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At least 865 records · Page 48

Human Factors of Remotely Piloted Aircraft Systems: Lessons from Incident Reports

It has been estimated that aviation accidents are typically preceded by numerous minor incidents arising from the same causal factors that ultimately produced the accident. Accident databases provide in-depth information on a relatively small number of occurrences, however incident databases have the potential to provide insights into the human factors of Remotely Piloted Aircraft System (RPAS) operations based on a larger volume of less-detailed reports. Currently, there is a lack of incident data dealing with the human factors of unmanned aircraft systems. An exploratory study is being conducted to examine the feasibility of collecting voluntary critical incident reports from RPAS pilots. Twenty-three experienced RPAS pilots volunteered to participate in focus groups in which they described critical incidents from their own experience. Participants were asked to recall (1) incidents that revealed a system flaw, or (2) highlighted a case where the human operator contributed to system resilience or mission success. A total of 90 incidents were reported. This presentation begins with an overview of RPAS human factors and then considers the emerging issues identified by pilot reports.

incidents↗

Human Performance Contributions to Safety in Commercial Aviation

Every day in aviation, pilots, air traffic controllers, and other front-line personnel perform countless correct judgments and actions in a variety of operational environments. These judgments and actions are often the difference between an accident and a non-event. Ironically, data on these behaviors are rarely collected or analyzed. Data-driven decisions about safety management and design of safety-critical systems are limited by the available data, which influence how decision makers characterize problems and identify solutions. Large volumes of data are collected on the failures and errors that result in infrequent incidents and accidents, but in the absence of data on behaviors that result in routine successful outcomes, safety management and system design decisions are based on a small sample of nonrepresentative safety data. This assessment aimed to find and document “safety successes” made possible by human operators. With many Aeronautics Research Mission Directorate (ARMD) Programs and Projects focusing on increased automation and autonomy and decreased human involvement, failure to fully consider the human contributions to successful system performance in civil aviation represents a significant risk — a risk that has not been recognized to date. Without understanding how humans contribute to safety, any estimate of predicted safety of autonomous capabilities is incomplete and inherently suspect. Furthermore, understanding the ways in which humans contribute to safety can promote strategic interactions among safety technologies, functions, procedures and the people using them. Without this understanding, the full benefits of an integrated, optimized human/technology or autonomous system will not be realized. Historically, safety has been consistently defined in terms of the occurrence of accidents or recognized risks (i.e., in terms of things that go wrong). These adverse outcomes are explained by identifying their causes, and safety is restored by eliminating or mitigating these causes. An alternative to this approach is to focus on what goes right and identify how to replicate that process. Focusing on the rare cases of failures attributed to “human error” provides little information about why human performance routinely prevents adverse events. Hollnagel has proposed that things go right because people continuously adjust their work to match their operating conditions. These adjustments become increasingly important as systems continue to grow in complexity. Thus, the definition of safety should reflect not only “avoiding things that go wrong” but “ensuring that things go right.” The basis for safety management requires developing an understanding of everyday activities. However, few mechanisms to monitor everyday work exist in the aviation domain, which limits opportunities to learn how designs function in reality. This concept of safety thinking and safety management is reflected in the emerging field of resilience engineering. According to Hollnagel, a system is resilient if it can sustain required operations under expected and unexpected conditions by adjusting its functioning prior to, during, or following changes, disturbances, and opportunities. To explore “positive” behaviors that contribute to resilient performance in commercial aviation, the assessment team examined a range of existing sources of data about pilot and air traffic control (ATC) tower controller performance, including subjective interviews with domain experts and objective aircraft flight data records. These data were used to identify strategies that support resilient performance, methods for exploring and refining those strategies in existing data, and proposed methods for capturing and analyzing new data.

Null, Cynthia H.↗

Human Performance Contributions to Safety in Commercial Aviation

In the commercial aviation domain, large volumes of data are collected and analyzed on the failures and errors that result in infrequent incidents and accidents, but in the absence of data on behaviors that contribute to routine successful outcomes, safety management and system design decisions are based on a small sample of non- representative safety data. Analysis of aviation accident data suggests that human error is implicated in up to 80% of accidents, which has been used to justify future visions for aviation in which the roles of human operators are greatly diminished or eliminated in the interest of creating a safer aviation system. However, failure to fully consider the human contributions to successful system performance in civil aviation represents a significant and largely unrecognized risk when making policy decisions about human roles and responsibilities. Opportunities exist to leverage the vast amount of data that has already been collected, or could be easily obtained, to increase our understanding of human contributions to “things going right” in commercial aviation. The principal focus of this assessment was to identify current gaps and explore methods for identifying human “success” data generated by the aviation system, from personnel and within the supporting infrastructure.

