Publications

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Efforts are being pursued to develop and qualify a system-level model of a reactor core isolation (RCIC) steam-turbine-driven pump. The model is being developed with the intent of employing it to inform the design of experimental configurations for full-scale RCIC testing. The model is expected to be especially valuable in sizing equipment needed in the testing. An additional intent is to use the model in understanding more fully how RCIC apparently managed to operate far removed from its design envelope in the Fukushima Daiichi Unit 2 accident. RCIC modeling is proceeding along two avenues that are expected to complement each other well. The first avenue is the continued development of the system-level RCIC model that will serve in simulating a full reactor system or full experimental configuration of which a RCIC system is part. The model reasonably represents a RCIC system today, especially given design operating conditions, but lacks specifics that are likely important in representing the off-design conditions a RCIC system might experience in an emergency situation such as a loss of all electrical power. A known specific lacking in the system model, for example, is the efficiency at which a flashing slug of water (as opposed to a concentrated jet of steam) could propel the rotating drive wheel of a RCIC turbine. To address this specific, the second avenue is being pursued wherein computational fluid dynamics (CFD) analyses of such a jet are being carried out. The results of the CFD analyses will thus complement and inform the system modeling. The system modeling will, in turn, complement the CFD analysis by providing the system information needed to impose appropriate boundary conditions on the CFD simulations. The system model will be used to inform the selection of configurations and equipment best suitable of supporting planned RCIC experimental testing. Preliminary investigations with the RCIC model indicate that liquid water ingestion by the turbine decreases the developed turbine torque; the RCIC speed then slows, and thus the pump flow rate to the RPV decreases. Subsequently, RPV water level decreases due to continued boiling and the liquid fraction flowing to the RCIC decreases, thereby accelerating the RCIC and refilling the RPV. The feedback cycle then repeats itself and/or reaches a quasi-steady equilibrium condition. In other words, the water carry-over is limited by cyclic RCIC performance degradation, and hence the system becomes self-regulating. The indications achieved to date with the system model are more qualitative than quantitative. The avenues being pursued to increase the fidelity of the model are expected to add quantitative realism. The end product will be generic in the sense that the RCIC model will be incorporable within the larger reactor coolant system model of any nuclear power plant or experimental configuration.

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Accident management is an important component to maintaining risk at acceptable levels for all complex systems, such as nuclear power plants. With the introduction of self-correcting, or inherently safe, reactor designs the focus has shifted from management by operators to allowing the system's design to manage the accident. Inherently and passively safe designs are laudable, but nonetheless extreme boundary conditions can interfere with the design attributes which facilitate inherent safety, thus resulting in unanticipated and undesirable end states. This report examines an inherently safe and small sodium fast reactor experiencing a beyond design basis seismic event with the intent of exploring two issues: (1) can human intervention either improve or worsen the potential end states and (2) can a Bayesian Network be constructed to infer the state of the reactor to inform.

This document provides Sandia National Laboratories’ meeting notes and presentations at the Society for Modeling and Simulation Power Plant Simulator conference in Jacksonville, FL. The conference was held January 26-28, 2015, and SNL was invited by the U.S. nuclear industry to present Fukushima modeling insights and lessons learned.

Accident management is an important component to maintaining risk at acceptable levels for all complex systems, such as nuclear power plants. With the introduction of self - correcting, or inherently safe, reactor designs the focus has shifted from management by operators to allowing the syste m's design to manage the accident. While inherently and passively safe designs are laudable, extreme boundary conditions can interfere with the design attributes which facilitate inherent safety , thus resulting in unanticipated and undesirable end states. This report examines an inherently safe and small sodium fast reactor experiencing a beyond design basis seismic event with the intend of exploring two issues : (1) can human intervention either improve or worsen the potential end states and (2) can a Bayes ian Network be constructed to infer the state of the reactor to inform (1). ACKNOWLEDGEMENTS The author s would like to acknowledge the U.S. Department of E nergy's Office of Nuclear Energy for funding this research through Work Package SR - 14SN100303 under the Advanced Reactor Concepts program. The authors also acknowledge the PRA teams at A rgonne N ational L aborator y , O ak R idge N ational L aborator y , and I daho N ational L aborator y for their continue d contributions to the advanced reactor PRA mission area.

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The MELCOR and MACCS computer codes require inputs for radionuclide inventory and decay heat in order to simulate severe accident phenomenology, source term, and consequences. Therefore, Sandia National Laboratories ( SNL ) has developed accurate and automated methods using SCALE 6 in conjunction with automation scripts to directly generate MELCOR and MACCS input records for radionuclide inventories and decay heating. Consistent information between the two codes is essential for realistic modeling of severe nuclear accidents it is the radionuclide inventory and decay power that are the principal safety problems of any severe accident that assumes successful reactor shutdown. SNL plans to conduct best-estimate calculations and uncertainty analyses using MELCOR and MACCS for several reactors, including units 1 - 3 of Fukushima Daiichi, which entail accurate and technically-scrutable information for the input models. The SNL methodology for generating radionuclide and decay power inputs directly from SCALE6 calculations is mostly presented in the context of supporting Fukushima modeling efforts. Furthermore, certain historical assumptions used by MELCOR and MACCS in the abstraction of radionuclide classes and their properties are reviewed, and these assumptions are compared to modern code predictions by SCALE6.

