Short-term Station Research Articles

A Novel Approach for Predicting the Mid–Long-Term Radiation Dose in the Case of a Hypothetical STSBO Nuclear Accident for an Operating Nuclear Power Plant

Four severe nuclear accident scenarios have been identified for operating nuclear power plants (ONPPs). However, there is a research gap in predicting the mid–long-term radiation doses for these scenarios. This study aims to address this gap by proposing a novel approach for predicting the mid–long-term radiation dose in the case of a hypothetical short-term station blackout (STSBO) scenario, one of the aforementioned scenarios. Firstly, the Weather Research and Forecasting (WRF) model was coupled with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) (WRF-HYSPLIT) model to establish an atmospheric transport and diffusion model for airborne radionuclides, and the regularity of the atmospheric transport and diffusion for the airborne radionuclides was determined. Subsequently, the Residual Radioactive Material Guidelines (RESRAD) OFFSITE (RESRAD-OFFSITE) code was utilized to establish a radiation dose model for predicting the mid–long-term radiation dose resulting from the airborne radionuclides, and the evolution of the mid–long-term radiation dose was analyzed. Finally, the proposed approach was applied to an ONPP, and the results were used to predict the mid–long-term public radiation dose. The results indicated that the total radiation dose would be lower than the dose limit recommended by the International Commission on Radiological Protection (1 mSv/yr) from the second month to the 100th year after the hypothetical STSBO nuclear accident, and the total radiation dose would decrease slowly over time. Recommendations are made for offsite emergency response measures. These research findings can assist ONPPs in analyzing their environmental impacts in the event of an STSBO scenario.

Pearson Correlation and Chi-Squared Methods Applied to the Sensitivity and Uncertainty Analysis of Hydrogen Generation During a Boiling Water Reactor STSBO Using MAAP5 and AZTUSIA

Abstract After the nuclear accident at the Fukushima Daiichi nuclear power plant, hydrogen generation has resurfaced as a key issue, with respect to its quantification in the simulation of severe accidents (SAs), because of the potential risk of deflagration or detonation, which would compromise the integrity of the plant facilities. Severe accident simulation codes may have different models to predict cladding oxidation rates and the consequent hydrogen generation. Thus, sensitivity and uncertainty analysis can be applied to determine which input variables of the code have a significant impact in the implemented models, and consequently on the overall predicted values of key figures of merit. In this work, the generation of hydrogen in the reactor core during a short-term station blackout (STSBO) is studied as a figure of merit for a boiling water reactor (BWR). The scope of the analysis is until the failure of the reactor pressure vessel (RPV). This study shows a sensitivity and uncertainty analysis through the complementary calculations of two methods: Chi-squared parameter and Pearson simple correlation coefficient (PSCC). The severe accident simulation code was MAAP 5.0.3 (Modular Accident Analysis Program, EPRI, USA) and the AZTUSIA code (AZtlan Tool for Uncertainty and SensItivity Analysis, Mexico) was used as the statistical tool for the sensitivity and uncertainty analysis. Ten uncertain parameters were chosen, of which two are MAAP models options, and the values were generated via the simple random Monte Carlo technique. The results show that the Chi-squared parameter in conjunction with correlation coefficients provides a more comprehensive understanding of the impact of both quantitative and qualitative input variables on the figures of merit.

Improved Analysis of the Short-Term Station Blackout Accidents of the Peach Bottom Unit-2 Reactor with ASTEC Including Radiological Impact and Statistical Analysis with JRODOS

