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The CRADA objective is to 1) Generalize web tools for data analytics and machine learning, and 2) Develop and test a smart-alarm system at Jefferson Lab.
We present initial results from a proof-of-concept ?smart alarm? 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. Our approach leverages a trained neural network to not only identify an anomalous machine state, but also to identify the root-cause by pinpointing the specific element or region responsible. We developed an inverse model trained on data collected during normal operations. Using the inverse model, measurements from the machine are used to compute machine settings, which are then compared to EPICS setpoints. Instances when predictions differ from EPICS setpoints by a user-defined threshold are flagged as anomalies, and the user is alerted to the issue. We present the results of our data collection efforts, model training and performance, and initial performance metrics.
The determination of the disposition route for a legacy drum in a nuclear facility at the Y-12 National Security Complex is supported by non-destructive testing of the drum and its contents. An advanced radiation measurements organization at the Y-12 National Security Complex proposed a series of active measurements using a portable x-ray generator, imaging the drum and its contents with 360-degree visualization. In rooms near the current storage location for the drum, Criticality Accident Alarm System (CAAS) annunciation is degraded, and Personal Radiation Dosimetry Instruments (PRDI’s) are routinely worn by personnel. The Y-12 Development Organization has procured advanced radiation detection devices and a variety of radiation sources for use in active measurements. These devices are being used to support Y-12 operations and safety engineering disciplines to safely address complex challenges throughout the complex. This paper presents the results of experimental testing of the PRDI’s used at Y-12 with the x-ray generator [1], and notes the unique utilization of a decommissioned facility for this work.
The goal of this presentation is to use available data from Health Physics Research Reactor (HPRR) operation to create a benchmark report for inclusion in the ICSBEP, as a Criticality Accident Alarm System (CAAS) shielding benchmark.
The researcher worked on the Fermilab Mu2e Experiment. This experiment is in the construction phase. This experiment will look at a large number of stopped muon decays to search for the stopped muon converting directly into an electron with no other particles (i.e. neutrinos). The signature will be a nearly mono-energetic electron with the momentum of just below the rest mass energy of the muon. The researcher worked with in the Cosmic Ray Veto group (cosmic rays are a major source of background) and he also worked on the global alarms system.
The goal is to use available data from Health Physics Research Reactor (HPRR) operation to create a benchmark report for inclusion in the International Criticality Safety Benchmark Evaluation Project (ICSBEP) Handbook, or SINBAD, as a criticality accident alarm system (CAAS) shielding benchmark.
The Oak Ridge National Laboratory (ORNL) Health Physics Research Reactor (HPRR) was a research reactor designed and built at ORNL in 1961. The critical assembly was using a highly enriched uranium and molybdenum alloy as the fuel, and it could be operated in steady-state or burst modes. The reactor was used for about 25 years to produce a lot of publications related to dosimetry, radiobiology and radiation detectors testing before its decommissioning in 1987. In recent years, the idea of using legacy operation data from the to create a valuable critical accident alarm system shielding benchmark arose. Such a benchmark has been submitted to the International Criticality Safety Benchmark Experiment Project (ICSBEP) Technical Review Group for a potential inclusion in the 2022 version of the handbook. Another way to use the valuable data from the operation of the HPRR is to evaluate the feasibility of the creation of a subcritical or prompt supercritical benchmark for inclusion in the ICSBEP or the International Reactor Physics Experiments Evaluation Project (IRPhEP) handbooks. To initiate a burst, the HPRR had to be operated in a slightly subcritical state for a few minutes. Then, the insertion of the burst control rod would greatly increase the reactivity of the system and start the burst. No critical configuration of the HPRR critical assembly could be located. The only information available concerns stable subcritical and prompt supercritical states, found in a burst experiments’ logbook. In the recovered logbook pages, information about 8 different bursts is available. The information includes the rods positions before and during a burst, the recorded subcritical reactor period and reactivity, and the burst fission yield derived from the temperature elevation sulfur pellet irradiation analysis. By using the HPRR logbook information and the as-built drawings of the critical assembly, a highly detailed model of the HPRR was created with SCALE 6.2.4/KENO-VI. Eight KENO-VI models were created to replicate the sub-critical assembly configurations described in the eight bursts from the recovered logbook pages. KENO-VI calculates k eff and it can be linked to a reactivity value in cents by using the delayed neutron fraction B eff , also calculated by KENO-VI. KENO-VI can also be used to model the prompt super-critical configurations of the HPRR and to assess the similarity with the burst measurements by comparing the calculated k eff and the measured fission yields between each burst. Unfortunately, high uncertainty exist and the obtained discrepancies between experiments and calculation results are high, compromising the creation of a valuable critical benchmark from HPRR operation data. The reasons of the discrepancies and potential ways to solve them are explored.
