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At least 19 records

Development of Real-Time Quantitative Safety Evaluation Method for Nuclear Power Plants

Human operators involved with large process control systems such as nuclear power plants (NPPs) perform significant roles in the operation of systems. In this reason, deficiencies in human factors such as poor HSI (Human System Interface) design, procedure, and training, are significant contributing factors to NPPs incidents and accidents. In this regard, many kinds of studies including human reliability analysis (HRA), operation support system, automation, have been conducted in order to reduce human errors. Among these fields of studies, automation has its own specialty since it mainly focuses on eliminating the human error-inducing tasks to reduce human error, while other topics are primarily focusing on reducing human error by aiding human operators during operation. It is commonly believed that replacing human operators to the automated system would guarantee greater efficiency, lower workloads, and fewer human errors. However, these beliefs are not always true in large and complex systems such as NPP, since excessive automation can generate new roles for human operators, change human operators' activities in unexpected ways, and inducing 'out-of-the-loop' (OOTL) problem. Especially, although human error probability drastically increases during abnormal situations in NPP due to overload of information, high workload, and short time available for diagnosis, automation is not actively adopted since conventional machine learning techniques are neither capable nor reliable to handle overwhelmingly complex abnormal situations in NPP. Recently, newly developed 'deep learning' techniques have been actively applied to many fields, and some of these studies proved that the artificial intelligences (AI) based on deep learning techniques show better performance than that of human being. In 2015, deep Q-network (DQN) which is one of the deep learning techniques was developed and applied to train AI that automatically plays various Atari 2800 games, and this AI surpassed the human-level playing in many kind of games. Also in 2016, 'AlphaGo', which was developed by 'Google Deepmind' based on deep learning technique to play the game of Go, was defeated Se-dol Lee who is the World Go champion with score of 4:1. In the point of view that the game of Go is considered as one of the challenging area in machine learning field, it is verified that these new machine learning techniques can perform well, even better than the human being in high-complexity tasks. Previously, most of AI applications in nuclear field were focusing on supporting human operators' tasks such as diagnosis and decision making by rapid classification and prediction, based on fixed training data sets. However, it is expected that AI can be applied to solve more complicated problems (i.e. automation during abnormal states in NPP) by adopting advanced machine learning methodologies. In order to apply the automation system in NPP, firstly it is needed to evaluate the status of the plant quantitatively in real-time, since the information about which state is better (i.e. safe) or worse (i.e. unsafe) is essential for training properly working automation AI. Currently, there are several methodologies for NPP safety evaluation such as probabilistic safety assessment (PSA), safety performance indicator (SPI). However, existing methodologies are not feasible for development of automated system since they are not capable for the cases which are not included in the underlying model, not able to consider delicate changes in the NPP, and hard to evaluate in real-time. Therefore, development of new NPP safety evaluation methodology which can cover previously mentioned requirements is needed to conduct the study on NPP automation further. In this study, the concept of early warning score (EWS) in medical field was adopted to develop the methodology which can evaluate the safety level of the NPP quantitatively and in real-time. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

A development framework for artificial intelligence based distributed operations support systems

Advanced automation is required to reduce costly human operations support requirements for complex space-based and ground control systems. Existing knowledge based technologies have been used successfully to automate individual operations tasks. Considerably less progress has been made in integrating and coordinating multiple operations applications for unified intelligent support systems. To fill this gap, SOCIAL, a tool set for developing Distributed Artificial Intelligence (DAI) systems is being constructed. SOCIAL consists of three primary language based components defining: models of interprocess communication across heterogeneous platforms; models for interprocess coordination, concurrency control, and fault management; and for accessing heterogeneous information resources. DAI applications subsystems, either new or existing, will access these distributed services non-intrusively, via high-level message-based protocols. SOCIAL will reduce the complexity of distributed communications, control, and integration, enabling developers to concentrate on the design and functionality of the target DAI system itself.

