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After approximately 50 years, NASA is restarting efforts to develop nuclear thermal propulsion (NTP) for interplanetary missions. Building upon nuclear engine tests performed from the late 1950s to the early 1970s, the present research and testing focuses on advanced materials and fabrication methods. A number of transient tests have been performed to evaluate materials performance under high-temperature, high-flux conditions, with several more experiments in the pipeline for future testing. The measured data obtained from those tests are being used to validate the Griffin reactor multiphysics code for this particular type of application. Griffin was developed at Idaho National Laboratory (INL) using the MOOSE framework. This article describes the simulation results of the SIRIUS-CAL calibration experiment in the Transient Reactor Test Facility (TREAT). SIRIUS-CAL was the first transient test conducted on NASA fuels, and although the test was performed with a relatively low core peak power, the test specimen survived a temperature exceeding 900 K. Griffin simulations of the experiment successfully matched the reactor’s power transient after calibrating the initial control rod position to match the initial reactor period. The thermal-hydraulics model largely matches the time-dependent response of a thermocouple located within the experiment specimen to within the uncertainty estimate. However, the uncertainty range is significant and must be reduced in the future.

High temperature gas cooled reactors (HTGR) are a candidate for timely Gen-IV reactor technology deployment because of high technology readiness and walk-away safety. Among HTGRs, pebble bed reactors (PBRs) have attractive features such as low excess reactivity and online refueling. Pebble bed reactors pose unique challenges to analysts and reactor designers such as continuous burnup distribution depending on pebble motion and recirculation, radiative heat transfer across a variety of gas-filled gaps, and long design basis transients such as pressurized and depressurized loss of forced circulation. Modeling and simulation is essential for both the PBR’s safety case and design process. In order to verify and validate the new generation codes the Nuclear Energy Agency (NEA) Data bank provide a set of benchmarks data together with solutions calculated by the participants using the state of the art codes of that time. An important milestone to test the new PBR simulation codes is the OECD NEA PBMR-400 benchmark which includes thermal hydraulic and neutron kinetic standalone exercises as well as coupled exercises and transients scenarios. In this work, the reactor multiphysics code MAMMOTH and the thermal hydraulics code Pronghorn, both developed by the Idaho National Laboratory (INL) within the multiphysics object-oriented simulation environment (MOOSE), have been used to solve Phase 1 exercises 1 and 2 of the PBMR-400 benchmark. The steady state results are in agreement with the other participants’ solutions demonstrating the adequacy of MAMMOTH and Pronghorn for simulating PBRs.

In this work we develop a quantitative decision metric for spatial discretization methods of the SN equations. The quantitative decision metric utilizes performance data from selected test problems for computing a fitness score that is used for the selection of the most suitable discretization method for a particular SN transport application. The fitness score is aggregated as a weighted geometric mean of single performance indicators representing various performance aspects relevant to the user. Thus, the fitness function can be adjusted to the particular needs of the code practitioner by adding/removing single performance indicators or changing their importance via the supplied weights. Within this work a special, broad class of methods is considered, referred to as nodal methods. This class is naturally comprised of the DGFEM methods of all function space families. Within this work it is also shown that the Higher Order Diamond Difference (HODD) method is a nodal method. Building on earlier findings that the Arbitrarily High Order Method of the Nodal type (AHOTN) is also a nodal method, a generalized finite-element framework is created to yield as special cases various methods that were developed independently using profoundly different formalisms. A selection of test problems related to a certain performance aspect are considered: an Method of Manufactured Solutions (MMS) test suite for assessing accuracy and execution time, Lathrop’s test problem for assessing resilience against occurrence of negative fluxes, and a simple, homogeneous cube test problem to verify if a method possesses the thick diffusive limit. The contending methods are implemented as efficiently as possible under a common SN transport code framework to level the playing field for a fair comparison of their computational load. Numerical results are presented for all three test problems and a qualitative rating of each method’s performance is provided for each aspect: accuracy/efficiency, resilience against negative fluxes, and possession of the thick diffusion limit, separately. The choice of the most efficient method depends on the utilized error norm: in Lp error norms higher order methods such as the AHOTN method of order three perform best, while for computing integral quantities the linear nodal (LN) method is most efficient. The most resilient method against occurrence of negative fluxes is the simple corner balance (SCB) method. A validation of the quantitative decision metric is performed based on the NEA box-inbox suite of test problems. The validation exercise comprises two stages: first prediction of the contending methods’ performance via the decision metric and second computing the actual scores based on data obtained from the NEA benchmark problem. The comparison of predicted and actual scores via a penalty function (ratio of predicted best performer’s score to actual best score) completes the validation exercise. It is found that the decision metric is capable of very accurate predictions (penalty < 10%) in more than 83% of the considered cases and features penalties up to 20% for the remaining cases. An exception to this rule is the third test case NEA-III intentionally set up to incorporate a poor match of the benchmark with the “data” problems. However, even under these worst case conditions the decision metric’s suggestions are never detrimental. Suggestions for improving the decision metric’s accuracy are to increase the pool of employed data, to refine the mapping of a given configuration to a case in the database, and to better characterize the desired target quantities.

