AGR-2 Data Qualification Report for ATR Cycles 151B-2, 152A, 152B, 153A, 153B and 154A

This report documents the data qualification status of AGR-2 fuel irradiation experimental data from Advanced Test Reactor (ATR) Cycles 152A, 152B, 153A, 153B, and 154A, as recorded in the Nuclear Data Management and Analysis System (NDMAS). The AGR-2 data streams addressed include thermocouple (TC) temperatures, sweep gas data (flow rate, pressure, and moisture content), and fission product monitoring system (FPMS) data for each of the six capsules in the experiment. A total of 13,400,520 every minute instantaneous TC and sweep gas data records were received and processed by NDMAS for this period. Of these data, 8,911,791 records (66.5% of the total) were determined to be Qualified based on NDMAS accuracy testing and data validity assessment. For temperature, there were 4,266,081 records (74% of the total TC data) that were Failed due to TC instrument failures. For sweep gas flows, there were 222,648 gas flow records (2.91% of the flow data) that were Failed. The inlet gas flow failures due to gas flow cross-talk and leakage problems that occurred after Cycle 150A were corrected by using the same gas mixture in all six capsules and the Leadout. For FPMS data, NDMAS received and processed preliminary release rate and release-to-birth rate ratio (R/B) data for three reactor cycles (Cycles 149B, 150B, and 151A) . This data consists of 45,983 release rate records and 45,235 R/B records for the 12 radionuclides reported. The qualification status of these FPMS data has been set to In Process until receipt of Quality Assurance-approved data generator reports. All of the above data have been processed and tested using a SAS®-based enterprise application software system, stored in a secure Structured Query Language database, made available on the NDMAS Web portal (http://ndmas.inl.gov), and approved by the INL STIM for release to both internal and appropriate external Very High Temperature Reactor Program participants

AGR-2 Data Qualification Report for ATR Cycles 149B, 150A, 150B, 151A, and 151B

This report provides the data qualification status of AGR-2 fuel irradiation experimental data from Advanced Test Reactor (ATR) cycles 149B, 150A, 150B, 151A, and 151B), as recorded in the Nuclear Data Management and Analysis System (NDMAS). The AGR-2 data streams addressed include thermocouple temperatures, sweep gas data (flow rate, pressure, and moisture content), and fission product monitoring system (FPMS) data for each of the six capsules in the experiment. A total of 3,307,500 5-minute thermocouple and sweep gas data records were received and processed by NDMAS for this period. There are no AGR-2 data for cycle 150A because the experiment was removed from the reactor. Of these data, 82.2% were determined to be Qualified based on NDMAS accuracy testing and data validity assessment. There were 450,557 Failed temperature records due to thermocouple failures, and 138,528 Failed gas flow records due to gas flow cross-talk and leakage problems that occurred in the capsules after cycle 150A. For FPMS data, NDMAS received and processed preliminary release rate and release-to-birth rate ratio (R/B) data for the first three reactor cycles (cycles 149B, 150B, and 151B). This data consists of 45,983 release rate records and 45,235 R/B records for the 12 radionuclides reported. The qualification status of these FPMS data has been set to In Process until receipt of QA-approved data generator reports. All of the above data have been processed and tested using a SAS®-based enterprise application software system, stored in a secure Structured Query Language database, and made available on the NDMAS Web portal (http://ndmas.inl.gov) for both internal and external VHTR project participants

Online Monitoring Technical Basis and Analysis Framework for Large Power Transformers; Interim Report for FY 2012

The Light Water Reactor Sustainability program at Idaho National Laboratory (INL) is actively conducting research to develop and demonstrate online monitoring (OLM) capabilities for active components in existing Nuclear Power Plants. A pilot project is currently underway to apply OLM to Generator Step-Up Transformers (GSUs) and Emergency Diesel Generators (EDGs). INL and the Electric Power Research Institute (EPRI) are working jointly to implement the pilot project. The EPRI Fleet-Wide Prognostic and Health Management (FW-PHM) Software Suite will be used to implement monitoring in conjunction with utility partners: the Shearon Harris Nuclear Generating Station (owned by Duke Energy for GSUs, and Braidwood Generating Station (owned by Exelon Corporation) for EDGs. This report presents monitoring techniques, fault signatures, and diagnostic and prognostic models for GSUs. GSUs are main transformers that are directly connected to generators, stepping up the voltage from the generator output voltage to the highest transmission voltages for supplying electricity to the transmission grid. Technical experts from Shearon Harris are assisting INL and EPRI in identifying critical faults and defining fault signatures associated with each fault. The resulting diagnostic models will be implemented in the FW-PHM Software Suite and tested using data from Shearon-Harris. Parallel research on EDGs is being conducted, and will be reported in an interim report during the first quarter of fiscal year 2013

