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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

“Shoulda, Coulda, Woulda”: Conceptualizing the Differences in Trust Between Human-Human Teaming and Human-Machine Teaming

Intelligent decision support systems (IDSSs) are machine teammates designed to facilitate better human decision-making in high-consequence domains such as health care, power grid operations, and fraud detection. IDSSs identify patterns in datasets and provide intelligent decision-making recommendations to human teammates. However, previous research indicates that humans often trust IDSS recommendations less than the recommendations from their human teammates, even when the machine teammate is more accurate. To conceptualize why trust differs, we review the literature surrounding trust, error, and predictability. Then, we compile and compare participant trust ratings and decision-making in an abridged systematic review of previous studies manipulating teammate type, error rate, and error type. Finally, we conduct a content analysis of participants’ qualitative responses to trust queries from a survey on generative language models. Results suggest that humans may trust IDSS teammates less than other human teammates because of differences in (1) interaction complexity, (2) blame attribution, and (3) swift trust. We conclude that human factors practitioners should collaborate with data scientists and domain experts to build and maintain trust in IDSSs by anthropomorphizing algorithms, matching mental models, and considering individual differences.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A Typology of Decision-Making Tasks for Visualization

Despite decision-making being a vital goal of data visualization, little work has been done to differentiate decision-making tasks within the field. While visualization task taxonomies and typologies exist, they often focus on more granular analytical tasks that are too low-level to describe large complex decisions, which can make it difficult to reason about and design decision-support tools. In this paper, we contribute a typology of decision-making tasks that were iteratively refined from a list of design goals distilled from a literature review. Our typology is concise and consists of only three tasks: CHOOSE, ACTIVATE, and CREATE. Although decision types originating in other disciplines exist, we provide definitions for these tasks that are suitable for the visualization community. Our proposed typology offers two benefits. First, the ability to compose and hierarchically organize the tasks enables flexible and clear descriptions of decisions with varying levels of complexities. Second, the typology encourages productive discourse between visualization designers and domain experts by abstracting the intricacies of data, thereby promoting clarity and rigorous analysis of decision-making processes. We demonstrate the benefits of our typology through four case studies, and present an evaluation of the typology from semi-structured interviews with experienced members of the visualization community who have contributed to developing or publishing decision support systems for domain experts. Our interviewees used our typology to delineate the decision-making processes supported by their systems, demonstrating its descriptive capacity and effectiveness. Finally, we present preliminary findings on the usefulness of our typology for visualization design.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Autonomous System Subversion Tactics: Prototypes and Recommended Countermeasures

One of the fielding requirements for Advanced and Small Modular Reactors (AR/SMR) is the ability to support remote and autonomous operations. Autonomous Control Systems (ACS) are found on platforms such as Autonomous Space Vehicles, Cruise Missiles, and advanced driver-assistance systems. Each of these ACS implementations depends upon a set of decision support subsystems responsible for supporting Autonomous Mission Managers (names vary based upon field and author preferences). These Autonomous Mission Managers receive inputs from system sensors (e.g., LIDAR collection from an automobile travelling down a street; transients from a nuclear reactor), and perform a set of classifications (e.g., Red Traffic Light; Small Pedestrian at 10m; Load Rejection; Single Coolant Pump Trip), and then use these classifications in combination with recommendation algorithms to achieve platform goals (e.g., Stop the Vehicle at the Traffic Light, Avoid the Small Pedestrian; Trip the Reactor to prevent a Safety Event). The design, implementation, and fielding of an ACS capability will alter the cyber-attack surface such that existing risk management plans will need to be updated to include how to protect and defend against data-science and decision-support-system attack classes. These attack classes would include protection of the design and training environments where algorithm selection and testing and training data would be obvious attack vectors. These attack classes would also require an informed set of detection and response procedures to identify anomalous behaviors and document best practices for anomaly assessment and vulnerability mitigation and remediation. Last year we published a Cyber Threat Assessment Methodology for Autonomous and Remote Operations for AR/SMRs along with a companion publication on Cyber Attack and Defense Use Cases. The focus of the methodology was on describing and enumerating ACS processes, components, and functions such that security engineers could: evaluate subversion options against the target; identify threat actor attributes and capabilities derived from each subversion option; and identify security controls and response countermeasures. The Use Cases document offered detailed methodology examples including an assessment of a Military Base SMR, an Autonomous System Decision Loop, and implementation of AR/SMR Machine Learning algorithms. Our proposal at the end of last year was to focus on implementation of subversion prototypes related to the last Use Case area: AR/SMR Machine Learning (ML) Algorithms. We included six attack scenarios in our Use Cases paper: a Poisoning Attack against ML functions implemented using an FPGA; a Trojaning Attack against ML classifiers exploiting the excitability of Nuclear Engineers; a Backdooring Attack against ML Training environments to ensure persistence of an attack vector; a False Positive Evasion Attack against multi-factor Access Control Systems using clever inputs; an Inference Attack against ML models by an Insider with access to the Operational environment; and an Adversarial Reprogramming Attack against a Material Access Control Video Surveillance System. At the beginning of this year these six attack scenarios were provided to our research teams at Georgia Tech and Idaho State University and each team successfully implemented a subversion attack against a ML implementation to include transient misclassifications. While this is a notable outcome from this type of research, this paper offers the reader insight into not only how to structure and execute these types of attacks, but into the thought process behind how the researcher investigated the problem space, performed initial algorithm implementation, and the trial-and-error behind arriving at the successful subversion prototypes. We include in this paper a set of associated Scenarios on how these subversion prototypes could be implemented and an initial set of guidance for AR/SMR architects, Nuclear Regulators, and Cyber Defenders to implement awareness and defense capabilities into their current operational portfolios.

