Past Operating Experience Research Articles

Equilibrium core modeling of a pebble bed reactor similar to the Xe-100 with SCALE

As the nuclear industry moves towards licensing and constructing advanced reactors, new attention has been focused on the advanced reactor designs that have past operational experience, such as pebble-bed high-temperature gas-cooled reactors (PB-HTGRs). Pebble-bed reactor designs have many advantages, such as their higher operating temperatures and online refueling capabilities. However, high-fidelity computational modeling of pebble-bed reactor designs, from reactor startup to operation at equilibrium, is more challenging compared to conventionally fueled reactors due to the continuous movement of the fuel pebbles through the reactor during operation. In previous work at Oak Ridge National Laboratory (ORNL), the SCALE Leap-In method for Cores at Equilibrium (SLICE) was developed around tools within the SCALE code system. This iterative method can effectively generate pebble-bed reactor zone-wise fuel inventories at equilibrium core operation within a reasonable computational time. The objective of this work was to further verify the ORNL SLICE method and to investigate the impact of considering temperature profiles during the application of the method. The SLICE method was applied to a modular high-temperature gas-cooled reactor design based upon publicly available design specifications of the Xe-100 pebble-bed reactor. Upon comparing the results from the SLICE method to published literature, the differences in the eigenvalue keffective were on the order of several hundred pcm (percent millirho). To investigate one possible cause of these differences, a study looking at the sensitivity of the full-core equilibrium keffective and discharge nuclide inventory to temperature was performed by developing equilibrium cores of two additional temperature profiles. From this temperature study, differences on the order of hundreds of pcm for the full-core equilibrium keffective, and up to 15% difference for the discharge inventories were found. These results indicated the strong dependence on temperature that needs to be considered for future work in equilibrium modeling of PB-HTGRs.

Reliability modeling in a predictive maintenance context: A margin-based approach

Current system reliability methods (typically based on fault trees [FTs] or reliability block diagrams) can effectively propagate reliability data from the asset level to the system level in order to identify system-critical points. However, the asset reliability data employed are an approximated integral representation of past industry-wide operational experience, and thus neglect an asset's present health status (obtainable, for example, from online monitoring data and diagnostic assessments) and forecasted health projection (when available from prognostic models). Asset health should be informed solely by that specific asset's current and historical performance data and should not be an approximated integral representation of past industry-wide operational experience (as currently performed by system reliability models through Bayesian updating processes). Sensor data, diagnostic assessments, and prognostic assessments are in fact not considered in plant reliability models used to inform system engineers as to which assets are the most critical. In addition, propagation of quantitative health data from the asset level to the system level is made challenging by the diverse nature and structure of health data elements (e.g., vibration spectra, temperature readings, and expected failure time). Ideally, in a predictive maintenance context, system reliability models would support decision making by propagating available health information from the asset level to the system level to provide a quantitative snapshot of system health and identify the most critical assets. This paper directly addresses these two goals by proposing a different approach to reliability modeling—one that relies on asset diagnostic/prognostic assessments and monitoring data to measure asset health. Propagation of health data from the asset level to the system level is performed through FT models, not in terms of probability but rather in terms of margin, with margin being the “distance” between the asset's present status and an undesired event (e.g., failure or unacceptable performance). Per a cause-effect lens, while classical reliability models target the effect associated with asset performance, a margin-based approach focuses on the cause of the undesired asset performance (i.e., its health). Hence, thinking of reliability in terms of margins implies decision-making processes based on causal reasoning. We show how FT models can be solved using a margin-based reliability mindset, and how this process can effectively assist system engineers in identifying which assets are the most critical to system performance.

