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At least 19 records

Installation of Computerized Procedure System and Advanced Alarm System in the Human Systems Simulation Laboratory

This report describes the installation of two advanced control room technologies, an advanced alarm system and a computerized procedure system, into the Human Systems Simulation Laboratory (HSSL). Installation of these technologies enables future phases of this research by providing a platform to systematically evaluate the effect of these technologies on operator and plant performance.

60 APPLIED LIFE SCIENCES↗

HYBRID ALARM SYSTEMS: COMBINING SPATIAL ALARMS AND ALARM LISTS FOR OPTIMIZED CONTROL ROOM OPERATION

The US Department of Energy (DOE) is sponsoring research, development, and deployment on Light Water Reactor Sustainability (LWRS), in which the Idaho National Laboratory (INL) is working closely with nuclear utilities to develop technologies and solutions to help ensure the safe operational life extension of current nuclear power plants. One of the main areas of focus is control room modernization. Within control room modernization, alarm system upgrades present opportunities to meet the broader goals of the LWRS project in demonstrating the use and safety of the advanced instrumentation and control (I&C) technologies and the short-term and longer term objectives of the plant. In this paper, we review approaches for and human factors issues behind upgrading alarms in the main control room of nuclear power plants.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Computational Human Performance Modeling For Alarm System Design

The introduction of new technologies like adaptive automation systems and advanced alarms processing and presentation techniques in nuclear power plants is already having an impact on the safety and effectiveness of plant operations and also the role of the control room operator. This impact is expected to escalate dramatically as more and more nuclear power utilities embark on upgrade projects in order to extend the lifetime of their plants. One of the most visible impacts in control rooms will be the need to replace aging alarm systems. Because most of these alarm systems use obsolete technologies, the methods, techniques and tools that were used to design the previous generation of alarm system designs are no longer effective and need to be updated. The same applies to the need to analyze and redefine operators’ alarm handling tasks. In the past, methods for analyzing human tasks and workload have relied on crude, paper-based methods that often lacked traceability. New approaches are needed to allow analysts to model and represent the new concepts of alarm operation and human-system interaction. State-of-the-art task simulation tools are now available that offer a cost-effective and efficient method for examining the effect of operator performance in different conditions and operational scenarios. A discrete event simulation system was used by human factors researchers at the Idaho National Laboratory to develop a generic alarm handling model to examine the effect of operator performance with simulated modern alarm system. It allowed analysts to evaluate alarm generation patterns as well as critical task times and human workload predicted by the system.

99 GENERAL AND MISCELLANEOUS↗

Next ALarM System

NALMS is an alarm system application designed for availability, integrability, and extensibility. The NALMS development was driven by SLAC's efforts to replace the Alarm Handler, due for deprecation as a Motif-based application, and to introduce process improvements addressing hierarchy implementation overhead, limited operator engagement, and operator display integration.

Garrahan, Jacqueline↗

Methods for Detector Placement and Analysis of Criticality Accident Alarm Systems

Determining the optimum placement to minimize the number of detectors for a criticality accident alarm system (CAAS) in a large manufacturing facility is a complex problem. There is typically a target for the number of detectors that can be used over a given zone of the facility. A study to optimize detector placement typically begins with some initial guess at the placement of the detectors and is followed by either predictive calculations of accidents at specific locations or adjoint calculations based on preferred detector locations. Within an area of a facility, there may be a large number of potential criticality accident sites. For any given placement of the detectors, the list of accident sites can be reduced to a smaller number of locations at which accidents may be difficult for detectors to detect. Developing the initial detector placement and determining the list of difficult accident locations are both based on the practitioner's experience. Simulations following fission particles released from an accident location are called 'forward calculations.' These calculations can be used to answer the question 'where would an alarm be triggered?' by an accident at a specified location. Conversely, 'adjoint calculations' start at a detector site using the detector response function as a source and essentially run in reverse. These calculations can be used to answer the question 'where would an accident be detected?' by a specified detector location. If the number of accidents, P, is much less than the number of detectors, Q, then forward simulations may be more convenient and less time-consuming. If Q is large or the detectors are not placed yet, then a mesh tally of dose observed by a detector at any location must be computed over the entire zone. If Q is much less than P, then adjoint calculations may be more efficient. Adjoint calculations employing a mesh tally can be even more advantageous because they do not rely on a list of specific difficult-to-detect accident sites, which may not have included every possible accident location. Analog calculations (no biasing) simply follow particles naturally. For sparse buildings and line-of-sight calculations, analog Monte Carlo (MC) may be adequate. For buildings with internal walls or large amounts of heavy equipment (dense geometry), variance reduction may be required. Calculations employing the CADIS method use a deterministic calculation to create an importance map and a matching biased source distribution that optimize the final MC to quickly calculate one specific tally. Calculations employing the FW-CADIS method use two deterministic calculations (one forward and one adjoint) to create an importance map and a matching biased source distribution that are designed to make the MC calculate a mesh tally with more uniform uncertainties in both high-dose and low-dose areas. Depending on the geometry of the problem, the number of detectors, and the number of accident sites, different approaches to CAAS placement studies can be taken. These are summarized in Table I. SCALE 6.1 contains the MAVRIC sequence, which can be used to perform any of the forward-based approaches outlined in Table I. For analog calculations, MAVRIC simply calls the Monaco MC code. For CADIS and FW-CADIS, MAVRIC uses the Denovo discrete ordinates (SN) deterministic code to generate the importance map and biased source used by Monaco. An adjoint capability is currently being added to Monaco and should be available in the next release of SCALE. An adjoint-based approach could be performed with Denovo alone - although fine meshes, large amounts of memory, and long computation times may be required to obtain accurate solutions. Coarse-mesh SN simulations could be employed for adjoint-based scoping studies until the adjoint capability in Monaco is complete. CAAS placement studies, especially those dealing with mesh tallies, require some extra utilities to aid in the analysis. Detectors must receive a minimum dose rate in order to alarm; therefore, a simple yes/no plot could be more useful to the analyst than a standard dose rate contour plot. Alarm systems that require several detectors to alarm simultaneously for a given accident would need to combine several yes/no plots in order to show accident sites where multiple detectors would be triggered. This could require a plot of 'areas of coverage' that is mapped over the building geometry. Several new utilities (which will be part of SCALE 6.2) were created to help with CAAS analysis.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Report for 2.2.1 Task 5: Develop and Document a State-Based Alarm System for a Nuclear Power Plant Control Room Using Machine Learning. Light Water Reactor Sustainability Program report

