Software Requirements Specification for RELAP-7
This document contains the software requirements for the RELAP-7 code.
97 MATHEMATICS AND COMPUTING↗
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RELAP-7 Software Verification and Validation Plan: Requirements Traceability Matrix (RTM) Update and Code Verification Strategy
This document describes the recent progress of the RELAP-7 Software Verification and Validation Plan (SVVP). The RELAP-7 RTM has been updated by adding the validation requirement items, rearranging the requirement items, and introducing the classifier to better describe the RELAP-7 V&V status at a specified point in time. Also, from the perspective of nuclear system code validation, six items from the list of LWR safety-relevant phenomena were identified as validation data gaps. Lastly, motivated by the needs revealed from the previous RELAP-7 IV&V activities, the independent verification plan for RELAP-7 has been established. The verification method, procedure, and verification test coverage suite discussed in this document will be applied to the future work for the RELAP-7 independent verification.
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Additional Model Datasets and Results to Accelerate the Verification and Validation of RELAP-7
The RELAP-7 code verification and validation activities are ongoing under the code assessment plan proposed in the previous document (INL-EXT-16-40015). Among the list of V&V test problems in the ‘RELAP-7 code V&V RTM (Requirements Traceability Matrix)’, the RELAP-7 7-equation model has been tested with additional demonstration problems and the results of these tests are reported in this document. In this report, we describe the testing process, the test cases that were conducted, and the results of the evaluation.
97 MATHEMATICS AND COMPUTING↗
Validation of RELAP-7 for Forced Convective and Natural Circulation Reactor Flows
New reactor designs such as small modular reactors and newly proposed passive safety systems rely on natural circulation two-phase flow cooling of the reactor core under accident conditions. Without a pump to drive the flow, the circulation rate is a strong function of void fraction. Under these conditions where the flow rate, pressure drop, and heat transfer are all closely coupled through the mixture density, the accurate prediction of void fraction is pivotal. Furthermore, while these passive systems boast a more reliable cooling option, the sensitivity of the system to two-phase flow instabilities is more likely than in forced convective cooling and can result in dangerous density wave or flow excursion conditions. Improvement of one-dimensional system codes and further development of three-dimensional code capability are all important efforts in addressing these issues. The proposed work is a critical step towards providing confidence through validation of the capability of thermal-hydraulic tools to meet the emerging challenges in reactor simulation. Historically, system analysis codes develop a stiffness over time due to fine-tuning of adjustable code parameters that result in a reluctance or inability to adopt improved models. Therefore, a rigorous effort should be invested in preparation for RELAP-7 to identify required modeling improvements of constitutive relations and validation of the code applicability to the wide range of possible reactor flow conditions. Part of this validation involves the uncertainty quantification which is a critical step in the Code Scaling, Applicability, and Uncertainty (CSAU) methodology and Best-Estimate Plus Uncertainty (BEPU) licensing method. BEPU allows recovery of safety margins and potentially optimizing plant performance, resulting in economic benefit for the reactor operators. Beginning with the collection of past experimental data covering prototypic LWR conditions, this document reviews three critical datasets available for the validation of system codes. The experimental methods, procedures, facilities, instrumentation, and supplementary information are consolidated to aid the validation efforts of this work and future projects. These three datasets (824 conditions) are chosen due to their wide acceptance and demonstrated application for code validation. Two experimental datasets from the Nuclear Power Engineering Corporation (NUPEC) span prototypic conditions of Boiling Water Reactors (BWR) and Pressurized Water Reactors (PWR). These datasets are labeled as the BFBT (BWR Full-size Fine-mesh Bundle Test) and the PSBT (PWR Subchannel and Bundle Test) datasets respectively. The third dataset is taken in an internally heated annulus that is scaled based on a BWR subassembly and includes two-phase flows important to post-blowdown analysis. Together, the datasets consider the full range of pressure and mass flux expected in LWR forced convective flows from steady-state to long term cooling conditions. Recognizing the deficiency of available data under low-pressure, low-flow, a new natural circulation dataset consisting of 107 conditions is obtained. With system pressures ranging between 145 and 950 kPa, natural circulation is driven by a heat flux up to 275 kW/m2 in a 3 m long heated region of the annulus test section which is followed by a 2 m long unheated region before entering a condenser. Stable natural circulation flow rates in this facility were measured to be between 182 and 590 kg/m2-s. Pressure and temperature are measured at ten locations in the facility. In addition, measurements of void fraction, interfacial area concentration, and gas velocity are obtained at five axial locations in the annulus test section. The RELAP5-3D code demonstrates good accuracy for void fraction predictions in BFBT and PSBT benchmarks, but shows relatively large discrepancy in the annulus experiments at lower pressures. In two-phase natural circulation, large oscillations are observed in many system parameters during the simulation of 19 experiment cases which were conducted at low pressures, and the model shows good predictive performance with the remaining 88 experiment cases. However, some discrepancies in void fraction and temperature predictions are found which may require improved constitutive modeling under these low-pressure, low-flow condition. The quantified uncertainty in void fraction predictions in three forced convection benchmarks due to uncertainties in boundary conditions and geometries are relatively small, except for a few cases in the annulus experiment. The output uncertainty of the natural circulation model is quantified by considering the measurement error in boundary conditions, and it is shown that the output uncertainty ranges are relatively small for the range of conditions considered. Through this work, a clear pathway is provided for validation of RELAP7 once the code is available.