After the severe accident at Fukushima, the importance of BWR design and related structures and their contribution to the severe accident progression has increased. Fuel channel boxes, absorber crosses, water rods, and smaller primary containment design of the BWR have been considered in the ASTEC code to increase the knowledge of BWR design and associated models. The previously developed ASTEC model for Peach Bottom Unit-2 was updated to include modern GE14 10x10 fuel assemblies with realistic fission product inventories. The CASMO5 code predicted the fission product inventory and burnup for GE14 10x10 fuel assemblies based on real plant data obtained from the ENRESA samples. First, a Short-Term Station Blackout (ST-SBO) analysis was performed to compare the impact of the old and new fuel assembly designs on the accident progression and radiological consequences. Second, a short-term station blackout with a stuck open safety relief valve (ST-SBO SOSRV) was considered for modern fuel assemblies. The actuation of the safety valve resulted in a much lower corium ejection and a longer transient to basemat failure in the cavity. The scatter of corium ejection amounts between the considered scenarios showed the importance of the design of the bottom head and the penetration points in BWRs. In both cases, failure of the drywell head flange and release of radionuclides to the environment occurred. Higher burnup and radionuclide inventory in the 10x10 assemblies resulted in a larger release compared to the previous design. Radiological analysis using JRODOS was performed for both cases and the maximum total effective gamma dose rate was estimated to be 67.89 mSv/h and 119.46 mSv/h for ST-SBO and ST-SBO SOSRV, respectively. The statistical analysis and the number of records in the considered cities around the Peach Bottom Plant showed the distribution over the region and the risk factors of the populated cities. The collaborative use of three codes in this study allows users to identify the fission product inventory with CASMO5 and investigation of the severe accident scenario with ASTEC and identifying radiological impact of the released radioactive isotopes to the environment with JRODOS code.

Deep-learning post-processing of short-term station precipitation based on NWP forecasts

Post-processing methods that rely on fusion-grided forecast products can reduce systematic biases from Numerical Weather Prediction (NWP) precipitation forecasts. However, these methods also limit the capability to forecast precipitation accurately at local stations. We constructed a Station-based Precipitation Post-processing Model (SPPM) that utilizes deep-learning algorithms, predominantly convolutional layers and ResNet modules. Based on 390 meteorological stations in North China and European Centre for Medium-Range Weather Forecasts Highest-resolution (ECMWF-HRES) forecast data, the SPPM utilizes multi-level atmospheric forecast variables and geographic variables in a small area centered on a station as predictors. The results show that the SPPM improved the threat score (TS) by 4.29%, 3.66%, 15.63%, 61.08%, and 295.83% for precipitation thresholds of 0.1, 3.0, 10.0, 20.0, and 50.0 mm/3 h, respectively. We then examined the sensitivity of predictors using the interpretable deep-learning technique Layer-wise Relevance Propagation (LRP). The results indicate that the NWP total precipitation (TP) from ECMWF is the most sensitive and important factor, followed by the low-level (850 hPa) field, single-level field, and geographic variables. Notably, TP becomes increasingly important with larger forecast grades, while the importance of variables at other levels remains relatively constant. The majority of stations exhibit consistent importance rankings as mentioned above. Finally, possible causes of variables' insensitivity at medium-level (500 hPa) and high-level (200 hPa) were discussed.

Effect of mitigation strategies in the severe accident uncertainty analysis of the OPR1000 short-term station blackout accident

Integrated severe accident codes should be capable of simulating not only specific physical phenomena but also entire plant behaviors, and in a sufficiently fast time. However, significant uncertainty may exist owing to the numerous parametric models and interactions among the various phenomena. The primary objectives of this study are to present best-practice uncertainty and sensitivity analysis results regarding the evolutions of severe accidents (SAs) and fission product source terms and to determine the effects of mitigation measures on them, as expected during a short-term station blackout (STSBO) of a reference pressurized water reactor (optimized power reactor (OPR)1000). Three reference scenarios related to the STSBO accident are considered: one base and two mitigation scenarios, and the impacts of dedicated severe accident mitigation (SAM) actions on the results of interest are analyzed (such as flammable gas generation). The uncertainties are quantified based on a random set of Monte Carlo samples per case scenario. The relative importance values of the uncertain input parameters to the results of interest are quantitatively evaluated through a relevant sensitivity/importance analysis.