Adler, Richard M.↗

A Cis-Lunar Propellant Infrastructure for Flexible Path Exploration and Space Commerce

This paper describes a space infrastructure concept that exploits lunar water for propellant production and delivers it to users in cis-lunar space. The goal is to provide responsive economical space transportation to destinations beyond low Earth orbit (LEO) and enable in-space commerce. This is a game changing concept that could fundamentally affect future space operations, provide greater access to space beyond LEO, and broaden participation in space exploration. The challenge is to minimize infrastructure development cost while achieving a low operational cost. This study discusses the evolutionary development of the infrastructure from a very modest robotic operation to one that is capable of supporting human operations. The cis-lunar infrastructure involves a mix of technologies including cryogenic propellant production, reusable lunar landers, propellant tankers, orbital transfer vehicles, aerobraking technologies, and electric propulsion. This cislunar propellant infrastructure replaces Earth-launched propellants for missions beyond LEO. It enables users to reach destinations with smaller launchers or effectively multiplies the user s existing payload capacity. Users can exploit the expanded capacity to launch logistics material that can then be traded with the infrastructure for propellants. This mutually beneficial trade between the cis-lunar infrastructure and propellant users forms the basis of in-space commerce.

Oeftering, Richard C.↗

TCL4 UTM (UAS Traffic Management) Nevada 2019 Flight Tests, Airspace Operations Laboratory (AOL) Report

The Unmanned Aircraft Systems (UAS) Traffic Management (UTM) research project has been developing and testing concept ideas for enabling small UAS (sUAS) operations in low altitude airspace (ground to 400 feet). To do this, NASA has organized a series of flight test demonstrations. Technology Capability Level-4 (TCL4) flight tests were conducted at a Nevada, USA test site, during June 2019. The testing resulted in over 300 data collection flights using eight live rotorcraft, 15 simulated vehicles, involving six flight crews and five Unmanned Aerial System (UAS) Service Suppliers (USS). The TCL4 approach was designed to demonstrate five scenarios that set up five diverse sets of UAS events and activities. The Nevada test site focused on three of these scenarios: an incoming weather front, a concert event with an incident requiring an emergency response, and a scenario where multiple vehicles experienced Communication, Navigation, and Surveillance (CNS) issues. The test site created their scenarios to each have three phases and were required to complete three executions of each scenario, for a total of nine missions per Nevada vehicle per scenario. This document presents data collected from participants during the TCL4-Nevada flight test that provides information about how much and how well operators were able to make use of UTM functions and information, with the goal of exploring what the minimum information requirements and/or best practices might be in TCL4 operations. The driving enquiry was: how do UTM tools and features support (human) operators leading to safe and effective conduct of large-scale beyond visual line of sight (BVLOS) sUAS operations in “urban canyon” environments? As with the data collected during previous similar tests (e.g., TCL3, Martin et al., 2019), the quality of the UTM information exchanged, and the meaningfulness and therefore usefulness of this information, were all focal points of the questions asked and the data collected. Data aligned with five human-system attributes to indicate that UTM provided information that contributed to users’ ability to operate safely and effectively within UTM, but that information was not always complete and was sometimes unclear.

UTM↗

Computerized operator support system and human performance in the control room - 262

An operator aid technology to assist nuclear plant operators maintain situation awareness and detect faults earlier than would be possible using conventional control room technologies is described. Coined the Computerized Operator Support System (COSS), this technology is aimed at the human-machine interface. The COSS has been implemented as a prototype system operating in the control room environment of a full-scale simulator for a commercial nuclear power plant. A human factors assessment that involved two licensed reactor crews was performed to evaluate crew performance. This paper describes the development of the COSS, the underlying fault-detection architecture, and feedback received based on operator-in-the-loop studies. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Horizontally Integrated Informatics to Support Science Operations in Human Spaceflight

Many systems will be designed and developed for the Artemis program. A subset of these systems will be relevant to scientific activities conducted during the program, but it is important to consider that these scientific activities will be conducted within the context of the larger program. Thinking about the program data holistically, and deliberately structuring its data products in a way that preserves the data’s mission context will enable new opportunities for real-time science support, and long-term analysis of the mission and its data for generations to come (see [1] for an example). Here, we show that horizontally integrated mission data products are valuable by describing the SSERVI RISE2 field program. Temporally contextualized data introduces relationships that do not need to be predefined. One field that greatly benefits from freeform relationship building between disparate datasets is anomaly investigation, where investigators need to follow lines of evidence that may not be a priori apparent. Building relationships between data enables accident investigators to react faster to time-sensitive incidents during EVA operation and other critical human spaceflight activities. Another advantage is the long-term data preservation that enables unplanned or yet-to-be conceived applications or study of this data many years in the future. As Artemis activities become more defined, having a horizontally integrated data management plan will be paramount to ensuring successful mission context is captured for generations to come.