The Y-12 National Security Complex and the Uranium Processing Facility (UPF) selected the Mirion CAAS-3S as the Criticality Accident Alarm System for UPF and for Y-12 facilities replacing their legacy CAAS as part of efforts to extend the facility lifespans. As part of this process, the CAAS-3S system was exposed to a high radiation dose and dose rate during reactor testing with the Godiva-IV fast burst reactor. The reactor testing was designed around preliminary analyses that determined Y-12 and UPF requirements, and simulations of the radiation field within the reactor facility were used to determine reactor operating parameters, CAAS equipment locations, and the design of a neutron shield wall. This paper presents the design and results of the testing, and discusses how the test results were interpreted by criticality safety engineers at Y-12 and UPF.
In support of planned site activities, the existing basis for criticality safety in nuclear facilities is being revisited. One such technical basis is the demonstration of Criticality Accident Alarm System (CAAS) coverage in tube vaults at Y-12 National Security Complex. The configuration of the vaults places significant shielding between the source and the detector stations and is dissimilar to drum storage or other processes. This paper describes tube vault storage at Y-12, describes the basis for the application of the 2022 minimum accident for system design (MASD) to the analysis, and presents how the accident is implemented in the analysis.
Abstract not provided.
Abstract not provided.
Radiation Portal Monitors (RPMs) have been installed worldwide to scan vehicles and cargo for the presence of radiological and nuclear materials. In field operations, the sensitivity of these systems is typically limited by the relatively high rates of nuisance alarms that usually must be followed up with secondary inspections. We have developed a machine-learning based alarm analysis system that has been deployed at numerous locations in the U.S. and internationally. Our Enhanced Radiological Nuclear Inspection and Evaluation (ERNIE) analysis software and its derivatives have demonstrated increased sensitivity to radiological and nuclear material of concern while reducing nuisance alarms by as much as an order of magnitude.
This study evaluated rule-based and machine learning (ML) based automated fault detection and diagnostics (AFDD) algorithms for detecting refrigerant undercharge faults in residential heating, ventilation, and air conditioning (HVAC) systems, using actual building data and a minimal set of features. The ML-based algorithms included Decision Tree (DT) and K-Nearest Neighbors (KNN). Both the rule-based and ML-based algorithms demonstrated the capability to detect refrigerant undercharge faults of -30% or more. Both types of algorithms exhibited false alarms before the implementation of a false alarm mitigation algorithm, which motivated the development of such a mitigation strategy. After applying the mitigation, false alarms were substantially reduced, with the rule-based algorithm decreasing to 0.6% and the ML-based algorithms reaching 0%, while maintaining strong detection performance. Although the rule-based algorithm initially showed lower performance compared to the ML-based algorithms, its detection accuracy improved after mitigation to a level comparable to the ML-based algorithms. These results confirm that combining false alarm mitigation with both rule-based and ML-based AFDD algorithms significantly enhances practical reliability while preserving robust fault detection capabilities. Furthermore, the findings demonstrate the potential for field deployment of these algorithms in residential HVAC systems and highlight the importance of minimizing false alarms.
A principal performance-enabling, or performance-limiting, component of Ground-Moving-Target-Indication (GMTI) radar systems is the antenna. Undesired clutter leakage into antenna sidelobes can be particularly problematic, generating undesired false alarms. GMTI system antennas can be designed with characteristics and features to allow discriminating and depressing/suppressing problematic sidelobe leakage of clutter and other undesired signals. We offer analysis and design guidelines for doing so.
This work presents a novel deep learning model for early, accurate, and robust detection, recognition, and temporal localization of multi-type events in large-scale power systems. The proposed method develops a unified 1-D fully convolutional network (FCN) model that takes time series of raw frequency signals measured from a power system as input, extracts distinguishing features, and predicts at every temporal point in the time series if an event is happening and what the type of the event is. Compared to existing methods, the proposed model eliminates the necessity for hand-crafted feature extraction or complicated data pre-processing, can flexibly handle input signals of arbitrary length, and precisely infer the event occurrence time. Most importantly, the model is intentionally trained with incomplete patterns, such that it is more robust to partial features of an event which is common in real-world online recognition, resulting in early alarm for power system failures. Extensive experimental results demonstrate that the proposed method achieves superior performance to the state-of-the-art, and also shows strong robustness to noise and system oscillations.