Best-practice severe accident uncertainty and sensitivity analysis for a short-term SBO sequence of a reference PWR using MAAP5

The integrated severe accident codes like MELCOR and MAAP5 include numerous uncertain models and parameters representing a wide range of physico-chemical phenomena for a comprehensive plant simulation, consequently making relevant uncertainty analysis a non-trivial task. In this paper, best-practice uncertainty and sensitivity analysis was performed to statistically quantify uncertainties associated with the analysis results of interest and identify potentially important uncertain parameters, based on the key modeling parameters employed in MAAP5. Three reference scenarios related to a short-term station blackout (STSBO) accident of a reference pressurized water reactor (PWR: OPR1000), were considered for the purpose: one base scenario and two mitigation scenarios to investigate the impact of dedicated severe accident mitigation (SAM) actions on the results of interest. A series of uncertainty and sensitivity analyses were carried out for these model parameters. Uncertainties for the results of interest were quantified through a random set of the Monte Carlo samples per case scenario, with values statistically sampled from the probability distributions of the individual model parameters. In addition, the relative importance of these model parameters to each relevant analysis result was then quantitatively evaluated through the sensitivity and importance analysis. Analysis results and relevant insights are summarized in terms of particular points of interest.

Construction of the Central Arctic Sea Ice Structure and Acoustic Velocity Model at the Short-Term Ice Station During N11 CHINARE

Construction of the Central Arctic Sea Ice Structure and Acoustic Velocity Model at the Short-Term Ice Station During N11 CHINARE

Effects of ATF cladding properties on PWR responses to an SBO accident: A sensitivity analysis

To investigate the effects of ATF cladding properties on the plant responses to severe accidents, short-term station blackout simulations and sensitivity analyses on hypothetical cladding materials were performed using MAAP code. For sensitivity analyses, 7 hypothetical cladding materials were used. Hypo-0 was the reference cladding whose properties are anticipated to show the poorest fuel performance during an STSBO. The input property values for Hypo-1 through Hypo-6 were selected by changing each variable independently while keeping the others at their reference values. The onset times for core uncovery and cladding oxidation were similar for all the simulation cases, implying that single property cannot affect the event times. A main conclusion is that a large enthalpy of cladding can delay the increase in temperatures of fuel and cladding by playing a role as a heat sink after the core uncovery occurs, resulting in the delayed hot leg rupture.

Material Interactions in Severe Accidents – Benchmarking the MELCOR V2.2 Eutectics Model for a BWR-3 Mark-I Station Blackout: Part II – Uncertainty Analysis

Single case comparisons between severe accident simulations can provide detailed insights into severe accident model behavior, however, they cannot offer insights into model uncertainty, sensitivity to uncertain parameters, or underlying model biases. In this analysis, the single case benchmark comparison of the MELCOR material interaction models for a station blackout (SBO) scenario of a boiling water reactor (BWR) using representative Fukushima Daiichi Unit 1 boundary conditions is expanded to include an uncertainty analysis. As part of this uncertainty analysis, 1200 simulations are performed for each material interaction model (2400 total), with random sampling of 14 uncertain MELCOR input parameters. Input parameters are selected for their impact on models representing core degradation processes. These include candling, fuel rod failure, debris quenching and dryout. The analysis performed here is not a traditional “best-estimate” uncertainty analysis that uses best-estimate parameters or identifies best-estimate figure of merit distributions. Instead, it is an exploratory uncertainty analysis that identifies and interrogates underlying model form biases of the two material interaction models (eutectics and interactive materials models). Uniform distributions are applied to all uncertain parameters to ensure coverage of the model parameter uncertainty space. Key findings from this study include underlying model form biases exhibited by material interaction models, and notable differences in accident progression outcomes between the material interaction models. This uncertainty study extends and confirms the conclusions from the first part of this study, which compared the impact of material interaction modeling on simulation of a short-term station blackout scenario with representative Fukushima Daiichi Unit I boundary conditions. In particular, this study confirms that the eutectics model generally exhibits accelerated degradation and failure of fuel components, the core plate, and the lower head. The eutectics model also has a tendency to exhibit a greater degree of core degradation, greater debris mass formation, and larger debris mass ejection. Finally, the eutectics model exhibits higher maximum temperatures for fuel, cladding, particulate debris, oxidic molten pool, and metallic molten pool components than the interactive materials model; interactive materials model simulations exhibit a soft “limitation” on maximum temperatures that is related to the temperature at which material relocation occurs.