B F Feist↗

Uncrewed Lunar Surface Operations and Support Activities

Sustained human presence on the surface of the Moon and future missions to Mars require increased independence from surface crews and Earth-based mission control to operate efficiently, safely, and reliably. The time for surface crews to perform tasks will be limited. Extravehicular activities by surface personnel are burdensome and time-consuming, even when a continuous human presence on the surface occurs. Identifying and balancing human/automation roles and tasks and infusing automation and autonomy practices early in a system’s lifecycle will be essential to achieve mission objectives. Among these objectives are attaining a sustained human presence, improving performance and mission effectiveness, reducing operations and maintenance (O&M) costs, and ensuring operations that are robust to communication delays. To achieve these objectives, an operational shift toward increased automation and autonomy with less reliance on humans is needed. Uncrewed lunar surface operations and support activities occur when surface crews are not present or are independent of surface crew timeline activities requiring no surface crew oversight or intervention. These uncrewed surface opportunities can also be planned to minimize crew workload that avoids routine maintenance and support tasks, thus maximizing crew exploration time. Uncrewed preparations such as staging and prepositioning equipment and materials before the crew arrives could improve crew task efficiency. Additional opportunities exist to conduct uncrewed science, exploration, and utilization. Uncrewed surface architecture functions can include science and exploration; habitation; launch and landing support; surface communication and navigation; surface power generation and distribution; human surface mobility; lifting, handling, manipulating; excavation, construction, and site preparation; logistics management; maintenance and repair; surface resource utilization; integrated site operations and shared support services (e.g., site scheduling/prioritization, dust mitigation/contamination control, and surface safety). Early robotic lunar surface campaigns will provide information on the availability of resources, such as oxygen and water, and demonstrate surface-based technologies. After the Artemis III human lunar return mission, a series of landers will deliver surface systems, cargo, supplies, science packages, spare parts, and commodities. A balance of crewed and uncrewed surface operations will enable a sustained lunar surface presence at the South Pole of the Moon at a site that will be known as the Artemis Base Camp (ABC). It is envisioned that base camp operations on and around the Moon will then help prepare for the mission durations and activities needed to support the first human mission to Mars. Before long-duration crew missions to the base camp can occur, the necessary surface infrastructure will be pre-deployed and verified operational. Surface assets will be teleoperated and remotely managed from Earth. Additionally, robotic and short-duration crewed missions to the ABC will ensure the site’s merit to achieve long-term science objectives, availability of usable resources, and that terrain, seasonal variations, and illumination conditions are acceptable. ABC will consist of different areas where specific functions and services are rendered, including: • Launch and Landing Area • Habitation Area • Power Production Area • Resource Areas Launch and Landing Area—The launch and landing area will support associated functions for the arrival and departure of vehicles, such as crewed landing and ascent and uncrewed cargo deliveries and offloading. It will evolve from an unimproved site at the beginning of the exploration campaign to a more sustainable landing and launch area that can support repeated arrivals and departures. Initial uncrewed Lunar Terrain Vehicle (LTV) surface operations may include emplacement of navigation beacons and communication equipment, real-time video and photography of landing/liftoff events, and element repositioning, such as portable utility power (PUP) (applicable for other landed assets at other areas). Site preparations, such as surface leveling, soil compaction, and berm/path construction, may be needed for a more sustainable launch and landing area capable of accommodating vehicles that are increasingly more reusable and reduce the effects of plume surface interactions and ejecta impacts on nearby surface assets. During the ABC missions, cargo and logistics will be delivered to the lunar surface via robotic cargo landers before the crew arrives. These shipments, which can arrive in pressurized logistics carriers, will deliver the logistics necessary to support a crewed mission and include items such as food, water, equipment spares, etc. Providing the capability to retrieve, offload, and