Rethinking Reliability in Terms of Margins

Current reliability approaches were designed to assess and quantify the reliability associated with complex systems such as nuclear power plants (NPPs). These approaches are generally based on classical Boolean logic structures such as event trees (ETs) and fault trees (FTs) [Rausand, 2020]. The outcome obtained by combining FTs and ETs is the set of minimal cuts sets (MCSs), with each MCS representing a unique combination of BEs that leads to an undesired outcome (e.g., core damage). Probabilistic evaluation of a MCS is performed by evaluating the product of the probability values associated with each BE. A relevant factor here is that the probability values associated with BEs used in the plant models are updated at least every 4 years based on past operational experience and through the use of a Bayesian statistical process [Siu, 1998]. Hence, the probability value of a BE associated with a physical asset (e.g., a centrifugal pump or motor-operated valve) in no way reflects that asset’s actual condition and performance.
 This fact plays a major role in the application of plant reliability models to support risk-informed decisions. With the particular goal of reducing operation and maintenance costs, existing NPPs are moving from corrective and periodic maintenance toward new types of predictive maintenance strategies [Agarwal, 2021]. This transition is designed such that maintenance is conducted only when the asset requires it (i.e., prior to undergoing imminent failure). And though these benefits cannot be achieved through actual reliability modelling methods and currently employed reliability data, they can be achieved by employing asset-monitoring sensors, automated data acquisition systems, data analysis methods, and improved decision-making processes. Combined, these resources can provide precise information on the health of an asset, track its degradation trends, and estimate its expected failure time. Based on such information, maintenance operations can be scheduled and performed for each asset on an as-needed basis. This dynamic context of predictive maintenance operations requires new methods of data analysis, the propagation of asset health information from the asset level to the system level, and the optimization of plant resources.
 This paper provides an alternative reliability approach designed for a predictive maintenance context in which a direct link is created between monitoring data and decision-making. Rather than thinking of reliability in terms of system/asset probability of failure, we propose a reliability mindset based on the concept of margin [Mandelli, 2023]. An asset’s health is quantified by determining its margin, based on the asset’s current and historical monitoring data. The margin values of the monitored asset are then propagated through system reliability models (e.g., FTs or reliability block diagrams) to identify the assets that are more critical to guarantee system operation. We show how a margin-based approach can be used assess asset health, based solely on current and historic monitoring data (e.g., condition-based, anomaly detection, diagnostic, and prognostic data) [Xingang, 2021]. A margin-based approach directly addresses the limitations of classical reliability modelling approaches and provides a snapshot of system health—given the availability of monitoring data. These two different approaches are designed to address different types of decisions: classical reliability models support static decisions (e.g., a set frequency of periodic maintenance or surveillance operations) based on past operational experience, whereas a margin-based approach directly supports dynamic decisions involving maintenance operations that should only be performed when necessary, based on monitoring data (i.e., a predictive maintenance context).

Causal Approach to Integrate Component Health Data into System Reliability Models

Two of the challenges of current plant reliability approaches are the ability to integrate plant health data, and to support decision making. Condition based data and diagnostic/prognostic information are in fact not considered into plant reliability models to inform system engineers on the most critical components. Currently, the propagation of quantitative health data from the component to the system level is a challenge given the diverse nature/structure of the data. On the other hand, plant reliability methods (which are typically based on fault-trees or reliability block diagrams) can effectively propagate data from the component to the system level, but values of failure rates or failure probabilities are an approximated integral representation of the past industry-wide operational experience, and it neglects the present component health status (e.g., diagnostic and condition-based data) and health projection (when available from prognostic data). Our first claim is that system reliability models should propagate health information from the component to the system/plant level in order to provide a quantitative snapshot of system/plant health and identify the most critical components. Our second claim is that component health should be informed solely by that specific component current and historical performance data and should not be an approximated integral representation of the past industry-wide operational experience. This paper is directly supporting these two claims by proposing a different approach to perform reliability modeling which relies on available component diagnostic, prognostic and condition-based data to measure component health, and it propagates this information through fault tree models. The propagation of health data from the component to the system level is performed not in terms of probability, but in terms of margins where margin is defined as the “distance” between the present actual status and an undesired event (e.g., failure or unacceptable performance). Through a cause-effect lens, while classical reliability models target the effect associated to a component performance, a margin-based approach focuses on the cause of an undesired component performance (i.e., component health). Hence, thinking of reliability in terms of margins implies decision making based on causal reasoning. We will show how fault tree models can be solved using a margin language and how this process can effectively assist system engineers to identify the most critical components.

