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At least 1,801 records · Page 100
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

An Approach to Dynamic Human Reliability Analysis and Its Data Collection Framework

Human reliability analysis (HRA) is a method for evaluating human errors in a variety of complex systems such as nuclear power plants, military systems, aircraft, and chemical plants. Most HRA methods currently used by regulatory institutes or utilities are called static HRA and are carried out by simple worksheets or simple calculators. To date, there are many unsolved or intrinsic challenges in static HRA. For example, existing static HRA does not realistically model and evaluate human actions as they would be performed at actual systems. There is no method with HRA to objectively estimate the time required for human actions despite being essential to HRA processes. In addition, many HRA methods still rely on a dataset generated prior to the 1980s, from unrelated industry experience or simply from expert judgment. Accordingly, this study attempted to research how to overcome the challenges of existing HRA via dynamic risk assessment (a.k.a., simulation-based or computation-based risk assessment) techniques. First, this study developed a dynamic HRA method, named as PRocedure-based Investigation Method of EMRALD Risk Assessment – HRA (PRIMERA-HRA). The PRIMERA-HRA mainly concentrates on providing HRA analysts with specific guidelines on how to reasonably model human actions, assign human reliability data and evaluate output of simulation within a dynamic probabilistic risk assessment tool, called as Event Modeling Risk Assessment using Linked Diagrams (EMRALD). Second, this study also developed a module for performance shaping factors (i.e., the key concept in HRA quantification) applicable to dynamic HRA, then implemented it based on PRIMERA-HRA within the EMRALD tool. Third, this study developed an HRA data collection framework to support dynamic HRA, called as Simplified Human Error Experimental Program (SHEEP). Originally, the SHEEP study aimed to support static HRA and its data collection, but recently extended the scope to the new technologies such as dynamic HRA or HRA for advanced reactors. SHEEP focuses on the use of data collected from simplified simulators to complement—but not replace—data collection studies using full-scope simulators and actual operators. To date, many experiments were conducted under the SHEEP framework. Multiple analyses, such as human performance analysis, human error analysis, task complexity analysis, learning effect analysis and time distribution analysis, were also carried out using the collected data. Then, based on the major insights, an approach to inferring full-scope data based on simplified simulator data was proposed. The PRIMERA-HRA and SHEEP research are expected to evaluate human actions more realistically than existing static HRA, provide an opportunity to collect more HRA data with reasonable cost and labor, then contribute to enhance the quality of HRA.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

An Approach to Dynamic Human Reliability Analysis and Its Data Collection Framework

Human reliability analysis (HRA) is a method for evaluating human errors in a variety of complex systems such as nuclear power plants, military systems, aircraft, and chemical plants. Most HRA methods currently used by regulatory institutes or utilities are called static HRA and are carried out by simple worksheets or simple calculators. To date, there are many unsolved or intrinsic challenges in static HRA. For example, existing static HRA does not realistically model and evaluate human actions as they would be performed at actual systems. There is no method with HRA to objectively estimate the time required for human actions despite being essential to HRA processes. In addition, many HRA methods still rely on a dataset generated prior to the 1980s, from unrelated industry experience or simply from expert judgment. Accordingly, this study attempted to research how to overcome the challenges of existing HRA via dynamic risk assessment (a.k.a., simulation-based or computation-based risk assessment) techniques. First, this study developed a dynamic HRA method, named as PRocedure-based Investigation Method of EMRALD Risk Assessment – HRA (PRIMERA-HRA). The PRIMERA-HRA mainly concentrates on providing HRA analysts with specific guidelines on how to reasonably model human actions, assign human reliability data and evaluate output of simulation within a dynamic probabilistic risk assessment tool, called as Event Modeling Risk Assessment using Linked Diagrams (EMRALD). Second, this study also developed a module for performance shaping factors (i.e., the key concept in HRA quantification) applicable to dynamic HRA, then implemented it based on PRIMERA-HRA within the EMRALD tool. Third, this study developed an HRA data collection framework to support dynamic HRA, called as Simplified Human Error Experimental Program (SHEEP). Originally, the SHEEP study aimed to support static HRA and its data collection, but recently extended the scope to the new technologies such as dynamic HRA or HRA for advanced reactors. SHEEP focuses on the use of data collected from simplified simulators to complement—but not replace—data collection studies using full-scope simulators and actual operators. To date, many experiments were conducted under the SHEEP framework. Multiple analyses, such as human performance analysis, human error analysis, task complexity analysis, learning effect analysis and time distribution analysis, were also carried out using the collected data. Then, based on the major insights, an approach to inferring full-scope data based on simplified simulator data was proposed. The PRIMERA-HRA and SHEEP research are expected to evaluate human actions more realistically than existing static HRA, provide an opportunity to collect more HRA data with reasonable cost and labor, then contribute to enhance the quality of HRA.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Proof-of-Concept for Sensor Modeling in MOOSE for the Design of Autonomous Nuclear Reactor Control

