ data for 232,233,234,235,236,238 U. Because of the unreliable and sparse nature of $\overline{TKE}$ experimental data, future work will use more reliable experimental $\bar{v}$ data to infer the $\overline{TKE}$ model (and likely other internal CGMF parameters) for uranium isotopes. Such work has been performed previously using a sensitivity analysis and Kalman filter methods.
07 ISOTOPE AND RADIATION SOURCES↗
Establishing robust correction schemes for improved and reliable ARM-AOS aerosol optical data products
Aerosol light absorption and scattering of solar radiation play an important role in the earth’s atmosphere in terms of direct and semi-direct radiative forcing. Optical parameters of importance to the US Department of Energy (DOE) climate models include absorption and scattering coefficients, single scattering albedo (SSA), absorption Angstrom exponents (AAE), and the asymmetry parameter (g). These parameters depend on aerosol size, shape and composition (refractive index), and are spectrally sensitive in the shortwave region. Additionally, these parameters have a complex dependency on the emission source, especially for carbonaceous aerosols. The DOE Atmospheric Radiation Measurement (ARM) user facility has deployed aerosol observing systems (AOS) containing several filter-based instruments to measure and constrain aerosol optical properties and related parameters at multiple sites worldwide. For measurement of aerosol light absorption, the AOS includes filter-based instruments (particle soot absorption photometer and tricolor absorption photometer) that infer particle-phase aerosol absorption coefficients at nominal red, green, and blue wavelength bands from the attenuation (ATN) of light passing through a particulate filter on which aerosols are deposited. Measurement of aerosol scattering is done in situ using nephelometers. By combining inferred absorption coefficients from filter-based ATN measurements and in situ scattering coefficients, value-added products (VAPs) such as SSA, AAE, and g are derived.
54 ENVIRONMENTAL SCIENCES↗
Modeling the reliability of complex systems with multiple data sources: a statistical engineering case study
Abstract Not Provided
99 GENERAL AND MISCELLANEOUS↗
Performance Monitoring of Residential Hot Water Distribution Systems
Current water distribution systems are designed such that users need to run the water for some time to achieve the desired temperature, wasting energy and water in the process. We developed a wireless sensor network for large-scale, long time-series monitoring of residential water end use. Our system consists of flow meters connected to wireless motes transmitting data to a central manager mote, which in turn posts data to our server via the internet. This project also demonstrates a reliable and flexible data collection system that could be configured for various other forms of end use metering in buildings. The purpose of this study was to determine water and energy use and waste in hot water distribution systems in California residences. We installed meters at every end use point and the water heater in 20 homes and collected 1s flow and temperature data over an 8 month period. For a typical shower and dishwasher events, approximately half the energy is wasted. This relatively low efficiency highlights the importance of further examining the energy and water waste in hot water distribution systems.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Upgrade of the Minos+ Experiment Data Acquisition for the High Energy NuMI Beam Run
The Minos+ experiment is an extension of the Minos experiment at a higher energy and more intense neutrino beam, with the data collection having begun in the fall of 2013. The neutrino beam is provided by the Neutrinos from the Main Injector (NuMI) beam-line at Fermi National Accelerator Laboratory (Fermilab). The detector apparatus consists of two main detectors, one underground at Fermilab and the other in Soudan, Minnesota with the purpose of studying neutrino oscillations at a base line of 735 km. The original data acquisition system has been running for several years collecting data from NuMI, but with the extended run from 2013, parts of the system needed to be replaced due to obsolescence, reliability problems, and data throughput limitations. Specifically, we have replaced the front-end readout controllers, event builder, and data acquisition computing and trigger processing farms with modern, modular and reliable devices with few single points of failure. The new system is based on gigabit Ethernet TCP/IP communication to implement the event building and concatenation of data from many front-end VME readout crates. The simplicity and partitionability of the new system greatly eases the debugging and diagnosing process. As a result, the new system improves throughput by about a factor of three compared to the old system, up to 800 megabits per second, and has proven robust and reliable in the current run.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
On-line fission products measurements during a PWR severe accident: the French DECA-PF project
