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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Why is the winner the best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multi- center study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), im- age preprocessing (97%), data curation (79%), and post- processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

Eisenmann, Matthias↗
DOE OSTI2021

Laboratory earthquake forecasting: A machine learning competition

Earthquake prediction, the long-sought holy grail of earthquake science, continues to confound Earth scientists. Could we make advances by crowdsourcing, drawing from the vast knowledge and creativity of the machine learning (ML) community? We used Google’s ML competition platform, Kaggle, to engage the worldwide ML community with a competition to develop and improve data analysis approaches on a forecasting problem that uses laboratory earthquake data. The competitors were tasked with predicting the time remaining before the next earthquake of successive laboratory quake events, based on only a small portion of the laboratory seismic data. The more than 4,500 participating teams created and shared more than 400 computer programs in openly accessible notebooks. Complementing the now well-known features of seismic data that map to fault criticality in the laboratory, the winning teams employed unexpected strategies based on rescaling failure times as a fraction of the seismic cycle and comparing input distribution of training and testing data. In addition to yielding scientific insights into fault processes in the laboratory and their relation with the evolution of the statistical properties of the associated seismic data, the competition serves as a pedagogical tool for teaching ML in geophysics. The approach may provide a model for other competitions in geosciences or other domains of study to help engage the ML community on problems of significance.

58 GEOSCIENCES↗
DOE OSTI2020

Sterilization of N95 Respirators via Gamma Radiation: Comparison of Post-sterilization Efficacy

This study evaluated gamma irradiation for sterilization and reuse of two models of N95 respirators after gamma radiation sterilization as a method to increase availability of N95 respirators during a shortage. The Sandia National Laboratories Gamma Irradiation Facility was used to irradiate two different models of N95 filtering facepiece respirators at doses ranging from 0 kGy(tissue) to 50 kGy(tissue). The following tests were used to determine the efficacy of the respirator after irradiation sterilization: Ambient Aerosol Condensation Nuclei Counter Quantitative Fit Test, tensile test, strain cycling, oscillatory dynamic mechanical analysis, microscopic image analysis of fiber layers, and electrostatic field measurements. Both of the respirator models exhibited statistically significant changes after gamma irradiation as shown by the Quantitative Fit Test, electrostatic testing and the aerosol testing. The change in electrostatic capability of the filter reduced the efficiency of challenging particles near the 200 nm size by approximately 40-50%. Both tested respirators showed statistically significant changes associated with gamma sterilization. However, our results indicate that choices in materials and manufacturing methods to achieve N95 filtration lead to different magnitudes of damage when exposed to gamma radiation at sterilization relevant doses. This damage results in lower filtration performance. While our sample size (2 different types of respirators) was small, we did observe a change in electrostatic properties on a filter layer that coincided with the failure on the Quantitative Fit Test.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

System Study: Auxiliary Feedwater 1998-2020

This report presents an unreliability evaluation of the auxiliary feedwater (AFW) system at 69 U.S. commercial nuclear reactors. Demand, run hour, and failure data from calendar year 1998–2020 for selected components were obtained from the Institute of Nuclear Power Operations (INPO) Industry Reporting and Information System (IRIS), formerly the INPO Consolidated Events Database (ICES). The unreliability results are trended for the most recent 10-year period while yearly estimates for system unreliability are provided for the entire active period. Statistically significant decreasing trends were identified in the industry-wide estimates of AFW system start-only unreliability and AFW system 8-hour mission unreliability.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Assessment of EPRI’s Tan Delta Approach to Manage Cables in Submerged Environments: Statistical Review of EPRI Data