Autonomous operation is essential for the deployment of microreactors and fission batteries, both in terrestrial and space applications. For this reason, recent studies have investigated autonomous control by using adaptive model predictive control and multi-objective optimization for heat pipe–cooled microreactors under normal and heat pipe failure conditions. However, prototypes of microreactors and fission batteries do not exist yet, and even the design space has not been narrowed down conclusively, making the instrumentation and control system design difficult. For this reason, there is a need for flexible computational capabilities to create a numerical stand-in of potential microreactor and fission battery designs. The latter can be used to design and test control strategies to support autonomous operations. In this poster, we describe the initial implementation of a pluggable sensor system for the easy implementation of realistic sensor models in the multiphysics object-oriented simulation environment (MOOSE) framework. This new capability will enable MOOSE users to create a numerical stand-in of microreactors and fission batteries, ultimately allowing them to easily test new control algorithms, and instrumentation strategies for advanced systems in the design phase.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

ECAR-5363 Rev 0 MARVEL Environmental Assessment Inhalation Dose Consequence Calculations

The following dose consequence analysis is performed to support the Microreactor Applications Research Validation and Evaluation Project (MARVEL) environmental assessment. The purpose of the MARVEL project is to develop a nuclear microreactor applications test bed at Idaho National Laboratory (INL) to perform research and development on various operational features of microreactors to ultimately improve integration of microreactors to end-user applications. The MARVEL concept is a 100-kilowatt thermal (kWth) and approximately 20-kilowatt electric (kWe) generating microreactor. The MARVEL system will be located in the INL TREAT facility in the north high bay equipment pit. This ECAR analyzes a maximum reasonably foreseeable accident for MARVEL as required by “Recommendations for Analyzing Accidents under NEPA.” Consistent with the recommendations from this guidance, this ECAR considers the median air dispersion parameters for INL in evaluation accident releases from the MARVEL reactor concept and documents the dose from the maximum reasonably foreseeable accident.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2024

Model-Based Detection of Coordinated Attacks (DCA) in Distribution Systems

The fast-paced growth in digitization of smart grid components enhances system observability and remote-control capabilities through efficient communication. However, enhanced connectivity results in heightened system vulnerability towards cybersecurity risks in the cyber-physical power system. Coordinated cyber-attacks (CCA), when undetected, lead to system-wide impact in terms of large disturbances or widespread outages. Detecting CCA in the cyber layer is critical to thwart cyber-attacks in real-time before the attack impacts the physical system. The challenge of locating CCA stems from the complex grid dynamics, making it difficult to distinguish between normal operational variations and cyber-attack impact. CCA often employs multiple attack vectors targeting geographically distributed components, further complicating CCA identification. Existing research in intrusion detection is primarily focused on the transmission network and limited to detecting individual attacks. In this paper, a novel proactive DCA strategy is proposed for early detection of CCA by establishing correlations among distinct attack events through model-based reinforcement learning that utilizes abductive reasoning to conclude the attacker goal. The solution includes understanding the system model, learning the system dynamics, and correlating individual cyber-attacks to extract the attacker’s objective. The developed learning algorithm identifies the most probable attack path to reach the attacker’s objective by predicting the next attack steps. A DNP3-based cyber-physical co-simulation testbed is developed to test the proposed algorithm using the IEEE 13-node test feeder.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Artificial Intelligence for Autonomous Molecular Design: A Perspective