42 ENGINEERING↗
DOE OSTI2026

Evolving language of pediatric anxiety in electronic health records

Objectives This study aimed to identify and quantify semantic drift (ie, the change in semantic meaning over time) within expert-defined anxiety-related (AR) terminology and compare it to common electronic health record (EHR) vocabulary across longitudinal pediatric clinical notes. Materials and Methods A corpus of pediatric clinical notes from 2009 to 2022 was analyzed using computational methods. Semantic drift for each term was quantified using cosine similarity between annual temporal word embeddings. Contextual meaning was examined through changes in nearest neighbors across years. The Laws of Semantic Change were applied to assess the influence of word frequency and polysemy. Vocabulary terms were categorized as AR or common EHR. Results 98% of AR terminology maintained a cosine similarity between 0.00 and 0.50, indicating moderate semantic stability, whereas 90% of common EHR terms remained between 0.00 and 0.25, showing greater contextual stability overall. Frequent terms exhibited minimal change (Frequency Coefficient = 0.04), whereas highly polysemous or abbreviated terms showed less stability (Polysemy Coefficient = 0.630). AR terminology drifted more slowly than general EHR vocabulary (Type Coefficient = −0.179), further supported by significant year–type interactions (Coef = −0.09 to −0.523). Discussion Although anxiety-related terminology demonstrates slower semantic drift than general EHR vocabulary, subtle contextual shifts still occur that may affect downstream interpretability and retrieval in automated systems. Conclusion Continuous linguistic monitoring and adaptive modeling are essential to maintain semantic fidelity and ensure the long-term reliability of clinical decision support systems as healthcare documentation evolves.

Pediatric anxiety disorders↗
DOE OSTI2022

Evolution of Programmatic Asset Lifecycle Planning at MESA

Early on in 2018 Sandia recognized the Microsystems Engineering, Science and Applications (MESA) Programmatic Asset Lifecycle Planning capability to be unpredictable, inconsistent, reactive, and unable to provide strong linkage to the sponsor's needs. The impetus for this report is to share learnings from MESA's journey towards maturing this capability. This report describes re-building the foundational elements of MESA's Programmatic Asset Lifecycle Planning capability using a risk-based, Multi-Criteria Decision Analysis (MCDA) approach. To begin, MESA's decades-old Piano Chart + Ad Hoc Hybrid Methodology is described with a narrative of its strengths and weaknesses. Then its replacement, the MCDA /Analytical Hierarchy Process, is introduced with a discussion of its strengths and weaknesses. To generate a realistic Programmatic Asset Lifecycle Planning budget outlook, MESA used its rolling 20-year Extended Life Program Plan (MELPP) as a baseline. The new MCDA risk-based prioritization methodology implements DOE/NNSA guidelines for prioritization of DOE activities and provides a reliable, structured framework for combining expert judgement and stakeholder preferences according to an established scientific technique. An in-house Hybrid Decision Support System (HDSS) software application was developed to facilitate production of several key deliverables. The application enables analysis of the prioritization decisions with charts to display and provide linkage of MESA's funding requests to the stakeholders' priorities, strategic objectives, nuclear deterrence programs, MESA priorities, and much more.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Architecture for Web-Based Visualization of Large-Scale Energy Domains: Preprint