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Advanced Steam-Electric Power Cycle for Advanced Reactor Concepts
This report presents improvements made to RELAP-7 for modeling Rankine power cycles, which are used on the secondary side of some advanced reactor concepts. For example, for a High-Temperature gas-cooled Reactor Pebble bed Module (HTR-PM), in the primary loop, helium transfers heat from the fuel to a steam generator, and on the secondary side, dry steam from the steam generator applies work to a turbine, which is converted into electrical power by a generator. After passing through the turbine, the steam passes through a condenser and pumped to the steam generator inlet, completing a Rankine cycle on the secondary side. This HTR-PM concept is used as an example to demonstrate a steam-electric power cycle capability in RELAP-7. This power cycle requires a number of different physical components, including a pump, compressor, turbine, steam generator, and condenser. The steam generator is modeled using a heat exchange between primary and secondary loops, and the condenser is modeled using a cooling source term. Recent efforts demonstrated preliminary models for the turbomachinery components (pump/compressor and turbine), but more physical models were required. Furthermore, several improvements were made for the robustness of mixed single-phase/two-phase flow present in a Rankine cycle.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Application of the new IAPWS Guideline on the fast and accurate calculation of steam and water properties with the Spline-Based Table Look-Up Method (SBTL) in RELAP-7
The numerical simulation of thermalhydraulic processes in nuclear power plants requires very accurate and extremely fast algorithms for calculating the thermophysical properties of water and steam. In order to provide such algorithms, the International Association for the Properties of Water and Steam (IAPWS) has adopted the new “IAPWS Guideline on the Fast Calculation of Steam and Water Properties with the Spline-Based Table Look-Up Method (SBTL)”. In this article, the SBTL method is applied to property functions of specific volume and specific internal energy (v,e) based on the scientific formulation IAPWS-95 and the latest IAPWS formulations for transport properties. From the newly generated SBTL functions, thermodynamic and transport properties as well as their derivatives and inverse functions are calculable in the fluid range of state for pressures up to 100 MPa and for temperatures up to 1273 K, including the metastable liquid and the metastable vapor regions. The SBTL functions reproduce the underlying formulations with an accuracy of 10–100 ppm and significantly reduced computing times. The SBTL method has been implemented into the nuclear reactor system safety analysis code RELAP-7 [2] to consider the real fluid behavior of water and steam in a novel 7-equation two-phase flow model.
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Simulations of single- and two-phase shock tubes and gravity-driven wave problems with the RELAP-7 nuclear reactor system analysis code
Propagation of pressure waves in single and two-phase flows is of key interest for nuclear safety analyses, reactor design, construction and life extension of nuclear reactors. In this work, we assess the capabilities of RELAP-7, the next-generation nuclear reactor system code, to solve single- and two-phase flows developing pressure waves. RELAP-7 is based on a 7-equation, two-phase flow model, with distinct phasic pressures. It is well established that these hyperbolic conservation laws can develop shocks and discontinuities and thus, require a stabilization numerical method. The all-Mach flow Entropy Viscosity Method (EVM) is now employed in RELAP-7 as a viscous stabilization approach. In this paper, we briefly describe the implementation of the EVM and use open-literature test cases that exhibit pressure waves to show that the entropy viscosity method performs adequately for these single- and two-phase cases.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Two-zone stratified wetwell model development and implementation for RELAP-7
The Reactor Core Isolation Cooling (RCIC) system consists of a turbine, pump and wetwell and is used in many US Boiling Water Reactors (BWRs) as an important heat removal system. The wetwell is a major heat sink within containment. Experimental investigations have shown that thermal stratification may occur in the wetwell during RCIC operation. Current systems-level analysis codes lack stratified wetwell models. Here, this paper describes the development of a stratified wetwell model for the RELAP-7 code. This paper uses a simple two-zone model for the wetwell water that can capture thermal stratification. This model assumes that a buoyant plume transports heat and mass from the steam injection site to the upper water zone. The buoyant plume assumption requires that steam is injected at a low enough rate so it adds negligible momentum. Model validation against experimental data shows similar trends of temperature field development in the suppression pool.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
RAVEN User Manual
RAVEN is a generic software framework to perform parametric and probabilistic analy- sis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncer- tainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is in- terested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assess- ment capability to RELAP-7, currently under develop-ment at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explaination of the input requirements, focalizing on the input structure.