Uncertainty analysis of ATF Cr-coated-Zircaloy on BWR in-vessel accident progression during a station blackout

The deposition of protective coatings on nuclear fuel cladding has been considered as a near-term Accident Tolerant Fuel (ATF) concept that will reduce the high-temperature oxidation rate and enhance accident tolerance of the cladding while providing additional benefits during normal operation and transients. In this study, an uncertainty analysis was employed to investigate the potential benefits of ATF Cr-coated-Zr cladding and canister for an unmitigated Short-Term Station Blackout (STSBO) sequence in a generic BWR plant using the MELCOR systems code. The MELCOR parameters that reflect the current state-of-knowledge of the relevant fuel assembly performance during core degradation were selected and characterized according to their ranges and distributions. An extensive set of simulations (240 MELCOR calculations) were performed for the Zr and Cr-coated-Zr cladding and canister materials, respectively, to determine their effect on core degradation with the associated uncertainties. The comparison between the Zr and Cr-coated-Zr calculations confirms that the use of ATF Cr-coated-Zr as cladding and canister component material in BWR might be an effective way to mitigate the accident progression and reduce the total hydrogen generation during the accident. The core degradation process was only delayed by less than a half hour, providing some additional time for compensatory actions to mitigate with the accident progression. In contrast, the effect of coated materials on total hydrogen generation was more substantial; i.e., hydrogen generation was almost reduced by half. In addition, a sensitivity analysis based on the Pearson and Spearman correlation coefficients was conducted to rank the significance of the considered parameter uncertainties. The Cr-coating failure temperature was identified as the dominant factor in the MELCOR simulations of core degradation and associated hydrogen generation. Understanding these effects will inform and guide researchers to focus on a more productive area of research and development for accident-tolerant fuel concepts and enhancement of core safety margins.

Best-practice severe accident analysis for the OPR1000 short-term SBO sequence using MELCOR2.2 and MAAP5

A short-term station blackout (STSBO) accident has been considered as one of risk dominant sequences for nuclear power plants (NPPs). Unless appropriate mitigation actions are not taken, a rapid damage of the reactor core and thereby a severe accident may result in. This paper presents best practice analysis results on the evolution of the severe accident sequences and fission product (FP) source terms expected during the STSBO accident of a reference pressurized water reactor. In addition, this study aims to analyze the impact of dedicated severe accident mitigation/management actions on the results of interest through the established modeling approach. As the modeling and simulation tools for the analysis, MELCOR2.2 and MAAP5.05, were used to compare the analysis results of interest such as the integrities of the reactor coolant system (RCS) pressure boundary and the reactor/containment building, the generation of flammable gases (such as hydrogen and carbon monoxide), and the environmental release of the FP cesium. As a key finding, this study demonstrated that both codes similarly predicted the environmental release of the FP cesium as well as the general trend of the evolution of the severe accident sequences and plant responses. Of particular interest was the substantial difference in predicting the generation of the flammable gases by the two codes. Relevant insights are summarized in terms of particular points of interest.

Nuclear fuel rod cladding oxidation and hydrogen production model based on diffusion theory in a multiphase environment of ZrO2, α-Zr(O), and β-Zr at high temperatures (1273 K–1800 K)

This paper proposes a mathematical model for the oxidation process of zirconium under the theory of oxygen diffusion in Zircaloy. The model considers ZrO2, α-Zr(O), and β-Zr phases at high temperatures (1273 K–1800 K) in an equivalent fuel rod. The model also considers the heat transfer phenomenon, the decay heat after shutdown, the heat released by the oxidation reaction, the loss of coolant water in the core and the heat transported by the steam produced. A computer program was coded in the C++ environment. The accident scenario of a BWR short term station blackout was simulated with this model. The results are compared with the ones obtained using MELCOR and RELAP/SCDAP codes. The comparison yielded an approximate result for total hydrogen production at the end of the simulation, with a difference of −2.7% compared with RELAP/SCDAP, and a difference of −1.11% with MELCOR. With the present model it is possible to calculate the growth of ZrO2, α-Zr(O), and β-Zr phases through the cladding.