Research conducted by the Electric Power Research Institute (EPRI) and other research institutions have concluded that water-trees are one of the leading degradation mechanisms that contribute to the loss of dielectric insulation strength in medium-voltage cable insulating materials in wet or submerged environments. The electrochemical reactions are caused by the combined effect of water presence and relatively high electrical stress. Records of cable failures provided by the licensees in response to Generic Letter (GL) 2007-01 (NRC 2007) have called into question the reliability of medium voltage cables in wetted or submerged environments. EPRI’s dissipation factor or Tan Delta testing guidelines and acceptance criteria have been adopted by most nuclear power plant operators as the primary tool for condition monitoring of medium voltage cables in wet or submerged environments. EPRI has been collecting member data since late 2009 to analyze and provide feedback to members, validate the EPRI-developed acceptance criteria guidelines, support analysis of test results, recommend appropriate actions for the "action required" category, and gather candidate cables for EPRI-sponsored forensic research on causes for insulation degradation. EPRI has collected data from 37 nuclear sites, which represent 44 operating units. The test results have been organized by insulation type, such as cross-linked polyethylene (XLPE); butyl rubber; black, pink, and brown ethylene-propylene rubber (EPR); and compact insulation (black and pink EPR)1. The data have been analyzed, and follow-up information was obtained from members for “action required” test results. EPRI has also performed correlations between Tan Delta tests and the information gathered under the EPRI forensic research on medium-voltage cables. In addition, EPRI has developed guidance by cable insulation type on how to systematically analyze Tan Delta test results. The analysis described here reviewed the two primary EPRI reports (EPRI 3002000557 and EPRI 3002005321) as well as two precedent EPRI reports (EPRI 1028262 and EPRI 1021070) that were cited in the primary reports. The principal results and conclusions from the project analyses are provided.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Sterilization of N95 Respirators via Gamma Radiation: Comparison of Post-sterilization Efficacy

This study evaluated gamma irradiation for sterilization and reuse of two models of N95 respirators after gamma radiation sterilization as a method to increase availability of N95 respirators during a shortage. The Sandia National Laboratories Gamma Irradiation Facility was used to irradiate two different models of N95 filtering facepiece respirators at doses ranging from 0 kGy(tissue) to 50 kGy(tissue). The following tests were used to determine the efficacy of the respirator after irradiation sterilization: Ambient Aerosol Condensation Nuclei Counter Quantitative Fit Test, tensile test, strain cycling, oscillatory dynamic mechanical analysis, microscopic image analysis of fiber layers, and electrostatic field measurements. Both of the respirator models exhibited statistically significant changes after gamma irradiation as shown by the Quantitative Fit Test, electrostatic testing and the aerosol testing. The change in electrostatic charge of the filter was correlated with a reduction in capturing particles near the 200 nm size by approximately 40-50%. Both tested respirators showed statistically significant changes associated with gamma sterilization. However, our results indicate that choices in materials and manufacturing methods to achieve N95 filtration lead to different magnitudes of damage when exposed to gamma radiation at sterilization relevant doses. This damage results in lower filtration performance. While our sample size (2 different types of respirators) was small, we did observe a change in electrostatic properties on a filter layer that coincided with the failure on the Quantitative Fit Test and reduction in aerosol filtering efficiency. Key Words: N95 respirators, respirators, airborne transmission, pandemic prevention, COVID-19, gamma sterilization

COVID-19↗
DOE OSTI2022

System Study: Emergency Power System 1998-2020

This report presents an unreliability evaluation of the emergency power system (EPS) at 104 U.S. commercial nuclear power plants. Demand, run hours, and failure data from 1998 through 2020 for selected components were obtained from the Institute of Nuclear Power Operations (INPO) Industry Reporting and Information System (IRIS), formerly the INPO Consolidated Events Database (ICES). The unreliability results are trended for the most recent 10 year period while yearly estimates for system unreliability are provided for the entire active period. Statistically significant decreasing trends were identified in the industry-wide estimates of EPS system start-only mission as well as EPS system unreliability (8-hour mission).

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Uncertainty Quantification Enabled by Automatic Differentiation for Hydrodynamic Simulation of Shock‐to‐Detonation Transition in High Explosives

