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High-fidelity simulations of the run-in process for a pebble-bed reactor

Pebble-bed reactors (PBRs) rely on a continual feed of fuel pebbles being cycled through the core. As a result, they require a “run-in” period in order to reach an equilibrium state. The run-in period for a PBR is a complex, time-dependent problem that requires the injection of new fuel, different types of fuel, and power increases. This complexity in the run-in makes it important to capture the physical processes in order to generate an accurate representation. The present work details the creation of a high-fidelity Monte Carlo methodology for analyzing the run-in and subsequent approach to equilibrium for PBRs. The methodology entails a Python module wrapped around Serpent so as to perform neutronics calculations, move pebbles, refuel the core, and discharge pebbles, thereby modeling the explicit behavior of the PBR run-in. Further, three run-in simulations (a constant temperature profile, a linear temperature profile, and a constant temperature profile using control rods) were examined in order to identify the key physical phenomena present in the run-in process. Utilizing kugelpy, we found the inclusion of a temperature profile to be important for accurately capturing a discharge burnup (around 141 MWd/kg), a consistent k-eff (around 1.005), and an average pebble power (around 2.5 kW/pebble) that all fall within acceptable limits.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Capturing the run-in of a pebble-bed reactor by using thermal feedback and high-fidelity neutronics simulations

Modeling the run-in of a pebble-bed reactor (PBR) can be challenging as a result of changes in the power, fuel type, and temperatures that occur throughout the run-in period. Previous work utilized high-fidelity neutronics simulations or lower-fidelity coupled neutronics/thermal-hydraulics models to capture the general characteristics of the run-in process. Here, the present work employs high-fidelity neutronics simulations (using Serpent) coupled with thermal-hydraulics simulations (using Griffin–Pronghorn) to capture the thermal feedback present during the run-in and approach to equilibrium for a PBR. Incorporating thermal feedback enables important distinctions to be made about conditions occurring inside the core, as the power distribution, discharge burnup, and isotopic compositions are all affected by the temperature distribution.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Cyclic Run-In of Interlocking Metasurfaces

Interlocking metasurfaces were mechanically latched and unlatched for 500 cycles in order to see how they break-in with use. Different surface manufacture conditions were used in this work. Various characterization criteria were used to identify differences.

Garber, Kevin Wayne [Sandia National Laboratories ↗

Parameter Study of the Running-In Process for the Generic Pebble Bed Reactor (GPBR200)

The run-in period of a pebble bed reactor is complex and difficult to model given significant heterogeneity in core composition, power, and temperature. While it is understood that the initial composition of the core should eventually result in the same equilibrium core composition, the approach to equilibrium can vary significantly depending on factors such as start-up fuel enrichment and power ramp rate. To explore this, a high-fidelity model of the General Pebble Bed Reactor was used to vary power ramp schemes and start-up core compositions. It was found that both the initial core composition and power ramp rate had a significant impact on the flow rate of pebbles during early time steps, with higher ramp rates and low enrichment resulting in non-physical flow rates. Power ramp rate alone was found to dictated maximum pebble power peaking observed during the run-in process, with higher ramp rates resulting in greater peak pebble powers. Start-up fuel enrichment and power ramp were found to both impact total fuel consumption, although the impact of start-up fuel enrichment was generally secondary to ramp rate.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

Framework For Performing Time-dependent Pebble Bed Reactor Simulations

The present work details the creation of a high-fidelity Monte Carlo methodology for analyzing the run-in and subsequent approach to equilibrium for PBRs. The methodology entails a Python module wrapped around Serpent so as to perform neutronics calculations, move pebbles, refuel the core, and discharge pebbles, thereby modeling the explicit behavior of the PBR run-in. The code kugelpy is within the GitHub repository `pyrates`.

