Analysis of the Operational and Safety Features of the In-Core Bubbling System of the Molten Salt Fast Reactor

A foreseen feature of the Molten Salt Fast Reactor is the adoption of a bubbling system for the removal of gaseous and metallic fission products (FPs). This mechanism injects helium bubbles into the core to remove FPs from the salt through floating and mass transfer mechanisms for metallic and gaseous FPs, respectively. The present work is aimed at analyzing this helium bubbling system, focusing on gaseous FPs. We investigate both operational and safety-related features in order to get information useful for the design and to assess the convenience of its adoption. Accordingly, our investigations split into two strands: (1) analyzing the characteristics of the bubbling system itself and (2) assessing the safety features of the reactor in its presence. In order to perform the above analyses, we add the capability to simulate production, transport, and mass transfer of an arbitrary number of gaseous FPs to a preexisting multiphysics solver, built with the OpenFOAM suite. In terms of operational characterization, our analyses quantify the removal efficiency through a characteristic removal time and estimate the poisoning effect of gaseous FPs. In addition, we evaluate the activity and decay heat of the removed gas, which is an aspect crucial for the design of the off-gas unit, and the effect of the bubbling system on the power versus the fuel mass flow rate curve, which is a possible control mechanism. Among our safety-related studies, we first evaluate the void coefficient, determining upper bounds on the helium flow rate in order to avoid prompt supercriticality in case of prompt loss of helium injection. The latter accidental scenario is also analyzed considering the thermal-hydraulic dynamics of the system. We also discuss another accident: complete loss of helium removal

Nonlinear Dynamic Modeling, Simulation And Characterization Of The Mesoscale Neuron-electrode Interface

Extracellular neuroelectronic interfacing has important applications in the fields of neural prosthetics, biological computation and whole-cell biosensing for drug screening and toxin detection. While the field of neuroelectronic interfacing holds great promise, the recording of high-fidelity signals from extracellular devices has long suffered from the problem of low signal-to-noise ratios and changes in signal shapes due to the presence of highly dispersive dielectric medium in the neuron-microelectrode cleft. This has made it difficult to correlate the extracellularly recorded signals with the intracellular signals recorded using conventional patch-clamp electrophysiology. For bringing about an improvement in the signalto-noise ratio of the signals recorded on the extracellular microelectrodes and to explore strategies for engineering the neuron-electrode interface there exists a need to model, simulate and characterize the cell-sensor interface to better understand the mechanism of signal transduction across the interface. Efforts to date for modeling the neuron-electrode interface have primarily focused on the use of point or area contact linear equivalent circuit models for a description of the interface with an assumption of passive linearity for the dynamics of the interfacial medium in the cell-electrode cleft. In this dissertation, results are presented from a nonlinear dynamic characterization of the neuroelectronic junction based on Volterra-Wiener modeling which showed that the process of signal transduction at the interface may have nonlinear contributions from the interfacial medium. An optimization based study of linear equivalent circuit models for representing signals recorded at the neuron-electrode interface subsequently iv proved conclusively that the process of signal transduction across the interface is indeed nonlinear. Following this a theoretical framework for the extraction of the complex nonlinear material parameters of the interfacial medium like the dielectric permittivity, conductivity and diffusivity tensors based on dynamic nonlinear Volterra-Wiener modeling was developed. Within this framework, the use of Gaussian bandlimited white noise for nonlinear impedance spectroscopy was shown to offer considerable advantages over the use of sinusoidal inputs for nonlinear harmonic analysis currently employed in impedance characterization of nonlinear electrochemical systems. Signal transduction at the neuron-microelectrode interface is mediated by the interfacial medium confined to a thin cleft with thickness on the scale of 20-110 nm giving rise to Knudsen numbers (ratio of mean free path to characteristic system length) in the range of 0.015 and 0.003 for ionic electrodiffusion. At these Knudsen numbers, the continuum assumptions made in the use of Poisson-Nernst-Planck system of equations for modeling ionic electrodiffusion are not valid. Therefore, a lattice Boltzmann method (LBM) based multiphysics solver suitable for modeling ionic electrodiffusion at the mesoscale neuron-microelectrode interface was developed. Additionally, a molecular speed dependent relaxation time was proposed for use in the lattice Boltzmann equation. Such a relaxation time holds promise for enhancing the numerical stability of lattice Boltzmann algorithms as it helped recover a physically correct description of microscopic phenomena related to particle collisions governed by their local density on the lattice. Next, using this multiphysics solver simulations were carried out for the charge relaxation dynamics of an electrolytic nanocapacitor with the intention of ultimately employing it for a simulation of the capacitive coupling between the neuron and the v planar microelectrode on a microelectrode array (MEA). Simulations of the charge relaxation dynamics for a step potential applied at t = 0 to the capacitor electrodes were carried out for varying conditions of electric double layer (EDL) overlap, solvent viscosity, electrode spacing and ratio of cation to anion diffusivity. For a large EDL overlap, an anomalous plasma-like collective behavior of oscillating ions at a frequency much lower than the plasma frequency of the electrolyte was observed and as such it appears to be purely an effect of nanoscale confinement. Results from these simulations are then discussed in the context of the dynamics of the interfacial medium in the neuron-microelectrode cleft. In conclusion, a synergistic approach to engineering the neuron-microelectrode interface is outlined through a use of the nonlinear dynamic modeling, simulation and characterization tools developed as part of this dissertation research