transport the logistics closer to the ABC site before the arrival of the crew will increase the overall efficiency of crew operations once they arrive. In the sustained phase of exploration, other supporting services may be needed, such as lander propellant servicing, surface power services, commodity refreshes, and additional inspection, maintenance, and repair capabilities, to sustain a cadence of extended personnel stays and cargo arrivals and departures. Habitation Area—Uncrewed support to surface habitation could involve supporting activation and pre-entry operations of the habitat while the crew is in orbit at the Gateway outpost preparing for a surface landing. Surface Habitat (SH) uncrewed operations may include bringing the cabin environment to a habitable temperature and air mix and activating other critical crew support systems. Potential crop production uncrewed tasks in the SH could also include autonomous watering and tending. Additionally, when the crew departs, the SH enters dormancy for the long period of uncrewed operation. A logistical staging area could also be collocated near the SH. If so, staging operations for crew supplies, waste re-location, and recycling operations may be opportunities for uncrewed operations. Power Production Area—The Fission Surface Power (FSP) element and its supporting distribution equipment provide power to surface elements as needed across the ABC to supplement day-to-day operations and survive lunar nights. Uncrewed support of this power system includes any initial LTV-assisted deployments of cables and other distributed equipment, associated electrical connections, and system testing and activation operations. Robotically performing some inspections, maintenance, or repair tasks on the power distribution equipment could reduce the surface crew workload. Resource Area— Uncrewed resource prospecting, mapping, and characterizing possible resource sites is likely to be time-consuming and represents an opportunity for uncrewed operations between crewed missions. Uncrewed mobile equipment operations will be needed in the extreme environments of permanently shadowed locations where resource extractions occur. As In-Situ Resource Utilization (ISRU) pilot plant operations begin, uncrewed surface support activities with available mobile and portable assets (LTV, PUP, etc.) will better support these operations. Any produced commodities can be stored at a centralized storage location for future use. Also associated with these operations is the use of mobile robotic excavators for resource acquisition and robotic/autonomous regolith processing. The waste tailings generated during excavation and regolith processing would also need to be transported and deposited at a dedicated location. Surface assets will continue operating between crew visits to maintain surface capabilities, conduct lunar surface science, technology demonstrations, and public outreach opportunities. Additionally, certain sustaining tasks that would consume valuable crew time could be performed before crew arrival, or after their departure. This capability may offer more affordable options to construct, activate, test, and maintain a broad set of surface assets. Telerobotically operated human surface mobility systems, such as the LTV and Pressurized Rover (PR), can be utilized for various tasks. Surface environmental conditions pose a distinct challenge for all these activities. Surface illumination and localized shadows are one such factor. Night-survival operations could consist of thermal management, battery pre-charging, and load shedding. Some surface systems may hibernate through the night and then awake and continue nominal operations. Uncrewed mobile assets may use a more adaptive approach to optimize their power and operations; one method is to follow the sunlight. Night-survival operations may be initiated remotely by teleoperation, automated, or accomplished by supervised autonomous operation. The ability to pre-deploy and control remote assets in orbit or on Mars before the arrival of the mission crew is a key capability that can be simulated on the moon. The base camp provides a venue where these advanced operational concepts, technologies, and autonomous methods and techniques, including the incorporation of time delays to simulate Earth-Mars latency can be replicated to help buy down future Mars mission risks. This paper will examine the evolution of uncrewed lunar surface operations and support activities. It will also discuss the lunar surface environmental conditions (thermal, lighting, terrain, topography, communications) along with the challenges they pose on uncrewed surface operations, and the performance of these activities with limited to minimal human interaction and/or teleoperation. Since lunar missions include Mars mission analogs, such investigation provides the framework for future uncrewed Mars mission support.

