Engineering topics

Joe, Jeffrey Clark

Publications and source records attributed to Joe, Jeffrey Clark.

8 records

DOE OSTI2016

Migration of Older to New Digital Control Systems in Nuclear Power Plant Main Control Rooms

The United States (U.S.) Department of Energy (DOE) Office of Nuclear Energy (NE) has the primary mission to advance nuclear power by resolving socio-technical issues through research and development (R&D). One DOE-NE activity supporting this mission is the Light Water Reactor Sustainability (LWRS) program. LWRS has the overall objective to sustain the operation of existing commercial nuclear power plants (NPPs) through conducting R&D across multiple “pathways,” or R&D focus areas. The Advanced Instrumentation, Information, and Control (II&C) Systems Technologies pathway conducts targeted R&D to address aging and reliability concerns with the legacy instrumentation and control (I&C) and related information systems in operating U.S. NPPs. This work involves (1) ensuring that legacy analog II&C systems are not life-limiting issues for the LWR fleet, and (2) implementing digital II&C technology in a manner that enables broad innovation and business improvement in the NPP operating model. Under the LWRS Advanced II&C pathway, Human Factors experts at Idaho National Laboratory (INL) have been conducting R&D in support of NPP main control room (MCR) modernization activities. Work in prior years has focused on migrating analog I&C systems to new digital I&C systems (). In fiscal year 2016 (FY16), one new focus area for this research is migrating older digital I&C systems to new and advanced digital I&C systems. This report summarizes a plan for conducting a digital-to-digital migration of a legacy digital I&C system to a new digital I&C system in support of control room modernization activities.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2015

Proof-of-Concept Demonstrations for Computation-Based Human Reliability Analysis. Modeling Operator Performance During Flooding Scenarios

The United States (U.S.) Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) program has the overall objective to help sustain the existing commercial nuclear power plants (NPPs). To accomplish this program objective, there are multiple LWRS “pathways,” or research and development (R&D) focus areas. One LWRS focus area is called the Risk-Informed Safety Margin and Characterization (RISMC) pathway. Initial efforts under this pathway to combine probabilistic and plant multi-physics models to quantify safety margins and support business decisions also included HRA, but in a somewhat simplified manner. HRA experts at Idaho National Laboratory (INL) have been collaborating with other experts to develop a computational HRA approach, called the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER), for inclusion into the RISMC framework. The basic premise of this research is to leverage applicable computational techniques, namely simulation and modeling, to develop and then, using RAVEN as a controller, seamlessly integrate virtual operator models (HUNTER) with 1) the dynamic computational MOOSE runtime environment that includes a full-scope plant model, and 2) the RISMC framework PRA models already in use. The HUNTER computational HRA approach is a hybrid approach that leverages past work from cognitive psychology, human performance modeling, and HRA, but it is also a significant departure from existing static and even dynamic HRA methods. This report is divided into five chapters that cover the development of an external flooding event test case and associated statistical modeling considerations.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2015

Human Performance Modeling for Dynamic Human Reliability Analysis

Part of the U.S. Department of Energy’s (DOE’s) Light Water Reac- tor Sustainability (LWRS) Program, the Risk-Informed Safety Margin Charac- terization (RISMC) Pathway develops approaches to estimating and managing safety margins. RISMC simulations pair deterministic plant physics models with probabilistic risk models. As human interactions are an essential element of plant risk, it is necessary to integrate human actions into the RISMC risk framework. In this paper, we review simulation based and non simulation based human reliability analysis (HRA) methods. This paper summarizes the founda- tional information needed to develop a feasible approach to modeling human in- teractions in RISMC simulations.

human reliability analysis↗

DOE OSTI2015

Establishing a value chain for human factors in nuclear power plantcontrol room modernization

Commercial nuclear power plants in the United States (U.S.) have operated reliably and efficiently for decades. With the life extensions of plants now being planned for operation beyond their original operating licenses, there are opportunities to achieve even greater efficiencies, while maintaining high operational reliabilities, with strategic, risk- and economically-informed, upgrades to plant systems and infrastructure. The U.S. Department of Energy’s Light Water Reactor Sustainability (LWRS) program supports the commercial nuclear industry’s modernization efforts through research and development (R&D) activities across many areas to help establish the technical and economic bases for modernization activities. The Advanced Instrumentation, Information, and Control Systems Technologies pathway is one R&D focus area for the LWRS program, and has researchers at Idaho National Laboratory working with select utility partners to use human factors and instrumentation and controls R&D to help modernize the plant’s main control room. However, some in the nuclear industry have not been as enthusiastic about using human factors R&D to inform life extension decision making. Part of the reason for this may stem from uncertainty decision-makers have regarding how human factors fits into the value chain for nuclear power plant control room modernization. This paper reviews past work that has attempted to demonstrate the value of human factors, and then describes the value chain concept, how it applies to control room modernization, and then makes a case for how and why human factors is an essential link in the modernization value chain.