Holbrook, Jon B.↗

Monitoring for Flight Path Management: Inputs to Pilot Monitoring Training

The commercial aviation industry world-wide has identified a need for improved pilot monitoring (e.g., ICAO, 2016). More specifically, aviation safety data indicate that failures in pilots' monitoring for flight path management (FPM) have contributed to a range of undesired outcomes: accidents, major upsets, and non-compliance with ATC guidance. The FAA has further stated that these types of FPM failures are likely to worsen with the increasingly complex air traffic control systems and FPM concepts proposed for NextGen (https://www.faa.gov/nextgen/what_is_nextgen/) operations (e.g., see Hah et al., 2017). An important element of this additional complexity will be the introduction of new automation or artificial intelligence that is intended to work with the flight crew but can add additional monitoring burdens. One potential mitigation for this situation is to enhance pilot training for effective monitoring. NASA Ames Research Center was asked to identify and evaluate training approaches that have the potential to enhance pilots' ability to effectively monitor for FPM (with the result of improved awareness). The focus of this work is to identify, develop or validate training guidance to improve pilot monitoring/awareness regarding FPM and mitigate the recent trend of accidents and incidents, especially Loss of Control (LOC) events. The result of this work should be guidance for FAA training and standards organizations—such as the Air Carrier Training Aviation Rulemaking Committee (ACT ARC)—to aid in reducing the risk of incidents and accidents due to inadequate pilot monitoring/awareness.

Feary, Mike↗

Biocybernetic Closed-Loop System for Mitigating Hazardous States of Awareness

The past century of passenger flight has seen continuous improvement in aviation safety by the aerospace industry. However, while commercial aviation accident rates have continued to decline, human error-related incident and accident rates remain remarkably constant across all types of aviation (Shappell, et al., 2007). Unfortunately, this level of human error is unacceptable when considering projections for increased traffic volume (FAA, 2009), and is likely to yield more incidents and accidents unless a more complete understanding of operator error is achieved and remediations are implemented. One area of interest highlighted by researchers is Hazardous States of Awareness (HSAs) that can result from deficiencies in the design and inappropriate use of human-machine interfaces. Identifying and mitigating HSAs is critical for reducing operator errors. One promising approach uses psychophysiological measures which enable automated systems to adapt to the operator?s state and modify modes of operation to support optimal human performance (Scerbo, 2007). This paper will survey previous research and describe future directions for the application of psychophysiological measures of operators derived from cortical and autonomic assessment to perform real-time adaptive modulation of human-automation task mode mixes. The authors will present a summary of previous work done at NASA LaRC and Old Dominion University using a Psychophysiologically Adaptive System (PAS) in which the level of automation of the NASA Multi-Attribute Task Battery was modulated based on Engagement Indices derived from the users? electroencephalogram (Pope, Bogart, & Bartolome, 1995; for review see, Scerbo, Freeman, & Mikulka, 2003). Future theoretical and methodological directions for this type of closed-loop research will be discussed. Specifically, the capacity for this type of PAS to maintain effective operator state and to enable validation of candidate physiological indices will be described. Consideration will also be given to critical system characteristics (e.g., engagement indices, methods for invoking changes among system states, individual differences among users, etc.) that have been or still need to be studied. The potential of the PAS approach for interactive system design and prototyping will also be described. Examples of adaptive automation flight deck concepts in recent experiments will be highlighted and discussed.

Chad L Stephens↗

A Data-Driven Approach to Recognizing and Understanding Human Contributions to Aviation Safety

Data-driven decisions about safety management and design of safety-critical systems are limited by the available data, which influence, and are influenced by, how decision makers characterize problems and identify solutions. In the commercial aviation domain, large volumes of data are collected and analyzed on the failures and errors that result in infrequent incidents and accidents, but in the absence of data on safety-producing behaviors, safety management and system design decisions are based on a small sample of non-representative safety data. Analysis of aviation accident data suggests that human error is implicated in up to 80% of accidents, which has been used to justify future visions for aviation in which the roles of human operators are greatly diminished or eliminated in the interest of creating a safer aviation system. However, failure to fully consider the human contributions to aviation safety represents a significant and largely unrecognized risk when making policy decisions about safety management and system design. Opportunities exist to leverage the vast amount of data that have already been collected, or could be easily obtained, to increase our understanding of human contributions to safety in commercial aviation. This presentation will focus on those opportunities as well as the challenges associated with collecting and analyzing data on operators’ safety-producing behaviors.