Mark E Lewis↗

Virtual Environment Computer Simulations to Support Human Factors Engineering and Operations Analysis for the RLV Program

The Army-NASA Virtual Innovations Laboratory (ANVIL) was recently created to provide virtual reality tools for performing Human Engineering and operations analysis for both NASA and the Army. The author's summer research project consisted of developing and refining these tools for NASA's Reusable Launch Vehicle (RLV) program. Several general simulations were developed for use by the ANVIL for the evaluation of the X34 Engine Changeout procedure. These simulations were developed with the software tool dVISE 4.0.0 produced by Division Inc. All software was run on an SGI Indigo2 High Impact. This paper describes the simulations, various problems encountered with the simulations, other summer activities, and possible work for the future. We first begin with a brief description of virtual reality systems.

Lunsford, Myrtis Leigh↗

Development of the Delphi display concept for alternative explanations decision making support - 120

Human operators of advanced technology systems, such as nuclear power, command and control, and deepwater oil drilling, must be able to evaluate and respond to a wide array of operational anomalies. Unfortunately, these circumstances are often complex and ambiguous, complicating decision making tasks. One common decision making error, called confirmation bias, occurs when decision makers prematurely focus on one explanation instead of systematically considering all plausible alternative explanations that could equally well account for an operational anomaly (Nickerson, 1998). The Delphi display is designed to counteract decision makers' tendencies towards confirmation bias and to encourage them to consider all plausible explanations, including high risk explanations, they might otherwise overlook. An initial Delphi display prototype was designed to help identify the underlying cause of operational anomalies in the complex, high-consequence technology of deepwater drilling. This prototype decision aid incorporates major indicators related to operational anomalies and presents corresponding plausible explanations for each indicator, ranging from high-risk system-critical explanations to lower-risk but still significant events. The Delphi display incorporates several innovative features to help decision makers detect and monitor operational anomalies and view the relationships among indicators and their associated plausible explanations. Preliminary evaluations of an early prototype Delphi display for detecting kicks in deepwater oil drilling operations have been promising. Moreover, the methodology used to design and develop the Delphi display can be readily generalized to numerous other operational contexts that require humans to make system-critical decisions. To illustrate, an initial Delphi display design for nuclear power plant control is described. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

In Situ Measurements of Surface Texture with Virtual Environments Support Science-Driven Human Surface Operations on the Moon and Beyond

Visualization tools enabling real-time scientific analysis are important for supporting future astronaut operations on the lunar surface. Such tools can be built into virtual environments to support scientific investigations, as well as situational awareness, real-time decision making, and efficient communication between astronauts and ground and support systems. Understanding how these tools can be optimized for science is essential for upcoming Artemis missions. In this contribution, we discuss how measurements of surface texture at multiple length scales can greatly enhance in situ science on/of the Moon, and eventually Mars, asteroids, and beyond. Roughness measurements at various wavelengths directly support objectives defined in the Artemis Science Plan, including (O1) “understanding planetary processes,” (O2) “understanding volatile cycles,” and (O3) “interpreting the impact history of the Earth-Moon system” . Key scientific analyses enabled by texture measurements at different length scales include: ● Sub-centimeter scales: Texture measurements can help constrain lava flow crystallinity, lava rheology, emplacement flow dynamics, and cooling histories (O1). Measurements of lacunarity (voids in fractal fill space) can shed light on eruptive volatile content, residence time of migrating volatiles, and near-surface volume available for micro-cold trapping of volatiles (O1, O2). ● Centimeter–meter scales: Texture measurements can be used for the differentiation of individual lava flows, the reconstruction of local stratigraphies and emplacement sequences, characterization of post-emplacement surface modification processes (O1, O3). Derived roughness (polarization) metrics can be used in the detection of water ice and characterization of ice properties (e.g., purity, grade, depth, abundance). ● Hectometer–Kilometer scales: Texture measurements can be used to differentiate major geologic surface units and surface structures (O1), constrain the presence of abundant ground ices (O2), and analyze surface modification and estimate surface age (O3). Real-time measurements of surface texture across these multiple length scales will enable efficient sample identification and scientific investigations by future astronauts. To support these investigations and the objective classification of surface texture, virtual environments employed by astronauts should be able to instantaneously convert raw data into processed data (e.g., digital terrain and elevation models) and derived metrics (e.g., RMS, std, Hurst, CPR) and perform statistical analyses (e.g., PCA, outliers, correlation matrices). Such tools are being developed and tested by the Resource Exploration and Science of our Cosmic Environment (RESOURCE) team, a node of NASA’s Solar System Exploration Research Virtual Institute (SSERVI), and are an excellent example of the powerful synergies of human and robotic ground assets critical in the return of humans to the Moon.