Following the Fukushima accident, a lot of recommendations was drawn by international organizations (IAEA, OECD, NUGENIA network...) in order to improve the safety in such accidental conditions and mitigate their consequences. One of these recommendations was to improve the robustness of the instrumentation, which was dramatically lacking at Fukushima, as well as to better determine the Source Term involved in nuclear accident. The DECA-PF project (Diagnosis of a degraded reactor core through Fission Product measurements) was elaborated in this context and selected as one of 21 collaborative R and D projects in the field of nuclear safety and radioprotection, funded in May 2013 by the French National Research Agency. Over the months following the Fukushima accident, a CEA crisis team was held in order to analyze on-line the situation taking into account the data delivered by TEPCO and other organizations. Despite the difficulties encountered concerning the reliability of these data, the work performed showed the high capacity of Fission Products (FP) measurements to get a diagnosis relative to the status of the reactors and the spent fuel pools (SFP). Based on these FP measurements, it was possible to conclude that the main origin of the releases was coming from the cores and not from the SFP, in particular for SFP-4 which was of high concern, and that the degradation level of the reactors was very large, including probably an extensive core melting. To improve the reliability of this kind of diagnosis, the necessity to get such measurements as soon as possible after the accident and as near as possible from the reactor was stressed. In this way the present DECA-PF project intends to develop a new and innovative instrumentation taking into account the design of the French nuclear power plants on which sand bed filters have been implemented for severe accident management. Three complementary techniques, devoted to measure the FP release on-line, are being studied: - Gamma spectrometry, with an industrial objective to build a prototype aimed at improving the capacity of the present radiation monitoring system, - Gas chromatography, for the quantification of the fission gases (Xe, Kr) as well as potential carbon oxides produced in case of Molten Corium Concrete Interaction, - Optical absorption spectroscopy, the objective of this most innovative technique being to quantify the tetra-oxide of ruthenium, which could be produced in case of lower head failure, and the gaseous forms of iodine (molecular and organic) released in the environment. A global description and the present status of this project is presented, focusing on the Source Term establishment at the outlet stack of the sand bed filters and on the perspectives of implementation of the on-line gamma spectrometry equipment. (authors)
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
INTEGRATION OF DATA ANALYTICS WITH SYSTEM HEALTH PROGRAMS
Industry equipment reliability and asset management programs are essential elements that help ensure the safe and economical operation of nuclear power plants. The effectiveness of these programs is addressed in several industry developed and regulatory programs. However, these programs have proven to be labor intensive and expensive. There is an opportunity to significantly enhance the collection, analysis, and use of this information to provide more cost-effective plant operation. Additionally, there is an acute industry need to leverage advanced technology to reduce costs and improve operational effectiveness. The goal of this paper is to provide effective and efficient analytical methods and tools to support risk-informed decisions for the equipment reliability and asset management programs at nuclear power plants. This is accomplished by creating a direct bridge between component health/lifecycle data and decision making (e.g., maintenance scheduling and project prioritization). Here we are supporting typical system engineer decisions regarding maintenance activity scheduling and component ageing management. This is performed in a risk-informed context where herein the term “risk” is broadly constructed to include both plant reliability and economics. This framework combines data analytics tools to analyze equipment reliability data with risk-informed methods designed to support system engineer decisions (e.g., maintenance and replacement schedules, optimal maintenance posture) in a customizable workflow. A challenge is that the structure of this workflow strongly depends on the decision that needs to be made, the type of data available, and the constraints that need to be considered. Current methods are designed to provide specific answers to specific problems; however, these methods might prove to be inadequate even when problem settings slightly change (e.g., different types of requirements, additional dependencies between system reliability and economics). We tackled this challenge by designing framework in a flexible and modular fashion such that the user can assemble and customize his/her own workflow that integrates SSC economic lifecycle models (e.g., maintenance and replacement costs), system reliability models, and optimization methods.
97 - MATHEMATICS AND COMPUTING↗
Integrating renewal process modeling with Probabilistic Physics-of-Failure: Application to Loss of Coolant Accident (LOCA) frequency estimations in nuclear power plants
Renewal process modeling is used for the failure prediction of hardware components under periodic maintenance. While most studies utilized data-driven approaches to estimate the input parameters for renewal process models, this paper initiates a line of research to integrate renewal process modeling with probabilistic models of underlying mechanisms associated with physical degradation and maintenance. At this stage of the research, the methodology integrates Markov modeling with Probabilistic Physics-of-Failure (PPoF) models of degradation, while maintenance is treated by a data-driven approach. This methodology is valuable to obtain a more accurate estimation of component reliability and availablity, especially when (i) components are highly reliable, and failure data are limited, (ii) historical data are unreliable due to changes in design, operation, and maintenance, or (iii) advanced technologies have emerged limiting operational data. The methodology explicitly incorporates the underlying spatiotemporal causes of failure into the renewal model, allowing to rank the criticality of causal factors to improve maintenance and mitigation strategies. Although the new methodology is applicable for component reliability and availability analysis in diverse industries, this report demonstrates its value for estimating frequencies of a Loss-Of-Coolant Accident (LOCA), which is an initiating event in Probabilistic Risk Assessment (PRA) of Nuclear Power Plants (NPPs).