In legacy control rooms the operators might have 20 to 30 alarms to deal with on 12 control boards, during a reactor trip. The new digital alarm system in the upgraded control room will display between 150 and 200 alarm points on a single screen. This overload of information prevents the operators from identifying any abnormal alarms quickly without having to scroll through the alarm list looking for the abnormal alarms. One way to tackle this problem could be to attempt to create a state-based alarm system which would identify plant states and would identify which alarms are expected in those states and suppress them from the alarm screen so that any unexpected alarms would be displayed and easily identified to the operators. This can be done today but is very labor intensive in that an operator or training instructor would be required to run scenarios and log the alarms to be suppressed. The hope is that Machine Learning (ML) can provide an alternative solution with less manual effort required.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Plan for IER-443 Testing of the Y-12 and AWE Criticality Accident Alarm System Detectors at the Godiva IV Burst Reactor

This document provides the scope and details of the “Plan for Testing the Y-12 and AWE Criticality Accident Alarm System Detectors at the Godiva IV Burst Reactor”. Due to the relative simplicity of the testing goals, scope, and methodology, the NCSP Manager approved execution of the test when ready. No preliminary CED-1 or final design CED-2 reports were required or issued. The test will subject Criticality Accident Alarm System (CAAS) detectors supplied by Y- 12 and AWE to very intense and short duration mixed neutron and gamma radiation fields. The goals of the test will be to (1) substantiate functionality, for both existing and newly acquired Y- 12 CAAS detectors, and (2) the ability of the AWE detectors to provide quality temporal dose information after a hypothetical criticality accident. ANSI/ANS-8.3.1997 states that the “system shall be sufficiently robust as to actuate an alarm signal when exposed to the maximum radiation expected”, which has been defined at Y-12, in Documented Safety Analyses (DSAs), to be a dose rate of 10 Rad/s. ANSI/ANS-8.3.1997 further states that “alarm actuation shall occur as a result of a minimum duration transient” which may be assumed to be 1 msec. The pulse widths and dose rates which will be achieved in this test will exceed these requirements. Pulsed radiation fields will be produced by the Godiva IV fast metal burst reactor at the National Criticality Experimental Research Center (NCERC) at the Nevada National Security Site (NNSS). The magnitude of the pulses and the relative distances to the detectors will be varied to afford a wide range of radiation fluence and pulse widths. The magnitude of the neutron and gamma fields will be determined by reactor temperature rise to fluence and dose conversions which have been previously established through extensive measurements performed under IER-147. The requirements for CAAS systems to detect and alarm under a “minimum accident of concern” as well as other functional requirements specified in ANSI/ANS-8.3.1997, are not included in the test. Routine and periodic maintenance and calibrations performed at Y-12 and AWE are intended to provide adequate confirmation for meeting these requirements.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Development of a Smart Alarm System for the CEBAF Injector

RadiaSoft and Jefferson Laboratory are working together to develop a machine-learning-based smart alarm system for the CEBAF injector. Because of the injector’s large number of parameters and possible fault scenarios, it is highly desirable to have an autonomous alarm system that can quickly identify and diagnose unusual machine states. We present our work on artificial neural networks designed to identify such undesirable machine states. In particular, we test both auto-encoders and inverse models as possible tools for differentiating between normal and abnormal states. These models are being developed using both supervised and unsupervised learning techniques, and are being trained using CEBAF injector data collected during dedicated machine studies as well as during regular operations. Lastly, we discuss tradeoffs between the two types of models.