97 MATHEMATICS AND COMPUTING↗
RAVEN User Manual
RAVEN is a generic software framework to perform parametric and probabilistic analy- sis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncer- tainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is in- terested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assess- ment capability to RELAP-7, currently under develop-ment at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explaination of the input requirements, focalizing on the input structure.
97 MATHEMATICS AND COMPUTING↗
RAVEN User Manual
RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assessment capability to RELAP-7, currently under development at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explanation of the input requirements, focalizing on the input structure.
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Implementation of Depletion Architecture in the MAMMOTH Reactor Physics Application
Nuclear fuel experiments conducted at the Transient Reactor Test Facility (TREAT) will be analyzed with the MAMMOTH reactor physics application, currently under development at Idaho National Laboratory. MAMMOTH natively couples with the BISON, RELAP-7, and Rattlesnake applications within the MOOSE framework. The methods currently implemented allow for simulating the irradiation of fuel from the beginning of life in a nuclear reactor until it is placed in TREAT for fuel testing using the same finite element mesh throughout the analysis thereby retaining a very high level of resolution and fidelity. The calculation of the isotopic distribution in fuel requires the solution to the decay and transmutation equations coupled to the neutron transport equation. This work focuses on two significant improvements to the MAMMOTH micro-depletion capability. First, the depletion architecture within MAMMOTH is modified to improve the flexibility and speed of burnup calculations. The results of various benchmark tests demonstrate the accuracy of the modified implementation. The second improvement involves the expansion of the data functionality. MAMMOTH can now read the decay data files used by the Oak Ridge Isotope Generation (ORIGEN) code, thus enabling access to data of more than 1,600 isotopes. Comparison of ORIGEN and MAMMOTH for a selected benchmark problem was performed. Both codes yield very similar results, with differences of 0.03% that can be attributed to differences in precision between the two codes. Future work will focus on comparisons between MAMMOTH and ORIGEN for more complex problems.
07 ISOTOPE AND RADIATION SOURCES↗
Architecture Improvements and Retesting of the Microscopic Depletion Implementation in the MAMMOTH Reactor Physics Application
Nuclear fuel experiments conducted at the Transient Reactor Test Facility (TREAT) will be analyzed with the MAMMOTH reactor physics application, currently under development at Idaho National Laboratory. MAMMOTH natively couples with the BISON, RELAP-7, and Rattlesnake applications within the MOOSE framework. The implemented methods allow for simulating the irradiation of fuel in a nuclear reactor from the beginning of life in a nuclear reactor until it is placed in TREAT for fuel testing using the same finite element mesh throughout the analysis retaining a very high level of resolution and fidelity. The calculation of the isotopic distribution in fuel requires the solution to the decay and transmutation equations coupled to the neutron transport equation. This report focuses on three significant improvements to the MAMMOTH micro-depletion capability. First, the depletion architecture within MAMMOTH is modified to improve the flexibility and speed of burnup calculations. The results of various benchmark tests demonstrate that the refactoring of the previously implemented capability does not introduce any regression. The second improvement involves the expansion of the data functionality. MAMMOTH can now read the decay data files used by the Oak Ridge Isotope Generation (ORIGEN) code, thus enabling access to data of more than 1,600 isotopes. The third improvement is the capability to perform depletion at a constant power level which was verified via a comparison to ORIGEN.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Automated Control for Nuclear Thermal Propulsion Start-Up using MOOSE-based Applications
This report presents a Griffin/Bison/RELAP-7 numerical model of a prototypical NTP system that features fuel assemblies arranged in rings, and which was designed to simulate rapid startup transients. The physics modeled include full-core neutronics, assembly-wise heat conduction, and conjugate heat transfer, with the balance of plant mainly imposed through boundary conditions. In addition, various forms of automated reactivity control were deployed by using the MOOSE to autonomously drive the model and simulate the reactor transitioning from assumed initial conditions to nominal power in a fraction of a minute. To generate the cross-sections of the neutronics model, and in an effort to simultaneously account for the tremendous axial temperature gradients in the reactor and to limit the number of state points required for cross-section generation, the average component