42 ENGINEERING↗
PV Systems Reliability and Outreach
The project purpose was to collaborate with the Electric Power Research Institute (EPRI) to reach out to the photovoltaic (PV) industry to cooperate, collaborate and facilitate establishment and access to PV plant data for use in the Photovoltaic Reliability Operations and Maintenance (PVROM) database. Sandia National Laboratories (SNL) and EPRI conducted this outreach to expand and accelerate the implementation of PVROM by gathering more data for analyzing trends, with a goal of accelerating the adoption of PV systems as a primary power generation source in the United States. SNL and EPRI analyzed the data from the PVROM database to assess industry trends in operations and maintenance (O&M) and develop guidelines for the industry. More specifically, this project was intended to leverage SNL’s expertise in PV plant reliability data collection and analysis, with data sharing partners that own or operate PV systems who wanted to learn about rigorous reliabilitybased data collection to track issues over time and improve O&M activities. The partnership also worked to host multiple workshops as part of the SNL/EPRI PV Systems Symposium to gather industry feedback on O&M process improvement and data standards, and share insights learned from the collaboration between SNL and EPRI in the area of PV system reliability. Additional outreach included multiple ‘expert elicitation’ interviews led by EPRI to gather best practices and paths forward for improving/standardizing PV plant O&M and asset management practices. Technical accomplishments included further development of the PVROM database process, sharing best practices for reliability data collection and improving industry understanding of existing O&M practices, important knowledge, and new and novel techniques for benefitting EPRI utility partners and the broader PV industry. Improved reliability at a PV power plant leads to a lower levelized cost of energy (LCOE), which is reflected most directly in its operating expenses. Electric utilities that utilize PV as a larger share of plants they own and operate are interested in having a high degree of availability or ‘uptime’, similar to other power generation sources in their service area. Having low cost, reliable PV generation can lead to fewer costs that need to be recovered by the utility over the lifetime of the power plant, and less regulatory uncertainty when considering bulk transmission and distribution system reliability. The public and taxpayers benefit by the electric utility passing through lower electricity costs.
14 SOLAR ENERGY↗
An Experimental Investigation of Human Performance Differences Depending on Simulator Complexity
As a different approach to collect human reliability analysis (HRA) data compared to the full-scope simulator studies, Idaho National Laboratory (INL) has attempted to collect HRA data based on Simplified Human Error Experimental Program (SHEEP), which uses a simplified simulator and student participants. To date, INL has considered the SHEEP approach using simplified simulators such as Rancor Microworld and Compact Nuclear Simulator to complement – not replace – full-scope studies as well as to mainly collect HRA data for estimating nominal/basic human error probabilities (HEPs) needed in the HRA quantification process. This study is a part of the project aiming to suggest how to support full-scope data collection studies based on SHEEP. This paper first introduces major tasks within the SHEEP framework. Then, as one of the major tasks, why and how we have planned to experimentally investigate human performance differences depending on simulator complexity are mainly introduced in this paper.
99 GENERAL AND MISCELLANEOUS↗
DEVELOPMENT AND APPLICATION OF RISK ANALYSIS TOOLKIT FOR PLANT RESOURCE OPTIMIZATION
This paper presents the development of methods and tools that are being designed to optimize plant operations (e.g., maintenance/replacement schedules and optimal maintenance postures for plant components) in a manner that is more cost effective than current approaches and makes better use of available component health and cost data. These methods include both data- and model-based optimization methods. Model-based optimization methods directly include reliability and cost models to determine an optimal plant operational strategy. We consider gradient-based and evolutionary (based on genetic algorithms) optimization methods. The second class of methods target more specific use cases (e.g., project schedule optimization) and are not based on reliability models directly, but they require specific component reliability and cost data. This class of methods is based on variants of the knapsack problem with an aim to determine an optimal project schedule that maximizes the overall NPV. This paper also presents multi-objective methods designed to identify an optimal maintenance posture based on a Pareto frontier analysis. Rather than dictating the “right” tradeoff (i.e., identify the absolute best posture), we show how it is possible to perform a trade space exploration approach (i.e., identify value and costs of several postures and let the analysis account for desired value and cost metrics). This is performed by identifying maintenance postures that maximize value (e.g., system availability) and minimize operational costs, i.e., the Pareto frontier in a value-cost trade space. For all these methods we present detailed applicative examples that show their validity from a decision-making perspective.