Safety↗

Going beyond reliability to robustness and resilience in space systems

The words reliability, robustness, and resilience, are often used interchangeably to describe tough and dependable systems but the distinctions between them suggest how to design more serviceable space systems. Reliability is simply the quality of consistently performing well. A system that dependably meets its design requirements in the specified environments is reliable. The designers may not consider themselves responsible for failures under unanticipated conditions. Robustness is the capability of performing without failure under a wide range of conditions, which can go beyond the expected range to include possible off-nominal conditions. Resilience is the ability to recover from or adapt to damaging events, such as failures, accidents, external disruptions, and repurposing. Such changes are usually unanticipated. They often invalidate the usual operating assumptions and cause system failure. Reliability, robustness, and resilience describe dependable performance under increasingly difficult conditions, first the specified environment, then a wider possible environment, and finally unanticipated damaging events. These three are increasingly desirable and increasingly difficult to achieve. Engineering for resilience would design systems that can ignore or repair failures, survive accidents, and recover from disruptions. Increasing the resilience of space systems, the ability to perform after unanticipated events, would greatly increase space crew safety. Improving reliability and robustness can be done by dealing with known sources of problems, but improving resilience requires implementing a general approach to reducing the impact of unknown future events. Two contrasting approaches are reducing system complexity and adding supervisory control. The need for resilience has been claimed for decades but little has been accomplished. Systems designers assume that they understand requirements, technologies, designs, architectures, integration, testing, operations, and environments. The potential problems of changes, failures, accidents, unknown environments, and unknown unknowns are ignored. Systems designers are typically overconfident and ignore the need for robustness and resilience.

Harry W Jones↗

Going Beyond Reliability to Robustness and Resilience in Space Life Support Systems

The words reliability, robustness, and resilience are often used interchangeably to describe tough and dependable systems but the distinctions between them suggest how to design more serviceable space systems. Reliability is simply the quality of consistently performing well. A system that dependably meets its design requirements in the specified environment is reliable. The designers may not consider themselves responsible for failures under unanticipated conditions. Robustness is the capability of performing without failure under a wide range of conditions, which can go beyond the expected range to include possible off-nominal conditions. Resilience is the ability to recover from or adapt to unanticipated damaging events, such as failures, accidents, external disruptions, and repurposing. Such changes can invalidate the usual operating assumptions and cause system failure. Reliability, robustness, and resilience describe dependable performance under increasingly difficult conditions, first the specified environment, then a wider possible environment, and finally unanticipated damaging conditions. These three qualities are increasingly desirable and increasingly difficult to achieve. Engineering for resilience would design systems that can ignore or repair failures, survive accidents, and recover from unanticipated disruptions. Increasing the resilience of space systems would greatly increase space crew safety. Improving reliability and robustness requires dealing with known problems, but improving resilience requires implementing a general approach to reducing the impact of unknown future events. The need for robustness and resilience has been stated for decades but little has been done. Systems designers often assume that they understand everything they need to know. The potential failures caused by changes, failures, accidents, unknown environments, and unknown unknowns can be ignored. Such overconfidence can lead to neglect of reliability, robustness, and resilience.

Harry W. Jones↗

Going Beyond Reliability to Robustness and Resilience in Space Life Support Systems

The words reliability, robustness, and resilience are often used interchangeably to describe tough and dependable systems but the distinctions between them suggest how to design more serviceable space systems. Reliability is simply the quality of consistently performing well. A system that dependably meets its design requirements in the specified environment is reliable. The designers may not consider themselves responsible for failures under unanticipated conditions. Robustness is the capability of performing without failure under a wide range of conditions, which can go beyond the expected range to include possible off-nominal conditions. Resilience is the ability to recover from or adapt to unanticipated damaging events, such as failures, accidents, external disruptions, and repurposing. Such changes can invalidate the usual operating assumptions and cause system failure. Reliability, robustness, and resilience describe dependable performance under increasingly difficult conditions, first the specified environment, then a wider possible environment, and finally unanticipated damaging conditions. These three qualities are increasingly desirable and increasingly difficult to achieve. Engineering for resilience would design systems that can ignore or repair failures, survive accidents, and recover from unanticipated disruptions. Increasing the resilience of space systems would greatly increase space crew safety. Improving reliability and robustness requires dealing with known problems, but improving resilience requires implementing a general approach to reducing the impact of unknown future events. The need for robustness and resilience has been stated for decades but little has been done. Systems designers often assume that they understand everything they need to know. The potential failures caused by changes, failures, accidents, unknown environments, and unknown unknowns can be ignored. Such overconfidence can lead to neglect of reliability, robustness, and resilience.