Effect of ATF Cr-coated-Zircaloy on BWR In-vessel Accident Progression during a Station Blackout

The deposition of protective coatings on nuclear fuel cladding has been considered as a near-term Accident Tolerant Fuel (ATF) concept that will reduce the high-temperature oxidation rate and enhance accident tolerance of the cladding while providing additional benefits during normal and transient. In this study, the performance of the proposed ATF concept of Cr-coated-Zircaloy is assessed using a generic Boiling Water Reactor MELCOR plant model considering a Short-term Station Blackout (STSBO) scenario. Simulation results indicate that the use of Cr-coated-Zircaloy as cladding and canister material mitigates the core degradation process as compared to the traditional Zircaloy cladding and canister design. The onset of fuel degradation and collapse is delayed by over thirty minutes, and the extent of fuel degradation is reduced. Specifically, the gross in-vessel hydrogen generation decreased by almost a factor of three. Although the eutectic reaction between Cr-coating and Zircaloy could cause an early failure of the coating, the improvement in the delay of fuel degradation is still notable. Additionally, a thicker coating is found helpful to obtain additional coping time and to decrease hydrogen generation. In addition to the eutectic formation that may compromise Cr-coated Zr, a different failure mode is identified for the Cr-coated-Zr when compared to Zircaloy; i.e., a complete melt of base material leads to component collapse before the coating is oxidized and consumed. These findings can help the industry focus on productive areas of research and development for accident-tolerant fuel concepts and enhancement of core safety margins.

Extending Limited In Situ Mountain Weather Observations to the Baseline Climate: A True Verification Case Study

The availability of in situ atmospheric observations decreases with elevation and topographic complexity. Data sets based on numerical atmospheric modeling, such as reanalysis data sets, represent an alternative source of information, but they often suffer from inaccuracies, e.g., due to insufficient spatial resolution. sDoG (statistical Downscaling for Glacierized mountain environments) is a reanalysis data postprocessing tool designed to extend short-term weather station data from high mountain sites to the baseline climate. In this study, sDoG is applied to ERA-Interim predictors to produce a retrospective forecast of daily air temperature at the Vernagtbach climate monitoring site (2640 MSL) in the Central European Alps. First, sDoG is trained and cross-validated using observations from 2002 to 2012 (cross-validation period). Then, the sDoG retrospective forecast and its cross-validation-based uncertainty estimates are evaluated for the period 1979–2001 (hereafter referred to as the true evaluation period). We demonstrate the ability of sDoG to model air temperature in the true evaluation period for different temporal scales: day-to-day variations, year-to-year and season-to-season variations, and the 23-year mean seasonal cycle. sDoG adds significant value over a selection of reference data sets available for the site at different spatial resolutions, including state-of-the-art global and regional reanalysis data sets, output by a regional climate model, and an observation-based gridded product. However, we identify limitations of sDoG in modeling summer air temperature variations particularly evident in the first part of the true evaluation period. This is most probably related to changes of the microclimate around the Vernagtbach climate monitoring site that violate the stationarity assumption underlying sDoG. When comparing the performance of the considered reference data sets, we cannot demonstrate added value of the higher resolution data sets over the data sets with lower spatial resolution. For example, the global reanalyses ERA5 (31 km resolution) and ERA-Interim (80 km resolution) both clearly outperform the higher resolution data sets ERA5-Land (9 km resolution), UERRA HARMONIE (11 km resolution), and UERRA MESCAN-SURFEX (5.5 km resolution). Performance differences among ERA5 and ERA-Interim, by contrast, are comparably small. Our study highlights the importance of station-scale uncertainty assessments of atmospheric numerical model output and downscaling products for high mountain areas both for data users and model developers.