Stewart, RyanH. [Idaho National Laboratory (INL), ↗

Optimization Methodology of Pebble Bed HTGR Start-Up and Running-in Strategy

In recent years interest in advanced reactor technologies has increased significantly. However, the methods used for analysis of traditional nuclear reactors are insufficient to consider all the different and varied advanced reactor designs without further development. One promising advanced reactor design is the pebble bed reactor (PBR). PBRs possess unique operational and fuel cycle features that require the development of specific analysis methodologies to adequately design and analyze the systems. There is a need in PBR research for a capability to analyze and optimize the process of transitioning from the start-up reactor core to the equilibrium core (known as the “running-in” of the reactor). The start-up of a PBR and the transition to the equilibrium core is a complex, multi-physics challenge that has not yet been well researched and has many opportunities for design, analysis, and optimization of the process. In this research, a methodology is defined to consider the potential strategies in PBR start-up and run-in to the equilibrium core. Multiple candidate software are considered, and their pros and cons are discussed for the PBR-specific application in the methodology. A preliminary software selection for the physics engine is made, and initial verification of key modules is performed. Software to support optimization of the reactor run-in strategies through reduced order modeling (ROM) and machine learning are considered. Challenges for a full implementation of the methodology are discussed. Additional code selections and verification of models relevant to the application are needed before full demonstration of the methodology can be achieved and an optimal strategy determined.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Measuring characteristic differences between high- and low-performing discharges on the MegaJOuLe Neutron Imaging Radiography (MJOLNIR) DPF

A dense plasma focus (DPF) is a compact coaxial plasma gun, which completes its discharge as a Z-pinch, producing short (<100 ns) pulses of ions, x rays, and/or neutrons. Lawrence Livermore National Laboratory recently constructed and began operating a new device, the MJOLNIR (MegaJOuLe Neutron Imaging Radiography) DPF, which is designed for single-pulse flash neutron radiography. This device has achieved neutron yields of up to 4.1×10 11 neutrons/pulse at 3.3 MA peak current, and higher-current commissioning is under way. Like most DPFs, MJOLNIR exhibits variable yields in some configurations. Here, we present evidence of the role of parasitic current paths within the gun in stochastically influencing the yield. First through “conditioning shots,” where new hardware has been introduced, we show that increased run-down and run-in speeds correlate with higher yields. These observations are consistent with current being delivered to the electrodes but not to the main plasma sheath, degrading the implosion-driving force. Once nominal conditions are established, we correlate low-performing discharges with smaller current dip and associated voltage spike for a fixed machine configuration. A snow-plow model is able to recreate small-magnitude current dips through the introduction of a parasitic current path, and particle-in-cell simulations establish how parasitic current paths lower the ion beam energy available to produce neutrons. Finally, we observe an increased likelihood of shots with low yield and smaller current dip with increasing fill pressure.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Effects of Testosterone Supplementation on Ghrelin and Appetite During and After Severe Energy Deficit in Healthy Men

Abstract Background Severe energy deficits cause interrelated reductions in testosterone and fat free mass. Testosterone supplementation may mitigate those decrements, but could also reduce circulating concentrations of the orexigenic hormone ghrelin, thereby exacerbating energy deficit by suppressing appetite. Objective To determine whether testosterone supplementation during severe energy deficit influences fasting and postprandial ghrelin concentrations and appetite. Design and methods Secondary analysis of a randomized, double-blind trial that determined the effects of testosterone supplementation on body composition changes during and following severe energy deficit in nonobese, eugonadal men. Phase 1 (PRE-ED): 14-day run-in; phase 2: 28 days, 55% energy deficit with 200 mg testosterone enanthate weekly (TEST; n = 24) or placebo (PLA; n = 26); phase 3: free-living until body mass recovered (end-of-study; EOS). Fasting and postprandial acyl ghrelin and des-acyl ghrelin concentrations and appetite were secondary outcomes measured during the final week of each phase. Results Fasting acyl ghrelin concentrations, and postprandial acyl and des-acyl ghrelin concentrations increased in PLA during energy deficit then returned to PRE-ED values by EOS, but did not change in TEST (phase-by-group, P < 0.05). Correlations between changes in free testosterone and changes in fasting acyl ghrelin concentrations during energy deficit (ρ = -0.42, P = 0.003) and body mass recovery (ρ = -0.38; P = 0.01) were not mediated by changes in body mass or body composition. Transient increases in appetite during energy deficit were not affected by testosterone treatment. Conclusions Testosterone supplementation during short-term, severe energy deficit in healthy men prevents deficit-induced increases in circulating ghrelin without blunting concomitant increases in appetite. Clinical Trials Registration www.clinicaltrials.gov NCT02734238 (registered 12 April 2016).