97 - MATHEMATICS AND COMPUTING↗
Data Imbalance, Uncertainty Quantification, and Transfer Learning in Data‐Driven Parameterizations: Lessons From the Emulation of Gravity Wave Momentum Transport in WACCM
Abstract Neural networks (NNs) are increasingly used for data‐driven subgrid‐scale parameterizations in weather and climate models. While NNs are powerful tools for learning complex non‐linear relationships from data, there are several challenges in using them for parameterizations. Three of these challenges are (a) data imbalance related to learning rare, often large‐amplitude, samples; (b) uncertainty quantification (UQ) of the predictions to provide an accuracy indicator; and (c) generalization to other climates, for example, those with different radiative forcings. Here, we examine the performance of methods for addressing these challenges using NN‐based emulators of the Whole Atmosphere Community Climate Model (WACCM) physics‐based gravity wave (GW) parameterizations as a test case. WACCM has complex, state‐of‐the‐art parameterizations for orography‐, convection‐, and front‐driven GWs. Convection‐ and orography‐driven GWs have significant data imbalance due to the absence of convection or orography in most grid points. We address data imbalance using resampling and/or weighted loss functions, enabling the successful emulation of parameterizations for all three sources. We demonstrate that three UQ methods (Bayesian NNs, variational auto‐encoders, and dropouts) provide ensemble spreads that correspond to accuracy during testing, offering criteria for identifying when an NN gives inaccurate predictions. Finally, we show that the accuracy of these NNs decreases for a warmer climate (4 × CO 2 ). However, their performance is significantly improved by applying transfer learning, for example, re‐training only one layer using ∼1% new data from the warmer climate. The findings of this study offer insights for developing reliable and generalizable data‐driven parameterizations for various processes, including (but not limited to) GWs.
54 ENVIRONMENTAL SCIENCES↗
Distinguishing isotropic and anisotropic signals for X-ray total scattering using machine learning
Understanding structure–property relationships is essential for advancing technologies based on thin films. X-ray pair distribution function (PDF) analysis can access relevant atomic structure details spanning local-, mid- and long-range structure. While X-ray PDF has been adapted for thin films on amorphous substrates, measurements on single-crystal substrates are necessary to accurately determine structure origins for some thin film materials, especially those for which the substrate changes the accessible structure and properties. However, when measuring films on single-crystal substrates, high-intensity anisotropic Bragg spots saturate 2D detector images, overshadowing the thin films' isotropic scattering signal. This renders previous data processing methods for films on amorphous substrates unsuitable for films on single-crystal substrates. To address this measurement need, we developed IsoDAT2D, an innovative data processing approach using unsupervised machine learning algorithms. The program combines dimensionality reduction and clustering algorithms to separate thin film and single-crystal substrate X-ray scattering signals. We use SimDAT2D , a program we developed to generate simulated thin film data, to validate IsoDAT2D . Here we also use IsoDAT2D to isolate X-ray total scattering signal from a thin film on a single-crystal substrate. The resulting PDF data are compared with similar data processed using previous methods, especially substrate subtraction for single-crystal and amorphous substrates. PDF data from IsoDAT2D -identified X-ray total scattering data are significantly better than from single-crystal substrate subtraction, but not as reliable as PDF data from amorphous substrate subtraction. With IsoDAT2D , there are new opportunities to expand PDF to a wider variety of thin films, including those on single-crystal substrates, with which new structure–property relationships can be elucidated to enable fundamental understanding and technological advances.
36 MATERIALS SCIENCE↗
artdaq
The artdaq toolkit is a data-acquisition framework designed for high-energy physics experiments. It provides a flexible, reliable backbone for data transfers and has several locations where users can perform custom analysis tasks using the art framework.
Flumerfelt, EricL. [Fermi National Accelerator Lab↗