Quantifying the effects of uncertainty in a reactive burn model on the run-to-detonation time in high explosives (HEs) provides a robust methodology for assessing the probability of an HE failing the IHE qualification standard. Moreover, uncertainty quantification helps evaluate whether the model calibration accurately represents data outside the calibration set. This study uses a specialized hydrodynamic simulation code for modeling detonation to determine the run-to-detonation time of the HE PBX 9502 for various impact velocities. To quickly approximate uncertainties in the model, a surrogate was constructed using a Taylor series expansion centered at the mean of the input parameters. To obtain the sensitivities required for constructing the Taylor series, HYP-percomplex Automatic Differentiation (HYPAD) was implemented. HYPAD is a methodology for infusing existing codes with automatic differentiation capabilities by augmenting variables with one or more imaginary units to compute step-size independent partial derivatives. These derivatives are accurate to machine precision with respect to the implemented numerical algorithm, meaning their accuracy reflects that of the underlying method (e.g., integration or discretization schemes). Using reduced order modeling techniques, the mean and standard deviation of the run-to-detonation time of a shock within PBX 9502 were computed for a number of initial impact velocities. A weighted least squares regression was then performed to obtain a best fit curve and prediction interval for the computed statistics. Historical data points from explosively driven wedge tests were utilized to validate the prediction interval, ensuring its reliability in predicting future outcomes. With this prediction interval and a known safety constraint curve, the most probable point of failure and the probability of failure for the HE PBX 9502 were determined.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

EMP-Resilient Electric Grid Transformer Analysis

The response of a high - voltage (HV) transformer to fast rise time voltages, such as that from a electromagnetic pulse (EMP) can result in interruption of power distribution and possibly system failure. To help identify these potential occurrences, it is necessary to develop a transformer model that not only captures the input/output response of the transformer but also the internal behavior. The model constructed should cover the frequency band of interest while capturing the internal physical and electrical characteristics. This broad-band, high-fidelity model would enable the prediction of unwanted effects through simulation. A proposed modeling scheme for a HV transformer is described in Part 1 of this report. Part 2 of this report details assessments of internal voltage and electrical field holdoff testing of transformer insulation dielectric breakdown, including comparison of low frequency (DC/60 Hz) holdoff to rise times characteristic of lightning (1 s) and EMP E1 transients (10 - 30 ns). This initial project is a path toward establishing electrical grid transformer failure criteria for EMP voltage transients. We developed modeling methods and measured breakdown electrical field statistical distributions for direct current, 60 Hz, lightning and EMP characteristic voltage rise times. Methods of nanosecond-scale capacitive discharge unit high voltage source development, suggestions for derating of 60 Hz insulation maximum electrical fields for EMP nanosecond pulse voltage withstand rating, and potential methods for increasing transformer resilience to such fast rise time pulses are described.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Brittle failure analysis and modeling of high-burnup PWR fuel cladding alloys

The aim of this research is the development of methods for predicting mechanical behavior and identification of limiting conditions to prevent brittle failure of high-burnup (HBU) pressure water reactor (PWR) fuel cladding alloys. A finite element (FE) model of the ring compression test (RCT) was created to analyze the failure behavior of zirconium-based alloys with radial hydrides during the RCT. An elastic-plastic material model describes the zirconium alloy. The stress-strain curve needed for the elastic-plastic material model was derived by inverse finite element analyses. Cohesive zone modeling is used to reproduce sudden load drops during RCT loading. Based on the failure mechanism in non-irradiated ZIRLO (R) claddings, a micro-mechanical model was developed that distinguishes between brittle failure along hydrides and ductile failure of the zirconium matrix. Two different cohesive laws representing these types of failure are present in the same cohesive interface. The key differences between these constitutive laws are the cohesive strength, the stress at which damage initiates, and the cohesive energy, which is the damage energy dissipated by the cohesive zone. Statistically generated matrix-hydride distributions were mapped onto the cohesive elements and simulations with focus on the first load drop were performed. Computational results are in good agreement with the RCT results conducted on high-burnup M5 (R) samples. It could be shown that crack initiation and propagation strongly depend on the specific configuration of hydrides and matrix material in the fracture area.