Rapid-cycling synchrotron for multi-megawatt proton facility at Fermilab

The Fermilab accelerator complex delivers intense high-energy proton beams to a variety of fixed-target scientific programs, including a flagship long-baseline neutrino program. With the advent of the Deep Underground Neutrino Experiment (DUNE) and Long Baseline Neutrino Facility (LBNF) program there is strong motivation for a 2.4 MW beam power upgrade of the Fermilab proton facility. We show the Fermilab proton facility can achieve 2.4 MW with a new rapid-cycling synchrotron (RCS) to replace the Fermilab Booster and we provide a comprehensive technical analysis of the RCS-based facility design. Past design efforts and operational experience at the Fermilab Booster, J-PARC RCS, and Oak Ridge SNS are leveraged to provide strong empirical precedent for the design. We provide a parametric study of slip-stacking accumulation, RCS extraction energy, space-charge limits, beampipe aperture, eddy current heating, injection foil heating, and lattice optics. The 2.4 MW benchmark for the long baseline neutrino program is achieved independently of a previously proposed multi-GeV linac program, but we assess the impact the linac upgrade would have on RCS performance.

Analysis of events related to cracks and leaks in the reactor coolant pressure boundary

The presence of cracks and leaks in the reactor coolant pressure boundary may jeopardise the safe operation of nuclear power plants. Analysis of cracks and leaks related events is an important task for the prevention of their recurrence, which should be performed in the context of activities on Operating Experience Feedback. In response to this concern, the EU Clearinghouse operated by the JRC-IET supports and develops technical and scientific work to disseminate the lessons learned from past operating experience. In particular, concerning cracks and leaks, the studies carried out in collaboration with IRSN and GRS have allowed to identify the most sensitive areas to degradation in the plant primary system and to elaborate recommendations for upgrading the maintenance, ageing management and inspection programmes. An overview of the methodology used in the analysis of cracks and leaks related events is presented in this paper, together with the relevant results obtained in the study.

A hybrid architecture of cognitive decision engine based on particle swarm optimization algorithms and case database

The architecture of cognitive decision engine should enable fast decision making, long-term knowledge accumulating based on past operating experience, and capabilities of knowledge updating to adapt to new situations. In this paper, a hybrid architecture of cognitive decision engine based on particle swarm optimization algorithms and case database is proposed. Considering the user’s quality of service preferences and the wireless situations, how to determine the radio’s link parameters such as modulation type, symbol rate, and transmit power can be formulated as a multi-objective optimization problem. In the architecture, this problem is solved by using particle swarm optimization algorithms, which make cognitive radio have the fast decision-making ability when facing unknown wireless situations. The case database, which stores the past running experiences of the cognitive radio is also integrated into the proposed architecture to improve the radio’s response speed and endows the radio with the ability of learning from its previous operating experiences. Simulation results show the effectiveness of the architecture, and the proposed cognitive decision engine can dynamically and properly reconfigure the radio according to the changes in wireless environment.

A revision of proton machine quality assurance for wobbled-proton-beam therapy

Periodic checks for proton machine quality assurance (QA) are significant for machine users safely and accurately to provide proton-beam treatment for cancer. Our aim in this study was to describe a revision to proton machine QA procedures for wobbled-proton-beam therapy at the Shizuoka Cancer Center (SCC) in Japan. The previous daily, monthly, and annual QA procedures were determined by reference to our past operational experience and to QA papers for medical accelerators. The revised QA procedures were initiated in May 2011 after preliminary measurements to decide baselines for the QA procedures. This paper presents the proton machine QA procedures and the results of representative QA measurements. Three action levels were decided on by reference to the American Association of Physicists in Medicine Task Group 142 report. Tolerances of inspection action were decided on based on the provisional operational results and actual fluctuations of the QA measurement for a year, and those of scheduled action and stop-treatment action were determined by reference to the machine QA papers and those of the inspection action. No deviation from the tolerance of the scheduled action has been observed so far. Although a few QA procedures exceeded the tolerance of the inspection action, these excesses were resolved by inspection and improvement of the respective measuring procedure within the designated QA time. Hereafter, the proton machine QA procedures proposed in this study will be performed continuously at the SCC to assure patient safety and accurate operation of proton therapy.