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Sandia National Laboratories (SNL) plans to conduct uncertainty analyses (UA) on the Fukushima Daiichi unit (1F1) plant with the MELCOR code. The model to be used was developed for a previous accident reconstruction investigation jointly sponsored by the US Department of Energy (DOE) and Nuclear Regulatory Commission (NRC). However, that study only examined a handful of various model inputs and boundary conditions, and the predictions yielded only fair agreement with plant data and current release estimates. The goal of this uncertainty study is to perform a focused evaluation of uncertainty in core melt progression behavior and its effect on key figures-of-merit (e.g., hydrogen production, vessel lower head failure, etc.). In preparation for the SNL Fukushima UA work, a scoping study has been completed to identify important core melt progression parameters for the uncertainty analysis. The study also lays out a preliminary UA methodology.

This paper describes the knowledge advancements from the uncertainty analysis for the State-of- the-Art Reactor Consequence Analyses (SOARCA) unmitigated long-term station blackout accident scenario at the Peach Bottom Atomic Power Station. This work assessed key MELCOR and MELCOR Accident Consequence Code System, Version 2 (MACCS2) modeling uncertainties in an integrated fashion to quantify the relative importance of each uncertain input on potential accident progression, radiological releases, and off-site consequences. This quantitative uncertainty analysis provides measures of the effects on consequences, of each of the selected uncertain parameters both individually and in interaction with other parameters. The results measure the model response (e.g., variance in the output) to uncertainty in the selected input. Investigation into the important uncertain parameters in turn yields insights into important phenomena for accident progression and off-site consequences. This uncertainty analysis confirmed the known importance of some parameters, such as failure rate of the Safety Relief Valve in accident progression modeling and the dry deposition velocity in off-site consequence modeling. The analysis also revealed some new insights, such as dependent effect of cesium chemical form for different accident progressions. (auth)

In response to the accident at the Fukushima Daiichi nuclear power station in Japan, the U.S. Nuclear Regulatory Commission and US. Department of Energy agreed to jointly sponsor an accident reconstruction study as a means of assessing the severe accident modeling capability of the MELCOR code and developing an understanding of the likely accident progression. Objectives of the project included reconstruction of the accident progressions using computer models and accident data, and validation of MELCOR and the Fukushima models against plant data. In this study Sandia National Laboratories developed MELCOR 2.1 models of Fukushima Daiichi Units 1 (IFI), 2, and 3 as well as the Unit 4 spent fuel pool. This paper reports on the analysis of the 1F1 accident. Details are presented on the modeled accident progression, hypothesized mode of failures in the reactor pressure vessel (RPV) and containment pressure boundary, and release of fission products to the environment. The MELCOR-predicted RPV and containment pressure trends compare well with available measured pressures. Conditions leading up to the observed explosion of the reactor building are postulated based on this analysis where drywell head flange leakage is thought to have led to accumulation of flammable gases in the refueling bay. The favorable comparison of the results from the analyses with the data from the plant provides additional confidence in MELCOR to reliably predict real-world accident progression. The modeling effort has also provided insights into future data needs for both model development and validation.

Developing a big picture understanding of a severe accident is extremely challenging. Operating crews and emergency response teams are faced with rapidly evolving circumstances, uncertain information, distributed expertise, and a large number of conflicting goals and priorities. Severe accident management guidance (SAMGs) provides support for collecting information and assessing the state of a nuclear power plant during severe accidents. However, SAMGs developers cannot anticipate every possible accident scenario. Advanced Probabilistic Risk Assessment (PRA) methods can be used to explore an extensive space of possible accident sequences and consequences. Using this advanced PRA to develop a decision support system can provide expanded support for diagnosis and response. In this paper, we present an approach that uses dynamic PRA to develop risk-informed "Smart SAMGs". Bayesian Networks form the basis of the faster-than-real-time decision support system. The approach leverages best-available information from plant physics simulation codes (e.g., MELCOR). Discrete Dynamic Event Trees (DDETs) are used to provide comprehensive coverage of the potential accident scenario space. This paper presents a methodology to develop Smart procedures and provides an example model created for diagnosing the status of the ECCS valves in a generic iPWR design.

Data, a brief description of key boundary conditions, and results of Sandia National Laboratories’ ongoing MELCOR analysis of the Fukushima Unit 2 accident are given for the reactor core isolation cooling (RCIC) system. Important assumptions and related boundary conditions in the current analysis additional to or different than what was assumed/imposed in the work of SAND2012-6173 are identified. This work is for the U.S. Department of Energy’s Nuclear Energy University Programs fiscal year 2014 Reactor Safety Technologies Research and Development Program RC-7: RCIC Performance under Severe Accident Conditions.

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In response to the accident at the Fukushima Daiichi nuclear power station in Japan, the US Nuclear Regulatory Commission and Department of Energy agreed to jointly sponsor an accident reconstruction study as a means of the assessing severe accident modeling capability of the MELCOR code. Objectives of the project included reconstruction of the accident progressions using computer models and accident data, and validation of the MELCOR code and the Fukushima models against plant data.