With the growing penetration of inverter-based distributed energy resources and increased loads through electrification, power systems analyses are becoming more important and more complex. Moreover, these analyses increasingly involve the combination of interconnected energy domains with data that are spatially and temporally increasing in scale by orders of magnitude, surpassing the capabilities of many existing analysis and decision-support systems. We present the architectural design, development, and application of a high-resolution web-based visualization environment capable of cross-domain analysis of tens of millions of energy assets, focusing on scalability and performance. Our system supports the exploration, navigation, and analysis of large data from diverse domains such as electrical transmission and distribution systems, mobility and electric vehicle charging networks, communications networks, cyber assets, and other supporting infrastructure. We evaluate this system across multiple use cases, describing the capabilities and limitations of a web-based approach for high-resolution energy system visualizations.

grid modernization↗
DOE OSTI2022

Image transformers for classifying acute lymphoblastic leukemia

Cancer is the leading cause of death by disease in American children. Each year, nearly 16,000 children in the United States and over 300,000 children globally are diagnosed with cancer. Leukemia is a form of blood cancer that originates in the bone marrow and accounts for one-third of pediatric cancers. This disease occurs when the bone marrow contains 20% or more immature white blood cell blasts. Acute lymphoblastic leukemia is the most prevalent leukemia type found in children, with half of all annual cases in the U.S. diagnosed for subjects under 20 years of age. To diagnose acute lymphoblastic leukemia, pathologists often conduct a morphological bone marrow assessment. This assessment determines whether the immature white blood cell blasts in bone marrow display the correct morphological characteristics, such as size and appearance of nuclei. Pathologists also use immunophenotyping via multi-channel flow cytometry to test whether certain antigens are present on the surface of blast cells; the antigens are used to identify the cell lineage of acute lymphoblastic leukemia. These manual processes require well-trained personnel and medical professionals, thus being costly in time and expenses. Computerized decision support via machine learning can accelerate the diagnosis process and reduce the cost. Training a reliable classification model to distinguish between mature and immature white blood cells is essential to the decision support system. Here, we adopted the Vision Transformer model to classify white blood cells. The Vision Transformer achieved superb classification performance compared to state-of-the-art convolutional neural networks while requiring less computational resources for training. Additionally, the latent self-attention architecture provided attention maps for a given image, providing clues as to which portion(s) of the image were significant in decision-making. We applied the Vision Transformer model and a convolutional neural network model to an acute lymphoblastic leukemia classification dataset of 12,528 samples and achieved accuracies of 88.4% and 86.2%.

Cho, Priscilla↗
DOE OSTI2023

Cycles-L: A Coupled, 3-D, Land Surface, Hydrologic, and Agroecosystem Landscape Model

Managing landscapes to increase agricultural productivity and environmental stewardship can be informed by spatially-distributed models that operate at spatial and temporal scales that are intervention-relevant. This paper presents Cycles-L, a landscape-scale agroecosystem and hydrologic modeling system, using as a test case a watershed in Pennsylvania. Cycles-L emerges from melding the landscape and hydrology structure of Flux-PIHM, a 3-D land surface hydrologic model, with the agroecosystem processes in the Cycles model. Consequently, Cycles-L can simulate processes affected by topography, soil heterogeneity, and management practices, owing to its physically-based hydrology that can simulate horizontal and vertical transport of solutes with water. The model was tested at a 730-ha experimental watershed within the Mahantango Creek watershed. Cycles-L simulated well stream water and mineral nitrogen discharge (Nash-Sutcliffe coefficient 0.55 and 0.60, respectively) and grain yield (root mean square error 1.2 Mg ha -1 ). Cycles-L outputs are as good or better than those obtained with the uncoupled Flux-PIHM (water discharge) and Cycles (grain yield) models. Modeled spatial patterns of nitrogen fluxes like denitrification illustrate the combined control of crop management and topography. For example, denitrification is almost twice as high when simulated with Cycles-L than when simulated with Cycles 1-D. Due to its spatial and temporal resolution, Cycles-L fills a gap in the availability of models that operate at a scale relevant to evaluate interventions in the landscape. Cycles-L can become a central component in tools for climate change scenario analysis, precision agriculture, precision conservation, and artificial intelligence-based decision support systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