Harry W. Jones↗

Revisiting the Soyuz-1 Parachute Failure in the Context of Safety in the Modern Era

The Soyuz‑1 accident remains one of the most consequential parachute related failures in human spaceflight history and provides enduring lessons for modern Entry, Descent, and Landing (EDL) system design. Occurring during the height of the Cold War and the Space Race, the mission unfolded under extraordinary political and schedule pressure as the Soviet Union sought to maintain its early leadership in space achievements following the death of chief designer Sergei Korolev. Despite unresolved propulsion, electrical, and parachute system deficiencies, Soyuz‑1 proceeded to launch and immediately encountered critical inflight anomalies, including a failed solar panel deployment, attitude control issues, and communication dropouts. Upon reentry, a malfunction in the parachute system, driven by a primary main canopy that failed to deploy, and subsequent entanglement of the reserve main canopy with the primary drogue parachute, resulted in insufficient deceleration and the fatal crash of cosmonaut Vladimir Komarov. Subsequent investigations revealed deep rooted cultural and organizational issues within the Soviet space program, including inadequate testing, suppression of dissent, undocumented last minute design changes, and the absence of integrated parachute system verification. More than 200 design flaws were identified after the accident, and firsthand accounts, including those from Yuri Gagarin, highlighted widespread concern prior to launch. Over time, the Soviet program implemented substantial reforms: systematic design corrections, rigorous process documentation, and an extensive series of drop tests that ultimately transformed the Soyuz system into one of the world’s most reliable human-rated return vehicles. This paper examines the technical architecture of the Soyuz‑1 parachute system, reconstructs the likely deployment sequence and failure mechanism, and analyzes the cultural contributors that shaped the accident. The study draws parallels to modern spacecraft parachute development, emphasizing the critical importance of integrated system testing, transparent engineering culture, and continuous hardware surveillance. These lessons remain directly relevant to today’s NASA and Commercial Crew Programs (CCP), where the Government continues to refine its understanding of aggregate risk and strengthen overall astronaut safety in the face of increasingly complex parachute systems.

Aaron L Morris↗

Two decades of DOE investment lays the foundation for TRISO-fueled reactors

Tristructural isotropic (TRISO) coated particle fuel is a robust, microencapsulated fuel form developed originally for use in high-temperature gas-cooled reactors (HTGRs). The particles consist of a spherical fissile kernel surrounded by several layers of pyrocarbon and a silicon carbide (SiC) layer (Figure 1). The particles are formed into cylindrical or spherical fuel forms using a resinated graphite matrix material for insertion into an HTGR. The kernel and coating layers together act to retain fission products within the particle during normal reactor operation and during postulated accidents; TRISO particles can maintain structural integrity at extremely high temperatures, reaching as high as approximately 1,600°C in limiting HTGR accidents. This limits the fission product activity circulating in the helium coolant and the activity released to the environment during accidents. Acceptable performance of TRISO particles is therefore essential for reactor safety.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Domestic and International Standards for Nuclear Criticality Safety – Overview and Status

The domestic and international consensus standards for nuclear criticality safety (NCS) have provided guidance for staff performing hands-on work in operations with fissionable materials. These consensus standards have contributed directly to the significant reduction in the rate of criticality accidents in process facilities since the 1940s. The last known criticality accident inside the United States was in 1978 (nearly 43 years ago) at the Idaho Chemical Processing Plant, and outside the United States, an accident occurred at Tokai-mura, Japan, in 1999 (23 years ago). The domestic consensus standards for NCS include the American Nuclear Society (ANS) standards. The ANS Standards Board, the NCS Consensus Committee, and the ANS-8 Subcommittee oversee the development and maintenance of these standards. There are currently eighteen standards in the ANS-8 series. Currently, there are six ANS-8 standards in revision mode and eleven in a maintenance mode with one new standard under development. The international consensus standards for NCS calculations, procedures, and practices are maintained and developed within the International Organization for Standardization (ISO), Technical Committee 85 on Nuclear Energy, Subcommittee 5 on Nuclear Fuel Technology, and Working Group 8, “Nuclear Criticality Safety.” Eleven standards are currently available, three standards are in revision mode, and two standards are development. This paper provides the NCS community with an overview and status report of domestic and international NCS consensus standards to stimulate interest and to support their continued development.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Domestic and International Standards for Nuclear Criticality Safety - Overview & Status [Abstract]