Domain-aware artificial intelligence has been increasingly adopted in recent years to expedite molecular design in various applications, including drug design and discovery. Recent advances in areas such as physics-informed machine learning and reasoning, software engineering, high-end hardware development, and computing infrastructures are providing opportunities to build scalable and explainable AI molecular discovery systems. This could improve a design hypothesis through feedback analysis, data integration that can provide a basis for the introduction of end-to-end automation for compound discovery and optimization, and enable more intelligent searches of chemical space. Several state-of-the-art ML architectures are predominantly and independently used for predicting the properties of small molecules, their high throughput synthesis, and screening, iteratively identifying and optimizing lead therapeutic candidates. However, such deep learning and ML approaches also raise considerable conceptual, technical, scalability, and end-to-end error quantification challenges, as well as skepticism about the current AI hype to build automated tools. To this end, synergistically and intelligently using these individual components along with robust quantum physics-based molecular representation and data generation tools in a closed-loop holds enormous promise for accelerated therapeutic design to critically analyze the opportunities and challenges for their more widespread application. This article aims to identify the most recent technology and breakthrough achieved by each of the components and discusses how such autonomous AI and ML workflows can be integrated to radically accelerate the protein target or disease model-based probe design that can be iteratively validated experimentally. Taken together, this could significantly reduce the timeline for end-to-end therapeutic discovery and optimization upon the arrival of any novel zoonotic transmission event. Our article serves as a guide for medicinal, computational chemistry and biology, analytical chemistry, and the ML community to practice autonomous molecular design in precision medicine and drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1995

Determining the Loci of Anomalies Using Minimal Causal Models

A new technique is described for attention focusing for use in anomaly detection in complex physical systems. The technique involves reasoning about the distance between two frequency distributions, and it is used to detect both anomalous system parameters and 'broken' causal dependencies. These two forms of information together isolate the locus of anomalous behavior in the system being monitored.

qualitative reasoning naive physics causal reasoni↗
DOE OSTI2014

Semantic Pattern Analysis for Verbal Fluency Based Assessment of Neurological Disorders

In this paper, we present preliminary results of semantic pattern analysis of verbal fluency tests used for assessing cognitive psychological and neuropsychological disorders. We posit that recent advances in semantic reasoning and artificial intelligence can be combined to create a standardized computer-aided diagnosis tool to automatically evaluate and interpret verbal fluency tests. Towards that goal, we derive novel semantic similarity (phonetic, phonemic and conceptual) metrics and present the predictive capability of these metrics on a de-identified dataset of participants with and without neurological disorders.

concept reasoning↗
DOE OSTI2020

A Probabilistic Model-Based Diagnostic Framework for Nuclear Engineering Systems

A fault diagnostic framework was investigated in this study for applications in thermal–hydraulic systems of nuclear power plants. The proposed framework consists of quantitative model-based diagnosis, statistical change detection and probabilistic reasoning. The use of physics-based diagnostic models provides high detection sensitivity and allows noise and measurement uncertainty to be incorporated robustly. Performance-related parametric models for each component are constructed based on first principles. Numerical model residuals are generated using the concept of analytical redundancy. Statistical change detection methods are employed to detect non-zero residuals in the presence of uncertainty. The diagnosis task is performed using Bayesian inference to detect and localize possible faults. Application to a single-phase heat exchanger for demonstration showed that the proposed probabilistic framework can provide improved results in comparison with traditional approaches while remaining less sensitive to false alarms in the presence of measurement and modeling uncertainty.