An ensemble method for nuclei detection of overlapping cervical cells

The Pap test is a preventive approach that requires specialized and labor-intensive examination of cytological preparations to track potentially cancerous cells from the internal and external cervix surface. A cytopathologist must analyze many microscopic fields while screening for abnormal cells. Therefore the expectation is that a support decision system could assist with screening the most relevant cells, for example, by identifying sub-cellular abnormalities, such as changes in the nuclei features. Here, this work proposes a computational method for cervical nuclei detection aimed to reduce the workload of cytopathologists.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Hierarchical median narrow band for level set segmentation of cervical cell nuclei

This paper presents a novel hierarchical nuclei segmentation algorithm for isolated and overlapping cervical cells based on a narrow band level set implementation. Our method applies a new multiscale analysis algorithm to estimate the number of clusters in each image region containing cells, which turns into the input to a narrow band level set algorithm. We assess the nuclei segmentation results on three public cervical cell image databases. Overall, our segmentation method outperformed six state-of-the-art methods concerning the number of correctly segmented nuclei and the Dice coefficient reached values equal to or higher than 0.90. We also carried out classification experiments using features extracted from our segmentation results and the proposed pipeline achieved the highest average accuracy values equal to 0.89 and 0.77 for two-class and three-class problems, respectively. Furthermore, these results demonstrated the suitability of the proposed segmentation algorithm to integrate decision support systems for cervical cell screening.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Explainable Artificial Intelligence in Endocrinological Medical Research

Artificial Intelligence (AI) has been a part of the medical community for decades in the form of Clinical Decision Support Systems (CDSS) to aide physicians in diagnosis and categorization of patients (1). Recent years have seen a shift from expert-derived models to the integration of machine learning (ML) algorithms to drive the output of these AI systems due to the ability of ML models to more accurately make predictions by exploiting higher dimensional and often complex data. In many cases ML models gain their advantage in accuracy by capture complex and often non-linear relationships between features being used to make the prediction. However, the hype and excitement around these methods are tempered by the clinical utility of often black-box solutions driven by skepticism of results that are difficult for practitioners to not only interpret but explain to their patients (1-3). This skepticism is not unfounded as multiple examples of black-box solutions identifying incidental correlates as the key predictors have highlighted the potential bias in a training set, or reward system, that a ML model may exploit; for example a model discerning wolves from huskies based on snow in the background rather than features of the dogs (1, 4, 5).

Endocrinological, artificial intelligence, diabete↗
DOE OSTI2022

CyclusJS: A Distributed Web-based Fuel Cycle Visual Analytics. Research Performance (Final Report)

The overall aim of the CyclusJS project has been to design novel approaches that support fuel cycle simulations and empower decision-makers and researchers with better tools to compare and contrast results from multiple simulations to understand the landscape of potential outcomes better. In particular, nuclear fuel cycle simulations focus on modeling the nuclear industry and ecosystem at a macroscopic level, and the analysis tools connect such simulations with decision support systems. As a demonstration of the novelty and effectiveness of the proposed approach, we study scenarios for transitioning from one technology, Light Water Reactors (LWR), to a newer Sodium-cooled Fast breeder Reactor (SFR) technology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI1996

Nuclear Safety [Vol. 37, No. 1, January-March 1996]