Recent interest for the development of high-temperature gas reactors has increased the need for more advanced understanding of flow characteristics in randomly packed pebble beds. A proper understanding of these flow characteristics can provide a better idea of the cooling capabilities of the system in both normal operation and accident scenarios. In order to enhance the accuracy of computationally efficient, intermediate fidelity modeling, high-fidelity simulation may be used to generate correlative data. For this research, NekRS, a GPU-enabled spectral-element computational fluid dynamics code, was used in order to produce the high-fidelity flow data for beds of 1,568 and 45,000 pebbles. Idaho National Lab's Pronghorn porous media code was used as the intermediate fidelity code. The results of the high-fidelity model were separated into multiple concentric regions in order to extract porosity and velocity averages in each region. The porosity values were input into the Pronghorn model and the resulting velocity profile was compared with that from NekRS. Both cases were run with a Reynolds number of 20,000 based on pebble diameter. The Pronghorn results were found to significantly overestimate the velocity in the outermost region indicating that changes in the porosity alone do not cause the difference in fluid velocity. We conclude that further work is necessary to develop a more effective drag coefficient correlation for the near-wall region and improve predictive capabilities of intermediate fidelity models.

The development of nuclear reactors that utilize pebble fuel has drastically increased the demand for improving the capabilities to simulate the packed beds found in these reactors. The complex flow fields found in a pebble bed make computational fluid dynamics (CFD) simulations time consuming and costly. Intermediate fidelity porous media models, however, are capable of approximating these flow fields in a much more computationally efficient manner. These models require the use of closures to capture the effects of complex flow phenomena without modeling them explicitly. This research employs data obtained from high-fidelity CFD simulations of a pebble bed to improve the drag closures used in porous media models in the near-wall region of the bed. Specifically, NekRS, a GPU-enabled spectral element CFD code, was used to simulate a bed of 1,568 pebbles at multiple Reynolds numbers. The case was divided into five concentric subdomains to extract radial profiles of the average porosity, velocity, and wall shear in each subdomain. A model consistent with the high-fidelity model was created in Idaho National Laboratory's Pronghorn porous media code and the KTA correlation was chosen as the drag closure of comparison. It was found that the KTA correlation overestimates the velocity in the near-wall region. An investigation of the drag coefficients between the two codes revealed that the KTA correlation underestimated the form factor in the outermost region while overestimating it in the inner four regions. This analysis in this work has revealed the underlying inaccuracy in the near-wall region of the KTA correlation and has set up the process for using high-fidelity simulation to predict more accurate drag coefficients a priori, rather than with a manual velocity-matching approach. This process will allow for the development of an improved drag closure for use in porous media models.