temperatures and hydrogen densities in the cooling channels were correlated to the average fuel and moderator temperatures, and fixed axial profiles were derived for nominal conditions and then used during the transient. With this approximation, a tractable cross-section library tabulated with fuel/moderator temperatures and CD angles was generated using Serpent. The full-core SPH correction procedure and the CD decusping technology in Griffin, respectively, ensure preservation of the multiplication factor and reaction rates at state points, along with a reasonably accurate reactivity worth between tabulated CD angles, despite using a coarse mesh. Feedback from other physics was calculated by modeling one representative fuel assembly per ring, along with the corresponding fuel and moderator cooling channels. To limit power overshoots during startup, another layer of multiphysics coupling was added to the model in order to automatically control the drums. Two different technologies presented herein showed outstanding performance in this regard: (1) a novel hybrid PID controller based on both power and reactivity signals, and (2) a PGC that relies on kinetics parameters and reactivity coefficients to predict future behavior and adjust the desired signal accordingly. A challenging benchmark was devised, featuring a power demand curve that exponentially increases by a factor of 500 within 30 seconds, then levels out after that. Both control approaches create a simulated power curve that closely follows the power demand curve and limits power overshoots to 1% or less. While the former approach requires more tuning of the internal parameters, the latter requires additional knowledge of the reactivity feedback coefficients and rates of change of the corresponding variables, including fuel and moderator temperature, which could be difficult to dynamically measure for a real NTP system. Fortunately, some inaccuracy in these quantities will not drastically degrade the PGC performance. Subsequently, a more realistic startup sequence was considered, in which the mass flow rate and outlet pressures are ramped up to model bootstrap and thrust build-up phases prior to reaching steady-state conditions, demonstrating the ability of the hybrid PID and PGCs to handle such transients, with both types of controllers exhibiting very similar behavior. Nevertheless, a significant chamber temperature overshoot was observed, caused by the demanded power signal and assumed mass flow rate. This issue could be mitigated by deploying a reactor controller that follows the chamber temperature signal and actuates both the control valves and drums (rather than using a power signal based solely on the drums to control reactivity). Enhancement of the hydrogen fluid properties available in MOOSE, as well as a better understanding of prototypical initial conditions, are also needed to further enhance this startup model. Finally, a study was performed to model decay heat post-shutdown, and to prepare for extending this model to predict shutdown behavior and post-shutdown pulsed cooling requirements.
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Development of Segregated Thermal-Hydraulics Solvers in MOOSE
The simulation of fluid flows is an essential part of the design and analysis of nuclear systems. Algorithms able to simulate flows at different fidelity levels are available in the Multiphysics Object-Oriented Simulation Environment (MOOSE) and MOOSE-based applications such as Pronghorn \cite{novak2018pronghorn}, Pronghorn-Subchannel, RELAP-7, and SAM. Currently, significant effort is being invested in the development of coarse-mesh Computational Fluid Dynamics (CFD) capabilities within MOOSE and Pronghorn for the simulation of Generation IV nuclear reactors. Traditionally, the solution algorithms in MOOSE have relied on Newton or quasi-Newton methods (such as the preconditioned Jacobian-free Newton-Krylov method) where residuals and Jacobians (or approximations thereof) are constructed. Both Newton and quasi-Newton methods require the solution of a linear system at each nonlinear Newton iteration with the Jacobian as the system matrix. The Jacobian contains blocks originating from all variables in the problem (i.e., for thermal-hydraulics at least pressure, velocities, and temperature). Due to the formulation of the problem in a general multiphysics setting on unstructured mesh, creating a good preconditioner for the linear system can be challenging, thus many fluid applications have utilized direct solver-based methods such as LU factorization. However, with increasing system size and complexity in multi-dimensional problems, the direct solution of linear systems becomes computationally expensive both in execution time and and memory. For this reason, recent effort has focused on adapting segregated solution algorithms for CFD problems in MOOSE. These algorithms use fixed-point iteration between segregated systems whose assembly and preconditioning are easier those of the monolithic system. Initial results show that the segregated solution algorithm outperforms the monolithic approach in terms of memory usage and for large 3D problems in terms of CPU time as well.