Simbruner, Kai↗
DOE OSTI2025

Potentials of mean force fail to describe chemical bond-breaking in solution

Many liquid phase studies assume that the potential energy surfaces of reacting molecules are the same as in the gas phase, neglecting complex solvent dynamics that can completely alter the nature of chemical reactivity. Even studies that include solvent effects typically only consider them in an average, equilibrium way as part of a potential of mean force (PMF). In this work, we use mixed quantum/classical simulations to compare how equilibrium and non-equilibrium solvent motions affect the photodissociation of a simple diatomic molecule, NaK + , in liquid tetrahydrofuran. A PMF analysis shows that as the excited-state molecule dissociates with the solvent at equilibrium, the bonding electron remains associated with K + at short bond distances but eventually localizes on Na + at the end of dissociation. When we examine non-equilibrium dynamical photodissociation trajectories, however, we find that they fall into three distinct categories: about a quarter of them have the bonding electron mainly associated with Na + , another quarter stay mainly associated with K + , and about half have the bonding electron shared roughly equally between the two ions. The results show that equilibrium PMFs cannot accurately describe the dynamics of bond-breaking chemical reactions in solution because there is insufficient time for the solvent to reach equilibrium on the time scale over which bond dissociation occurs. Furthermore, our analysis shows that the solvent coupling between the electronic energy surfaces is similar at and away from equilibrium, suggesting that other factors, such as solute velocity-driven solvent memory effects, play a more important role in explaining the failure of the equilibrium PMF to predict the non-equilibrium dynamics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Natural Language Processing-Enhanced Nuclear Industry Operating Experience Data Analysis: Aggregation and Interpretation of Multi-Report Analysis Results

Industry-wide operating experience is a critical source of raw data for reliability and risk model parameter estimations for nuclear power plants. A large portion of operating experience data are failure events stored as reports that contain unstructured data, such as narratives. In current practice, a failure report is usually reviewed and manually coded by analysts. The coding is based on extracting several event characteristics such as system name, component type, sub-part type, failure mode, and failure cause. Event narratives are mostly used to help understand events and extract their characteristics. In this line of research, we aim to maximize the usage of event narratives by leveraging natural language processing (NLP) methods to automatically convert an event narrative to a causal graph. This research has promise to improve physical understanding of failure initiation and propagation and to facilitate use of non-failure data (e.g., near-misses and degradations) to complement the limited data pool of failures. In our previous work, we developed an NLP tool and applied it to analyze a number of licensee event reports submitted by U.S. nuclear power plants to the Nuclear Regulatory Commission. In this paper, we will report our recent research progress in aggregating the results of multiple reports, developing network model(s), and drawing statistical insights.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Genetic programming for interpretable, data-driven continuum damage models.

The damage mechanisms that lead to failure in engineering alloys have been studied extensively, but converting this knowledge into constitutive models that are suitable for engineering-scale analysis remains a challenge. Evolution laws for continuum damage have been developed in the past and have proven effective but suffer from many non-physical assumptions that inhibit the overall accuracy of the model. Further, the assumptions inherent in these existing models prevent them from being applicable to a broad class of materials. At the same time, computational models of fine-scale damage mechanisms continue to advance making it tractable to generate large training data sets through computer simulation. Data-driven machine learning approaches can leverage these data sets to avoid making limiting assumptions, and instead produce models directly from the results of microstructural simulations and/or experiments. Many of these machine learning approaches are rapid and accurate, but they offer little to no insight into the underlying relationships among state variables being discovered. Conversely, genetic programming symbolic regression (GPSR) is a machine learning method that produces analytic expressions relating the state variables, allowing maximal insight and interpretability. To that end, we propose using GPSR as a data-driven method of obtaining microstructurally informed continuum damage models. Data is generated using microstructural simulations of damage evolution, parameterized over microstructural statistics (i.e., pore shape) and nominally applied deformations. Analytic expressions for damage evolution are obtained from the data using GPSR, and these expressions are then utilized within a continuum constitutive model. Overall, this approach is a promising method of automatically obtaining analytic relations describing constitutive phenomena in a material.

Buche, Michael Robert↗
DOE OSTI2024

A retrospective review of von Neumann’s analysis of hidden variables in quantum mechanics

This article reviews the history of J. von Neumann’s analysis of hidden variables in quantum mechanics and the subsequent analysis by others. In his book The Mathematical Foundations of Quantum Mechanics , published in 1932, von Neumann performed an analysis of the consequences of introducing hidden parameters (hidden variables) into quantum mechanics. He arrived at two principal conclusions: first, hidden variables cannot be incorporated into the existing theory of quantum mechanics without major modifications, and second, if they did exist, the theory would have already failed in situations where it has been successfully applied. This analysis has been taken as an “incorrect proof” against the existence of hidden variables, possibly due to a mistranslation of the German word prufen . von Neumann’s so-called proof isn’t even wrong as such a proof does not exist, but it is an examination of the limitations imposed by internal consistency of the Hilbert space formulation of the theory. One of the earliest attempts to eliminate uncertainty, by D. Bohm, requires a major modification of quantum mechanics (observables are not represented by Hermitian operators), which supports von Neumann’s first principal conclusion. However, testing the Bohm theory requires constructing a physically impossible initial state. As such, the theory has no experimental consequences, so W. Pauli referred to it as an “uncashable check”. As there are no observable consequences, the Bohm theory is possibly a counterexample to von Neumann’s second conclusion that hidden variables in particular would have already led to a failure of the theory.