Bayesian network↗
DOE OSTI2020

Methodology to assess “no-touch” building audit software using simulated utility data

Building audits are conducted in many commercial buildings to identify opportunities to reduce energy costs and improve building operation. Because audits require significant effort by building engineers, they are usually only affordable for larger commercial buildings. “No-touch” building audit tools have thus been developed to identify potential savings based on a simplified analysis of building energy consumption patterns via high-level energy data such as monthly utility bills. This paper presents a comprehensive and standardized methodology to evaluate the accuracy of no-touch audit tools in detecting and diagnosing building energy problems and quantifying potential energy savings. The test suite is based on output data from a well-characterized set of building energy models, and the methodology is illustrated by applying it to a representative no-touch building audit tool. Results show that the tool estimates building energy end uses with reasonable accuracy but is less accurate in identifying probably causes of high energy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Applying ALARA Principles in the Design of New Radiological Facilities

The application of ALARA (As Low As Reasonably Achievable) principles to the design of new radiological facilities at Argonne National Laboratory provides a consistent radiation safety basis for future facility operations. The Department of Energy Rule 10 CFR 835 specifies design objectives to be adopted during the design of new facilities for controlling personal radiation exposure. One is to keep exposure levels below 20% of the applicable standards in 10 CFR 835.202. For a radiation worker 20% of the standard corresponds to 5 µSv hr -1 for a 2,000 hour work year. For a member of the public the DOE design objective corresponds to 0.2 mSv in a calendar year. For a new facility worst case radionuclides and their source strengths are chosen. Local shielding is specified to reduce exposure rates to less than 50 µSv hr -1 at 30 cm from the shielding. The current version MCNP6 of the Los Alamos radiation shielding computer program MCNP is then used to calculate the exposure rates elsewhere. Design modifications are made to meet the criteria. The calculations and resulting facility design modifications are discussed for two new radiological facilities.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2018

Intelligent Modeling for Nuclear Power Plant Accident Management

This study explores the viability of using counterfactual reasoning for impact analyses when understanding and responding to “beyond-design-basis” nuclear power plant accidents. Currently, when a severe nuclear power plant accident occurs, plant operators rely on Severe Accident Management Guidelines. However, the current guidelines are limited in scope and depth: for certain types of accidents, plant operators would have to work to mitigate the damage with limited experience and guidance for the particular situation. We aim to fill the need for comprehensive accident support by using a dynamic Bayesian network to aid in the diagnosis of a nuclear reactor’s state and to analyze the impact of possible response measures.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2014

Nose to tail, roots to shoots: spatial descriptors for phenotypic diversity in the Biological Spatial Ontology

Background: Spatial terminology is used in anatomy to indicate precise, relative positions of structures in an organism. While these terms are often standardized within specific fields of biology, they can differ dramatically across taxa. Such differences in usage can impair our ability to unambiguously refer to anatomical position when comparing anatomy or phenotypes across species. We developed the Biological Spatial Ontology (BSPO) to standardize the description of spatial and topological relationships across taxa to enable the discovery of comparable phenotypes. Results: BSPO currently contains 146 classes and 58 relations representing anatomical axes, gradients, regions, planes, sides, and surfaces. These concepts can be used at multiple biological scales and in a diversity of taxa, including plants, animals and fungi. The BSPO is used to provide a source of anatomical location descriptors for logically defining anatomical entity classes in anatomy ontologies. Spatial reasoning is further enhanced in anatomy ontologies by integrating spatial relations such as dorsal_to into class descriptions (e.g., ‘dorsolateral placode’ dorsal_to some ‘epibranchial placode’). Conclusions: The BSPO is currently used by projects that require standardized anatomical descriptors for phenotype annotation and ontology integration across a diversity of taxa. Anatomical location classes are also useful for describing phenotypic differences, such as morphological variation in position of structures resulting from evolution within and across species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Application of Monte Carlo Methods to Perform Uncertainty and Sensitivity Analysis on Inverse Water-Rock Reactions with NETPATH