Nuclear Safety is a journal that covers significant issues in the field of nuclear safety. Its primary scope is safety in the design, construction, operation, and decommissioning of nuclear power reactors worldwide and the research and analysis activities that promote this goal, but it also encompasses the safety aspects of the entire nuclear fuel cycle, including fuel fabrication, spent-fuel processing and handling, and nuclear waste disposal, the handling of fissionable materials and radioisotopes, and the environmental effects of all these activities. Table of Contents for this issue follows. GENERAL SAFETY CONSIDERATIONS: 1 A Decision Support System for Maintenance Management of a Boiling-Water Reactor Power Plant, J. H. Shen, A. Ray, and S. Levine; ACCIDENT ANALYSIS: 12 On Prediction of the Ignition Potential of Uranium Metal and Hydride, M. Epstein, W. Luangdilok, M. G. Plys, and H. K. Fauske; 26 An Overview of the Primary Parameters and Methods for Determining Condensation Heat Transfer to Containment Structures, J. Green and K. Almenas; DESIGN FEATURES: 49 Modem Tornado Design of Nuclear and Other Potentially Hazardous Facilities, J. D. Stevenson and Y. Zaho; U.S. NUCLEAR REGULATORY COMMISSION INFORMATION AND ANALYSES: 73 1994 Accident Sequence Precursor Program Results, R. J. Belles, J. W. Cletcher, D. A. Coplnger, B. W. Dolan, J. W. Minarick, and P. D. O'Reilly; ANNOUNCEMENTS: 93 American Institute of Chemical Engineers (AlChE) Spring 1997 National Meeting; 94 European Safety and Reliability Association International Conference on Safety and Reliability ESREL ’97; 95 Criticality Safety Challenges in the Next Decade; 96 21st International Symposium on the Scientific Basis for Nuclear Waste Management; 84 The Authors; 88 Letter to the Editor; 90 Indexes to Nuclear Safety, Volume 36.

05 NUCLEAR FUELS↗
DOE OSTI1996

Nuclear Safety [Vol. 37, No. 1, January-March 1996]

Nuclear Safety is a journal that covers significant issues in the field of nuclear safety. Its primary scope is safety in the design, construction, operation, and decommissioning of nuclear power reactors worldwide and the research and analysis activities that promote this goal, but it also encompasses the safety aspects of the entire nuclear fuel cycle, including fuel fabrication, spent-fuel processing and handling, and nuclear waste disposal, the handling of fissionable materials and radioisotopes, and the environmental effects of all these activities. Table of Contents for this issue follows. GENERAL SAFETY CONSIDERATIONS: 1 A Decision Support System for Maintenance Management of a Boiling-Water Reactor Power Plant, J. H. Shen, A. Ray, and S. Levine; ACCIDENT ANALYSIS: 12 On Prediction of the Ignition Potential of Uranium Metal and Hydride, M. Epstein, W. Luangdilok, M. G. Plys, and H. K. Fauske; 26 An Overview of the Primary Parameters and Methods for Determining Condensation Heat Transfer to Containment Structures, J. Green and K. Almenas; DESIGN FEATURES: 49 Modem Tornado Design of Nuclear and Other Potentially Hazardous Facilities, J. D. Stevenson and Y. Zaho; U.S. NUCLEAR REGULATORY COMMISSION INFORMATION AND ANALYSES: 73 1994 Accident Sequence Precursor Program Results, R. J. Belles, J. W. Cletcher, D. A. Copinger, B. W. Dolan, J. W. Minarick, and P. D. O'Reilly; ANNOUNCEMENTS: 93 American Institute of Chemical Engineers (AlChE) Spring 1997 National Meeting; 94 European Safety and Reliability Association International Conference on Safety and Reliability ESREL ’97; 95 Criticality Safety Challenges in the Next Decade; 96 21st International Symposium on the Scientific Basis for Nuclear Waste Management; 84 The Authors; 88 Letter to the Editor; 90 Indexes to Nuclear Safety, Volume 36.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

System risk quantification and decision making support using functional modeling and dynamic Bayesian network

Risk-informed decision-making requires a probabilistic assessment of the likelihood of success of control action, given the system status. This paper presents a systematic state transition modeling approach integrating dynamic probabilistic risk assessment with a decision-making process using a dynamic Bayesian network (DBN) coupled with functional modeling. A functional model designed with multilevel flow modeling (MFM) technique was used to build a system state structure inferred by energy, mass, and information flow so that one can verify the developed model with respect to system functionality. The MFM model represents the causal relationship among the nodes, which captures the structure of process parameters and control units. Each node may have multiple possible states, and the DBN structured by the MFM model represents the time-domain transitions among the defined states. Furthermore, the MFM-DBN integrated state transition modeling is a white-box approach that allows one to draw the system's risk profile by updating the system states and supports the decisions probabilistically with physical inference. An example of a simple heating system has been used to illustrate this process, including decision-making support based on quantitative risk profile. For demonstrating its applicability to a complex system operational decision making, a case study of station blackout accident scenario leading to the seal loss of coolant accident in a nuclear power plant is presented. The proposed approach effectively provided the risk profile along time for each option so that the operators can make the best decision, which minimizes the plant risk.