density matrix↗
DOE OSTI2023

Main Bearing Replacement and Damage - A Field Data Study on 15 Gigawatts of Wind Energy Capacity

This study seeks to establish a comprehensive baseline of knowledge for the replacement and damage of main bearings in wind turbines. The purpose of this report is to provide a high-level summary of the data set, methodology, and results of this work. Full technical details and an extended analysis will be made available in a future publication. We collected data on main bearing replacements and reported damage from industrial partners based in Europe and the United States. In total, we obtained data for 167 wind power plants, with a combined capacity of 15.3 gigawatts (GW). Most of the data set was comprised of land-based, three-point mount, spherical roller bearings. Within this data set were 689 instances of main bearing replacement. Analysis was undertaken in two parts: first, a statistical analysis of the main bearing time-to-replacement data using survival analysis techniques; second, quantitative and qualitative analyses of the obtained damage information. Our results showed that 10% of a fixed main bearing population would be expected to have been replaced by 10.5 years. This is close to half of the 20-year design value. Fitted parametric distributions then indicated that by year 20, between 22% and 25% of main bearings are expected to have been replaced. Analysis of the damage reports revealed spalling to be the main type of damage listed. The additional presence of surface damage in the collected data indicates that at least part of the spalling cases are likely due to surface-initiated rolling contact fatigue. At this stage is not clear what proportion of spalling cases result from "wear induced", surface-initiated and subsurface-initiated rolling contact fatigue. While this work provides important insights into the current state of main bearing replacements and damage, many questions remain. An ongoing and expanding data collection and analysis effort focused on wind turbine main bearings is therefore recommended.

17 WIND ENERGY↗
DOE OSTI2023

Probabilistic Multi-Hazard Performance Assessment of Concrete Structures in Nuclear Installations

Concrete structures in nuclear installations are subject to time-dependent degradation mechanisms that can deteriorate their physical and mechanical properties, potentially exacerbating the risk of structural failure under external forces such as a seismic event. Previous research has extensively investigated the seismic response of nuclear concrete structures and the associated risk, as well as their effect on structural components safety margins. However, substantial work is still necessary to incorporate concrete aging effects into such evaluations. In fact, most models in the literature assume pristine concrete conditions and do not account for the impact of aging on the structural components’ fragility curves. This work identifies relevant time-dependent degradation mechanisms and provides simplified models to predict the the evolution of key material properties based on data from the literature. Namely, this work focuses on the aging effects of corrosion, alkali–silica reaction (ASR), and irradiation on reinforced concrete within US Department of Energy (DOE) nuclear facilities and nuclear power plants (NPP) structures. Furthermore, degradation models based on literature data are presented that define the relationship between probabilistic material properties and the concrete’s age. In this work, sampled material properties served as input for a simplified finite element model (FEM) of a critical nuclear structural system, with the output of the FEM being the seismic response for a given ground motion. The results of the FEM were then used within a probabilistic performance assessment with a statistically significant number of samples. The research presented herein addresses the detrimental effects of hazards caused by natural phenomena on deteriorated concrete elements of nuclear installations. This work directly benefits the safety analysis performed on US DOE/ National Nuclear Security Administration (NNSA) nuclear facilities located in areas prone to seismic activity. The results presented herein could aid in the improvement of DOE-STD-1020, the DOE Standard that addresses seismic risk analysis and capacity evaluation in DOE facilities. DOE-STD-1020 refers to the requirements in American Society of Civil Engineers (ASCE) 4-98, now superseded by ASCE 4-16, that shall be met in performing dynamic response analyses and generating in-structure response spectra, provided that such requirements are consistent with the requirements of ASCE/Structural Engineering Institute (SEI) 43-05. Moreover, the results presented herein could also aid in the updating of section C3.1.1. of ASCE 4-16 to account for the effects of aging on the stiffness of reinforced elements and American Concrete Institute (ACI) 349.3R-18, “Report on Evaluation and Repair of Existing Nuclear Safety-Related Concrete Structures.” Ultimately, this work can assist the risk assessment of potential lifetime extension of the existing US commercial nuclear fleet (light water reactors) and the safety analysis of the emerging advanced nuclear reactors. The proposed proof-of-concept methodology employs open-source DOE computational tools and is transferable to commercial software commonly used by engineering firms.