Control room modernization↗

DOE OSTI2015

Requirements for Control Room Computer-Based Procedures for use in Hybrid Control Rooms

Many plants in the U.S. are currently undergoing control room modernization. The main drivers for modernization are the aging and obsolescence of existing equipment, which typically results in a like-for-like replacement of analogue equipment with digital systems. However, the modernization efforts present an opportunity to employ advanced technology that would not only extend the life, but enhance the efficiency and cost competitiveness of nuclear power. Computer-based procedures (CBPs) are one example of near-term advanced technology that may provide enhanced efficiencies above and beyond like for like replacements of analog systems. Researchers in the LWRS program are investigating the benefits of advanced technologies such as CBPs, with the goal of assisting utilities in decision making during modernization projects. This report will describe the existing research on CBPs, discuss the unique issues related to using CBPs in hybrid control rooms (i.e., partially modernized analog control rooms), and define the requirements of CBPs for hybrid control rooms.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

Simulation and Non-Simulation Based Human Reliability Analysis Approaches

Part of the U.S. Department of Energy’s Light Water Reactor Sustainability (LWRS) Program, the Risk-Informed Safety Margin Characterization (RISMC) Pathway develops approaches to estimating and managing safety margins. RISMC simulations pair deterministic plant physics models with probabilistic risk models. As human interactions are an essential element of plant risk, it is necessary to integrate human actions into the RISMC risk model. In this report, we review simulation-based and non-simulation-based human reliability assessment (HRA) methods. Chapter 2 surveys non-simulation-based HRA methods. Conventional HRA methods target static Probabilistic Risk Assessments for Level 1 events. These methods would require significant modification for use in dynamic simulation of Level 2 and Level 3 events. Chapter 3 is a review of human performance models. A variety of methods and models simulate dynamic human performance; however, most of these human performance models were developed outside the risk domain and have not been used for HRA. The exception is the ADS-IDAC model, which can be thought of as a virtual operator program. This model is resource-intensive but provides a detailed model of every operator action in a given scenario, along with models of numerous factors that can influence operator performance. Finally, Chapter 4 reviews the treatment of timing of operator actions in HRA methods. This chapter is an example of one of the critical gaps between existing HRA methods and the needs of dynamic HRA. This report summarizes the foundational information needed to develop a feasible approach to modeling human interactions in the RISMC simulations.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2014

Strategy for Migration of Traditional to Hybrid Control Boards in a Nuclear Power Plant

This strategy document describes the NUREG-0711 based human factors engineering (HFE) phases and associated elements required to support design, verification and validation (V&V), and implementation of new digital control room elements in a legacy analog main control room (MCR). Information from previous planning and analysis work serves as the foundation for creating a human-machine interface (HMI) specification for distributed control systems (DCSs) to be implemented as part of nuclear power plant (NPP) modernization. This document reviews ways to take the HMSI specification and use it when migrating legacy displays or designing displays with new functionality. These displays undergo iterative usability testing during the design phase and then an integrated system validation (ISV) in the full-scope control room training simulator. Following successful demonstration of operator performance using the systems during the ISV, the new DCS is implemented at the plant, first in the training simulator and then in the MCR. This document concludes with a sample project plan, including a 15-month timeline from DCS design through implementation. Included is a discussion of how the U.S. Department of Energy’s Human System Simulation Laboratory (HSSL) can be used to support design and V&V activities. This report completes a Level 4 (M4) milestone under the U.S. Department of Energy’s (DOE’s) Light Water Reactor Sustainability (LWRS) Program.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2014

Task Decomposition in Human Reliability Analysis

In the probabilistic safety assessments (PSAs) used in the nuclear industry, human failure events (HFEs) are determined as a subset of hardware failures, namely those hardware failures that could be triggered by human action or inaction. This approach is top-down, starting with hardware faults and deducing human contributions to those faults. Elsewhere, more traditionally human factors driven approaches would tend to look at opportunities for human errors first in a task analysis and then identify which of those errors is risk significant. The intersection of top-down and bottom-up approaches to defining HFEs has not been carefully studied. Ideally, both approaches should arrive at the same set of HFEs. This question remains central as human reliability analysis (HRA) methods are generalized to new domains like oil and gas. The HFEs used in nuclear PSAs tend to be top-down— defined as a subset of the PSA—whereas the HFEs used in petroleum quantitative risk assessments (QRAs) are more likely to be bottom-up—derived from a task analysis conducted by human factors experts. The marriage of these approaches is necessary in order to ensure that HRA methods developed for top-down HFEs are also sufficient for bottom-up applications.

error of commission↗