42 ENGINEERING↗
Development of Segregated Thermal-Hydraulics Solvers in MOOSE
The simulation of fluid flows is an essential part of the design and analysis of nuclear systems. Algorithms able to simulate flows at different fidelity levels are available in the Multiphysics Object-Oriented Simulation Environment (MOOSE) and MOOSE-based applications such as Pronghorn \cite{novak2018pronghorn}, Pronghorn-Subchannel, RELAP-7, and SAM. Currently, significant effort is being invested in the development of coarse-mesh Computational Fluid Dynamics (CFD) capabilities within MOOSE and Pronghorn for the simulation of Generation IV nuclear reactors. Traditionally, the solution algorithms in MOOSE have relied on Newton or quasi-Newton methods (such as the preconditioned Jacobian-free Newton-Krylov method) where residuals and Jacobians (or approximations thereof) are constructed. Both Newton and quasi-Newton methods require the solution of a linear system at each nonlinear Newton iteration with the Jacobian as the system matrix. The Jacobian contains blocks originating from all variables in the problem (i.e., for thermal-hydraulics at least pressure, velocities, and temperature). Due to the formulation of the problem in a general multiphysics setting on unstructured mesh, creating a good preconditioner for the linear system can be challenging, thus many fluid applications have utilized direct solver-based methods such as LU factorization. However, with increasing system size and complexity in multi-dimensional problems, the direct solution of linear systems becomes computationally expensive both in execution time and and memory. For this reason, recent effort has focused on adapting segregated solution algorithms for CFD problems in MOOSE. These algorithms use fixed-point iteration between segregated systems whose assembly and preconditioning are easier those of the monolithic system. Initial results show that the segregated solution algorithm outperforms the monolithic approach in terms of memory usage and for large 3D problems in terms of CPU time as well.
42 ENGINEERING↗
Advancement of Functional Expansion Tallies Capabilities in Serpent 2
The need for accident tolerant fuels (ATFs) became self-evident after the series of unfortunate nuclear events at Fukushima Daiichi in 2011. Restarting the Transient Reactor Test Facility (TREAT) at INL is one component in this concerted effort to establish the ATF development and testing program under the United States Department of Energy. Prior to the closure of TREAT in 1994, each experiment required multiple low-power calibration tests before and after each run. Since then, computing advancements have set computer-based modeling and simulation (M and S) as the preferred calibration method. The anticipated savings are both temporal and monetary. MAMMOTH, a collection of M and S tools built from the finite element-based MOOSE Framework, has been established to support the designed experiments intended for TREAT. This collection of tools includes: 1) Rattlesnake, a striking diffusion-based neutronics code, 2) BISON, an advanced materials and fuels performance code, and 3) RELAP-7, a powerful thermal-hydraulics solver. Each is capable of independently performing its function accurately and effectively; unified, they create a powerful multiphysics reactor core M and S application. One unfortunate weakness in this configuration lies in the unique geometry and design of TREAT's core. Although simple to model, this poses a challenge to the diffusion solver in Rattlesnake. One alternative is to replace Rattlesnake with Serpent, a fully-fledged Monte Carlo (MC) neutronics code. Coincidentally, Rattlesnake calculations are frequently validated against Serpent simulations.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Automated power-following control for nuclear thermal propulsion startup and shutdown using MOOSE-based applications
This paper describes an investigation of various automated control strategies applied to a full-core multiphysics Griffin/Bison/RELAP-7 model of a prototypical nuclear thermal propulsion system. In all cases, control is achieved by actuating control drums based on the demanded power and predicted quantities from the numerical model. One key finding is that hybrid proportional integral derivative controllers – a novel type of controller that uses both power and reactivity predicted signals – can demonstrate a level of performance rivaling that of period-generated control. The former requires parameter tuning, while the latter mostly necessitates providing reactivity coefficients and temperature rates of change, which could be very challenging to accurately measure in real-time. In addition, decay heat plays an important role in determining cooling requirements during cooldown phases. A decay heat model, accounting for burn time and throttling, was derived, and is incorporated within the model to simulate the steady-state and shutdown phases and satisfyingly follow the power demand. However, temperature overshoots and non-constant specific impulse during throttling will necessitate further improvements.
33 ADVANCED PROPULSION SYSTEMS↗