Communication Pathways in the Light Water Reactor Sustainability Online Monitoring Project

Implementation of online monitoring and prognostics in existing U.S. nuclear power plants will involve coordinating the efforts of national laboratories, utilities, universities, and private companies. Large amounts of operational data, including failure data, are necessary for the development and calibration of diagnostic and prognostic algorithms. The ability to use data from all available resources will provide the most expeditious avenue to implementation of online monitoring in existing NPPs; however, operational plant data are often considered proprietary. Secure methods for transferring and storing data are discussed, along with a potential technology for implementation of online monitoring

Growth and real business cycles in Vietnam and the ASEAN-5. Does the trend shock matter?

We examine Vietnam’s economy together with its closest trade partners. We show that capital accumulation has been the primary growth engine since the start of its transition to the pro-market economy in 1986–the Doi Moi. We also show that the cyclical behavior of its macro-aggregates is similar to the one of its ASEAN-5 peers and other developing countries. We extend the standard small-open-economy RBC model by considering habit persistence and government consumption which allows a close match of the moments of the growth variables. At the business cycle frequency, transitory productivity shocks account for approximately one-half of Vietnam’s output variance, while country-risk and non-transitory productivity shocks account to close to one-fifth each. Regarding Solow residual's volatility, we find that the trend component merely accounts for 12% of this variance in Vietnam, while in Thailand it is only 6%. These findings refute “the cycle is the trend” hypothesis in Aguiar and Gopinath (2007), and align to those in García-Cicco, Pancrazi, and Uribe (2010) and Rhee (2017), in which the stationary component is overwhelmingly dominant. We claim that technological progress and productivity-enhancing measures are fundamental for Vietnam's economy to sustain a high growth

Prognostic Health Monitoring System: Component Selection Based on Risk Criteria and Economic Benefit Assessment

Prognostic health monitoring (PHM) is a proactive approach to monitor the ability of structures, systems, and components (SSCs) to withstand structural, thermal, and chemical loadings over the SSCs planned service lifespans. The current efforts to extend the operational license lifetime of the aging fleet of U.S. nuclear power plants from 40 to 60 years and beyond can benefit from a systematic application of PHM technology. Implementing a PHM system would strengthen the safety of nuclear power plants, reduce plant outage time, and reduce operation and maintenance costs. However, a nuclear power plant has thousands of SSCs, so implementing a PHM system that covers all SSCs requires careful planning and prioritization. This paper therefore focuses on a component selection that is based on the analysis of a component's failure probability, risk, and cost. Ultimately, the decision on component selection depend on the overall economical benefits arising from safety and operational considerations associated with implementing the PHM system

Prognostic and Health Management of Active Assets in Nuclear Power Plants

This paper presents the development of diagnostic and prognostic capabilities for active assets in nuclear power plants (NPPs). The research was performed under the Advanced Instrumentation, Information, and Control Technologies Pathway of the Light Water Reactor Sustainability Program. Idaho National Laboratory researched, developed, implemented, and demonstrated diagnostic and prognostic models for generator step-up transformers (GSUs). The Fleet-Wide Prognostic and Health Management (FW-PHM) Suite software developed by the Electric Power Research Institute was used to perform diagnosis and prognosis. As part of the research activity, Idaho National Laboratory implemented 22 GSU diagnostic models in the Asset Fault Signature Database and two wellestablished GSU prognostic models for the paper winding insulation in the Remaining Useful Life Database of the FW-PHM Suite. The implemented models along with a simulated fault data stream were used to evaluate the diagnostic and prognostic capabilities of the FW-PHM Suite. Knowledge of the operating condition of plant asset gained from diagnosis and prognosis is critical for the safe, productive, and economical long-term operation of the current fleet of NPPs. This research addresses some of the gaps in the current state of technology development and enables effective application of diagnostics and prognostics to nuclear plant assets