The domestic and international consensus standards for nuclear criticality safety (NCS) have provided guidance for staff performing hands-on work in operations with fissionable materials. These consensus standards have contributed directly to the significant reduction in the rate of criticality accidents in process facilities since the 1940s. The last known criticality accident inside the United States was in 1978 (nearly 43 years ago) at the Idaho Chemical Processing Plant, and outside the United States, an accident occurred at Tokai-mura, Japan, in 1999 (22 years ago). The domestic consensus standards for NCS include the American Nuclear Society (ANS) standards. The ANS Standards Board, the NCS Consensus Committee, and the ANS-8 Subcommittee oversee the development and maintenance of these standards. There are currently eighteen standards in the ANS-8 series. Currently, there are six ANS-8 standards in re- vision mode and eleven in a maintenance mode with one new standard under development. The international consensus standards for NCS calculations, procedures, and practices are maintained and developed within the International Organization for Standardization, Technical Committee 85 on Nuclear Energy, Subcommittee 5 on Nuclear Fuel Technology, and Working Group 8, “Nuclear Criticality Safety.” Eleven standards are currently available, three standards are in revision mode, and two standards are development. This paper provides the NCS community with an overview and status report of domestic and international NCS consensus standards to stimulate interest and to support their continued development.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Thermal-hydraulic and Fuel Performance Scoping Studies of a Flowing Water Capsule in TREAT

The restart of the Transient Reactor Test (TREAT) facility has provided a much needed capability for integral transient testing of nuclear fuel. This testing is necessary to qualify new fuel concepts such as those developed under the Accident Tolerant Fuel (ATF) program, increasing the burnup limit of light water reactor (LWR) fuels, or a variety of other programs under the Advanced Fuels Campaign (AFC). The ATF campaign has been the main driver behind the development and implementation of a variety of capsules for TREAT experiments. The Separate-Effect Test Holder (SETH) was a dry capsule that enabled testing of ATF concepts under reactivity-initiated accident (RIA) heating conditions. Following SETH, the Static Environment Rodlet Transient Test Apparatus (SERTTA) was developed to enable RIA testing in a static water environment. In efforts to support the need for future Loss-of-Coolant Accident (LOCA) tests, the Transient Water Irradiation System for TREAT (TWIST) capsule has been developed that allows for water to drain from around the fuel rod and lower the pressure to simulate LOCA conditions. All these capsules that have been developed for LWR fuel testing all lack the capability for forced convective cooling which in some applications may limit their ability to test under prototypic conditions. A design and modeling effort has been started to modify the TWIST capsule by adding a flow tube and impeller that can create flowing coolant conditions for the fuel rod. RELAP5-3D and BISON models are being used to study the differences between RIA, LOCA, Anticipated Operational Occurrences (AOO), LWR power cycling, and other transient scenarios under flowing conditions capable in the flowing capsule and the current stagnant water capsules (SERTTA and TWIST). The scoping study will provide guidance on the needed capabilities for the flowing capsule and the limitations of the currently developed capsule for LWR testing in TREAT.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Initial development of a generic fluoride salt-cooled reactor model

Fluoride high-temperature reactors (FHRs) are high-temperature, low-pressure reactor concepts that use tri-structural isotropic (TRISO) fuel and molten fluoride salt coolant. These reactors have the potential to provide both electrical power and high-temperature process heat. We used generic FHR parameters for a pebble-bed FHR to develop an initial model with fresh fuel for a generic FHR (gFHR) in MELCOR and SCALE (NEWT and KENO). In this paper, we present the development of our gFHR models, which will serve as the baseline for a sensitivity and uncertainty analysis to quantify the range of possible source terms for FHRs in severe accidents. We present MELCOR results for fuel and coolant temperatures through the core, a nodalization study for the steady-state thermal hydraulic model, and development of reactor physics models in SCALE. As this work progresses, these models will be used to calculate source terms for a loss-of-forced-flow accident and to conduct a sensitivity study on this accident to establish a range of possible source terms. SCALE will provide reactor physics parameters like isotopic inventory, decay heat generation, and temperature coefficients of reactivity. Using the uncertainty quantification tools within SCALE, we will generate distributions for those parameters and will use the uncertainty quantification code RAVEN or DAKOTA to sample those distributions in MELCOR to quantify the impact of reactor physics and thermal hydraulic uncertainties on FHR source terms. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Application of Constellation's best estimate alternate source term methodology