42 ENGINEERING↗
DOE OSTI2025

System Engineers and Decisions: It?s All about Knowledge

In order to guarantee that a system meets adequate levels of reliability and availability, system performances are continuously monitored and analyzed thanks to the technological advancements driving the Industry 4.0 revolution. An Industry 4.0 approach is typically based on advanced statistical, big data mining, machine learning, and internet-of-things methods designed to detect anomalies in the behavior of system, detect the most likely failure modes, and provide indications to system engineers on when maintenance activities should be performed before system performance are deemed unacceptable (which can be generated by diagnostic and prognostic methods). However, these analyses, which are designed to automatize and increase the efficacy of the system maintenance program, require large amount of data which can come in various forms: numeric, textual, images, sounds etc. Such data constitutes the historic knowledge benchmark to track system performances and support system engineer decisions. Here we claim that data is not sufficient to support this kind of analyses when applied to systems characterized by complex architectures and behaviors. Robust system engineer decisions require the ability to understand the system operational context that lies behind the observed data elements. In this respect, system models are in fact necessary to “put data in context” and capture relationships between data elements. Industry 4.0 methods require in fact contextual knowledge as a basis upon which hypotheses can be generated and assumptions tested. In our view, for complex systems, model-based system engineering (MBSE) models can afford this contextual knowledge, as they are typically used to describe systems architecture and dynamic behaviors. System knowledge is here intended as the blending of collected data and system architecture which takes the form of a “knowledge graph”. A knowledge graph is a database which consists of a large set of nodes (in our case an entity can be either a data or an MBSE element) which are linked to each other. The types of nodes and links follow a pre-defined topology, sometimes also refers as an ontology, that is designed to fit the actual decisions that needs to be performed. We show here how a knowledge graph can be defined to support system engineer maintenance decisions and how the same graph can be built based on system MBSE models and pre-processed data from numeric (through anomaly detections and diagnostic methods) and textual elements (through technical language processing TLP).

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Risk Analysis of Various Design Architectures for High Safety-significant Safety-related Digital Instrumentation and Control Systems of Nuclear Power Plants during Accident Scenarios

This report documents the plus-up activities performed by Idaho National Laboratory (INL) during Fiscal Year (FY) 2022 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk Informed Systems Analysis (RISA) Pathway, digital instrumentation and control (DI&C) risk assessment project. In FY 2019, the RISA Pathway initiated a project to develop a risk assessment strategy for delivering a strong technical basis to support effective, licensable, and secure DI&C technologies for digital upgrades/designs. An integrated risk assessment technology for the DI&C systems was proposed for this strategy, which aims to (1) provide a best-estimate, risk-informed capability to quantitatively and accurately estimate the safety margin obtained from plant modernization, especially for the high safety-significant safety-related (HSSSR) DI&C systems, (2) support and supplement existing advanced risk-informed DI&C design guides by providing quantitative risk information and evidence, (3) offer a capability of design architecture evaluation of various DI&C systems to support system design decisions and diversity and redundancy applications, (4) assure the long-term safety and reliability of HSSSR DI&C systems, and (5) reduce uncertainty in costs and support integration of DI&C systems in the plant. To achieve these technical goals and deal with the expensive licensing justifications from regulatory insights, the LWRS-developed framework instructs nuclear vendors and utilities on how to effectively lower the costs associated with digital compliance and speed industry advances by: (1) defining an integrated risk-informed analysis process for DI&C upgrade, including hazard analysis, reliability analysis, and consequence analysis, (2) applying systematic and risk-informed tools to address common cause failures (CCFs) and quantify corresponding failure probabilities for DI&C technologies, particularly software CCFs, (3) evaluating the impact of digital failures at the component level, system level, and plant level, and (4) providing insights and suggestions on designs to manage the risks, thus to support the development, licensing, and deployment of advanced DI&C technologies on nuclear power plant (NPPs). Adding diversity within system or components is the main means to eliminate and mitigate CCFs, but diversity also increases plant complexity and errors and may not address all sources of systematic failures. How to optimize the diversity and redundancy applications for the safety-critical DI&C systems remains a challenge. To deal with the technical issues in addressing potential software CCFs in HSSSR DI&C systems of NPPs and supporting relevant design optimization, the framework provides: ? An integrated best-estimate, risk-informed capability to address new technical digital issues quantitatively, accurately, and efficiently in plan modernization progress, such as software CCFs in HSSSR DI&C systems of NPPs ? A common and a modularized platform for DI&C designers, software developers, cybersecurity analysts, and plant engineers to efficiently predict and prevent risk in the early design stage of DI&C systems ? Technical bases and risk-informed insights to assist U.S. Nuclear Regulatory Commission (NRC) and industry to address and fulfill the risk-informed alternatives for evaluation of CCFs in HSSSR DI&C systems of NPPs ? An integrated risk-informed tool that offers a capability of design architecture evaluation of various DI&C systems to support system design decisions in diversity and redundancy applications. The plus-up research and development efforts of this project in FY 2022 are focused on methodology improvement of software CCF modeling and estimation, prevention analysis, importance analysis and risk analysis of various design architectures of HSSSR DI&C systems. This work greatly enhances the capability of the LWRS-developed framework for the risk assessment and design optimization of safety-critical DI&C systems. It should be noted that all the analyses are performed for the demonstration of the LWRS-developed framework, not for the evaluation of relevant systems. Results are obtained based on very limited design information and testing data.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