Online Monitoring Technical Basis and Analysis Framework for Large Power Transformers; Interim Report for FY 2012

The Light Water Reactor Sustainability program at Idaho National Laboratory (INL) is actively conducting research to develop and demonstrate online monitoring (OLM) capabilities for active components in existing Nuclear Power Plants. A pilot project is currently underway to apply OLM to Generator Step-Up Transformers (GSUs) and Emergency Diesel Generators (EDGs). INL and the Electric Power Research Institute (EPRI) are working jointly to implement the pilot project. The EPRI Fleet-Wide Prognostic and Health Management (FW-PHM) Software Suite will be used to implement monitoring in conjunction with utility partners: the Shearon Harris Nuclear Generating Station (owned by Duke Energy for GSUs, and Braidwood Generating Station (owned by Exelon Corporation) for EDGs. This report presents monitoring techniques, fault signatures, and diagnostic and prognostic models for GSUs. GSUs are main transformers that are directly connected to generators, stepping up the voltage from the generator output voltage to the highest transmission voltages for supplying electricity to the transmission grid. Technical experts from Shearon Harris are assisting INL and EPRI in identifying critical faults and defining fault signatures associated with each fault. The resulting diagnostic models will be implemented in the FW-PHM Software Suite and tested using data from Shearon-Harris. Parallel research on EDGs is being conducted, and will be reported in an interim report during the first quarter of fiscal year 2013

Assimilation of SMAP products for improving streamflow simulations over tropical climate region — is spatial information more important than temporal information?

Streamflow is one of the key variables in the hydrological cycle. Simulation and forecasting of streamflow are challenging tasks for hydrologists, especially in sparsely gauged areas. Coarse spatial resolution remote sensing soil moisture products (equal to or larger than 9 km) are often assimilated into hydrological models to improve streamflow simulation in large catchments. This study uses the Ensemble Kalman Filter (EnKF) technique to assimilate SMAP soil moisture products at the coarse spatial resolution of 9 km (SMAP 9 km), and downscaled SMAP soil moisture product at the higher spatial resolution of 1 km (SMAP 1 km), into the Soil and Water Assessment Tool (SWAT) to investigate the usefulness of different spatial and temporal resolutions of remotely sensed soil moisture products in streamflow simulation and forecasting. The experiment was set up for eight catchments across the tropical climate of Vietnam, with varying catchment areas from 267 to 6430 km2 during the period 2017–2019. We comprehensively evaluated the EnKF-based SWAT model in simulating streamflow at low, average, and high flow. Our results indicated that high-spatial resolution of downscaled SMAP 1 km is more beneficial in the data assimilation framework in aiding the accuracy of streamflow simulation, as compared to that of SMAP 9 km, especially for the small catchments. Our analysis on the impact of observation resolution also indicates that the improvement in the streamflow simulation with data assimilation is more significant at catchments where downscaled SMAP 1 km has fewer missing observations. This study is helpful for adding more understanding of performances of soil moisture data assimilation based hydrological modelling over the tropical climate region, and exhibits the potential use of remote sensing data assimilation in hydrology

Corporate Financial Distress of Industry Level Listings in an Emerging Market

Any critical analysis of the corporate financial distress of listed firms in international exchange would be incomplete without a serious dissection at the industry level because of the different levels of risks concerned. This paper considers the financial distress of listed firms at the industry level in Vietnam over the last decade. Two periods are considered, namely during the Global Financial Crisis (GFC) (2007 - 2009) and post-GFC (2010 - 2017). The logit regression technique is used to estimate alternative models based on accounting and market factors. The paper also extends the analysis to include selected macroeconomic factors that are expected to affect the corporate financial distress of listed firms at the industry level in Vietnam. The empirical findings confirm that the corporate financial distress prediction model, which includes accounting factors with macroeconomic indicators, performs much better than alternative models. In addition, the evidence confirms that the GFC had a damaging impact on each sector, with the Health & Education sector demonstrating the most impressive recovery post-GFC, and the Utilities sector recording a dramatic increase in bankruptcies post-GF