Abell, D. T.↗

CS-Studio Alarm System Based on Kafka

The CS-Studio alarm system was originally based on a relational database and the Apache ActiveMQ message service. The former was necessary to store configuration and state, while the latter communicated state updates and user actions. In a recent update, the combination of relational database and ActiveMQ have been replaced by Apache Kafka. We present how this simplified the implementation while at the same time improving performance.

Kasemir, Kay↗

Qualification of Y-12 Legacy Criticality Accident Alarm System Detectors

The Y-12 National Security Complex has had a criticality accident alarm system (CAAS) capable to detect a criticality accident since 1945. With the exception of Y-12’s new High-enriched Uranium Materials Storage facility (HEUMF), the system installed in Y-12’s existing facilities dates to 1957. This legacy system has received many upgrades since installation and detector stations have been removed and relocated as enriched uranium operations have evolved, but the basic design and electronic configuration of the system is the same as that in 1957.

61 RADIATION PROTECTION AND DOSIMETRY↗

Criticality Acccident Alarm System (CAAS) CSG-UM Hybrid Example

Criticality Accident Alarm System calculations combine methods needed for both criticality and shielding calculations. UM may be used for facility details, allowing import of existing facility drawing. CSG used for criticality cells.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Criticality Accident Alarm System Modeling for the Uranium Processing Facility

The Criticality Accident Alarm System (CAAS) for the Uranium Processing Facility (UPF) provides coverage for two primary processing facilities. For UPF, detailed documentation and drawings of the design are readily available. While this design information allows for some CAAS modeling features to match the actual design with a high level of accuracy, assumptions and simplifications are necessary as it is unrealistic and unnecessary to replicate many aspects of the design. A set of modeling guidelines and techniques were used to develop consistency between the MCNP6 models. While ensuring conservatism, overly conservative choices must be avoided to demonstrate the minimum accident of concern can be detected. To ensure proper modeling choices were made for the final models, a group of parametric studies were performed to evaluate the impact on the dose rates detected at the individual detectors. Some of these modeling choices and/or associated parametric studies include: - Modeling the grid steel area above the detectors as homogeneous stainless steel and determining the impact on detectability with an increasing density. - Modeling the presence of equipment in close proximity to the CAAS detectors. - Determining an appropriate material density to represent miscellaneous items not specifically modeled. - Evaluating the impact of sprinkler activation. The results of UPF CAAS analyses show that the modeling assumptions made based on the detailed design and parametric studies are appropriate and demonstrate accurate yet conservative models have been developed for detecting minimum accident of concern within the UPF.

61 RADIATION PROTECTION AND DOSIMETRY↗

Criticality Alarm Systems at the Idaho Chemical Processing Plant

The Idaho Chemical Processing Plant (ICPP) has a number of criticality alarm systems (CAS) in the various facilities. The complexity of the plant creates some unique CAS problems. A detailed review of the CAS has been made to determine the level of compliance with ANSI 8.3-1986. A major CAS upgrade project is currently in the design stage which will expand coverage and bring all CAS monitoring unshielded areas into full compliance with ANSI 8.3-1986. Because DOE Orders do not contain an exclusion that exempts the need for CAS coverage in shielded areas as provided in ANSI 8.3-1986, a future CAS upgrade project is in the planning stage which will also upgrade the CAS coverage in shielded portions of the plant.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Technical Basis for the Uranium Processing Facility Criticality Accident Alarm System

The technical basis for the Y-12 National Security Complex (Y-12) Uranium Processing Facility (UPF) Criticality Accident Alarm System (CAAS) 90% design has been completed. The CAAS design was evaluated for two primary processing facilities, the Main Processing Building (MPB) and the Salvage and Accountability Building (SAB), and for connected facilities known as the Personnel Support Building (PSB) and the Highly Enriched Uranium Materials facility (HEUMF) Connector (HCON), which connects UPF to existing Y-12 facilities. The evaluation focused on three primary areas to address requirements of ANSI/ANS-8.3: (1) detectability of the minimum accident of concern; (2) determination of areas where total dose could exceed 12 rad for the maximum anticipated accident yield; and (3) evaluation of the radiation tolerance of the detectors and associated control cabinets. The design was based on extensive analyses performed in MCNP6.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

Guide to Performing Computational Analysis of Criticality Accident Alarm Systems

This document provides guidance on performing computational analyses of Criticality Accident Alarm Systems (CAAS). A brief overview of ANSI/ANS-8.3-1997 (R2012), the standard that the United States Department of Energy uses to guide the placement of CAAS detectors, is provided with a focus on the parts of the standard that are most applicable to calculating CAAS detector responses and evaluating CAAS detector coverage. This is followed by a brief discussion of how CAAS detector response calculations differ from eigenvalue calculations that criticality safety practitioners perform regularly. The remainder of the document provides computational examples of how to determine the minimum accident of concern according to ANSI/ANS-8.3-1997 (R2012), how to calculate the response of a CAAS detector due to a specific criticality accident, and how to evaluate the coverage of a CAAS detector for criticality accidents. These practical examples are solved applying SCALE 6.1 and MCNP5, but the methodologies can be applied to other radiation transport codes with similar capabilities, including deterministic codes.

Miller, Thomas Martin↗