Application of the performance-goal based approach for establishing the SSE site specific response spectrum for new nuclear power plants in South Africa

Nuclear installation license holders in South Africa have become increasingly interested in the performance-goal based approach defined in the American Society of Civil Engineering Standard ASCE/SEI 43-05 for establishing the safe shutdown earthquake (SSE) site specific design response spectrum (SSRS) for new nuclear power plants. This approach has been adopted by the U.S. Nuclear Regulatory Commission (NRC) and has now been followed at more than 20 sites in that country. Quantitative performance goals are required when establishing seismic design basis parameters using the performance-goal based approach. However, the quantitative performance goals recommended in ASCE/SEI 43-05 were established based on country-specific operating experience and seismic probabilistic risk assessment (SPRA) applications conducted for existing plants designed and operated to meet specific safety criteria, set by a specific regulatory body. Whilst ASCE/SEI 43-05 provides enough flexibility for the selection of other user-specified quantitative performance goals, there is no guidance on how quantitative performance goals should be established in the absence of extensive operational experience accompanied by data derived from rigorous SPRA applications. This paper presents two practical approaches that can be used to provide a technical basis and to demonstrate the derivation of quantitative values of target performance goals when no data related to past and present operational experience exist to justify technical specifications.

Boiling water reactor with innovative safety concept: The Generation III+ SWR-1000

AREVA NP has developed an innovative boiling water reactor (BWR) SWR-1000 in close cooperation with German nuclear utilities and with support from various European partners. This Generation III+ reactor design marks a new era in the successful tradition of BWR and, with a net electrical output of approximately 1250 MWe, is aimed at ensuring competitive power generating costs compared to gas and coal fired stations. It is particularly suitable for countries whose power networks cannot facilitate large power plants. At the same time, the SWR-1000 meets the highest safety standards, including control of core melt accidents. These objectives are met by supplementing active safety systems with passive safety equipment of various designs for accident detection and control and by simplifying systems needed for normal plant operation on the basis of past operating experience. The plant is also protected against airplane crash loads. The functional capabilities and capacities of all new systems and components were successfully tested under realistic and conservative boundary conditions in large-scale test facilities in Finland, Switzerland and Germany. In general, the SWR-1000 design is based on well-proven analytical codes and design tools validated for BWR applications through recalculation of relevant experiments and independent licensing activities performed by authorities or their experts. The overview of used analytical codes and design tools as well as performed experimental validation programs is presented. Effective implementation of passive safety systems is demonstrated through the numerical simulation of transients and loss of coolant accidents (LOCAs) as well as through analytical simulation of a severe accident associated with the core melt. In the LOCA simulation presented the existing active core flooding systems were not used for emergency control: only passive systems were relevant for the analyses. Despite this – no core heat-up occurred. In the case of reactor core melting numerically is demonstrated that the molten core debris would be retained inside the reactor vessel due to the effective passive external water cooling of the vessel, keeping it completely intact. A short construction period of just 48 months from first concrete to provisional take over, flexible fuel cycle lengths of between 12 and 24 months and a high fuel discharge burn-up all contribute towards meeting economic goals. Realistic average availability for a plant lifetime of 60 years and 12 months cycle is 94.5%. Systems and plant design were reviewed by expert groups of European utilities. With the SWR-1000, AREVA NP has developed a design concept for a BWR plant that is now ready for commercial deployment and which fully meets the most stringent international requirements in terms of nuclear safety and nuclear regulatory.