Quantification of the effect of Cr-coated-Zircaloy cladding during a short term station black out

Accident Tolerant Fuel (ATF) has the potential to improve the inherent safety of current reactors. In particular, an ATF Cr-coated Zirconium alloy (Cr-coated-Zr) cladding can be an effective short-term approach to improve the cladding oxidation resistance without changing the cladding base materials. In order to appropriately assess the effect of such an ATF concept it is important to quantify the performance of such a cladding improvement not only during normal operation but also during postulated accidents. In this work, we examine the effect of Cr-coated-Zr cladding (Cr-Zr) during a short-term station blackout (STSBO) accident as well as quantify the uncertainty in the use of ATF Cr-coated-Zr cladding. This is accomplished using a coated cladding model developed for the MELCOR severe accident systems code; i.e., MELCOR-1.8.6-UDGC model. First, we quantify the effect of the coated cladding using a set of base case simulations that show the improved oxidation resistance of the Cr-coated-Zr cladding with the UDGC model parameters. Then, we quantify the uncertainty in these simulations by a Monte-Carlo analysis that examines the effect of the ATF clad input parameters (e.g., oxidation rate and failure temperature) on key output parameters; i.e., timing of rapid hydrogen generation, clad temperatures and hot leg creep rupture. Finally, we use regression analysis methods to estimate the importance of these input parameters. These analyses show that the Cr-coated-Zr cladding can delay rapid clad oxidation and generation of hydrogen by 3600 s to 7200 s with associated delays in rapid clad temperature increases. This additional time can be of benefit for compensatory operator actions. In contrast there is little effect on the timing of hot leg creep rupture since this failure is mainly controlled by core decay heat. We also find that these results are not significantly affected by the uncertainty in input parameters, and based on the regression analysis, the high temperature Zr oxidation rate has the major influence on the selected output parameters for the Cr-coated-Zr cladding.

SOARCA uncertainty analysis of a short-term station blackout accident at the Sequoyah nuclear power plant

The U.S. Nuclear Regulatory Commission initiated the state-of-the-art reactor consequence analyses (SOARCA) project to develop realistic estimates of the offsite radiological health consequences for potential severe reactor accidents. The SOARCA analysis of an ice condenser containment plant was performed because its relatively low design pressure and its reliance on igniters make it potentially susceptible to early containment failure from hydrogen combustion during a severe accident. The focus was on station blackout accident scenarios where all alternating current power is lost. Accident progression calculations used the MELCOR computer code and offsite consequence analyses were performed with MACCS. The analysis included more than 500 MELCOR and MACCS simulations to account for uncertainty in important accident progression and offsite consequence input parameters.Consequences from severe nuclear power plant accidents modeled in SOARCA are smaller than previously calculated. The delayed releases calculated provide more time for emergency response actions. The results show that early containment failure is very unlikely, even without successful use of igniters. The modeled behavior of safety valves is very important to this conclusion, but there is sparse data and a lack of established expert consensus on the failure rates under severe accident conditions. Even for scenarios resulting in early containment failure, the calculated individual latent fatal cancer risks are very small. Early and latent-cancer fatality risks are one focus of this paper. Regression results showing the most influential parameters are also discussed.

Real time transit demand prediction capturing station interactions and impact of special events

Demand for public transportation is highly affected by passengers’ experience and the level of service provided. Thus, it is vital for transit agencies to deploy adaptive strategies to respond to changes in demand or supply in a timely manner, and prevent unwanted deterioration in service quality. In this paper, a real time prediction methodology, based on univariate and multivariate state-space models, is developed to predict the short-term passenger arrivals at transit stations. A univariate state-space model is developed at the station level. Through a hierarchical clustering algorithm with correlation distance, stations with similar demand patterns are identified. A dynamic factor model is proposed for each cluster, capturing station interdependencies through a set of common factors. Both approaches can model the effect of exogenous events (such as football games). Ensemble predictions are then obtained by combining the outputs from the two models, based on their respective accuracy. We evaluate these models using data from the 32 stations on the Central line of the London Underground (LU), operated by Transport for London (TfL). The results indicate that the proposed methodology performs well in predicting short-term station arrivals for the set of test days. For most stations, ensemble prediction has the lowest mean error, as well as the smallest range of error, and exhibits more robust performance across the test days.