STOCHASTIC OPTIMAL POWER FLOW FOR REAL-TIME MANAGEMENT OF DISTRIBUTED RENEWABLE GENERATION AND DEMAND RESPONSE (Final Report)

To meet the grand challenge of a sustainable energy future, there has been a surge of interest in renewable energy. Today, the uncertainty associated with renewable resources is handled by using operating reserves. The high penetration of renewable resources, however, introduces difficult-to-control dynamics and challenges for power system operation. Decision support tools are necessary at the bulk system operational level to recognize and efficiently utilize renewable resources and distributed demand response products in concert with traditional grid resources. It is envisaged that responsive load can potentially have very significant cost advantages over either spinning or non-spinning ramping reserve. Critical decisions are made during hour(s)-ahead and real-time power system operation regarding the commitment and dispatch of generators to ensure power delivery is both reliable and economic. These decisions are typically made by a security constrained optimal flow, which determines future generator commitments, dispatches, and ensures adequate reserves are available in the event of a contingency (unexpected outage) or if future system conditions deviate from forecasts. However, security has been always based on a pre-specified subset of contingency constraints whose enforcement does not guarantee security under all possible future possibilities while also giving little or no weight to the likelihood of each contingent event or the severity of its consequences. Existing tools, which are based exclusively on deterministic optimization models, do not yield optimal operational decisions to address these new challenges, in terms of both reliability and cost-effectiveness. This project has focused on developing a stochastic optimal power flow (SOPF) framework, which integrates renewable resource uncertainty, load uncertainty, distributed storage (DS), demand response (DR) products, in a holistic manner to address the uncertainty associated with ever-increasing renewable resources, along with the inclusion of distributed demand response products in future power systems. A proof-of-concept problem was created using the Pennsylvania-Jersey-Maryland (PJM) power system network. Synthetic wind generation was added to the system to simulate 50% wind penetration. A 1-hour test of SOPF operation indicated more than 6% operational cost savings. The project continued by adding the Midwestern Independent System Operator (MISO) as a partner, with focus shifting from SOPF to Stochastic Look-Ahead Unit Commitment (SLAC). Unlike PJM, MISO is faced with significant renewable energy resources within its footprint and is challenged with substantial uncertainty in its operations. The SLAC distinguishes itself from existing tools that operators use. At best, today’s tools solve two to three cases independently, where one or two system parameters, such as forecasted load level (e.g., a low, base, and high forecast), are varied and the resulting scenarios are analyzed independently. The stochastic-based optimization of SLAC leverages statistical information from an ensemble of potential operational scenarios and their respective likelihood. The SLAC output can be translated into valuable information to the operator such as suggested commitments, optimal scheduling and dispatch of resources, reserve requirements at both locational and zonal resolutions, ramping availability and requirements, availability of demand response including operational guidance concerning the near-term and real-time coordination between distributed energy resources, and utilization of distributed storage resources. The developed SOPF/SLAC tool, a stand-alone tool compatible with existing EMSs, will provide system operators with unprecedented visibility, flexibility and predictability to these resources and operational guidance concerning the real-time coordination between DERs and DR/DS products. The game changing and practical impact of this disruptive technology will be dramatic and will usher in a new era in the electric power industry, wherein green energy concepts are fully embraced, and electric power costs are lowered throughout the nation.

42 ENGINEERING↗