Unconventional warfare: rebuilding U.S. special operations forces

For four decades after World War II, U.S. Special Operations Forces including Army Special Forces, Navy SEALs, Air Force special operations aircrews and Special Tactics Group suffered from mistrust and inadequate funding from the military services. They were nearly eliminated from the active force following the Vietnam War. But in the past fifteen years, special operations forces have risen from the ashes of the failed 1980 rescue of American hostages in Iran to become one of the most frequently deployed elements of the U.S. military. They are now adequately funded, better-equipped, and well-trained. Special operations forces are often the nation's first military response when faced with a crisis in today's uncertain and unstable international security environment.What caused this dramatic turnaround? As this book shows, it was a long way from congressional outrage at TV images of burned bodies of U.S. servicemen in the Iranian desert to the establishment of a special operations force of nearly 45,000 active and reserve personnel. The drama of how this happened sheds light on how public policy is made and implemented. It illustrates the complex interaction between internal forces within the special operations community, as well as between the executive and legislative branches of the U.S. government. The implementation of legislation establishing a special operations capability is seen to rebuild and protect these forces to an extent never imagined by the early quiet professionals. While offering insights into how the U.S. government makes policy, Susan Marquis also offers a revealing look at the special operations community, including their storied past, extreme training, and recent operational experience that continues to forge their distinctive organizational mission and culture. She describes the decade-long struggle to rebuild special operations forces, resulting in new SOF organizations with independence that is unique among U.S. military forces, an independence approaching that of a new military service.

Bayesian reliability analysis of a parallel system

Abstract Engineering systems are designed to meet the established reliability goals. A designer can better meet this objective if he is able to combine past operational experience about the system or its components with experimental data. At the initial stage of designing, it is often necessary to know about the reliability characteristics of the complete system. However, the operational experience about the complete system at this stage is either non-existent or expensive to realise. In such situations an alternative way of analysing the reliability characteristics of the complete system may be to use operational experience about the components or subunits of the system. In view of this, the present study deals with the Bayesian reliability analysis of a parallel system. Time-truncated failure information and prior belief about the failure rate of the components of the system have been used in the analysis.

Incorporating operational experience and design changes in availability forecasts

Abstract Reliability or availability forecasts which are based solely on past operating experience will be precise if the sample is large enough, and unbiased if nothing in the future design, environment, operating region or anything else changes. Unfortunately, life is never like that. This paper considers the methodology and philosophy of modifying forecasts based on past experience to take account also of changes in design, construction methods, operating philosophy, environment, operator training and so on, between the plants which provided the operating experience and the plant for which the forecasts is being made. This emphasises the importance of collecting, assessing, and learning from past data and of a thorough knowledge of future designs, and procurement, operation, and maintenance policies. The difference between targets and central estimates is also discussed. The paper concludes that improvements in future availability can be made by learning from past experience, but that certain conditions must be fulfilled in order to do so.