Methods were developed to quantify uncertainty and sensitivity for NETPATH inverse water-rock reaction models and to calculate dissolved inorganic carbon, carbon-14 groundwater travel times. The NETPATH models calculate upgradient groundwater mixing fractions that produce the downgradient target water chemistry along with amounts of mineral phases that are either precipitated or dissolved. Carbon-14 groundwater travel times are calculated based on the upgradient source-water fractions, carbonate mineral phase changes, and isotopic fractionation. Custom scripts and statistical code were developed for this study to facilitate modifying input parameters, running the NETPATH simulations, extracting relevant output, postprocessing the results, and producing graphs and summaries. The scripts read userspecified values for each constituent’s coefficient of variation, distribution, sensitivity parameter, maximum dissolution or precipitation amounts, and number of Monte Carlo simulations. Monte Carlo methods for analysis of parametric uncertainty assign a distribution to each uncertain variable, sample from those distributions, and evaluate the ensemble output. The uncertainty in input affected the variability of outputs, namely source-water mixing, phase dissolution and precipitation amounts, and carbon-14 travel time. Although NETPATH may provide models that satisfy the constraints, it is up to the geochemist to determine whether the results are geochemically reasonable. Two example water-rock reaction models from previous geochemical reports were considered in this study. Sensitivity analysis was also conducted to evaluate the change in output caused by a small change in input, one constituent at a time. Results were standardized to allow for sensitivity comparisons across all inputs, which results in a representative value for each scenario. The approach yielded insight into the uncertainty in water-rock reactions and travel times. For example, there was little variation in source-water fraction between the deterministic and Monte Carlo approaches, and therefore, little variation in travel times between approaches. Sensitivity analysis proved very useful for identifying the most important input constraints (dissolved-ion concentrations), which can reveal the variables that have the most influence on source-water fractions and carbon-14 travel times. Once these variables are determined, more focused effort can be applied to determining the proper distribution for each constraint. Second, Monte Carlo results for water-rock reaction modeling showed discrete and nonunique results. The NETPATH models provide the solutions that satisfy the constraints of upgradient and downgradient water chemistry. There can exist multiple, discrete solutions for any scenario and these discrete solutions cause grouping of results. As a result, the variability in output may not easily be represented by a single distribution or a mean and variance and care should be taken in the interpretation and reporting of results.

phase dissolution and precipitation amounts↗
NASA NTRS1983

Multiple ion streams in the near vicinity of the Space Shuttle

Differential measurements of ion flow direction and energy during the third Space Shuttle mission have revealed the existence of ion streams in the near vicinity of the Orbiter at angles of attack as great as 50 deg with respect to the ram direction and typically with 10 percent of the ram current intensity. Neither the source nor the mechanism by which these secondary ion streams were created are known at present; however, it is reasonably certain that they are not of geophysical origin, but result from the interaction of the Orbiter with its environmental ionospheric plasma. The energy of the secondary streams was observed to be very close to the ion ram energy, and they were therefore not detected by a standard planar Retarding Potential Analyzer instrument. This leaves open the question as to their existence in the vicinity of orbiting spacecraft in general. Possible connections between secondary ion streams and phenomena previously observed in the vicinity of ionospheric spacecraft are mentioned.

Stone, N. H.↗
NASA NTRS1995

Automated Consultation for the Diagnosis of Interplanetary Telecommunications

SHARP (Spacecraft Health Automated Reasoning Program) is a knowledge-based system for the diagnosis of problems in NASA's Deep Space Network (DSN) telecommunications system. This system provides the means of communication between a spacecraft and operations personnel at Jet Propulsion Laboratory. SHARP analyzes problems that occur in both the on-board spacecraft telecom subsystem, and the DSN.

SHARP spacecraft health automated reasoning progra↗
NASA NTRS1994

Automated Consultation for the Diagnosis for Interplanetary Telecommunications

SHARP (Spacecraft Health Automated Reasoning Program) is a knowledge-based consultant for diagnosing problems in NASA'a Deep Space Network (DSN) telecommunications link in near real-time...This paper describes the most recent version, which is an operational system used by spacecraft mission contollers; previous versions are described elsewhere...

DSN deep space network↗
NASA NTRS2006

A Bayesian Framework for Landing Site Selection During Autonomous Spacecraft Descent

The success of a landed space exploration mission depends largely on the final landing site. Factors influencing site selection include safety, fuel-consumption, and scientific return. This paper addresses the problem of selecting the best available landing site based on these factors in real-time during autonomous spacecraft descent onto a planetary surface. The problem is modeled probabilistically using Bayesian Networks (BNs). BNs provide a means of representing the causal relationships between variables that impact the quality of a landing site. The final landing site is determined via probabilistic reasoning based on terrain safety derived from on-board sensors, available fuel based on spacecraft descent dynamics, and regions of interest defined by mission scientists.

entry, descent, landing (EDL)↗

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