Safety analyses for a nuclear power plants need to consider postulated accidents that results in a risk of accidental release of radiation. The regulations require plant specific safety analysis reports to include an evaluation of the requirements of 10CFR50.67. Such a safety analysis report mandates limits such that calculated radiological consequences at certain locations do not exceed established limits in 10CFR50.67 following a postulated release of radioactivity. Analyses should demonstrate, with reasonable assurance, that these prescribed limits are complied with. Conventional methodologies utilize conservative approaches to address lack of uncertainty quantification in the utilized approaches, methods, and/or inputs. These built-in excess conservatisms often result in with compounding effects and hence overly conservative results in the estimated radiological consequences, which leads to inaccurate margin evaluation for operation and accident mitigation. Therefore, evaluating accurate dose consequences is needed for both operational and safety reasons. This paper illustrates the use of the best estimate source term (BEAST) methodology to the case of the loss of coolant accident (LOCA) dose analysis.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Sensitivity Analyses of Natural Convection in the AP1000 Passive Containment Cooling System Following LBLOCA Using CFD

The AP1000 power plant has implemented multiple layers of passive safety systems to enhance reactor safety and ensure system integrity during postulated scenarios, such as loss of coolant accident (LOCA) and main steam line break (MSLB). Among these, the passive containment cooling system (PCCS) is a safety-related system designed to prevent the containment from exceeding the design limits of both pressure and temperature following a postulated design basis accident, by transferring heat from the steel containment vessel to the atmosphere. During LOCA, natural air convection plays a vital role in removing heat from the steel containment vessel to the atmosphere. This is integrated with the condensate film from the Passive Containment Cooling Water Storage Tank (PCCWST). Air natural convection is also the only heat removal mechanism after the dry-out of the PCCWST and during the loss of shutdown decay heat removal (LOSDHR) event. Therefore, it is essential to investigate the thermal hydraulics behavior of the containment under transient conditions to ensure its safety and integrity. This paper presents a simplified CFD/ANSYS FLUENT model developed to analyze the impact of different parameters, such as air relative humidity, air temperature, and steel containment temperature, on the natural convection capability to remove the decay heat following LOCA. The model aims to examine the thermal behavior of the containment and provides a comprehensive understanding of the system's natural convection capability during postulated accidents. The results obtained from the model can be used to improve the safety and integrity of the containment system and provide valuable insights for future research in the field.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Scaling for Nuclear Reactor System: Overview

A scaling analysis is essential for predicting the behavior of a reactor system under various operating conditions, as well as for ensuring safe and reliable experimentation and adequate dataset generation for supporting the system code model development and assessment in accordance with USNRC guidelines 1.203. Testing and modeling can be carried out at various scales, from small test units to full-scale reactors, to validate the predictions produced by the analysis. However, due to the cost and challenges associated with full-scale testing, the nuclear industry uses scaled test facilities—integral effect test (IET) and separate effect test (SET)—to identify potential design issues and optimize the design to ensure regulatory compliance for safer operation and anticipated accident management. In general, IET facilities are non-nuclear thermal hydraulics test facilities. In contrast, SET facilities can be non-nuclear testing (e.g., steam generator, containment, pump system SETs) and nuclear testing (mostly fuel-material irradiation testing). IETs are used to study the system-level thermal-hydraulic behavior of the reactor coolant system (RCS) under different anticipated operational occurrences (AOOs) and design basis accidents (DBAs) such as loss-of-coolant-accident (LOCA) and loss-of-power (i.e., station blackout) and loss-of-flow (i.e., failure of RCS pump). Several LOCA scenarios could happen in reactor DBAs, such as main steam line break (MSLB), SG tube rapture (SGTR), break in direct vessel injection (DVI), and other small breaks LOCA. Scaling analysis determines the appropriate size and operating conditions for the test facility. This involves analyzing the system's thermal-hydraulic behavior at different scales and identifying the key parameters that affect the thermal-hydraulic behavior of the reactor. These key parameters can be identified so that the scaled facility can replicate the expected conditions in a prototypical setting. The scaled facility can simulate the same flow rates, pressure gradients, heat transfer coefficients, and fluid properties as the prototype. Scaling analysis is used to ensure that the results from these tests can be scaled up to predict the behavior of a full-scale prototype with an acceptable range of scaling distortions.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