Ariel N. Deutsch↗

OFMTutor: An operator function model intelligent tutoring system

The design, implementation, and evaluation of an Operator Function Model intelligent tutoring system (OFMTutor) is presented. OFMTutor is intended to provide intelligent tutoring in the context of complex dynamic systems for which an operator function model (OFM) can be constructed. The human operator's role in such complex, dynamic, and highly automated systems is that of a supervisory controller whose primary responsibilities are routine monitoring and fine-tuning of system parameters and occasional compensation for system abnormalities. The automated systems must support the human operator. One potentially useful form of support is the use of intelligent tutoring systems to teach the operator about the system and how to function within that system. Previous research on intelligent tutoring systems (ITS) is considered. The proposed design for OFMTutor is presented, and an experimental evaluation is described.

Jones, Patricia M.↗

Technology needs of the Exploration Initiative

An overview of the U.S. Space Exploration Initiative (SEI) is presented. The two primary objectives of the initiative are a return to the moon to create a permanent lunar base and a human mission to Mars. Even though mission architectural concepts are not yet defined, previous studies indicate that the SEI will require developments in numerous areas, including advanced engines for space transportation, in-space assembly and construction to support permanent basing of exploration systems in space, and advanced surface operations capabilities including satisfactory levels of power and surface roving vehicles, and technologies to safely support human space operations of long duration. The process of mission definition has begun and it is shown that it is possible to identify a family of fundamental functional building blocks from which all SEI mission architectures will be constructed.

Aldrich, Arnold↗

Innovative Test Operations to Support Orion and Future Human Rated Missions

This paper describes how the Orion program is implementing new and innovative test approaches and strategies in an evolving development environment. The early flight test spacecraft are evolving in design maturity and complexity requiring significant changes in the ground test operations for each mission. The testing approach for EM-2 is planned to validate innovative Orion production acceptance testing methods to support human exploration missions in the future. Manufacturing and testing at Kennedy Space Center in the Neil Armstrong Operations and Checkout facility will provide a seamless transition directly to the launch site avoiding transportation and checkout of the spacecraft from other locations.

Koenig, William J.↗

Intelligent resources for satellite ground control operations

This paper describes a cooperative approach to the design of intelligent automation and describes the Mission Operations Cooperative Assistant for NASA Goddard flight operations. The cooperative problem solving approach is being explored currently in the context of providing support for human operator teams and also in the definition of future advanced automation in ground control systems.

Jones, Patricia M.↗

Software Surrogate

In 1994, Blackboard Technology received a NASA Phase I SBIR award entitled "A Blackboard-Based Framework for Mixed-Initiative, Crewed- Space-System Applications." This research continued in Phase II at JSC, where a generic architecture was developed in which a software surrogate serves as the operator's representative in the fast-paced realm of nearly autonomous, intelligent systems. This SBIR research effort addressed the need to support human-operator monitoring and intervention with intelligent systems such as those being developed for NASA's crewed space program.

Source record↗

Why Didn't They Just Follow the Procedure?

It is often said that to err is human and that procedures are in place to prevent people from making mistakes. It's true that failures can be traced to human limitation, but what's more important is that all successes, all safe operations are the result of human capabilities. And while it's true that good procedures can help avoid error, it's also the case that procedures have their own limitations. This talk highlights the limits of procedures and the resilience people bring to operations. It discusses ways to change the common narrative that people are the creators of safety rather than only the source of error and failure, and it proposes an approach to the design of procedures that supports the human operator.

operations↗