42 ENGINEERING↗
DOE OSTI2021

Development of a compressive failure model for carbon fiber composites and associated uncertainties.

An approach to increase the value of carbon fiber for wind turbines blades, and other compressive strength driven designs, is to identify pathways to increase its cost-specific compressive strength. A finite element model has been developed to evaluate the predictiveness of current finite element methods and to lay groundwork for future studies that focus on improving the cost-specific compressive strength. Parametric studies are conducted to understand which uncertainties in the model inputs have the greatest impact on compressive strength predictions. Furthermore, a statistical approach is also presented that enables the micromechanical model, which is deterministic, to efficiently account for statistical variability in the fiber misalignment present in composite materials; especially if the results from the hexagonal and square pack models are averaged. The model was found to agree well with experimental results for a Zoltek PX-35 pultrusion. The sensitivity studies suggest that the fiber packing and the interface shear strength have the greatest impact on compressive strength prediction for the fiber reinforced polymer studied here. Based on the performance of the modeling approach presented in this work, it is deemed sufficient for future work which will seek to identify carbon fiber composites with improved cost-specific compressive strength.

36 MATERIALS SCIENCE↗
DOE OSTI2020

An Applied Strategy for Using Empirical and Hybrid Models in Online Monitoring

The monitoring of plant equipment for failure prediction is one of the key contributors to operation and maintenance (O&M) costs for a nuclear power plant (NPP) because O&M monitoring depends on labor-intensive activities that are required to meet high equipment reliability standards. These activities rely primarily on humans for information gathering, condition diagnosis, and predictive analysis. Online monitoring aims to automate these activities by relying on sensors to replace human information gathering and machine learning to replace human analysis and decision making. To facilitate automated monitoring, a systematic strategy for anomaly detection is needed to optimally use the available sensor data, empirical models, and physics-supported models. This strategy is essential to provide credible reasoning on why and when an empirical (i.e., purely data-driven) versus hybrid (i.e., physics-supported) approach should be used and to determine the ideal mix of these two approaches for a defined anomaly detection scope. The extant methods usually adopt an ad hoc trial-and-error approach that, in addition to being time-consuming and costly, is also highly subjective; it is impacted by the background and the skill set of the personnel making the decisions. Thus, such an approach cannot guarantee an optimum outcome. This represents the motivation of the current research effort, which is focused on devising a scientifically supported strategy for the optimum selection of anomaly detection methods. This report presents a detailed assessment of the main anomaly detection techniques within the empirical or hybrid method streams. Empirical methods include pattern, statistical, and causal inference. Hybrid methods include the use of physics models to train and test data methods, reduce data dimensionality, reduce data-model complexity, augment data, and reduce empirical uncertainty; hybrid methods also include the use of data to tune physics models. The listed techniques within these two streams represent the vast majority of techniques performed for anomaly detection. Using the techniques as outcomes, a strategy was developed to enable a systematic decision-making process to lead to one of these techniques. The strategy is driven by key decision points related to data relevance, simple modeling feasibility, data inference, physics-modeling value, data dimensionality, physics knowledge, method of validation, performance, data availability and suitability for training and testing, cause-effect, entropy inference, and model fitting. Each of these decision points in the strategy is explained in detail in this report with examples, along with the scientific basis behind the decisions and outcomes in common and simplified terminology. The strategy is developed for use by any NPP staff with basic engineering or science knowledge. A user-friendly graphical state flow diagram was also developed as a visual presentation of the strategy. The strategy was tested and demonstrated through two pilot projects for the application of anomaly detection at an NPP. Each pilot had two use cases: an initial case in which certain decisions were made that resulted in one or more empirical techniques and a revised use case where one or more key decisions were modified resulting in using a set of hybrid methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