Karl, J. Philip↗

Utah FORGE: Fault Shear Reactivation Experimental Data for Fluid Injection-Rate Controls on Seismic Moment

Included are experimental data recorded from shear experiments that specifically explore the link between fluid-injection rate and seismic moment resulting from shear reactivation of laboratory faults. Raw mechanical data from three experiments are included alongside corresponding MATLAB scripts that import and plot the data, as well as use it to calculate shear and normal stress. Experiments are performed on 2.5-3 inch long granitoid cores from the Utah FORGE EGS demonstration site, containing a single inclined fracture with small-scale roughness added to the fracture surface. The raw data included here were recorded from an aluminum triaxial pressure vessel (TEMCO) configured with three independent servo-controlled pumps, with distilled water used as the working fluid. The pumps control confining pressure, upstream pore pressure, and axial pressure, with each pump connected to a LabView interface to record applied pressures, cumulative injected water volumes, and pump flow rates. The downstream outlet from the fracture is closed to allow pressurization, which is measured by an external pressure transducer. A linear variable differential transformer (LVDT) attached to the axial piston measures axial displacement, from which we calculate shear displacement along the fracture. Additionally, P-wave transducers are used to record acoustic signatures, where acoustic emission events and maximum amplitudes are compared against seismic moment and shear slip velocity. Fluid injection rates range between 0.05 mL/min, 0.25 mL/min, and 0.75 mL/min for each experiment. Along-fault pressure distributions are progressively less uniform as injection rates increase, representing a switch from steady-state to transient conditions. Triggered shear displacement is used as a proxy for seismic moment and is indexed against cumulative injection volume and rate. Each experiment is performed under constant shear stress conditions, and the sample is fully saturated with DI water. Axial and confining stresses are applied to 3 MPa through pressure-stepping in 500 kPa increments. The pore pressure is held constant at 200 kPa prior to initiating the experiment, and initial axial displacement is recorded. The axial stress is then increased to initiate shear mobilization during the loading phase (run-in) until a peak steady state is achieved. The initial shear stress is reduced to approximately 80% of the peak shear stress by decreasing the axial stress, then held constant for the duration of each experiment.

15 GEOTHERMAL ENERGY↗

Griffin Software Development Plan

Griffin is a MOOSE-based reactor physics application for advanced reactor multiphysics modeling and simulation. The application is developed in a consistent multiphysics environment with strong software quality assurance. Griffin inherited most of the capabilities of MAMMOTH/Rattlesnake and is adopting the capabilities from PROTEUS that are needed in the code. The toolset includes a variety of deterministic radiation transport solvers for fixed source, k-eigenvalue, ad-joint, and subcritical multiplication, as well as transient solvers for point-kinetics, improved quasi-static, and spatial dynamics. The code contains the cross-section preparation capabilities applicable to fast and thermal reactors, including TRISO-fueled reactors. Core management capabilities include core performance, fuel depletion and shuffling, equilibrium core calculation, pebble-bed reactor run-in and equilibrium core, molten-salt reactor delayed neutron precursor drift, and control rod and drum movement with cusping correction. This software development plan presents the current and future capabilities and features in Griffin for the design and analysis of non-light-water reactor systems in steady-state and transient conditions.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