Produced Water Reuse Considerations For In-Situ Recovery: A Case Development

Abstract Steam-assisted methods for in-situ recovery in Canada typically operate at steam to oil ratios of approximately 3 to 1 and generate in the order of 2 to 5 barrels of produced water per barrel of production. To raise the large quantities of steam required for reservoir stimulation, once-through type steam generators are most commonly used. They are typically designed to produce about 80 per cent quality steam from soft, oil-free feedwater. Suncor Inc operates a cyclic steam injection pilot project near Fort Kent, Alberta. In the early 19805, Suncor planned an expansion of the 180 m3/d (1,130 bbl/d) facility to 800 m3/d (1,130 bbl/d). The expansion necessitated the development of a reliable water supply. Preliminary investigations into the feasibility of reusing produced water as the sale source of supply for the project expansion revealed this to be a costly and technically high risk option, given the specific produced water characteristics. As a result, an innovative alternative was developed to use a blend of produced water and municipal effluent from a nearby town as the water supply. This paper presents the rationale for the selection of this unique water supply and the process design considerations/or the resulting water treatment system. Background Several operators of in-situ recovery projects are interested in reusing produced water for steam generation in their commercial or semi-commercial projects. This interest has been stimulated by the often scarce supplies of fresh water available in the principal oil-producing regions of western Canada, and environmental pressures to explore alternatives to produced water disposal by injection. Suncor's planned expansion of their pilot project near Fort Kent, Alberta was forecast to require a water supply of 3,700 m3/d (20,000 bbl/d) for steam generation. CH2M HILL was retained to investigate the feasibility of reusing produced water as the steam generator feedwater supply. Feedwater Quality Requirements The once-through steam generators used in the expansion operate at a pressure of 13.8 MPa and produce 80 per cent quality steam. Typical manufacturer-specified feedwater quality requirements for these units are listed in Table 1. The maximum concentrations are based primarily on past operating experience rather than solubility considerations. Higher concentrations than those listed reduce the performance and service life of the steam generators. High concentrations of oil and hardness lead to coking and scaling respectively. Localized hot spots in the fouled region contribute to tube burnout. Oxygen leads to tube corrosion and a reduced service life. Iron is believed to attack the protective magnetite layer on the tubes. Uncertainties exist as to the maximum TDS and silica concentrations that can be tolerated in once-through steam generators. Operation with higher silica and TDS concentrations than those listed has recently been reported and successful operation could result in an increase in the maximum limits by the manufacturers. However, at the time of design there was insufficient experience at high pressure (14 MPa) and 80 per cent steam quality to justify exceeding the manufacturer's recommended feedwater quality criteria as the design basis for a project of this size.

Dynamic Behavior and Design of Offshore Caissons

Abstract Free-standing caissons are used for supporting flare pipes and single-well production platforms. However, caissons tend to be flexible and dynamically sensitive, and the static design practice may not be adequate for this type of practice may not be adequate for this type of structure. To assess motion effect on the integrity of the structural system and to quantify the allowable motion for safe operation on board a caisson platform, analytical and experimental studies of platform, analytical and experimental studies of the dynamic behavior of a caisson structure were conducted and are described here. The analytical simulations agree well statistically with The motion measurements. A caisson design procedure considering dynamic effects was developed Design considerations include ultimate strength failure, fatigue failure, excessive motion, and possible damage during installation. A key feature in an effective caisson design is that the upper part of the caisson should be made as small as possible so that wave loading and the caisson period can be minimized The fatigue design procedure was verified with past caisson operational experience. To illustrate past caisson operational experience. To illustrate the procedure, a flare-pipe support caisson in 185 ft of water was designed and analyzed. Introduction Free-standing caissons are used for supporting flare pipes or single-well production platforms. The attractiveness of a caisson structure lies in the potential economy and the short time required for potential economy and the short time required for fabrication and installation. However, a caisson tends to be flexible, and dynamic effects may increase the design requirements from both strength and functional standpoints. To assess the motion effect on the integrity of the structural system and to quantify the allowable motion level for effective operation on board a caisson platform, analytical and experimental studies of the dynamic behavior of a caisson structure were conducted, and a procedure was formulated for designing a caisson considering dynamic effects. Observations from the experimental data and computer simulations of the caisson behavior are described. Verification of the computer simulation and some useful information for developing and using such simulations as well as practical interpretation of the analytical results practical interpretation of the analytical results also are given. Differences between a static design and a dynamic design are illustrated in an example design of a flare-support caisson in 185 ft water. MOTION MEASUREMENT Motion data were taken from a caisson platform offshore Louisiana. General dimensions of the caisson are shown in Fig. 1. SPEJ P. 291

Steam vs. electricity for driving power station auxiliaries

During the past few years important changes effected in power station and system design have reduced operating expenses materially. At present, however, station designers are giving more attention to the reducing of investment costs. This reduction of plant investment quite naturally affects the plant auxiliaries as well as other station equipment. Many large stations built recently have practically all of the essential as well as the non-essential auxiliaries driven by electricity; but in other recent stations steam driven auxiliaries have been used quite extensively. In the light of present conditions and past operating experience, therefore, the A.I.E.E. power generation committee felt that a review of the characteristics and merits of both steam and electric drives would be in order. Accordingly, this committee has sponsored three papers to be presented at the Institute's forthcoming winter convention, and upon these papers the following three articles are based.