Nuclear Safety is a review journal that covers significant developments in the field of nuclear safety. Its scope includes the analysis and control of hazards associated with nuclear energy, operations involving fissionable materials, and the products of nuclear fission and their effects on the environment. Primary emphasis is on safety in reactor design, construction, and operation; however, the safety aspects of the entire fuel cycle, including fuel fabrication, spent-fuel processing, nuclear waste disposal, handling of radioisotopes, and environmental effects of these operations, are also treated. Table of Contents for this issue follows. GENERAL SAFETY CONSIDERATIONS: 325 Safety of Framatome Advanced Nuclear Steam Supply Systems Designs by J. A. Charles and D. Lange, 333 Book Review of Nuclear Accidents: Intervention Levels for the Protection of the Public by H. B. Piper; ACCIDENT ANALYSIS: 335 Living PRA Computer Systems by S. C. Dinsmore and H.-P. Balfanz, 343 Summary of ICAP Assessments of RELAP5/MOD2 by W. E. Driskell and R. G. Hanson; CONTROL AND INSTRUMENTATION: 352 Thermal Performance Monitoring System at Maanshan Nuclear Power Plant by H.-J. Chao, Y.-P. Lin, G.-H. Jou, L.-Y. Liao, and Y.-B. Chen; DESIGN FEATURES: 358 Warning Systems for Nuclear Power Plant Emergencies by J. H. Sorensen and D. S. Mileti; WASTE AND SPENT FUEL MANAGEMENT: 371 Activities Related to Waste Management Compiled by E G. Silver; OPERATING EXPERIENCES: 382 Steam Generator Tube Performance: Experience with Water-Cooled Nuclear Power Reactors During 1985 by O. S. Tatone and R. L. Tapping, 400 Systems Interaction Analyses: Concepts and Techniques (Part II) by M. D. Muhlheim and G. A. Murphy, 413 Reactor Shutdown Experience Compiled by J. W. Cletcher, 416 Operating U.S. Power Reactors Compiled by E G. Silver; RECENT DEVELOPMENTS: 440 General Administrative Activities Compiled by E G. Silver, 460 Reports, Standards, and Safety Guides by D. S. Queener, 466 Status of Power-Reactor Licensing Activities Compiled by E G. Silver, 470 Proposed Rule Changes as of Mar. 31, 1989; ANNOUNCEMENTS: 334 Proceedings Published, 351 CEC Seminar on Methods and Codes for Assessing the Off-Site Consequences of Nuclear Accidents, 357 Short Course on Multiphase Flow and Heat Transfer: Bases and Applications in A: The Nuclear Power Industry B: The Process Industries, 357 International Conference on Probabilistic Safety Assessment and Management, 478 International Topical Meeting on the Safety, Status, and Future of Non-Commercial Reactors and Irradiation Facilities, 475 The Authors.
Lawrence Livermore National Laboratory (LLNL) collects effluent samples from its sewer outfall at the B196 Sewer Monitoring Station (SMS). Effluent flow-proportional composite samples are collected at the SMS on a daily (midnight-to-midnight), weekly (Thursday through Wednesday), and “monthly” (composited from daily) basis; effluent grab samples are also collected each month at that same location. Certified contract laboratories analyzes these compliance samples, and results (Tables 1–3) are used to establish LLNL compliance with the 2019–2020 Wastewater Discharge Permit (Permit 1250) granted by the City of Livermore Water Resources Division (WRD). Supplemental analyses for biochemical oxygen demand (BOD) and total suspended solids (TSS) are performed onsite and reported (Table 4) for the calculation and assessment of sewer service charges. Quarterly, effluent is sampled for metals (24-hour composite) and cyanide (grab sample) concentrations (mg/L). The results are presented in Table 5. In addition to the sampling noted above, the SMS continuously monitors LLNL sewage effluent in real-time for flow rate, pH, metals, and radioactivity. Standard measurement methods are used to monitor flow and pH to determine permit compliance. Unique nonstandard analytical methods monitor for the presence of metals and radioactivity using x-ray fluorescence (XRF) and gamma spectroscopy, respectively. If an anomalous condition is detected by the monitoring system, an alarm activates LLNL’s Sewer Diversion Facility (SDF) and Livermore Water Reclamation Plant (LWRP) operators are notified that a potential release has occurred.
The first meeting that became the International Conference on Nuclear Criticality—ICNC—was held at the Los Alamos National Laboratory in February 1981. This presentation includes information on that meeting, including several experiments performed around that time period.