An adaptive Cartesian embedded boundary approach for fluid simulations of two- and three-dimensional low temperature plasma filaments in complex geometries

We review a scalable two- and three-dimensional computer code for low-temperature plasma simulations in multi-material complex geometries. Our approach is based on embedded boundary (EB) finite volume discretizations of the minimal fluid-plasma model on adaptive Cartesian grids, extended to also account for charging of insulating surfaces. We discuss the spatial and temporal discretization methods, and show that the resulting overall method is second order convergent, monotone, and conservative (for smooth solutions). Weak scalability with parallel efficiencies over 70\% are demonstrated up to 8192 cores and more than one billion cells. We then demonstrate the use of adaptive mesh refinement in multiple two- and three-dimensional simulation examples at modest cores counts. The examples include two-dimensional simulations of surface streamers along insulators with surface roughness; fully three-dimensional simulations of filaments in experimentally realizable pin-plane geometries, and three-dimensional simulations of positive plasma discharges in multi-material complex geometries. The largest computational example uses up to $800$ million mesh cells with billions of unknowns on $4096$ computing cores. Our use of computer-aided design (CAD) and constructive solid geometry (CSG) combined with capabilities for parallel computing offers possibilities for performing three-dimensional transient plasma-fluid simulations, also in multi-material complex geometries at moderate pressures and comparatively large scale.Comment: 40 pages, 21 figure

Application of Mass Transfer Models in Environmental Engineering

Generally, unit operation processes that are used in environmental engineering are involved in interfacial reaction where mass transfer is an extremely essential component for system optimization. The purposes of this chapter were intended to provide the information of both theoretical model development and engineering practice for mass transfer of important processes in environmental engineering. Those processes include, but are not limited to, (1) ozonation (gas–liquid process), (2) ion exchange (liquid–solid process), (3) biological activated carbon (liquid–solid process), (4) chlorination (gas–liquid process), and (5) carbonation (gas–liquid–solid process)

The effects of water and microstructure on the performance of polymer electrolyte fuel cells

n this paper, we present a comprehensive non-isothermal, one-dimensional model of the cathode side of a Polymer Electrolyte Fuel Cell. We explicitly include the catalyst layer, gas diffusion layer and the membrane. The catalyst layer and gas diffusion layer are characterized by several measurable microstructural parameters. We model all three phases of water, with a view to capturing the effect that each has on the performance of the cell. A comparison with experiment is presented, demonstrating excellent agreement, particularly with regard to the effects of water activity in the channels and how it impacts flooding and membrane hydration. We present several results pertaining to the effects of water on the current density (or cell voltage), demonstrating the role of micro-structure, liquid water removal from the channel, water activity, membrane and gas diffusion layer thickness and channel temperature. These results provide an indication of the changes that are required to achieve optimal performance through improved water management and MEA-component design. Moreover, with its level of detail, the model we develop forms an excellent basis for a multi-dimensional model of the entire membrane electrode assembly

Neurophysiological characterisation of neurons in the rostral nucleus reuniens in health and disease.

Evidence is mounting for a role of the nucleus reuniens (Re) in higher cognitive function. Despite growing interest, very little is known about the intrinsic neurophysiological properties of Re neurons and, to date, no studies have examined if alterations to Re neurons may contribute to cognitive deficits associated with normal aging or dementia. Work presented chapter 3 provides the first detailed description of the intrinsic electrophysiological properties of rostral Re neurons in young adult (~5 months) C57-Bl/6J mice. This includes a number of findings which are highly atypical for thalamic relay neurons including tonic firing in the theta frequency at rest, a paucity of hyperpolarisation-activated cyclic nucleotide–gated (HCN) mediated currents, and a diversity of responses observed in response to depolarising current injections. Additionally this chapter includes a description of a novel form of intrinsic plasticity which alters the functional output of Re neurons. Chapter 4 investigates whether the intrinsic properties of Re neurons are altered in aged (~15 month) C57-Bl/6J mice as compared to a younger control group (~5 months). The intrinsic properties were remarkably similar across age ranges suggesting that alterations to the intrinsic properties of Re neurons do not contribute to age-related cognitive deficits. Chapter 5 investigates whether alterations to the intrinsic properties of Re neurons occur in the J20 model of amyloidopathy. Alterations to the resting membrane potential (RMP), propensity to rebound fire, and a reduction in action potential (AP) width were observed. This suggests that alterations to the intrinsic properties of Re neurons may contribute to cognitive deficits observed in Alzheimer’s disease (AD). Chapter 6 investigates whether alterations to the intrinsic properties of Re neurons occur in a mouse model (CHMP2Bintron5) of frontotemporal dementia (FTD). Only subtle changes were observed suggesting that alterations to the intrinsic properties of Re neurons does not contribute to cognitive deficits observed in FTD linked to chromosome 3 (FTD-3)

Simulations of the irradiation and temperature dependence of the efficiency of tandem photoelectrochemical water-splitting systems