Iron–Chromium–Aluminum (FeCrAl) Cladding Oxidation Kinetics and Auxiliary Feedwater Sensitivity Analysis—Short-Term Station Blackout Simulation of Surry Nuclear Power Plant

Accident tolerant fuels (ATF) and steam generator (SG) auxiliary feedwater (AFW) extended operation are two important methods to increase the coping time for nuclear power plant safety response. In light of recent efforts to investigate such methods, we investigate both FeCrAl cladding oxidation kinetics and SG AFW sensitivity analyses, for the Surry nuclear power plant Short-Term Station Blackout simulation using the MELCOR YR 1.8.6 systems code. The first part describes the effects of FeCrAl cladding oxidation kinetics. Zircaloy cladding and two different oxidation models of FeCrAl cladding are compared. The initial hydrogen generation time (>0.5 kg) is used as the evaluation criterion for fuel degradation in a severe accident. Results showed that the more recent oxidation correlation by ORNL predicts much less hydrogen generation than Zircaloy cladding. The second part investigates the effects of three different methods of AFW injection into the SG secondary side. We considered three different methods of water injection; i.e., constant water injection into the secondary side (case 1); water injection based on secondary side water level in boiler region (case 2); water injection based on secondary side water level in the downcomer region (case 3). The case of constant water injection is the most straightforward, but it would have the tendency to overfill the SG with excess water. Water injection with downcomer level control is more reasonable but requires DC power to monitor level and to control AFW injection rate.

Behavior of cesium molybdate, Cs2MoO4, in severe accident conditions. (1) Partitioning of Cs and Mo among gaseous species

ABSTRACT In order to better understand the behavior of cesium in severe accident of Light Water Reactor (LWR), the high-temperature chemistry of Cs2MoO4 in H2O + H2 gas was studied. The pseudo–binary system, Cs2MoO4–MoO3, was thermochemically modeled with Redlich–Kister formulation to form a basis to analyze the high-temperature behavior of Cs2MoO4. The model prediction was compared with the thermogravimetric measurements of Cs2MoO4 in dry and humid argon, which revealed that the mass-loss rate was enhanced in humid atmosphere. The thermochemical model was further applied to predict the partitioning of cesium and molybdenum among gaseous species in the boiling water reactor-core degradation condition typical of short-term station blackout. Effects of the total pressure (3.5–75 bar) as well as the H2/H2O ratio (1/4000–2) were examined. CsOH(g) is the predominant cesium species, when the damaged fuel temperature is higher than 2000 K at higher steam pressures, but Cs2MoO4(g) would become more important as the steam cools toward the steam dome. The condensation of Cs2MoO4 occurs below ∼1900 and ∼1550 K at 75 and 3.5 bar, respectively. Besides, the ideal mixing of complex component model has also been examined for its simplicity. The latter gave satisfactory prediction as far as the condensed phase composition is concerned.

Predicting critical conditions in bicycle sharing systems

Bicycle sharing systems are eco-friendly transportation systems that have found wide application in Smart urban environments. Monitoring and analyzing the occupancy levels of the system's stations is crucial for guaranteeing the quality of the offered service. Advanced data mining solutions tailored to bicycle sharing data analysis are needed to support system managers to react to critical situations (e.g., lack of parked bicycles at a station) that can yield service disruption. This paper presents STation Occupancy Predictor (STOP), a data mining framework to predicting the occupancy levels (i.e., critical or non-critical) of the stations in the near future through Bayesian and associative classifiers. The prediction is made based on the current and past station occupancy values and on the temporal information associated with the predicted time instant. A classification model per station is generated on the collected data to support domain experts in understanding the underlying events. The model allows predicting short-term station occupancy levels and characterizing system usage to support planning maintenance activities in the medium term as well. As a case study, STOP has been thoroughly evaluated on real data acquired from the bicycle sharing system of New York City. The results demonstrate the effectiveness of the STOP system.