Development of an OpenFOAM multiphysics solver for solid fission products transport in the Molten Salt Fast Reactor

The analysis of innovative reactor concepts such as the Molten Salt Fast Reactor (MSFR) requires the development of new modeling and simulation tools. In the case of the MSFR, the strong intrinsic coupling between thermal-hydraulics, neutronics and fuel chemistry has led to the adoption of the multiphysics approach as a state-of-the-art paradigm. One of the peculiar aspects of liquid-fuel reactors such as the MSFR is the mobility of fission products (FPs) in the reactor circuit. Some FP species appear in form of solid precipitates carried by the fuel flow and can deposit on reactor boundaries (e.g., heat exchangers), potentially representing design issues related to the degradation of heat exchange performance or radioactive hotspots. The integration of transport models for solid particles in multiphysics codes is therefore relevant for the prediction of deposited fractions. To this aim, we develop a multiphysics solver based on the OpenFOAM library to address the issue of solid fission products transport. Single-phase incompressible thermal hydraulics are coupled with neutron diffusion, and advection-diffusion-decay equations are implemented for fission products concentrations. Particle deposition and precipitation are considered as well. The developed solver is tested on two different MSFR application to showcase the capabilities of the solver in steady-state simulation and to investigate the role of precipitation and turbulence modeling in the determination of particle concentration distributions

On the Application of Topology Optimisation Techniques to Thermal Management of Microelectronics Systems

In this paper, an autonomous thermal management design process based on a topological optimisation algorithm is presented. The numerical framework uses a finite element multiphysics solver to assess fluid flow and heat transfer, coupled with the Method of Moving Asymptotes approach for topology optimisation. The design framework is utilised to develop a copper heatsink for a simplified electronics package at two differing Reynolds numbers. In both cases, the final shape resembles a tree like structure rather than a more conventional fin structure

The demagnetization factor for randomly packed spheroidal particles

We investigate if the demagnetization factor for a randomly packed powder of magnetic spheroidal particles depend on the shape of the spheroidal particles and what the internal variation in magnetization is within such a powder. A spheroid is an ellipsoid of revolution, i.e. an ellipsoid with two semi-major axis being equal. The demagnetization factor is calculated as function of particle aspect ratio using two independent numerical models for several different packings, and assuming a relative permeability of 2. The calculated demagnetization factor is shown to depend on particle aspect ratio, not because of direct magnetic interaction but because the particle packing depend on the aspect ratio of the particles. The relative standard deviation of the magnetization across the powder was 3\%-8\%, increasing as the particle shape deviates from spherical, while the relative standard deviation within each particle was relatively constant around 5\%.Comment: 7 pages, 9 figure