The instantaneous efficiency of an operating photoelectrochemical solar-fuel-generator system is a complicated function of the tradeoffs between the light intensity and temperature-dependence of the photovoltage and photocurrent, as well as the losses associated with factors that include ohmic resistances, concentration overpotentials, kinetic overpotentials, and mass transport. These tradeoffs were evaluated quantitatively using an advanced photoelectrochemical device model comprised of an analytical device physics model for the semiconducting light absorbers in combination with a multi-physics device model that solved for the governing conservation equations in the various other parts of the system. The model was used to evaluate the variation in system efficiency due to hourly and seasonal variations in solar irradiation as well as due to variation in the isothermal system temperature. The system performance characteristics were also evaluated as a function of the band gaps of the dual-absorber tandem component and its properties, as well as the device dimensions and the electrolyte conductivity. The modeling indicated that the system efficiency varied significantly during the day and over a year, exhibiting local minima at midday and a global minimum at midyear when the solar irradiation is most intense. These variations can be reduced by a favorable choice of the system dimensions, by a reduction in the electrolyte ohmic resistances, and/or by utilization of very active electrocatalysts for the fuel-producing reactions. An increase in the system temperature decreased the annual average efficiency and led to less rapid ramp-up and ramp-down phases of the system, but reduced midday and midyear instantaneous efficiency variations. Careful choice of the system dimensions resulted in minimal change in the system efficiency in response to degradation in the quality of the light absorbing materials. The daily and annually averaged mass of hydrogen production for the optimized integrated system compared favorably to the daily and annually averaged mass of hydrogen that was produced by an optimized stand-alone tandem photovoltaic array connected electrically to a stand-alone electrolyzer system. The model can be used to predict the performance of the system, to optimize the design of solar-driven water splitting devices, and to guide the development of components of the devices as well as of the system as a whole

Applications of CFD Simulations on Chemical Processing Equipment Designs

The objective of this work is to achieve process intensification by seeking optimal equipment design with CFD investigations. In this work, two projects on chemical equipment design have been discussed. The first project is on design and optimization of fractal distributor in a novel ion-exchanger. Flow distributors are adopted extensively by chemical industry to distribute an incoming process stream uniformly to the downstream equipment. Currently, the performance of chemical equipment installed with conventional distributor is severely undermined due to poor flow distribution. For conventional distributors such as spray nozzle distributors, their design concept is based on maintaining very high pressure drop across the whole device with very little opening areas through orifices. Fractal distributors can achieve high outlet densities with low pressure drop due to their inherent self-similarity feature. To investigate the performance of fractal distributor, a novel ion-exchanger equipped with fractal distributor was proposed and manufactured. With comparison against conventional distributor, fractal distributor is proven to be able to offer much better flow distribution inside ion-exchanger by both CFD and experimental investigations. To seek optimal performance, the design space of fractal distributor has been explored with CFD studies. The influence of key design parameters such as channel aspect ratio was investigated and fractal distributor with “deep and narrow” channels were found to achieve superior performance. While conducting large scale design explorations, automation tools were developed to handle massive number of study cases. The second project focuses on design explorations of a novel oil-water separator. The flow pattern was investigated first with single phase studies. An improved design was proposed with draft tube diameter ratio of 0.6 and a larger twisting angle of impeller. The new impeller design was shown to have better separation efficiency from experiments. Later, the design has been studied with multiphase simulation with population balance model. With the challenge of lacking available kernels in low Reynolds number flow, a new coalesce kernel was proposed. The model offers as a comprehensive tool to understand flow pattern and phase separation process inside the device

Plasma propulsion simulation using particles

This perspective paper deals with an overview of particle-in-cell / Monte Carlo collision models applied to different plasma-propulsion configurations and scenarios, from electrostatic (E x B and pulsed arc) devices to electromagnetic (RF inductive, helicon, electron cyclotron resonance) thrusters, with an emphasis on plasma plumes and their interaction with the satellite. The most important elements related to the modeling of plasma-wall interaction are also presented. Finally, the paper reports new progress in the particle-in-cell computational methodology, in particular regarding accelerating computational techniques for multi-dimensional simulations and plasma chemistry Monte Carlo modules for molecular and alternative propellan

The physiological variability of channel density in hippocampal CA1 pyramidal cells and interneurons explored using a unified data-driven modeling workflow

Every neuron is part of a network, exerting its function by transforming multiple spatiotemporal synaptic input patterns into a single spiking output. This function is specified by the particular shape and passive electrical properties of the neuronal membrane, and the composition and spatial distribution of ion channels across its processes. For a variety of physiological or pathological reasons, the intrinsic input/output function may change during a neuron’s lifetime. This process results in high variability in the peak specific conductance of ion channels in individual neurons. The mechanisms responsible for this variability are not well understood, although there are clear indications from experiment and modeling that degeneracy and correlation among multiple channels may be involved. Here, we studied this issue in biophysical models of hippocampal CA1 pyramidal neurons and interneurons. Using a unified data-driven simulation workflow and starting from a set of experimental recordings and morphological reconstructions obtained from rats, we built and analyzed several ensembles of morphologically and biophysically accurate single cell models with intrinsic electrophysiological properties consistent with experimental findings. The results suggest that the set of conductances expressed in any given hippocampal neuron may be considered as belonging to two groups: one subset is responsible for the major characteristics of the firing behavior in each population and the other responsible for a robust degeneracy. Analysis of the model neurons suggests several experimentally testable predictions related to the combination and relative proportion of the different conductances that should be expressed on the membrane of different types of neurons for them to fulfill their role in the hippocampus circuitry